Question: MASK: Reconfiguring the GPU memory hierarchy to facilitate multi-application concurrency.

This document introduces Project Adam, an innovative distributed system designed for efficient and scalable training of large deep neural network models. These models, despite their high accuracy on complex visual recognition tasks, are notoriously time-consuming to train and demand substantial computational resources. Project Adam, built using commodity server machines, distinguishes itself by achieving remarkable performance, scalability, and task accuracy in visual recognition tasks. The system's high efficiency and scalability are attributed to a whole-system co-design strategy that optimizes and balances workload computation and communication. By embracing asynchrony at all levels, Project Adam not only boosts performance but also enhances the accuracy of the trained models. Remarkably, it outperforms previous benchmarks by using 30 times fewer machines to train a large model with 2 billion connections to twice the accuracy in the same time frame as the previous record holder for the ImageNet 22,000 category image classification task. Additionally, the results highlight that task accuracy correlates positively with the size of the model. These findings underscore the potential of a distributed systems-driven approach to deep learning, even when using current training algorithms.

**Question**: MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency

**Documents**: A System Architecture for Efficient Multithreaded Execution on GPUs
Graphics Processing Units (GPUs) have evolved from fixed-function pipelines to fully programmable parallel processors. However, they still suffer from poor multithreaded performance due to the Single Instruction Multiple Thread (SIMT) execution model and a memory hierarchy designed for graphics workloads. We propose a new system architecture for GPUs that improves multithreaded performance by overcoming these limitations. Our architecture combines the advantages of both SIMT and Multithreaded Execution (MTE) models, while providing a memory hierarchy optimized for general-purpose workloads. We evaluate our architecture using a set of benchmarks from different domains and compare it against state-of-the-art GPUs. Our results show significant improvements in performance and efficiency, demonstrating the potential of our architecture for efficient multithreaded execution on GPUs.

**Similar Documents**: This paper presents a novel system architecture designed to enhance multithreaded performance on Graphics Processing Units (GPUs). Despite the transition from fixed-function pipelines to fully programmable parallel processors, GPUs continue to struggle with suboptimal multithreaded execution, primarily due to the constraints imposed by the Single Instruction Multiple Thread (SIMT) model and a memory hierarchy tailored for graphics applications. To address these issues, we introduce an architecture that merges the strengths of SIMT

In this paper, we characterize Google applications, based on a one-month Google trace with over 650k jobs running across over 12000 heterogeneous hosts from a Google data center. On one hand, we carefully compute the valuable statistics about task events and resource utilization for Google applications, based on various types of resources (such as CPU, memory) and execution types (e.g., whether they can run batch tasks or not). Resource utilization per application is observed with an extremely typical Pareto principle. On the other hand, we classify applications via a K-means clustering algorithm with optimized number of sets, based on task events and resource usage. The number of applications in the K-means clustering sets follows a Pareto-similar distribution. We believe our work is very interesting and valuable for the further investigation of Cloud environment.

Sparse matrix matrix (SpMM) multiplication is involved in a wide range of scientific and technical applications. The computational requirements for this kind of operation are enormous, especially for large matrices. This paper analyzes and evaluates a method to efficiently compute the SpMM product in a computing environment that includes graphics processing units (GPUs). Some libraries to compute this matricial operation can be found in the literature. However, our strategy (FastSpMM) outperforms the existing approaches because it combines the use of the ELLPACK-R storage format with the exploitation of the high ratio computation/memory access of the SpMM operation and the overlapping of CPU–GPU communications/computations by Compute Unified Device Architecture streaming computation. In this work, FastSpMM is described and its performance evaluated with regard to the CUSPARSE library (supplied by NVIDIA), which also includes routines to compute SpMM on GPUs. Experimental evaluations based on a representative set of test matrices show that, in terms of performance, FastSpMM outperforms the CUSPARSE routine as well as the implementation of the SpMM as a set of sparse matrix vector products.

OpenCL FPGA has recently gained great popularity with emerging needs for workload acceleration such as Convolutional Neural Network (CNN), which is the most popular deep learning architecture in the domain of computer vision. While OpenCL enhances the code portability and programmability of FPGA, it comes at the expense of performance. The key challenge is to optimize the OpenCL kernels to efficiently utilize the flexible hardware resources in FPGA. Simply optimizing the OpenCL kernel code through various compiler options turns out insufficient to achieve desirable performance for both compute-intensive and data-intensive workloads such as convolutional neural networks . In this paper, we first propose an analytical performance model and apply it to perform an in-depth analysis on the resource requirement of CNN classifier kernels and available resources on modern FPGAs. We identify that the key performance bottleneck is the onchip memory bandwidth. We propose a new kernel design to effectively address such bandwidth limitation and to provide an optimal balance between computation, on-chip, and off-chip memory access. As a case study, we further apply these techniques to design a CNN accelerator based on the VGG model. Finally, we evaluate the performance of our CNN accelerator using an Altera Arria 10 GX1150 board. We achieve 866 Gop/s floating point performance at 370MHz working frequency and 1.79 Top/s 16-bit fixed-point performance at 385MHz. To the best of our knowledge, our implementation achieves the best power efficiency and performance density compared to existing work.

Processing-In-Memory (PIM) is an effective technique that reduces data movements by integrating processing units within memory. The recent advance of “big data” and 3D stacking technology make PIM a practical and viable solution for the modern data processing workloads. It is exemplified by the recent research interests on PIM-based acceleration. Among them, TESSERACT is a PIM-enabled parallel graph processing architecture based on Micron’s Hybrid Memory Cube (HMC), one of the most prominent 3D-stacked memory technologies. It implements a Pregel-like vertex-centric programming model, so that users could develop programs in the familiar interface while taking advantage of PIM. Despite the orders of magnitude speedup compared to DRAM-based systems, TESSERACT generates excessive crosscube communications through SerDes links, whose bandwidth is much less than the aggregated local bandwidth of HMCs. Our investigation indicates that this is because of the restricted data organization required by the vertex programming model. In this paper, we argue that a PIM-based graph processing system should take data organization as a first-order design consideration. Following this principle, we propose GraphP, a novel HMC-based software/hardware co-designed graph processing system that drastically reduces communication and energy consumption compared to TESSERACT. GraphP features three key techniques. 1) “Source-cut” partitioning, which fundamentally changes the cross-cube communication from one remote put per cross-cube edge to one update per replica. 2) “Two-phase Vertex Program”, a programming model designed for the “source-cut” partitioning with two operations: GenUpdate and ApplyUpdate. 3) Hierarchical communication and overlapping, which further improves performance with unique opportunities offered by the proposed partitioning and programming model. We evaluate GraphP using a cycle accurate simulator with 5 real-world graphs and 4 algorithms. The results show that it provides on average 1.7 speedup and 89% energy saving compared to TESSERACT.

A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate the needs of machine learning algorithms and represent an intermediate point between the efficiency of ASICs and the programmability of general-purpose processors. However, acceleration with FPGAs still requires long development cycles and extensive expertise in hardware design. To tackle this challenge, instead of designing an accelerator for a machine learning algorithm, we present TABLA, a framework that generates accelerators for a class of machine learning algorithms. The key is to identify the commonalities across a wide range of machine learning algorithms and utilize this commonality to provide a high-level abstraction for programmers. TABLA leverages the insight that many learning algorithms can be expressed as a stochastic optimization problem. Therefore, learning becomes solving an optimization problem using stochastic gradient descent that minimizes an objective function over the training data. The gradient descent solver is fixed while the objective function changes for different learning algorithms. TABLA provides a template-based framework to accelerate this class of learning algorithms. Therefore, a developer can specify the learning task by only expressing the gradient of the objective function using our high-level language. Tabla then automatically generates the synthesizable implementation of the accelerator for FPGA realization using a set of hand-optimized templates. We use Tabla to generate accelerators for ten different learning tasks targeted at a Xilinx Zynq FPGA platform. We rigorously compare the benefits of FPGA acceleration to multi-core CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650 Ti, and Tesla K40) using real hardware measurements. TABLA-generated accelerators provide 19.4x and 2.9x average speedup over the ARM and Xeon processors, respectively. These accelerators provide 17.57x, 20.2x, and 33.4x higher Performance-per-Watt in comparison to Tegra, GTX 650 Ti and Tesla, respectively. These benefits are achieved while the programmers write less than 50 lines of code.

The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug.
 This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.

Graphs are a fundamental data representation that has been used extensively in various domains. In graph-based applications, a systematic exploration of the graph such as a breadth-first search (BFS) often serves as a key component in the processing of their massive data sets. In this paper, we present a new method for implementing the parallel BFS algorithm on multi-core CPUs which exploits a fundamental property of randomly shaped real-world graph instances. By utilizing memory bandwidth more efficiently, our method shows improved performance over the current state-of-the-art implementation and increases its advantage as the size of the graph increases. We then propose a hybrid method which, for each level of the BFS algorithm, dynamically chooses the best implementation from: a sequential execution, two different methods of multicore execution, and a GPU execution. Such a hybrid approach provides the best performance for each graph size while avoiding poor worst-case performance on high-diameter graphs. Finally, we study the effects of the underlying architecture on BFS performance by comparing multiple CPU and GPU systems, a high-end GPU system performed as well as a quad-socket high-end CPU system.

Modern personal computers have embraced increasingly powerful Graphics Processing Units (GPUs). Recently, GPU-based graphics acceleration in web apps (i.e., applications running inside a web browser) has become popular. WebGL is the main effort to provide OpenGL-like graphics for web apps and it is currently used in 53% of the top-100 websites. Unfortunately, WebGL has posed serious security concerns as several attack vectors have been demonstrated through WebGL. Web browsers» solutions to these attacks have been reactive: discovered vulnerabilities have been patched and new runtime security checks have been added. Unfortunately, this approach leaves the system vulnerable to zero-day vulnerability exploits, especially given the large size of the Trusted Computing Base of the graphics plane. We present Sugar, a novel operating system solution that enhances the security of GPU acceleration for web apps by design. The key idea behind Sugar is using a dedicated virtual graphics plane for a web app by leveraging modern GPU virtualization solutions. A virtual graphics plane consists of a dedicated virtual GPU (or vGPU) as well as all the software graphics stack (including the device driver). Sugar enhances the system security since a virtual graphics plane is fully isolated from the rest of the system. Despite GPU virtualization overhead, we show that Sugar achieves high performance. Moreover, unlike current systems, Sugar is able to use two underlying physical GPUs, when available, to co-render the User Interface (UI): one GPU is used to provide virtual graphics planes for web apps and the other to provide the primary graphics plane for the rest of the system. Such a design not only provides strong security guarantees, it also provides enhanced performance isolation.

Recent advances in multi-core and many-core processors requires programmers to exploit an increasing amount of parallelism from their applications. Data parallel languages such as CUDA and OpenCL make it possible to take advantage of such processors, but still require a large amount of effort from programmers.
 A number of parallelizing source-to-source compilers have recently been developed to ease programming of multi-core and many-core processors. This work presents and evaluates a number of such tools, focused in particular on C-to-CUDA transformations targeting GPUs. We compare these tools both qualitatively and quantitatively to each other and identify their strengths and weaknesses.
 In this paper, we address the weaknesses by presenting a new classification of algorithms. This classification is used in a new source-to-source compiler, which is based on the algorithmic skeletons technique. The compiler generates target code based on skeletons of parallel structures, which can be seen as parameterisable library implementations for a set of algorithm classes. We furthermore demonstrate that the presented compiler requires little modifications to the original sequential source code, generates readable code for further fine-tuning, and delivers superior performance compared to other tools for a set of 8 image processing kernels.


Question: MIMO wireless systems with linear precoding techniques.

This document provides an overview of Space-Time Wireless Communications, offering a comprehensive introduction to the subject. The book, published by the University of Cambridge Press, includes a catalog record available from the British Library, and its ISBN is 0 521 82615 2 for the hardback edition. The contents cover a variety of topics crucial to understanding space-time wireless communications, starting with an exploration of polarization and field diverse channels, which are detailed on page 27. The text then delves into antenna array topology (page 28) and discusses degenerate channels (page 29). The implications of reciprocity in wireless communications are analyzed on page 31. A significant portion of the book is dedicated to Space-Time (ST) channel and signal models, providing readers with a deep understanding of the underlying principles and practical applications in the field of MIMO wireless linear precoding.

The document includes lists of figures (page xiv) and tables (page xxii), which enhance the reader's comprehension and facilitate the visualization of complex concepts. A preface (page xxiii) sets the stage for the detailed content that follows, while lists of abbreviations (page xxvi) and symbols (page xxix) ensure clarity and precision in the use of technical language. This resource is invaluable for students, researchers, and professionals seeking to expand their knowledge in the field of MIMO wireless linear precoding, as it provides a thorough grounding in the foundational aspects of space-time wireless communications.

We develop channel estimation agorithms for millimeter wave (mmWave) multiple input multiple output (MIMO) systems with one-bit analog-to-digital converters (ADCs). Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem is formulated as a one-bit compressed sensing problem. We propose a modified EM algorithm that exploits sparsity and has better performance than the conventional EM algorithm. We also present a second solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem. The simulation results show that GAMP can reduce mean squared error in the important low and medium SNR regions.

AN IMPORTANT class of theoretical and practical problems in communication and control is of a statistical nature. Such problems are: (i) Prediction of random signals; (ii) separation of random signals from random noise; (iii) detection of signals of known form (pulses, sinusoids) in the presence of random noise. In his pioneering work, Wiener [1]3 showed that problems (i) and (ii) lead to the so-called Wiener-Hopf integral equation; he also gave a method (spectral factorization) for the solution of this integral equation in the practically important special case of stationary statistics and rational spectra. Many extensions and generalizations followed Wiener’s basic work. Zadeh and Ragazzini solved the finite-memory case [2]. Concurrently and independently of Bode and Shannon [3], they also gave a simplified method [2) of solution. Booton discussed the nonstationary Wiener-Hopf equation [4]. These results are now in standard texts [5-6]. A somewhat different approach along these main lines has been given recently by Darlington [7]. For extensions to sampled signals, see, e.g., Franklin [8], Lees [9]. Another approach based on the eigenfunctions of the WienerHopf equation (which applies also to nonstationary problems whereas the preceding methods in general don’t), has been pioneered by Davis [10] and applied by many others, e.g., Shinbrot [11], Blum [12], Pugachev [13], Solodovnikov [14]. In all these works, the objective is to obtain the specification of a linear dynamic system (Wiener filter) which accomplishes the prediction, separation, or detection of a random signal.4 ——— 1 This research was supported in part by the U. S. Air Force Office of Scientific Research under Contract AF 49 (638)-382. 2 7212 Bellona Ave. 3 Numbers in brackets designate References at end of paper. 4 Of course, in general these tasks may be done better by nonlinear filters. At present, however, little or nothing is known about how to obtain (both theoretically and practically) these nonlinear filters. Contributed by the Instruments and Regulators Division and presented at the Instruments and Regulators Conference, March 29– Apri1 2, 1959, of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS. NOTE: Statements and opinions advanced in papers are to be understood as individual expressions of their authors and not those of the Society. Manuscript received at ASME Headquarters, February 24, 1959. Paper No. 59-IRD—11. A New Approach to Linear Filtering and Prediction Problems

An experimental study of a dual-polarized L-probe patch antenna is presented. The antenna is designed to operate at around 1.8 GHz. A "dual-feed" technique is introduced to achieve high isolation between two input ports. The proposed antenna has an impedance bandwidth of 23.8% (SWR/spl les/2), 15% (SWR/spl les/1.5) and an isolation over 30 dB. In array designs, techniques for improving the isolation between two adjacent elements of an antenna array are also investigated. A two-element array with more than 30 dB isolation is designed and tested.

This letter presents a CMOS outphasing class-D power amplifier (PA) with a Chireix combiner. Two voltage-mode class-D amplifiers used in the outphasing system were designed and implemented with a 0.18-mum CMOS process. By applying the Chireix combiner technique, drain efficiency of the outphasing PA for CDMA signals was improved from 38.6% to 48% while output power was increased from 14.5 to 15.4 dBm with an adjacent channel power ratio of -45 dBc.

Attractive features of time-hopping spread-spectrum multiple-access systems employing impulse signal technology are outlined, and emerging design issues are described. Performance of such communications systems in terms of achievable transmission rate and multiple-access capability are estimated for both analog and digital data modulation formats under ideal multiple-access channel conditions.

As well known, the near-optimal K-best detection is popular in multiple-input and multiple-output (MIMO) systems. In this paper, we first propose the joint approaches of K-best detection and polar decoding. For joint detection and decoding (JDD) approach, both hard and soft decisions are considered. The simplified successive cancellation (SSC) decoding is exploited for hard decision, and the successive cancellation list (SCL) decoding is used as soft decision. The system setup for JDD is als o introduced, in which the modulation points across several channels are considered together. Simulation results have demonstrated the performance advantage of the JDD algorithms over the separated ones. For 1/2-rate polar codes, JDD schemes show 50% complexity reduction compared to the separated ones. Furthermore, by employing SSC hard decoding, the JDD algorithm is promising for high-throughput and low-complexity application s.

Millimeter-wave (mm-wave) communications systems offer a promising solution to meeting the increasing data demands on wireless networks. Not only do mm-wave systems allow orders of magnitude larger bandwidths, they also create a high-dimensional spatial signal space due to the small wavelengths, which can be exploited for beamforming and multiplexing gains. However, the complexity of digitally processing the entire high-dimensional signal is prohibitive. By exploiting the inherent channel sparsity in beamspace due to highly directional propagation at mm-wave, it is possible to design near-optimal transceivers with dramatically lower complexity. In such beamspace MIMO systems, it is first necessary to determine the set of beams which define the low-dimensional communication subspace. In this paper, we address this beam selection problem and introduce a simple power-based classifier for determining the beamspace sparsity pattern that characterizes the communication subspace. We first introduce a physical model for a small cell which will serve as the setting for our analysis. We then develop a classifier for the physical model, and show its optimality for a class of ideal signals. Finally, we present illustrative numerical results and show the feasibility of the classifier in mobile settings.

In this paper, we provide a complete study on the training based channel estimation issues for relay networks that employ the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received training sequence by a sophisticatedly designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.

A 2 × 2 (four-element) multiple-input multiple-output (MIMO) patch antenna system is designed and fabricated for a 2.45-GHz ISM band operation. It uses complementary split-ring resonator (CSRR) loading on its ground plane for antenna miniaturization. This reduces the single-element antenna size by 76%. The total board size of the proposed MIMO antenna system, including the GND plane is 100 × 50 × 0.8 mm3, while the single-patch antenna element has a size of 14 × 18 mm2. The antenna is fabricated and tested. Measured results are in good agreement with simulations. A minimum measured isolation of 10 dB is obtained given the close interelement spacing of 0.17λ. The maximum measured gain for a single operating element is -0.8 dBi.

Coordinated Multi-Point (CoMP) transmission is a technology targeted for Long Term Evolution Advanced (LTE-A). The Joint Processing (JP) technique for CoMP can maximize system performance, which is achieved mainly with channel information-based beamforming algorithms. Precoding methods perform differently in various CoMP scenarios. In this paper, we propose a joint processing scheme in downlink CoMP transmission system. We apply block diagonal beamforming to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point.


Question: MIMO wireless systems with linear precoding techniques.

This paper introduces a novel quasi-orthogonal space-time block code (QOSTBC) design for multiple-input multiple-output (MIMO) wireless communication systems. The quest for complex orthogonal designs that offer full diversity and full transmission rate has been a significant challenge, especially for systems with more than two antennas. Previous research efforts have primarily focused on extending orthogonal designs to achieve space-time block codes with full diversity and a high transmission rate. In contrast, our work presents a distinctive approach by proposing rate one QOSTBCs that exhibit partial diversity. These codes are quasi-orthogonal, providing a compromise between the full diversity offered by orthogonal designs and the high transmission rate. A key innovation of our QOSTBCs is the decoding strategy, which operates on pairs of transmitted symbols rather than individual symbols, facilitating a more efficient and robust decoding process. This approach not only enhances the system's performance but also simplifies the decoding complexity, making it particularly suitable for MIMO wireless linear precoding applications.

By employing the lens antenna array, beamspace MIMO can utilize beam selection to reduce the number of required RF chains in mmWave massive MIMO systems without obvious performance loss. However, to achieve the capacity-approaching performance, beam selection requires the accurate information of beamspace channel of large size, which is challenging, especially when the number of RF chains is limited. To solve this problem, in this paper we propose a reliable support detection (SD)-based channel estimation scheme. Specifically, we propose to decompose the total beamspace channel estimation problem into a series of sub-problems, each of which only considers one sparse channel component. For each channel component, we first reliably detect its support by utilizing the structural characteristics of mmWave beamspace channel. Then, the influence of this channel component is removed from the total beamspace channel estimation problem. After the supports of all channel components have been detected, the nonzero elements of the sparse beamspace channel can be estimated with low pilot overhead. Simulation results show that the proposed SD-based channel estimation outperforms conventional schemes and enjoys satisfying accuracy, even in the low SNR region.

Multicarrier CDMA is a multiplexing approach in which modulated QAM symbols are spread over multiple OFDMA tones by using a generally complex spreading sequence. Effectively, a QAM symbol is repeated over multiple tones. Low density signature (LDS) is a version of CDMA with low density spreading sequence allowing us to take advantage of a near optimal ML receiver with practically feasible complexity. In this paper, we propose a new multiple access scheme so called sparse code multiple access (SCMA) which still enjoys the low complexity reception technique but with better performance compared to LDS. In SCMA, the procedure of bit to QAM symbol mapping and spreading are combined together and incoming bits are directly mapped to a multidimensional codeword of an SCMA codebook set. Each layer or user has its dedicated codebook. Shaping gain of a multidimensional constellation is the main source of the performance improvement in comparison to the simple repetition of QAM symbols in LDS. In general, SCMA codebook design is an optimization problem. A systematic sub-optimal approach is proposed here for SCMA codebook design.

Vehicle-to-vehicle (V2V) communications aim to enhance driver safety and traffic efficiency by using the recently designated frequency bands in the 5.9 GHz range in Europe. Due to the time-frequency selective fading behavior of the vehicular communication channel, multi-antenna techniques can provide enhanced link conditions by means of diversity processing. This paper highlights the integration of a four-element (N =4) linear array antenna into the roof-top compartment of a vehicle to conduct Multiple-Input Multiple-Output (MIMO) high-resolution mobile-to-mobile channel measurements.

For millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO) systems, codebook-based analog beamforming (including transmit precoding and receive combining) is usually used to compensate the severe attenuation of mmWave signals. However, conventional beamforming schemes involve complicated search among predefined codebooks to find out the optimal pair of analog precoder and analog combiner. To solve this problem, by exploring the idea of turbo equalizer together with the tabu search (TS) algorithm, we propose a Turbo-like beamforming scheme based on TS, which is called Turbo-TS beamforming in this paper, to achieve near-optimal performance with low complexity. Specifically, the proposed Turbo-TS beamforming scheme is composed of the following two key components: 1) Based on the iterative information exchange between the base station (BS) and the user, we design a Turbo-like joint search scheme to find out the near-optimal pair of analog precoder and analog combiner; and 2) inspired by the idea of the TS algorithm developed in artificial intelligence, we propose a TS-based precoding/combining scheme to intelligently search the best precoder/combiner in each iteration of Turbo-like joint search with low complexity. Analysis shows that the proposed Turbo-TS beamforming can considerably reduce the searching complexity, and simulation results verify that it can achieve near-optimal performance.

Block diagonalization is one approach for linear preceding in the multiple-input multiple-output broadcast channel that sends multiple interference free data streams to different users in the same cell. Unfortunately, block diagonalization neglects other-cell interference (OCI), which limits the performance of users at the edge of the cell. This paper presents an OCI-aware enhancement to block diagonalization that uses a whitening filter for interference suppression at the receiver and a novel precoder using the interference-plus-noise covariance matrix for each user at the transmitter. For complex Gaussian matrix channels, the asymptotic sum rate of the proposed system is analyzed under a large antenna assumption for isotropic inputs and compared to conventional block diagonalization. The capacity loss due to OCI is quantified in terms of results from single-user MIMO capacity. Several numerical examples compare achievable sum rates, the proposed asymptotic rates, and the capacity loss, in low and high interference regimes.

Multiple-Input and Multiple-Output (MIMO) arrays provide more degrees of freedom than conventional phased arrays. They require that every transmitting element of the array can be identified when received. One way to achieve this is to give every element its own unique frequency. Offset-transmitters may be used to introduce MIMO or Doppler Division Multiple Access (DDMA) into phased-arrays without an excessive increase in waveform-generating hardware. Our dual-channel demonstrator IC can obtain a phase accuracy better than 1 degree and an spurious level of better than -65dBc for a single on-chip channel. This work investigates at X-band the effects of the limited on-chip isolation of 35dB, when multiple offset outputs are generated on a single chip for both beam steering and DDMA. In case of beam steering, the requirements on channel-to-channel isolation are less strict, making it well within reach. In the case of DDMA, we recommend increasing the channel-to-channel isolation by implementing multiple chips, in which case independent signals can be generated.

Wireless channels often include multipath propagation, in which the signal has more than one path from the transmitter to the receiver. Atmospheric reflection or refraction, as well as reflection from the ground or from objects such as buildings may cause this phenomena. The aim of this research is to study the effects of multipath environment on the performance of a proposed chaotic chirp spread spectrum system (CCSSS). The benchmark performance of the proposed system is investigated in term of bit error rate (BER) under multipath environment. A Quadrature Phase Shift Keying (QPSK) modulation scheme and a matched filter based on adaptive threshold decision were used in this work. To assess the performance of this system, simulations were performed to compare the proposed system with the traditional frequency modulation differential chaotic shift keying (DCSK) spread spectrum system. The proposed system may be considered as a well qualified communication system in multipath environment.

Choosing a proper waveform is a critical task for the implementation of multiple-input multiple-output (MIMO) radars. In addition to the general requirements for radar waveforms such as good resolution, low sidelobes, etc, MIMO radar waveforms also should possess good orthogonality. In this paper we give a brief overview of MIMO radar waveforms, which are classified into four categories: (1) time division multiple access (TDMA), (2) frequency division multiple access (FDMA), (3) Doppler division multiple access (DDMA), and (4) code division multiple access (CDMA). A special circulating MIMO waveform is also addressed The properties as well as application limitations of different waveforms are analyzed and compared. Some simulations results are also presented to illustrate the respective performance of different waveforms.

In this paper, we propose a novel adaptive beamforming algorithm for coprime array by compressive sensing the virtual uniform linear array signal. Based on the idea of coprime sampling, a much longer virtual uniform linear array can be generated from a coprime array. With a compressive sensing matrix, a connection can be built between the coprime array with fewer physical sensors and the virtual uniform linear array with much more virtual sensors. Hence, the proposed adaptive beamforming algorithm takes full advantage of the longer virtual array. The performance increment provided by the virtual array is much larger than the performance loss due to the introduced compressive sensing. Hence, the beam-former using the virtual array is expected to obtain much better performance than those using the coprime array directly. Simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithm.

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation of the original data. Well-known linear transformation methods include, for example, principal component analysis, factor analysis, and projection pursuit. A recently developed linear transformation method is independent component analysis (ICA), in which the desired representation is the one that minimizes the statistical dependence of the components of the representation. Such a representation seems to capture the essential structure of the data in many applications. In this paper, we survey the existing theory and methods for ICA.


Question: MIMO wireless communication using linear precoding techniques.

This paper introduces space-time block coding, a novel communication strategy for multiple-input multiple-output (MIMO) systems over Rayleigh fading channels using multiple transmit antennas. Data is encoded using space-time block codes and then divided into n streams, which are concurrently transmitted via n transmit antennas. At each receive antenna, the signal is a linear combination of the n transmitted signals, superimposed with noise. Maximum likelihood decoding is achieved through a simple decoupling of signals from different antennas, facilitated by the orthogonal structure of the space-time block code. This results in a decoding algorithm based solely on linear processing at the receiver. Space-time block codes are designed to maximize diversity order for a given number of transmit and receive antennas, ensuring straightforward decoding. The theoretical foundation of orthogonal designs is employed to construct these codes. However, it is noted that such codes only exist for a limited set of values of n. To address this, a generalization of orthogonal designs is proposed, enabling the creation of space-time block codes for real and complex constellations for any number of transmit antennas. These codes achieve optimal transmission rates for any number of transmit antennas when using real constellations like PAM. For complex constellations such as PSK and QAM, space-time block codes are designed to achieve half the maximum possible transmission rate. For specific cases with two, three, and four transmit antennas, space-time block codes are developed that achieve full, three-quarters, and three-quarters of the maximum possible transmission rate, respectively, using arbitrary complex constellations. The optimal tradeoff between decoding delay and the number of transmit antennas is also calculated, showing that many of the presented codes are optimal in this regard.

Magneto-inductive (MI) wireless communications is an emerging subject with a rich set of applications, including local area networks for the Internet-of-Things, wireless body area networks, in-body and on-chip communications, and underwater and underground sensor networks as a low-cost alternative to radio frequency, acoustic or optical methods. Practical MI networks include multiple access channel (MAC) mechanisms for connecting a random number of coils without any specific topology or coil orientation assumptions covering both short and long ranges. However, there is not any information theoretical modeling of MI MAC (MIMAC) capacity of such universal networks with fully coupled frequency selective channel models and exact 3-D coupling model of circular coils instead of long range dipole approximations. In this paper, K-user MIMAC capacity is information theoretically modeled and analyzed, and two-user MIMACs are modeled with explicitly detailed channel responses, bandwidths and coupled thermal noise. K-user MIMAC capacity is achieved through Lagrangian solution with K-user water-filling optimization. Optimum orientations maximizing capacity and received power are theoretically analyzed, and numerically simulated for two-user MIMACs. Constructive gain and destructive interference mechanisms on MIMACs are introduced in comparison with the classical interference based approaches. The theoretical basis promises the utilization of MIMACs in 5G architectures.

— As wireless communication evolves towards 5G, both fixed broadband and mobile broadband will play a crucial part in providing the Gbps infrastructure for a connected society. This paper proposes a Millimeter-wave Gbps Broadband (MGB) system as the solution to two critical problems in this evolution: last-mile access for fixed broadband and small cell backhaul for mobile broadband. The key idea is to use spectrum that is already available in the millimeter wave bands for fixed wireless access with optimized dynamic beamforming and massive MIMO infrastructure to achieve high capacity with wide area coverage. This paper explains the MGB concept and describes potential array architectures for realizing the system. Simulations demonstrate that with 500 MHz of bandwidth (at 39 GHz band) and 28 dBm transmission power (55 dBm EIRP), it is possible to provide more than 11 Gbps backhaul capacity for 96 small cells within 1-km radius.

An iterative procedure is presented for a fast and accurate analysis of scanning slotted waveguide arrays including the effects of mutual couplings and their dependence on the scanning angle. The analysis takes also into account the input transitions from the feeding network; each slotted waveguide being excited either at one end or at the centre. The procedure is applied to the simple case of a linear array of four slots, for which a full-wave analysis could be carried out for comparison. The proposed method is shown to provide excellent agreement with the full-wave computation still requiring extremely short computation times.

Orthogonal frequency division multiplexing (OFDM) is a special case of multicarrier transmission where a single DataStream is transmitted over a number of lower rate subcarriers. In July 1998, the IEEE standardization group decided to select OFDM as the basis for their new 5-GHz standard aiming a range of data stream from 6 up to 54 Mbps. This new standard is the first one to use OFDM in packetbased communications. In wireless communication, concept of parallel transmission of symbols is used to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and losses and also some of its application. Keywords— Orthogonal frequency division multiplexing(OFDM); BER; ISI; PAPR; DVB; DAB.

This paper presents a new design concept of a beam steerable high gain phased array antenna based on WR28 waveguide at 29 GHz frequency for fifth generation (5G) full dimension multiple input multiple output (FD-MIMO) system. The 8×8 planar phased array is fed by a three dimensional beamformer to obtain volumetric beam scanning ranging from −60 to +60 degrees both in azimuth and elevation direction. Beamforming network (BFN) is designed using 16 set of 8×8 Butler matrix beamformer to get 64 beam states, which control the horizontal and vertical angle. This is a new concept to design waveguide based high power three-dimensional beamformer for volumetric multibeam in Ka band for 5G application. The maximum gain of phased array is 28.5 dBi that covers 28.9 GHz to 29.4 GHz frequency band.

This letter presents a CMOS outphasing class-D power amplifier (PA) with a Chireix combiner. Two voltage-mode class-D amplifiers used in the outphasing system were designed and implemented with a 0.18-mum CMOS process. By applying the Chireix combiner technique, drain efficiency of the outphasing PA for CDMA signals was improved from 38.6% to 48% while output power was increased from 14.5 to 15.4 dBm with an adjacent channel power ratio of -45 dBc.

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

A concept of full-dimension MIMO (FD-MIMO) adopting 2-dimensional (2D) antenna array has been proposed with intent to cope with practical challenges of massive MIMO systems. In this paper, we investigate an FD-MIMO operation that can be transparently applied with current 3GPP LTE specifications, and propose a novel method that facilitates an accurate estimation of channel quality indicator (CQI) at base station (BS). We first introduce a feedback scheme in which horizontal and vertical dimension channel information are separately reported. To further improve accuracy of modulation and coding scheme (MCS) selection to maximize system throughput, we propose an enhanced feedback scheme that is helpful to more accurate CQI estimation at the BS. In the evaluation results, we show performance improvement of the proposed scheme over the conventional MIMO scheme.

1Department of Mobile Communications, School of Electrical Engineering and Computer Sciences, Technical University of Berlin, Berlin, Germany 2Wireless Networking, Signal Processing and Security Lab, Department of Electrical and Computer Engineering, University of Houston, Houston, TX 77004, USA 3Division of Communication Systems, Department of Electrical Engineering (ISY), Linköping University, SE-581 83 Linköping, Sweden 4Communications Laboratory, Faculty of Electrical Engineering and Information Technology, Dresden University of Technology, 01062 Dresden, Germany

Challenges associated with the scarcity of bandwidth and power in wireless communications have to be addressed. For the state-estimation problems discussed in the paper, observations about a common state are collected by physically distributed terminals. To perform state estimation, wireless sensor networks (WSNs) may share these observations with each other or communicate them to a fusion center for centralized processing. With K vector observations {yk(n)}K k=1 available, the optimal mean squared error (MSE) estimation of the state x(n) for the linear model is accomplished by a Kalman filter.


Question: MIMO wireless systems utilizing linear precoding techniques.

This dissertation delves into the realm of space-time codes (STC), a sophisticated signaling strategy designed to enhance coding and diversity gains while boosting spectral efficiency in wireless communications. STC capitalizes on the spatial and temporal diversity inherent in wireless links by seamlessly integrating error correction coding, modulation techniques, and array processing. This makes STC particularly advantageous for enhancing downlink performance, crucial in asymmetric applications like downstream Internet access. The dissertation makes three key contributions to STC. Initially, it develops analytical tools to establish performance boundaries for STC across different channel conditions, applying transfer function techniques to derive bounds for trellis-type STC over Rayleigh, Rician, and correlated fading environments. For block-type STC, an analytical framework is crafted to support complex orthogonal designs with variable signal cardinalities and array configurations. Secondly, the Virginia Tech Space-Time Advanced Radio (VT-STAR) is introduced, a multi-antenna hardware test bed that enables the characterization of multiple-input multiple-output (MIMO) channels and the validation of various space-time approaches. Lastly, two innovative space-time architectures, complemented by iterative processing principles, are proposed. One scheme extends STC's applicability to outdoor wireless communications by implementing iterative equalization/decoding for time dispersive channels. The other scheme utilizes iterative interference cancellation/decoding to address the error propagation issue in Bell-Labs Layered Space-Time Architecture (BLAST). The results highlight the significant energy and spectral efficiencies achievable through the integration of space-time coding, multiuser detection, array processing, and iterative decoding techniques.

As the number of wireless devices grows, the increasing demand for the shared radio frequency (RF) spectrum becomes a critical problem. Unlike wired communications in which, theoretically, more fibers can be used to accommodate the increasing bandwidth demand, wireless spectrum cannot be arbitrarily increased due to the fundamental limitations imposed by the physical laws. On the other hand, recent advances in free space optical (FSO) technologies promise a complementary approach to increase wireless capacity. However, high-speed FSO technologies are currently confined to short distance transmissions, resulting in limited mobility. In this paper, we briefly describe WiFO, a hybrid WiFi-FSO network for Gbps wireless local area network (WLAN) femtocells that can provide up to one Gbps per user while maintaining seamless mobility. While typical RF femtocells are non-overlapped to minimize inter-cell interference, there are advantages of using overlapped femtocells to increase mobility and throughput when the number of users is small. That said, the primary contribution of this paper will be a novel location assisted coding (LAC) technique used in the WiFO network that aims to increase throughput and reduce interference for multiple users in a dense array of femtocells. Both theoretical analysis and numerical experiments show orders of magnitude increase in throughput using LAC over basic codes.

We propose an enhanced physical random access channel (PRACH) preamble detector, which efficiently identifies non-orthogonal preambles while suppressing the noise rise. The proposed PRACH preamble detector reconstructs preambles from the received signal and efficiently utilizes them to improve the detection performance. Through simulations, we verify that the proposed detector successfully detects the desired preambles while suppressing the undesired noise rise, and, thus, it can detect ten non-orthogonal preambles simultaneously with an extremely low mis-detection probability lower than <inline-formula> <tex-math notation="LaTeX">$10^{-5}$ </tex-math></inline-formula>, while achieving a false alarm probability of approximately <inline-formula> <tex-math notation="LaTeX">$10^{-3}$ </tex-math></inline-formula>.

An experimental study of a dual-polarized L-probe patch antenna is presented. The antenna is designed to operate at around 1.8 GHz. A "dual-feed" technique is introduced to achieve high isolation between two input ports. The proposed antenna has an impedance bandwidth of 23.8% (SWR/spl les/2), 15% (SWR/spl les/1.5) and an isolation over 30 dB. In array designs, techniques for improving the isolation between two adjacent elements of an antenna array are also investigated. A two-element array with more than 30 dB isolation is designed and tested.

We propose a novel pilot sequence design to mitigate pilot contamination in multi-cell multiuser massive multiple-input multiple-output networks. Our proposed design generates pilot sequences in the multi-cell network and devises power allocation at base stations (BSs) for downlink transmission. The pilot sequences together with the power allocation ensure that the user capacity of the network is achieved and the pre-defined signal-to-interference-plus-noise ratio (SINR) requirements of all users are met. To realize our design, we first derive new closed-form expressions for the user capacity and the user capacity region. Built upon these expressions, we then develop a new algorithm to obtain the required pilot sequences and power allocation. We further determine the minimum number of antennas required at the BSs to achieve certain SINR requirements of all users. The numerical results are presented to corroborate our analysis and to examine the impact of key parameters, such as the pilot sequence length and the total number of users, on the network performance. A pivotal conclusion is reached that our design achieves a larger user capacity region than the existing designs and needs less antennas at the BS to fulfill the pre-defined SINR requirements of all users in the network than the existing designs.

In this paper, we propose a hybridly connected structure for hybrid beamforming in millimeter-wave (mmWave) massive MIMO systems, where the antenna arrays at the transmitter and receiver consist of multiple sub-arrays, each of which connects to multiple radio frequency (RF) chains, and each RF chain connects to all the antennas corresponding to the sub-array. In this structure, through successive interference cancelation, we decompose the precoding matrix optimization problem into multiple precoding sub-matrix optimization problems. Then, near-optimal hybrid digital and analog precoders are designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, we compare the performance of the proposed hybridly connected structure with the existing fully and partially connected structures in terms of spectral efficiency, the required number of phase shifters, and energy efficiency. Finally, simulation results are presented to demonstrate that the spectral efficiency of the hybridly connected structure is better than that of the partially connected structure and that its spectral efficiency can approach that of the fully connected structure with the increase in the number of RF chains. Moreover, the proposed algorithm for the hybridly connected structure is capable of achieving higher energy efficiency than existing algorithms for the fully and partially connected structures.

In this letter, we propose a novel feed network for circular polarization (CP) diversity antennas to enhance communications performances. The previous CP diversity systems adopted dual polarization diversity using right-hand circular polarization (RHCP) and left-hand circular polarization (LHCP) within a small space. However, the same rotational CPs need further antenna distance. To overcome the space limitation for the same CP diversity antenna, we propose a feed network for CP antenna using conventional CP with orthogonal polarization radiation characteristics of a linear polarization (LP) diversity system without additional antennas. The port isolation characteristics and impedance matching conditions are numerically verified for the diversity system and antenna characteristics. The proposed feed network with CP patch antennas is fabricated and measured in a reverberation chamber for diversity performances, and their validity is verified.

The rapid growth of traffic and number of simultaneously available devices leads to the new challenges in constructing fifth generation wireless networks (5G). To handle with them various schemes of non-orthogonal multiple access (NOMA) were proposed. One of these schemes is Sparse Code Multiple Access (SCMA), which is shown to achieve better link level performance. In order to support SCMA signal decoding channel estimation is needed and sparse Bayesian learning framework may be used to reduce the requirement of pilot overhead. In this paper we propose a modification of sparse Bayesian learning based channel estimation algorithm that is shown to achieve better accuracy of user detection and faster convergence in numerical simulations.

In this paper, we provide a complete study on the training based channel estimation issues for relay networks that employ the amplify-and-forward (AF) transmission scheme. We first point out that separately estimating the channel from source to relay and relay to destination suffers from many drawbacks. Then we provide a new estimation scheme that directly estimates the overall channels from the source to the destination. The proposed channel estimation well serves the AF based space time coding (STC) that was recently developed. There exists many differences between the proposed channel estimation and that in the traditional single input single out (SISO) and multiple input single output (MISO) systems. For example, a relay must linearly precode its received training sequence by a sophisticatedly designed matrix in order to minimize the channel estimation error. Besides, each relay node is individually constrained by a different power requirement because of the non-cooperation among all relay nodes. We study both the linear least-square (LS) estimator and the minimum mean-square-error (MMSE) estimator. The corresponding optimal training sequences, as well as the optimal preceding matrices are derived from an efficient convex optimization process.

This overview portrays the evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station's or radio port's coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment in multiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems.

This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.


Question: An ontology-driven method for traffic scene modeling, situational awareness dependent on traffic rules, and decision-making in automated vehicles.

This paper presents an innovative decision-making framework designed to enhance the safety of autonomous vehicles in complex urban environments, particularly at uncontrolled intersections and on narrow roads. Recognizing the limitations of current Advanced Driver Assistance Systems (ADAS) in these challenging scenarios, we have developed a system that employs an ontology-based Knowledge Base, enabling machines to interpret and apply traffic regulations intelligently. This Knowledge Base comprehensively includes detailed maps and traffic rules, facilitating autonomous vehicles to make informed decisions in accordance with Right-Of-Way protocols upon receiving collision warning signals. The system's decisions are seamlessly integrated with the vehicle's path planning module, allowing for dynamic route adjustments or immediate stops to prevent potential collisions, thereby significantly improving safety and compliance with traffic laws in urban settings.

As drivers back out of the driving task, when transported automatically by an intelligent car for a longer time, they are not always able to react properly, if a driver take over request occurs. This paper presents two ways, how to deal with this problem within the scope of a functional safety concept. Thereto, the difference between fully automatic and autonomous driving assistance systems is explained. Afterwards two different strategies to reach a safe state in consequence of a system boundary crossing are proposed. In the first case the fall back state is reached by a driver take over, in the second case by an automatic, active fail-safe mechanism. Subsequently the necessary components for monitoring and reaching a safe state and their embedment in a basic, functional architecture of a driving assistance system are described. In this context, special regard is paid to aspects of redundancy as well. In the end it is concluded, that the safety concept proposed here is crucial for guaranteeing enduring safety in an automatically driving car and in consequence for making automatic driving functions commercially ready for serial production.

Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the timeconsuming manual labelling process, typically applied per image. We notice that driving the car is itself a form of annotation. Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence. The average labelling time per image is reduced to 5 seconds and only an inexpensive dash-cam is required for data capture. We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.

Engineers and researchers in the automobile industry have tried to design and build safer automobiles, but traffic accidents are unavoidable. Patterns involved in dangerous crashes could be detected if we develop a prediction model that automatically classifies the type of injury severity of various traffic accidents. These behavioral and roadway patterns are useful in the development of traffic safety control policy. We believe that to obtain the greatest possible accident reduction effects with limited budgetary resources, it is important that measures be based on scientific and objective surveys of the causes of accidents and severity of injuries. This paper presents some models to predict the severity of injury that occurred during traffic accidents using three machine-learning approaches. We considered neural networks trained using hybrid learning approaches, decision trees and a concurrent hybrid model involving decision trees and neural networks. Experiment results reveal that among the machine learning paradigms considered the hybrid decision tree-neural network approach outperformed the individual approaches.

Both recognizing human behavior and understanding a user's mobility from sensor data are critical issues in ubiquitous computing systems. As a kind of user behavior, the transportation modes, such as walking, driving, etc., that a user takes, can enrich the user's mobility with informative knowledge and provide pervasive computing systems with more context information. In this paper, we propose an approach based on supervised learning to infer people's motion modes from their GPS logs. The contribution of this work lies in the following two aspects. On one hand, we identify a set of sophisticated features, which are more robust to traffic condition than those other researchers ever used. On the other hand, we propose a graph-based post-processing algorithm to further improve the inference performance. This algorithm considers both the commonsense constraint of real world and typical user behavior based on location in a probabilistic manner. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change point-based segmentation method and Decision Tree-based inference model, the new features brought an eight percent improvement in inference accuracy over previous result, and the graph-based post-processing achieve a further four percent enhancement.

At this moment Automous cars are probably the biggest and most talked about technology in the Robotics Reserach Community. In spite of great technolgical advances over past few years a full fledged autonomous car is still far from reality. This article talks about the existing system and discusses the possibility of a Computer Vision enabled driving being superior than the LiDar based system. A detailed overview of privacy violations that might arise from autonomous driving has been discussed in detail both from a technical as well as legal perspective. It has been proved through evidence and arguments that efficient and accurate estimation and efficient solution of the contraint satisfaction problem adressed in the case of autonomous cars are negatively correlated with the preserving the privacy of the user. It is a very difficult trade-off since both are very important aspects and has to be taken into account. The fact that one cannot compromise with the safety issues of the car makes it inevidable to run into serious privacy concerns that might have adverse social and political effects.

This paper proposes the recognition framework of car makes and models from a single image captured by a traffic camera. Due to various configurations of traffic cameras, a traffic image may be captured in different viewpoints and lighting conditions, and the image quality varies in resolution and color depth. In the framework, cars are first detected using a part-based detector, and license plates and headlamps are detected as cardinal anchor points to rectify projective distortion. Car features are extracted, normalized, and classified using an ensemble of neural-network classifiers. In the experiment, the performance of the proposed method is evaluated on a data set of practical traffic images. The results prove the effectiveness of the proposed method in vehicle detection and model recognition.

Currently autonomous or self-driving vehicles are at the heart of academia and industry research becauseof itsmulti-faceted advantages that includes improved safety, reduced congestion, lower emissions and greater mobility. Software is the key driving factor underpinning autonomy within which planning algorithms that are responsible for mission-critical decision making hold a significant position. While transporting passengers or goods from a givenorigin to agivendestination,motionplanningmethods incorporate searching for apath to follow, avoiding obstacles and generating the best trajectory that ensures safety, comfort and efficiency. A range of different planning approaches have beenproposed in the literature. Thepurpose of this paper is to reviewexisting approaches and then compare and contrast different methods employed for the motion planning of autonomous on-road driving that consists of (1)findingapath, (2) searching for the safestmanoeuvre and (3)determining themost feasible trajectory. Methods developed by researchers in each of these three levels exhibit varying levels of complexity and performance accuracy. This paper presents a critical evaluation of each of these methods, in terms of their advantages/disadvantages, inherent limitations, feasibility, optimality, handling of obstacles and testing operational environments. Based on a critical review of existingmethods, research challenges to address current limitations are identified and future research directions are suggested so as to enhance the performanceofplanningalgorithmsat all three levels. Somepromising areasof future focushave been identified as the use of vehicular communications (V2V and V2I) and the incorporation of transport engineering aspects in order to improve the look-ahead horizon of current sensing technologies that are essential for planning with the aim of reducing the total cost of driverless vehicles. This critical reviewon planning techniques presented in this paper, along with theassociateddiscussions on their constraints and limitations, seek to assist researchers in accelerating development in the emerging field of autonomous vehicle research. Crown Copyright 2015 Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

We study an algorithm that allows a vehicle to autonomously change lanes in a safe but personalized fashion without the driver's explicit initiation (e.g. activating the turn signals). Lane change initiation in autonomous driving is typically based on subjective rules, functions of the positions and relative velocities of surrounding vehicles. This approach is often arbitrary, and not easily adapted to the driving style preferences of an individual driver. Here we propose a data-driven modeling approach to capture the lane change decision behavior of human drivers. We collect data with a test vehicle in typical lane change situations and train classifiers to predict the instant of lane change initiation with respect to the preferences of a particular driver. We integrate this decision logic into a model predictive control (MPC) framework to create a more personalized autonomous lane change experience that satisfies safety and comfort constraints. We show the ability of the decision logic to reproduce and differentiate between two lane changing styles, and demonstrate the safety and effectiveness of the control framework through simulations.

On-road motion planning for autonomous vehicles is in general a challenging problem. Past efforts have proposed solutions for urban and highway environments individually. We identify the key advantages/shortcomings of prior solutions, and propose a novel two-step motion planning system that addresses both urban and highway driving in a single framework. Reference Trajectory Planning (I) makes use of dense lattice sampling and optimization techniques to generate an easy-to-tune and human-like reference trajectory accounting for road geometry, obstacles and high-level directives. By focused sampling around the reference trajectory, Tracking Trajectory Planning (II) generates, evaluates and selects parametric trajectories that further satisfy kinodynamic constraints for execution. The described method retains most of the performance advantages of an exhaustive spatiotemporal planner while significantly reducing computation.

Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.


Question: An ontology-driven method for traffic scene representation, regulation-based situational awareness, and decision-making in automated vehicles.

This paper explores the utilization of ontology-based traffic models to enhance the decision-making capabilities of autonomous vehicles, aiming for more efficient and effective navigation. The proposed methodology involves a dual-layered world model, with a detailed low-level component dedicated to trajectory planning, and a high-level abstract model designed to tackle traffic coordination challenges. Specifically, the CyberCars-2 project's abstract world model is detailed, which employs a topological lane segmentation approach. This segmentation is complemented by the incorporation of semantic relations that enrich the model's understanding of the traffic scenario. By representing the context in a semantically meaningful way, the system facilitates the development of a coherent and comprehensive driving control mechanism. This not only simplifies the realization of such a system but also streamlines its analysis, evaluation, and simulation processes, thereby contributing to the advancement of autonomous driving technology.

The DARPA Urban Challenge required robotic vehicles to travel more than 90 km through an urban environment without human intervention and included situations such as stop intersections, traffic merges, parking, and roadblocks. Team VictorTango separated the problem into three parts: base vehicle, perception, and planning. A Ford Escape outfitted with a custom drive-by-wire system and computers formed the basis for Odin. Perception used laser scanners, global positioning system, and a priori knowledge to identify obstacles, cars, and roads. Planning relied on a hybrid deliberative/reactive architecture to

Traffic safety is a severe problem around the world. Many road accidents are normally related with the driver's unsafe driving behavior, e.g. eating while driving. In this work, we propose a vision-based solution to recognize the driver's behavior based on convolutional neural networks. Specifically, given an image, skin-like regions are extracted by Gaussian Mixture Model, which are passed to a deep convolutional neural networks model, namely R*CNN, to generate action labels. The skin-like regions are able to provide abundant semantic information with sufficient discriminative capability. Also, R*CNN is able to select the most informative regions from candidates to facilitate the final action recognition. We tested the proposed methods on Southeast University Driving-posture Dataset and achieve mean Average Precision(mAP) of 97.76% on the dataset which prove the proposed method is effective in drivers's action recognition.

This paper presents model predictive control of a vehicle in a varying road-traffic environment for ecological (eco) driving. The vehicle control input is derived by rigorous reasoning approach of model based anticipation of road, traffic and fuel consumption in a crowded road network regulated by traffic signals. Model predictive control with Continuation and generalized minimum residual method for optimization is used to calculate the sequence of control inputs aiming at long run fuel economy maintaining a safe driving. Performance of the proposed eco-driving system is evaluated through simulations in AIMSUN microscopic transport simulator. In spite of nonlinearity and discontinuous movement of other traffic and signals, the proposed system is robust enough to control the vehicle safely. The driving behavior with fuel saving aspects is graphically illustrated, compared and analyzed to signify the prospect of the proposed eco-driving of a vehicle.

An important aspect of collision avoidance and driver assistance systems, as well as autonomous vehicles, is the tracking of vehicle taillights and the detection of alert signals (turns and brakes). In this paper, we present the design and implementation of a robust and computationally lightweight algorithm for a real-time vision system, capable of detecting and tracking vehicle taillights, recognizing common alert signals using a vehicle-mounted embedded smart camera, and counting the cars passing on both sides of the vehicle. The system is low-power and processes scenes entirely on the microprocessor of an embedded smart camera. In contrast to most existing work that addresses either daytime or nighttime detection, the presented system provides the ability to track vehicle taillights and detect alert signals regardless of lighting conditions. The mobile vision system has been tested in actual traffic scenes and the results obtained demonstrate the performance and the lightweight nature of the algorithm.

Advanced driver assistance systems (ADAS) are becoming more common in safety and convenience applications. The computer vision based ADAS described in this paper, is an add-on system, suitable to variety of cars. It detects vacant legal parking spots, and safely guides the vehicle into the selected parking. Detection can be performed in both indoor parking lots and along roadsides. The system is composed of three standard computer-connected webcams, which are attached to the vehicle. Upon slowing down, the system starts searching automatically for a right hand-side vacant parking spot, while being aware to parking color signs. Once detected, the parking orientation is determined, and the driver is notified. Once a parking is selected by the driver, the relative position between the vehicle and the parking spot is monitored. Vocal and visual parking guidance instructions are presented to the driver. In addition, if during parking, an object is moving on the road towards the car, a safety alert is given. The system is universal in the sense that, as an add-on system, it can be installed on any private 4-wheeled vehicle, and is suited to urban driving environment.

On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.

Driverless cars and driver-assisted vehicles are becoming prevalent in today's transportation systems. Companies like TESLA, Audi, and Google are among the leaders in this new era of the automotive industry. Modern vehicles, equipped with sophisticated navigation devices, complex driver-assisted systems, and a whole lot of safety features, bring broader impacts to our quality of life, economic development, and environmental sustainability. Instant safety messages such as pre-collision warnings, blind-spot detection, pedestrian and object awareness significantly improve the safety for drivers, passengers, and pedestrians. As a result, vehicles would be able to travel closely yet safely together, forming a platoon, thus resulting in a reduction of traffic congestion and fuel consumption. Driverless cars also have non-safety-related applications: which are used to facilitate traffic management and infotainment dissemination for drivers and passengers. Vehicular Ad hoc Network (VANET), the wireless communication technology enabling driverless cars, features not only dynamic topology but also high mobility. The vehicle nodes move rapidly on streets and highways. Their movements also depend on but not limited to road traffic, speed limits, and behavior of nearby vehicles. With massive amount of messages exchanged among driverless cars that command the cars' movements at high speeds and in close distances, any malicious alternation could lead to disastrous accidents. Message authentication to ensure data integrity is paramount to attack preparation. This paper presents a novel message authentication scheme that protects cars from bogus messages and makes VANET resilient to Denial-of-Service (DoS) attacks. The work includes a simulation framework that integrates vehicle and data traffic models to validate the effectiveness of the proposed message authentication scheme.

A survey is given on two decades of developments in the field, encompassing an increase in computing power by four orders of magnitude. The '4-D approach' integrating expectation-based methods from systems dynamics and control engineering with methods from AI has allowed to create vehicles with unprecedented capabilities in the technical realm: Autonomous road vehicle guidance in public traffic on freeways at speeds beyond 130 km/h, on-board-autonomous landing approaches of aircraft, and landmark navigation for AGV's, for road vehicles including turn-offs onto cross-roads, and for helicopters in low-level flight (real-time, hardware-in-the-loop simulations in the latter case).

Steering a car through traffic is a complex task that is difficult to cast into algorithms. Therefore, researchers turn to training artificial neural networks from front-facing camera data stream along with the associated steering angles. Nevertheless, most existing solutions consider only the visual camera frames as input, thus ignoring the temporal relationship between frames. In this work, we propose a Convolutional Long Short-Term Memory Recurrent Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and dynamic temporal dependencies of driving. Additionally, We introduce posing the steering angle regression problem as classification while imposing a spatial relationship between the output layer neurons. Such method is based on learning a sinusoidal function that encodes steering angles. To train and validate our proposed methods, we used the publicly available Comma.ai dataset. Our solution improved steering root mean square error by 35% over recent methods, and led to a more stable steering by 87%.

Recently, many studies have been conducted on increasing the automation level in cars to achieve safer and more efficient transportation. The increased automation level creates room for the drivers to shift their attention to non-driving related activities. However, there are cases that cannot be handled by automation where a driver should take over the control. This pilot study investigates a paradigm for keeping the drivers' situation-awareness active during autonomous driving by utilizing a social robot system, NAMIDA. NAMIDA is an interface consisting of three sociable driving agents that can interact with the driver through eye-gaze behaviors. We analyzed the effectiveness of NAMIDA on maintaining the drivers' attention to the road, by evaluating the response time of the drivers to a critical situation on the road. An experiment consisting of a take over scenario was conducted in a dynamic driving simulator. The results showed that existence of NAMIDA significantly reduced the response time of the drivers. However, surprisingly, NAMIDA without eye-gaze behaviors was more effective in reducing the response time than NAMIDA with eye-gaze behaviors. Additionally, the results revealed better subjective impressions for NAMIDA with eye-gaze behaviors behaviors.