Question: Full-duplex assisted user virtualization for mobile edge computing in fifth-generation networks.

This paper offers an overview and critical examination of wireless network virtualization, highlighting its potential to transform the deployment and operation of wireless networks by abstracting and sharing infrastructure and radio spectrum resources. This approach not only promises significant cost reductions but also facilitates smoother transitions to new technologies through network isolation. Despite the compelling vision, wireless network virtualization faces several significant hurdles before it can be widely implemented. These challenges encompass areas such as isolation mechanisms, control signaling optimization, resource discovery and allocation, mobility management, network management and operation, and security. Additionally, non-technical aspects such as governance and regulatory issues need to be addressed. The paper reviews existing work in the field and discusses key research issues and challenges. It delineates the fundamental aspects of wireless network virtualization, including motivations, framework, performance metrics, and enabling technologies. The paper concludes by exploring broader perspectives and future directions for the realization of wireless network virtualization, particularly in the context of 5G networks and mobile edge computing. This comprehensive approach to wireless network virtualization underscores its potential to support advanced services like full-duplex aided user virtualization for mobile edge computing in 5G networks.

As the number of electronic components of avionics systems is significantly increasing, it is desirable to run several avionics software on a single computing device. In such system, providing a seamless way to integrate separate applications on a computing device is a very critical issue as the Integrated Modular Avionics (IMA) concept addresses. In this context, the ARINC 653 standard defines resource partitioning of avionics application software. The virtualization technology has very high potential of providing an optimal implementation of the partition concept. In this paper, we study supports for full virtualization based ARINC 653 partitioning. The supports include extension of XML-based configuration file format and hierarchical scheduler for temporal partitioning. We show that our implementation can support well-known VMMs, such as VirtualBox and VMware and present basic performance numbers.

We propose and discuss the extension of software-defined networking (SDN) and OpenFlow principles to optical access/aggregation networks for dynamic flex-grid wavelength circuit creation. The first experimental demonstration of an OpenFlow1.0-based flex-grid λ-flow architecture for dynamic 150 Mb/s per-cell 4 G Orthogonal Frequency Division Multiple Access (OFDMA) mobile backhaul (MBH) overlays onto 10 Gb/s passive optical networks (PON) without optical network unit (ONU)-side optical filtering, amplification, or coherent detection, over 20 km standard single mode fiber (SSMF) with a 1:64 passive split is also detailed. The proposed approach can be attractive for monetizing optical access/aggregation networks via on-demand support for high-speed, low latency, high quality of service (QoS) applications over legacy fiber infrastructure.

Smart mobile devices have become ubiquitous, and people prefer to choose smartphone in daily life rather than use traditional personal computer. However, due to the hardware capability, performance of smartphones varies greatly and sometimes it cannot meet the demand of users. Furthermore, if smartphone is attacked by malicious application, the local private sensitive information will be leaked which in turn will cause huge losses. This paper proposes VSP, a Virtual Smartphone Platform. It offers a new way to enhance the capability of physical smartphone by providing virtual smartphone (VS) deployed in cloud. Users operate VS remotely through the thin-client of VSP on physical smartphones, and ignore the limits of physical mobile devices. The isolation of VS in cloud platform guarantees the security between VSes, and also prevents access to private sensitive information on physical devices. The evaluation indicates that average bandwidth cost with zlib is about 80 kBps and it is suitable for use in practice. Index Terms—Virtualization, Android, Remote Display, Cloud Computing, Ant Colony Optimization.

In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.

Thanks to the convergence of pervasive mobile communications and fast-growing online social networking, mobile social networking is penetrating into our everyday life. Aiming to develop a systematic understanding of the interplay between social structure and mobile communications, in this paper we exploit social ties in human social networks to enhance cooperative device-to-device communications. Specifically, as hand-held devices are carried by human beings, we leverage two key social phenomena, namely social trust and social reciprocity, to promote efficient cooperation among devices. With this insight, we develop a coalitional game theoretic framework to devise social-tie based cooperation strategies for device-to-device communications. We also develop a network assisted relay selection mechanism to implement the coalitional game solution, and show that the mechanism is immune to group deviations, individually rational, and truthful. We evaluate the performance of the mechanism by using real social data traces. Numerical results show that the proposed mechanism can achieve up-to 122% performance gain over the case without D2D cooperation.

Network Functions Virtualization (NFV) has recently emerged as one of the major technological driving forces that significantly accelerate the evolution of today's computer and communication networks. Despite the advantages of NFV, e.g., saving investment cost, optimizing resource consumption, improving operational efficiency, simplifying network service lifecycle management, lots of novel security threats and vulnerabilities will be introduced, thereby impeding its further development and deployment in practice. In this paper, we briefly report our threat analysis in the context of NFV, and identify the corresponding security requirements. The purpose is to establish a comprehensive threat taxonomy and provide a guideline to develop effective security countermeasures. Furthermore, a conceptual design framework for NFV based security management and service orchestration is presented, with an objective to dynamically and adaptively deploy and manage security functions on the demands of users and customers. A use case about NFV based access control is also developed, illustrating the feasibility and advantages of implementing NFV based security management and orchestration.

Code offloading is a promising way to accelerate mobile applications and reduce the energy consumption of mobile devices by shifting some computation to the cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices and the cloud. To address this challenge, in this paper, we consider to deploy edge nodes in close proximity to mobile devices and study how they benefit code offloading. We design an edge-centric code offloading system, called Echo, over a three-layer computing hierarchy consisting of mobile devices, the edge, and the cloud. A critical problem needs to be addressed by Echo is to decide which methods should be offloaded to which computing platform (the edge or the cloud). Different from existing offloading systems that let mobile devices individually make offloading decisions, Echo implements a centralized decision engine at the edge. This edge-centric design can fully exploit limited hardware resources at the edge to provide offloading services with the quality-of-service guarantee. Furthermore, we propose some novel mechanisms, e.g., lazy object transmission and differential object update, to further improve system performance. The results of a small-scale real deployment and trace-driven simulations show that Echo significantly outperforms existing code offloading systems at both execution time and energy consumption.

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.

As spectrum utilization efficiency is the major bottleneck in current wireless networking, many stakeholders discuss that spectrum should be shared rather than being exclusively allocated. Shared spectrum access raises many challenges which, if not properly addressed, degrades the performance level of the co-existing networks. Coexistence scenarios may involve two or more networks: same or different types; operated by the same or different operators. The complex interactions among the coexisting networks can be addressed by the machine learning tools which by their nature embrace uncertainty and can model the complex interactions. In this tutorial, we start with the basics of coexistence of wireless networks in the unlicensed bands. Then, we focus on WiFi and LTE-U coexistence. After providing a brief overview of machine learning topics such as supervised learning, unsupervised learning, reinforcement learning, we overview five particular examples which exploit learning schemes to enable efficient spectrum sharing entailing a generic cognitive radio setting as well as LTE and WiFi coexistence scenarios. We conclude with a list of challenges and research directions. I. SPECTRUM SHARING IN WIRELESS NETWORKS Spectrum sharing is the situation where at least two users or technologies are authorized to use the same portion of the radio spectrum on a non-exclusive manner1. We overview the current state of spectrum sharing and provide a taxonomy of spectrum sharing scenarios. We can list the main challenges in providing peaceful coexistence as follows: (i) scarcity of the resources, (ii) heterogeneity of the coexisting networks, (iii) power asymmetry, and (iv) lack of coordination, communication, and cooperation among the coexisting networks. II. COEXISTENCE IN THE UNLICENSED BANDS: THE CASE OF WIFI AND LTE-U The success of IEEE 802.11 networks in the unlicensed bands, i.e., 2.4 GHz and 5 GHz, has proved the efficiency and feasibility of using spectrum in a license-exempt manner. Currently, even the cellular providers consider expanding their network’s capacity with unlicensed spectrum to cope with the increasing wireless traffic demand. More particularly, Qualcomm’s 2013 proposal [1] of aggregating 5GHz bands with the licensed carriers of an LTE network has paved the way for LTE unlicensed networks. However, operation in the unlicensed bands has to address the coexistence challenges. For example, WiFi networks at 2.4 GHz bands, e.g., 802.11b/g/n, have to find the best channel 1S. Forge, R. Horvitz, and C. Blackman. Perspectives on the value of shared spectrum access. Final Report for the European Commission, 2012. among three non-overlapping channels for operation in a very-dense WLAN deployment. Additionally, 2.4 GHz band accommodates also non-WiFi technologies such as Bluetooth, ZigBee, or microwave ovens which all create interference on WLANs. As for 5GHz which has many more non-overlapping channels compared to 2.4 GHz, the more severe challenge is to coexist with technologies other than 802.11n/ac/ax networks, namely unlicensed LTE networks and radars. The main coexistence mechanism of WiFi is listen-beforetalk (LBT) which is also known as carrier sense multiple access with collision avoidance (CSMA/CA). A station with a traffic to transmit has to first check whether the medium is free or not. To decide on the state of the medium, two approaches exist: carrier sense and energy detection. In carrier sensing, a WiFi node decodes the preamble of a WiFi frame that is received above some energy detection level. The node extracts the information from the PLCP header which carries information about the occupancy duration of the medium by that ongoing flow. This mechanism is also referred to as channel reservation. With this information, a WiFi node knows when to re-start sensing the medium for a transmission opportunity. Energy detection (ED) is a simpler approach in which a candidate transmitter decides that the air is free if the signal level is below a predefined ED threshold. This approach is used for detecting inter-technology signals, where the received signal is not decodable, i.e., it belongs to other technologies (or corrupted WiFi signals). Despite its simplicity, ED requires more effort on a potential transmitter as it must constantly sense the energy level in the air to detect a transmission opportunity. As LTE follows a scheduled medium access on the licensedspectrum, there is no notion or necessity of politeness or LBT in more technical terms. However, it is vital for LTE unlicensed to implement such mechanisms for coexistence with WiFi and other unlicensed LTE networks at 5 GHz bands. Currently, frequency-domain sharing is a first step only. In other words, an LTE small cell first checks the channel activities and selects a clear channel, if any. For time sharing, there are two approaches taken by two variants of LTE unlicensed: duty cycling by LTE-U and LBT by License-Assisted-Access (LAA). LTE-U which is an industry-led effort lets small cells apply duty cycling where during the OFF periods WiFi can access the medium. As this approach does not mandate LBT before turning small cell transmissions on, it may degrade WiFi performance drastically. LAA requires LBT similar to WiFi’s CSMA/CA. LAA speciCROWNCOM2017 TUTORIAL 2 fication is led by 3GPP and aims to develop a global solution in contrast to LTE-U which is only compliant to countries like US, Korea, China where LBT is not mandatory. We overview basics of these two variants and list the major issues in their peaceful coexistence with WiFi networks. III. BACKGROUND ON MACHINE LEARNING We provide a sparse overview of learning approaches: supervised, unsupervised, and reinforcement learning. IV. THE ROLE OF MACHINE LEARNING IN SPECTRUM SHARING AND COEXISTENCE In this part, we examine the literature using ML approaches to solve the coexistence issues as our case studies. Is the channel idle or busy?This question is at the heart of coexistence of networks in a multi-channel environment, as the first step of coexistence is to choose a channel that is clear. For cognitive radio networks, it is mandatory to detect the channel state to avoid violating the rules of secondary spectrum access. Casting this question into a binary classification problem, authors [2] introduce several (un)supervised learning algorithms to correctly identify the state of a channel. While supervised approaches require the real channel state information from the Primary Users, unsupervised learning such as K-means does not require any input from the PUs which is a desirable property of classification scheme in a practical setting. Which unlicensed channel to select for each LAA SBS for inter-operator coexistence?As we expect multiple LAA operators deploy their small cells independently, there is surely the question of how to select an unlicensed channel to aggregate, particularly in case there are more cells than the number of available channels. One way of channel selection is to let every LAA BS learn from its own observations via trial-and-error, Q-learning [3]. Which unlicensed carrier to aggregate and how long to use this carrier?Q-learning framework can also be applied to an LAA setting where an LAA BS needs to select an unlicensed carrier and the transmission duration on the selected carrier [4]. Can WiFi exploit ML for defending itself against LTEU interference?Different than the literature which develops coexistence solutions to be deployed at the LTE base stations for WiFi/LTE setting, [5] proposes to also equip the WiFi APs with a tool that estimates the ON-duration of an existing LTE-U network in the neighborhood. Moreover, the developed solution can estimate the remaining airtime for the WiFi AP based on the LTE’s predicted ON duration. Key idea of WiPLUS is to detect the times where LTE-U has an ongoing transmission using the data passively collected from the MAC FSM of the NIC. However, although LTE-U signal may not be detected above the ED level, it may still have a severe impact on WiFi. Thus, PHY-layer analysis solely on signal level is short of detecting the moderate interference regime. WiPLUS overcomes this challenge by combining data from MAC FSM states and ARQ missing acknowledgments. Sampled data from a testbed has a lot of noise due to imperfections of the measuring devices and the complex interactions among the coexisting systems as well as PHY and MAC layers. WiPLUS applies K-means clustering to detect outliers on the estimated LTE-U on-durations. After filtering the data points based on the signal’s frequency harmonics, WiPLUS calculates the LTEU on-time as the average of the data points, each of which corresponds to an estimate of LTE-U on-time. Can we estimate WiFi link performance by learning from real-world link capacity measurements?In a multiAP setting, an AP can select the operation channel based on the expected capacity of the existing links. The traditional way is to take the SNIR-based capacity estimate into account, i.e., Shannon’s capacity formula. However, this capacity model may sometimes fail to represent the complex interactions between PHY and MAC layers, e.g., partially-overlapping channels in case of channel bonding in new 802.11ac/ax standards. The idea of [6] is to use supervised learning as a tool to model the complex interactions among many factors such as power and PHY rate of a neighboring WiFi link implicitly rather than modelling it explicitly. V. OPEN RESEARCH DIRECTIONS Machine learning-based solutions can embrace the complexity and uncertainty prevalent in the complex scenarios, especially hybrid horizontal spectrum sharing, by learning from the observations. However, a wireless network poses peculiar challenges such as the energy limitations, real-time operation, and sometimes fast changes in the operation environment that render learning less effective. We overview such challenges and conclude with some open questions in this

The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations


Question: Full-duplex supported user virtualization for mobile edge computing in 5G network environments.

This paper introduces an architectural framework, Software-Defined Network Virtualization (SDNV), aimed at seamlessly integrating Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) for service provisioning in future networks. The convergence of SDN and NFV has been a subject of significant research, driven by the potential synergies between these two transformative technologies. Nevertheless, merging SDN and NFV presents considerable challenges due to the complexity of coordinating diverse network elements and their intricate interactions. SDNV seeks to address these issues by offering a comprehensive blueprint that aligns the foundational concepts of SDN and NFV within a unified network architecture. This framework not only provides a roadmap for integrating SDN and NFV but also outlines the critical technical hurdles that must be overcome to achieve successful integration. Moreover, the paper highlights several key research areas that require further exploration to advance the field of SDN-NFV integration, inviting the research community to contribute to this burgeoning domain.

Question: Full-Duplex Aided User Virtualization for Mobile Edge Computing in 5G Networks

Documents: Full-Duplex Aided Virtualization for Mobile Edge Computing: An Enabler for 5G Networks
The advent of 5G networks has necessitated innovative approaches to enhance the efficiency and performance of mobile edge computing (MEC). One promising solution is the integration of full-duplex (FD) technology with MEC, enabling simultaneous transmission and reception, which can significantly increase spectral efficiency. This paper presents a novel framework for FD-aided user virtualization in MEC, designed to optimize resource allocation and reduce latency. The proposed scheme leverages FD capabilities to support virtualized users in MEC environments, thereby improving the overall network throughput and user experience. Through extensive simulations, the effectiveness of the proposed FD-aided virtualization scheme is demonstrated, showcasing its potential as a key enabler for the deployment of MEC in 5G networks. The study also discusses the challenges and limitations associated with FD-aided virtualization, providing insights for future research and development in this area.

Efficient flexibility and higher system scalability call for enhanced network performance, better energy consumption, lower infrastructure cost, and effective resource utilization. To accomplish this, an architectural optimization and reconstruction of existing cellular network is required. Network slicing is considered to be one of the key enablers and an architectural answer of communication system of 2020 and beyond. Traditional mobile operators provide all types of services to various kinds of customers through a single network, however, with the deployment of network slicing operators are now able to divide entire network into different slices each with its own configuration and specific Quality of Service (QoS) requirements. In a slice-based network, each slice will be considered as a separate logical network. In this way, the infrastructure utilization and resource allocation will be much more energy and cost efficient in comparison to traditional network. In this paper, we provided a comprehensive discussion on concept and system architecture of network slicing with particular focus on its business aspect and profit modeling. We throughly discussed two different dimensions of profit modeling, so called Own-Slice Implementation and Resource Leasing for Outsourced Slices. We further addressed open research directions and existing challenges with the purpose of motivating new advances and adding realistic solutions to this emerging technology.

The cloud was originally designed to provide general-purpose computing using commodity hardware and its focus was on increasing resource consolidation as a means to lower cost. Hence, it was not particularly adapted to the requirements of multimedia applications that are highly latency sensitive and require specialized hardware, such as graphical processing units. Existing cloud infrastructure is dimensioned to serve general-purpose workloads and to meet end-user requirements by providing high throughput. In this paper, we investigate the effectiveness of using this general-purpose infrastructure for serving latency-sensitive multimedia applications. In particular, we examine on-demand gaming, also known as cloud gaming, which has the potential to change the video game industry. We demonstrate through a large-scale measurement study that the existing cloud infrastructure is unable to meet the strict latency requirements necessary for acceptable on-demand game play. Furthermore, we investigate the effectiveness of incorporating edge servers, which are servers located near end-users (e.g., CDN servers), to improve end-user coverage. Specifically, we examine an edge-server-only infrastructure and a hybrid infrastructure that consists of using edge servers in addition to the cloud. We find that a hybrid infrastructure significantly improves the number of end-users served. However, the number of satisfied end-users in a hybrid deployment largely depends on the various deployment parameters. Therefore, we evaluate various strategies that determine two such parameters, namely, the location of on-demand gaming servers and the games that are placed on these servers. We find that, through both a careful selection of on-demand gaming servers and the games to place on these servers, we significantly increase the number of end-users served over the basic random selection and placement strategies.

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.

As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.

Cloud computing, enabled by virtualization technologies, is becoming a mainstream computing model. Many companies are starting to utilize the infrastructure-as-a-service (IaaS) cloud computing model, leasing guest virtual machines (VMs) from the infrastructure providers for economic reasons: to reduce their operating costs and to increase the flexibility of their own infrastructures. Yet, many companies may be hesitant to move to cloud computing due to security concerns. An integral part of any virtualization technology is the all-powerful hyper visor. A hyper visor is a system management software layer which can access all resources of the platform. Much research has been done on using hyper visors to monitor guest VMs for malicious code and on hardening hyper visors to make them more secure. There is, however, another threat which has not been addressed by researchers -- that of compromised or malicious hyper visors that can extract sensitive or confidential data from guest VMs. Consequently, we propose that a new research direction needs to be undertaken to tackle this threat. We further propose that new hardware mechanisms in the multi core microprocessors are a viable way of providing protections for the guest VMs from the hyper visor, while still allowing the hyper visor to flexibly manage the resources of the physical platform.

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.

The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.

This paper presents a quantitative study on the energy-traffic tradeoff problem from the perspective of entire Wireless Local Area Network (WLAN). We propose a novel Energy-Efficient Cooperative Offloading Model (E2COM) for energy-traffic tradeoff, which can ensure the fairness of energy consumption of mobile devices and reduce the computation repetition and eliminate the Internet data traffic redundancy through cooperative execution and sharing computation results. We design an Online Task Scheduling Algorithm (OTS) based on a pricing mechanism and Lyapunov optimization to address the problem without predicting future information on task arrivals, transmission rates and so on. OTS can achieve a desirable tradeoff between the energy consumption and Internet data traffic by appropriately setting the tradeoff coefficient. Simulation results demonstrate that E2COM is more efficient than no offloading and cloud offloading for a variety of typical mobile devices, applications and link qualities in WLAN.

We propose a novel framework to enable concurrent transmission of multiple users to an access node equipped with a massive multiple-input multiple-output (mMIMO), which encompasses and extends the conventional time-division duplex (TDD) protocol and the recently proposed semi-orthogonal multiple-access (SOMA). The new solution is referred to generalized semi-orthogonal multiple-access (GSOMA). To enable GSOMA, the users are grouped such that the users within each group are assigned mutually orthogonal pilot sequences. However the pilot sequences can be reused in different groups and the users in different group are coordinated such that their pilot sequences do not interfere by a semi-orthogonal mechanism enabled by a resource-offset sequence. We describe the general framework and analyze a case of GSOMA and show that a properly designed GSOMA can outperform the conventional TDD and SOMA.

The phenomenal growth in mobile data traffic calls for a drastic increase in mobile network capacity beyond current 3G/4G networks. In this paper, we propose a millimeter wave mobile broadband (MMB) system for the next generation mobile communication system (5G). MMB taps into the vast spectrum in 3-300 GHz range to meet this growing demand. We reason why the millimeter wave spectrum is suitable for mobile broadband applications. We discuss the unique advantages of millimeter waves such as spectrum availability and large beamforming gain in small form factors. We also describe a practical MMB system design capable of providing Gb/s data rates at distances up to 500 meters and supports mobility up to 350 km/h. By means of system simulations, we show that a basic MMB system is capable of delivering an average cell throughput and cell-edge throughput performances that is 10-100 times better than the current 20MHz LTE-Advanced systems.


Question: A method utilizing full-duplex assisted user virtualization for mobile edge computing in 5G network environments.

This paper offers an exhaustive review of Software-Defined Networking (SDN), a transformative approach aimed at addressing the complexities and challenges of traditional IP networks. SDN emerges as a solution to the problems of network management and reconfiguration, offering a fresh paradigm that decouples the control and data planes of the network. This separation promotes a (logical) centralization of network control, allowing for the programming of the network and breaking the vertical integration that has characterized current networks. The paper delves into the core concepts of SDN, elucidating its motivation, distinguishing features from traditional networking, historical roots, and the standardization efforts around this paradigm. It constructs a comprehensive understanding of SDN by examining its essential components in a layered, bottom-up approach. This includes a detailed analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. The paper also addresses cross-layer issues such as debugging and troubleshooting. Anticipating the future trajectory of SDN, the paper discusses ongoing research endeavors and the challenges they present, with a particular emphasis on the design of switches and control platforms. It explores critical aspects such as resiliency, scalability, performance, security, and dependability, as well as the potential opportunities for carrier transport networks and cloud providers. Ultimately, the paper positions SDN as a pivotal enabler for the evolution towards a software-defined future in networking, underlining its role in simplifying network management and facilitating network evolution.

To satisfy the delay constraint, the computation tasks can be offloaded to some computing servers, referred to as offloading destinations. Different to most of existing works which usually consider only a single type of offloading destinations, in this paper, we study the hybrid computation offloading problem considering diverse computation and communication capabilities of two types of offloading destinations, i.e., cloud computing servers and fog computing servers. The aim is to minimize the total energy consumption for both communication and computation while completing the computation tasks within a given delay constraint. It is quite challenging because the delay cannot be easily formulated as an explicit expression but depends on the embedded communication-computation scheduling problem for the computation offloading to different destinations. To solve the computation offloading problem, we first define a new concept named computation energy efficiency and divide the problem into four subproblems according to the computation energy efficiency of different types of computation offloading and the maximum tolerable delay. For each subproblem, we give a closed-form computation offloading solution with the analysis of communication-computation scheduling under the delay constraint. The numerical results show that the proposed hybrid computation offloading solution achieves lower energy consumption than the conventional single-type computation offloading under the delay constraint.

We propose and discuss the extension of software-defined networking (SDN) and OpenFlow principles to optical access/aggregation networks for dynamic flex-grid wavelength circuit creation. The first experimental demonstration of an OpenFlow1.0-based flex-grid λ-flow architecture for dynamic 150 Mb/s per-cell 4 G Orthogonal Frequency Division Multiple Access (OFDMA) mobile backhaul (MBH) overlays onto 10 Gb/s passive optical networks (PON) without optical network unit (ONU)-side optical filtering, amplification, or coherent detection, over 20 km standard single mode fiber (SSMF) with a 1:64 passive split is also detailed. The proposed approach can be attractive for monetizing optical access/aggregation networks via on-demand support for high-speed, low latency, high quality of service (QoS) applications over legacy fiber infrastructure.

Recent advances in virtualization technologies have made it feasible to host multiple virtual machines (VMs) in the same physical host and even the same CPU core, with fair share of the physical resources among the VMs. However, as more VMs share the same core/CPU, the CPU access latency experienced by each VM increases substantially, which translates into longer I/O processing latency perceived by I/O-bound applications. To mitigate such impact while retaining the benefit of CPU sharing, we introduce a new class of VMs called latency-sensitive VMs (LSVMs), which achieve better performance for I/O-bound applications while maintaining the same resource share (and thus cost) as other CPU-sharing VMs. LSVMs are enabled by vSlicer, a hypervisor-level technique that schedules each LSVM more frequently but with a smaller micro time slice. vSlicer enables more timely processing of I/O events by LSVMs, without violating the CPU share fairness among all sharing VMs. Our evaluation of a vSlicer prototype in Xen shows that vSlicer substantially reduces network packet round-trip times and jitter and improves application-level performance. For example, vSlicer doubles both the connection rate and request processing throughput of an Apache web server; reduces a VoIP server's upstream jitter by 62%; and shortens the execution times of Intel MPI benchmark programs by half or more.

We derive the uplink system model for In-band and Guard-band narrowband Internet of Things (NB-IoT). The results reveal that the actual channel frequency response (CFR) is not a simple Fourier transform of the channel impulse response, due to sampling rate mismatch between the NB-IoT user and long term evolution (LTE) base station. Consequently, a new channel equalization algorithm is proposed based on the derived effective CFR. In addition, the interference is derived analytically to facilitate the co-existence of NB-IoT and LTE signals. This letter provides an example and guidance to support network slicing and service multiplexing in the physical layer.

This article provides some fundamental indications about wireless communications beyond LTE/LTE-A (5G), representing the key findings of the European research project 5GNOW. We start with identifying the drivers for making the transition to 5G networks. Just to name one, the advent of the Internet of Things and its integration with conventional human-initiated transmissions creates a need for a fundamental system redesign. Then we make clear that the strict paradigm of synchronism and orthogonality as applied in LTE prevents efficiency and scalability. We challenge this paradigm and propose new key PHY layer technology components such as a unified frame structure, multicarrier waveform design including a filtering functionality, sparse signal processing mechanisms, a robustness framework, and transmissions with very short latency. These components enable indeed an efficient and scalable air interface supporting the highly varying set of requirements originating from the 5G drivers.

This paper discusses potential security requirements and mechanisms for 5G mobile networks. It does not intend to do so exhaustively, but rather aims at initiating and spurring the work towards a 5G security architecture.

Cloud Radio Access Network (C-RAN) is a novel mobile network architecture which can address a number of challenges the operators face while trying to support growing end-user's needs. The main idea behind C-RAN is to pool the Baseband Units (BBUs) from multiple base stations into centralized BBU Pool for statistical multiplexing gain, while shifting the burden to the high-speed wireline transmission of In-phase and Quadrature (IQ) data. C-RAN enables energy efficient network operation and possible cost savings on baseband resources. Furthermore, it improves network capacity by performing load balancing and cooperative processing of signals originating from several base stations. This paper surveys the state-of-the-art literature on C-RAN. It can serve as a starting point for anyone willing to understand C-RAN architecture and advance the research on C-RAN.

Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the in-depth understanding of the interference overheads in I/O channel in the driver domain and VMM.

The design and optimization of multicarrier communications systems often involve a maximization of the total throughput subject to system resource constraints. The optimization problem is numerically difficult to solve when the problem does not have a convexity structure. This paper makes progress toward solving optimization problems of this type by showing that under a certain condition called the time-sharing condition, the duality gap of the optimization problem is always zero, regardless of the convexity of the objective function. Further, we show that the time-sharing condition is satisfied for practical multiuser spectrum optimization problems in multicarrier systems in the limit as the number of carriers goes to infinity. This result leads to efficient numerical algorithms that solve the nonconvex problem in the dual domain. We show that the recently proposed optimal spectrum balancing algorithm for digital subscriber lines can be interpreted as a dual algorithm. This new interpretation gives rise to more efficient dual update methods. It also suggests ways in which the dual objective may be evaluated approximately, further improving the numerical efficiency of the algorithm. We propose a low-complexity iterative spectrum balancing algorithm based on these ideas, and show that the new algorithm achieves near-optimal performance in many practical situations

The integration of Device-to-Device (D2D) communication into a cellular network improves system spectral efficiency, increases capacity, reduces power consumption and also reduces traffic to the evolved NodeB (eNB). However, D2D communication generates interference to the conventional cellular network which deteriorates the system performance. In this paper, we propose a radio resource allocation and power control scheme to mitigate the interference using cell sectorization scheme while reusing uplink cellular resources by the D2D pairs. By simulations, we show that our proposed scheme improves the overall system performance compared with the existing methods.


Question: A full-duplex supported user virtualization technique for mobile edge computing in fifth-generation networks.

Beyond the Clouds: An Updated Berkeley Perspective on Cloud Computing

Cloud computing, the long-standing vision of computing as an on-demand utility, is poised to revolutionize significant segments of the IT sector. This transformative technology is making software even more compelling as a service and is redefining the landscape of IT hardware procurement and design. Innovative developers aiming to launch new Internet services no longer face the daunting challenge of substantial upfront hardware investments or the operational overhead typically associated with service deployment. They no longer need to grapple with the risks of overprovisioning for underperforming services, which can lead to wasted resources, or underprovisioning for unexpectedly popular services, potentially alienating potential customers and revenue opportunities. Furthermore, entities with large-scale, batch-oriented workloads can achieve results swiftly, as the cost of utilizing a thousand servers for an hour is equivalent to using a single server for a thousand hours. This unprecedented elasticity of resources, without incurring additional costs for scaling, marks a pivotal moment in the evolution of IT.

Cloud computing encompasses the provision of services over the Internet and the underlying datacenter infrastructure that supports these services. While the services have been traditionally labeled as Software as a Service (SaaS), we define a Cloud as the amalgamation of datacenter hardware and systems software. When a Cloud is accessible to the general public on a pay-as-you-go basis, it is denoted as a Public Cloud, and the service offered is Utility Computing. Conversely, a Private Cloud refers to the internal datacenter infrastructure of a business or organization, not open to the public. Thus, Cloud Computing is the sum of SaaS and Utility Computing, excluding Private Clouds.

Individuals and organizations can assume the roles of SaaS users or providers, or be users or providers of Utility Computing. This discourse primarily centers on SaaS Providers (Cloud Users) and Cloud Providers, areas that have received less scholarly attention. From a hardware perspective, three novel facets emerge in the realm of Cloud Computing:

1. Scalability: Cloud environments are designed to scale out rather than scale up, enabling seamless expansion by adding more commodity servers instead of upgrading individual machines.

2. Virtualization: The utilization of virtualization technologies allows for the creation of multiple virtual machines on a single physical server, optimizing resource usage and facilitating the provision of on-demand computing services.

3. Resilience: Cloud architectures are engineered to be highly resilient, incorporating redundancy and failover mechanisms to ensure continuous service availability.

These innovations are pivotal in shaping the future of computing, offering unparalleled flexibility and efficiency to

Machine-to-machine communication, M2M, will make up a large portion of the new types of services and use cases that the fifth generation (5G) systems will address. On the one hand, 5G will connect a large number of low-cost and low-energy devices in the context of the Internet of things; on the other hand it will enable critical machine type communication use cases, such as smart factory, automotive, energy, and e-health – which require communication with very high reliability and availability, as well as very low end-to-end latency. In this paper, we will discuss the requirements, enablers and challenges to support these emerging mission-critical 5G use cases. Keywords— 5G, NR, M2M, MTC, URLLC, Tactile Internet, Reliability, Latency.

With the explosive growth of wireless data, mobile edge caching has emerged as a promising paradigm to support mobile traffic recently, in which the service providers (SPs) prefetch some popular contents in advance and cache them locally at the network edge. When requested, those locally cached contents can be directly delivered to users with low latency, thus alleviating the traffic load over backhaul channels during peak hours and enhancing the quality-of-experience (QoE) of users simultaneously. Due to the limited available cache space, it makes sense for the SP to cache the most profitable contents. Nevertheless, users' true valuations of contents are their private knowledge, which is unknown to the SP in general. This information asymmetry poses a significant challenge for effective caching at the SP side. Further, the cached contents can be delivered with different quality, which needs to be chosen judiciously to balance delivery costs and user satisfaction. To tackle these difficulties, in this paper, we propose an optimal auction mechanism from the perspective of the SP. In the auction, the SP determines the cache space allocation over contents and user payments based on the users' (possibly untruthful) reports of their valuations so that the SP's expected revenue is maximized. The advocated mechanism is designed to elicit true valuations from the users (incentive compatibility) and to incentivize user participation (individual rationality). In addition, we devise a computationally efficient method for calculating the optimal cache space allocation and user payments. We further examine the optimal choice of the content delivery quality for the case with a large number of users and derive a closed-form solution to compute the optimal delivery quality. Finally, extensive simulations are implemented to evaluate the performance of the proposed optimal auction mechanism, and the impact of various model parameters is highlighted to obtain engineering insights into the content caching problem.

Non-orthogonal multiple access (NOMA) has been recently considered as a promising multiple access technique for fifth generation (5G) mobile networks as an enabling technology to meet the demands of low latency, high reliability, massive connectivity, and high throughput. The two dominants types of NOMA are: power-domain and code-domain. The key feature of power-domain NOMA is to allow different users to share the same time, frequency, and code, but with different power levels. In code-domain NOMA, different spread-spectrum codes are assigned to different users and are then multiplexed over the same time-frequency resources. This paper concentrates on power-domain NOMA. In power-domain NOMA, Successive Interference Cancellation (SIC) is employed at the receiver. In this paper, the optimum received uplink power levels using a SIC detector is determined analytically for any number of transmitters. The optimum uplink received power levels using the SIC decoder in NOMA strongly resembles the μ-law encoding used in pulse code modulation (PCM) speech companders.

Non-orthogonal multiple access (NOMA) is envisioned to be one of the promising radio access techniques for the fifth generation (5G) mobile networks. In this paper, a portable NOMA testbed based on software defined radio (SDR) is developed by transporting our NOMA system to mini personal computers (PCs). Moreover, the NOMA testbed has been enhanced from 5 MHz bandwidth to 10 MHz bandwidth. As the computation complexity grows higher with the increase of system bandwidth, the portable NOMA testbed is not competent to fully demonstrate the performance of our NOMA system due to the limitation of mini PC. For a better understanding of the NOMA testbed, the system architecture and scenario are introduced in brief. Then, the implementation of NOMA transceiver and the protocol stack are described separately. Finally, a series of experiments are carried out to evaluate its performance loss compared with the original NOMA system based on desktops. The experimental results indicate that the performance of processors may be a short slab for the development of portable SDR-based testbeds towards 5G networks.

Emerging mobile applications involve continuous sensing and complex computations on sensed data streams. Examples include cognitive apps (e.g., speech recognition, natural language translation, as well as face, object, or gesture detection and recognition) and anticipatory apps that proactively track and provide services when needed. Unfortunately, today's mobile devices cannot keep pace with such apps, despite advances in hardware capability. Traditional approaches address this problem by computation offloading. One approach offloads by sending sensed streams to remote cloud servers via cellular networks or to cloudlets via Wi-Fi, where a clone of the app runs [2, 3, 4]. However, cloudlets may not be widely deployed and access to cloud infrastructure may yield high network delays and can be intermittent due to mobility. Morever, users might hesitate to upload private sensing data to the cloud or cloudlet. A second approach offloads to accelerators by rewriting code to use DSP or GPU within mobile devices. However, using accelerators requires substantial programming effort and produces varied benefits for diverse codes on heterogeneous devices.

Virtualization techniques are the key in both public and private cloud computing environments. In such environments, multiple virtual instances are running on the same physical machine. The logical isolation between systems makes security assurance weaker than physically isolated systems. Thus, Virtual Machine Introspection techniques become essential to prevent the virtual system from being vulnerable to attacks. However, this technique breaks down the borders of the segregation between multiple tenants, which should be avoided in a public cloud computing environment. In this paper, we focus on building an encrypted Virtual Machine Introspection system, CryptVMI, to address the above concern, especially in a public cloud system. Our approach maintains a query handler on the management node to handle encrypted queries from user clients. We pass the query to the corresponding compute node that holds the virtual instance queried. The introspection application deployed on the compute node processes the query and acquires the encrypted results from the virtual instance for the user. This work shows our design and preliminary implementation of this system.

Non-orthogonal multiple access (NOMA) is a promising radio access technique for further cellular enhancements toward the 5th generation (5G) mobile communication systems. Single-user multiple-input multiple-output (SU-MIMO) is one of the key technologies in long term evolution (LTE) /LTE-Advanced systems. It is proved that NOMA combined with SU-MIMO techniques can achieve further system performance improvement. In this paper, we focus on the impact of rank optimization on the performance of NOMA with SU-MIMO in downlink. Firstly, a geometry based rank adjustment method is studied. Secondly, an enhanced feedback method for rank adjustment is discussed. The simulation results show that the performance gain of NOMA improves with the proposed two rank adjustment methods. Compared to orthogonal access system, large performance gains can be achieved for NOMA, which are about 23% for cell average throughput and 33% for cell-edge user throughput.

This paper presents an overview of radio interface technologies for cooperative transmission in 3GPP LTE-Advanced, i.e., coordinated multi-point (CoMP) transmission, enhanced inter-cell interference coordination (eICIC) for heterogeneous deployments, and relay transmission techniques. This paper covers not only the technical components in the 3GPP specifications that have already been released, but also those that were discussed in the Study Item phase of LTE-Advanced, and those that are currently being discussed in 3GPP for potential specification in future LTE releases. key words: LTE-Advanced, CoMP, eICIC, relay

As the fourth generation (4G) LTE-Advanced network becomes a commercial success, technologies for beyond 4G and 5G are being actively investigated from the research perspective as well as from the standardization perspective. While 5G will integrate the latest technology breakthroughs to achieve the best possible performance, it is expected that LTE-Advanced will continue to evolve, as a part of 5G technologies, in a backward compatible manner to maximize the benefit from the massive economies of scale established around the 3rd Generation Partnership Project (3GPP) LTE/LTE-Advanced ecosystem from Release 8 to Release 12. In this article we introduce a set of key technologies expected for 3GPP Release 13 and 14 with a focus on air interface aspects, as part of the continued evolution of LTE-Advanced and as a bridge from 4G to 5G.

The use of unmanned aerial vehicles (UAVs) as aerial base stations (BSs) is of great practical significance in future wireless networks, especially for on-demand deployment during a temporary event and emergency situation. Although prior works have demonstrated the performance improvement brought by the UAV mobility, they mainly focus on the delay-tolerant applications such as file transfer and data collection. As such, it is unknown if the UAV mobility is able to provide performance gain for delay-constrained applications, such as video conferencing and online gaming. Motivated by this, we study in this paper a UAV-enabled downlink orthogonal division multiple access (OFDMA) network where a UAV is dispatched to serve two ground users within a given flight period. By taking into account the delay-specified minimum-rate-ratio constraints of the users, our goal is to maximize the minimum user throughput by jointly optimizing the UAV trajectory and communication resource allocation. We show that the max-min user throughput in general decreases as the minimum-rate-ratio constraints become more stringent, which reveals a fundamental tradeoff between the throughput gain by exploiting the UAV mobility and the user delay requirement. Simulation results verify our theoretical findings and also demonstrate the effectiveness of our proposed design.


Question: An analysis of hitting times for the stochastic gradient Langevin dynamics algorithm.

This paper offers significant insights into the population likelihood function of Gaussian Mixture Models (GMMs) with M ≥ 3 components, focusing on two critical aspects. Firstly, it is demonstrated that even in the simplified scenario of equally-weighted, well-separated, and spherical Gaussian components, the population likelihood function can harbor detrimental local maxima. These local maxima can result in log-likelihood values that are significantly lower than those obtained at global optima, addressing a long-standing question posed by Srebro [21]. Secondly, the study reveals that the Expectation-Maximization (EM) algorithm, or a first-order variant thereof, initialized randomly, has a non-negligible probability (1 − e) of converging to these suboptimal local maxima. Additionally, the analysis indicates that the first-order EM variant is unlikely to converge to strict saddle points, suggesting that the algorithm's subpar performance is predominantly due to the presence of bad local maxima rather than saddle points. These findings underscore the critical role of initialization in practical applications of the EM algorithm, even under seemingly favorable conditions.

Note: In the transformation, the term "bad local maxima" was used to maintain the context's negative connotation, and "suboptimal" was introduced for variation. The phrase "will not converge to strict saddle points almost surely" was rephrased to "is unlikely to converge to strict saddle points," and "highly favorable settings" was used to replace "in highly favorable settings." The term "necessity" was kept to preserve the emphasis on the importance of careful initialization.

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation – rules for gradient backpropagation through stochastic variables – and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.

The continuous dynamical system approach to deep learning is explored in order to devise alternative frameworks for training algorithms. Training is recast as a control problem and this allows us to formulate necessary optimality conditions in continuous time using the Pontryagin’s maximum principle (PMP). A modification of the method of successive approximations is then used to solve the PMP, giving rise to an alternative training algorithm for deep learning. This approach has the advantage that rigorous error estimates and convergence results can be established. We also show that it may avoid some pitfalls of gradient-based methods, such as slow convergence on flat landscapes near saddle points. Furthermore, we demonstrate that it obtains favorable initial convergence rate periteration, provided Hamiltonian maximization can be efficiently carried out a step which is still in need of improvement. Overall, the approach opens up new avenues to attack problems associated with deep learning, such as trapping in slow manifolds and inapplicability of gradient-based methods for discrete trainable variables.

We derive optimal gambling and investment policies for cases in which the underlying stochastic process has parameter values that are unobserved random variables. For the objective of maximizing logarithmic utility when the underlying stochastic process is a simple random walk in a random environment, we show that a state-dependent control is optimal, which is a generalization of the celebrated Kelly strategy: The optimal strategy is to bet a fraction of current wealth equal to a linear function of the posterior mean increment. To approximate more general stochastic processes, we consider a continuous-time analog involving Brownian motion. To analyze the continuous-time problem, we study the diffusion limit of random walks in a random environment. We prove that they converge weakly to a Kiefer process, or tied-down Brownian sheet. We then find conditions under which the discrete-time process converges to a diffusion, and analyze the resulting process. We analyze in detail the case of the natural conjugate prior, where the success probability has a beta distribution, and show that the resulting limiting diffusion can be viewed as a rescaled Brownian motion. These results allow explicit computation of the optimal control policies for the continuoustime gambling and investment problems without resorting to continuous-time stochastic-control procedures. Moreover they also allow an explicit quantitative evaluation of the financial value of randomness, the financial gain of perfect information and the financial cost of learning in the Bayesian problem.

There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Dedicated to the marvelous random walk of Paul Erd} os through universities, continents, and mathematics Various aspects of the theory of random walks on graphs are surveyed. In particular, estimates on the important parameters of access time, commute time, cover time and mixing time are discussed. Connections with the eigenvalues of graphs and with electrical networks, and the use of these connections in the study of random walks is described. We also sketch recent algorithmic applications of random walks, in particular to the problem of sampling. 0. Introduction Given a graph and a starting point, we select a neighbor of it at random, and move to this neighbor; then we select a neighbor of this point at random, and move to it etc. The (random) sequence of points selected this way is a random walk on the graph. A random walk is a nite Markov chain that is time-reversible (see below). In fact, there is not much diierence between the theory of random walks on graphs and the theory of nite Markov chains; every Markov chain can be viewed as random walk on a directed graph, if we allow weighted edges. Similarly, time-reversible Markov chains can be viewed as random walks on undirected graphs, and symmetric Markov chains, as random walks on regular symmetric graphs. In this paper we'll formulate the results in terms of random walks, and mostly restrict our attention to the undirected case. 2 L. Lovv asz Random walks arise in many models in mathematics and physics. In fact, this is one of those notions that tend to pop up everywhere once you begin to look for them. For example, consider the shuuing of a deck of cards. Construct a graph whose nodes are all permutations of the deck, and two of them are adjacent if they come by one shuue move (depending on how you shuue). Then repeated shuue moves correspond to a random walk on this graph (see Diaconis 20]). The Brownian motion of a dust particle is random walk in the room. Models in statistical mechanics can be viewed as random walks on the set of states. The classical theory of random walks deals with random walks on simple , but innnite graphs, like grids, and studies their qualitative behaviour: does the random walk return to its starting point with probability one? does it return innnitely often? For example, PP olya (1921) proved that …

We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric l. We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of l. We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semimetric l under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.

We give concentration bounds for martingales that are uniform over finite times and extend classical Hoeffding and Bernstein inequalities. We also demonstrate our concentration bounds to be optimal with a matching anti-concentration inequality, proved using the same method. Together these constitute a finite-time version of the law of the iterated logarithm, and shed light on the relationship between it and the central limit theorem.

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

Many studies have shown that Deep Convolutional Neural Networks (DCNNs) exhibit great accuracies given large training datasets in image recognition tasks. Optimization technique known as asynchronous mini-batch Stochastic Gradient Descent (SGD) is widely used for deep learning because it gives fast training speed and good recognition accuracies, while it may increases generalization error if training parameters are in inappropriate ranges. We propose a performance model of a distributed DCNN training system called SPRINT that uses asynchronous GPU processing based on mini-batch SGD. The model considers the probability distribution of mini-batch size and gradient staleness that are the core parameters of asynchronous SGD training. Our performance model takes DCNN architecture and machine specifications as input parameters, and predicts time to sweep entire dataset, mini-batch size and staleness with 5%, 9% and 19% error in average respectively on several supercomputers with up to thousands of GPUs. Experimental results on two different supercomputers show that our model can steadily choose the fastest machine configuration that nearly meets a target mini-batch size.

Bayesian optimization is a prominent method for optimizing expensive-to-evaluate black-box functions that is widely applied to tuning the hyperparameters of machine learning algorithms. Despite its successes, the prototypical Bayesian optimization approach – using Gaussian process models – does not scale well to either many hyperparameters or many function evaluations. Attacking this lack of scalability and flexibility is thus one of the key challenges of the field. We present a general approach for using flexible parametric models (neural networks) for Bayesian optimization, staying as close to a truly Bayesian treatment as possible. We obtain scalability through stochastic gradient Hamiltonian Monte Carlo, whose robustness we improve via a scale adaptation. Experiments including multi-task Bayesian optimization with 21 tasks, parallel optimization of deep neural networks and deep reinforcement learning show the power and flexibility of this approach.


Question: A stochastic gradient Langevin dynamics examination through hitting time analysis.

This paper introduces a decision-theoretic framework for generalizing the Probably Approximately Correct (PAC) learning model, particularly tailored for applications involving neural networks and other learning paradigms. The proposed model is grounded in statistical decision theory, where the learner is presented with a sequence of examples. Each example is composed of an instance \(x\) from a set \(X\) and an outcome \(y\) from a set \(Y\). The goal is to develop a decision rule \(h: X \rightarrow A\), with \(h \in \mathcal{H}\), that minimizes the expected loss \(I(y, a)\) for every instance \(x\). Here, \(I\) is a real-valued loss function, and examples are drawn from an arbitrary joint distribution over \(X \times Y\). This model encompasses various learning scenarios, such as function learning from \(X\) to \(Y\), regression, and density estimation.

The paper presents theorems on the uniform convergence of empirical loss estimates to the true expected loss rates for specific decision rule spaces \(\mathcal{H}\), which, in turn, indicates learnability within a bounded sample size, ignoring computational complexity. An application of these theorems is showcased through distribution-independent upper bounds on the sample size required for learning with feedforward neural networks. These findings are underpinned by a generalized VC dimension concept for classes of real-valued functions, inspired by Vapnik and Pollard's work, and a capacity and metric dimension concept for function classes mapping into a bounded metric space.

The paper's contributions are significant in expanding the PAC model's applicability to more complex learning scenarios, such as those encountered in neural network learning, by providing a more nuanced understanding of learnability and sample complexity. This framework is particularly relevant for neural network learning, where the complexity of the decision rule space and the need for generalization are paramount.

Parameter-specific adaptive learning rate methods are computationally efficient ways to reduce the ill-conditioning problems encountered when training large deep networks. Following recent work that strongly suggests that most of the critical points encountered when training such networks are saddle points, we find how considering the presence of negative eigenvalues of the Hessian could help us design better suited adaptive learning rate schemes. We show that the popular Jacobi preconditioner has undesirable behavior in the presence of both positive and negative curvature, and present theoretical and empirical evidence that the socalled equilibration preconditioner is comparatively better suited to non-convex problems. We introduce a novel adaptive learning rate scheme, called ESGD, based on the equilibration preconditioner. Our experiments show that ESGD performs as well or better than RMSProp in terms of convergence speed, always clearly improving over plain stochastic gradient descent.

Neural networks have great success in many machine learning applications, but the fundamental learning theory behind them remains largely unsolved. Learning neural networks is NP-hard, but in practice, simple algorithms like stochastic gradient descent (SGD) often produce good solutions. Moreover, it is observed that overparameterization (that is, designing networks whose number of parameters is larger than statistically needed to perfectly fit the data) improves both optimization and generalization, appearing to contradict traditional learning theory. In this work, we extend the theoretical understanding of two and three-layer neural networks in the overparameterized regime. We prove that, using overparameterized neural networks, one can (improperly) learn some notable hypothesis classes, including two and three-layer neural networks with fewer parameters. Moreover, the learning process can be simply done by SGD or its variants in polynomial time using polynomially many samples. We also show that for a fixed sample size, the generalization error of the solution found by some SGD variant can be made almost independent of the number of parameters in the overparameterized network. ∗V1 appears on this date and V2 polishes writing and tightens parameters. Authors sorted in alphabetical order.

Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.

We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric l. We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of l. We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semimetric l under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.

To understand the dynamics of optimization in deep neural networks, we develop a tool to study the evolution of the entire Hessian spectrum throughout the optimization process. Using this, we study a number of hypotheses concerning smoothness, curvature, and sharpness in the deep learning literature. We then thoroughly analyze a crucial structural feature of the spectra: in non-batch normalized networks, we observe the rapid appearance of large isolated eigenvalues in the spectrum, along with a surprising concentration of the gradient in the corresponding eigenspaces. In batch normalized networks, these two effects are almost absent. We characterize these effects, and explain how they affect optimization speed through both theory and experiments. As part of this work, we adapt advanced tools from numerical linear algebra that allow scalable and accurate estimation of the entire Hessian spectrum of ImageNet-scale neural networks; this technique may be of independent interest in other applications.

Adjusting the learning rate schedule in stochastic gradient methods is an important unresolved problem which requires tuning in practice. If certain parameters of the loss function such as smoothness or strong convexity constants are known, theoretical learning rate schedules can be applied. However, in practice, such parameters are not known, and the loss function of interest is not convex in any case. The recently proposed batch normalization reparametrization is widely adopted in most neural network architectures today because, among other advantages, it is robust to the choice of Lipschitz constant of the gradient in loss function, allowing one to set a large learning rate without worry. Inspired by batch normalization, we propose a general nonlinear update rule for the learning rate in batch and stochastic gradient descent so that the learning rate can be initialized at a high value, and is subsequently decreased according to gradient observations along the way. The proposed method is shown to achieve robustness to the relationship between the learning rate and the Lipschitz constant, and near-optimal convergence rates in both the batch and stochastic settings (O(1/T ) for smooth loss in the batch setting, and O(1/ √ T ) for convex loss in the stochastic setting). We also show through numerical evidence that such robustness of the proposed method extends to highly nonconvex and possibly non-smooth loss function in deep learning problems. Our analysis establishes some first theoretical understanding into the observed robustness for batch normalization and weight normalization.

The softmax representation of probabilities for categorical variables plays a prominent role in modern machine learning with numerous applications in areas such as large scale classification, neural language modeling and recommendation systems. However, softmax estimation is very expensive for large scale inference because of the high cost associated with computing the normalizing constant. Here, we introduce an efficient approximation to softmax probabilities which takes the form of a rigorous lower bound on the exact probability. This bound is expressed as a product over pairwise probabilities and it leads to scalable estimation based on stochastic optimization. It allows us to perform doubly stochastic estimation by subsampling both training instances and class labels. We show that the new bound has interesting theoretical properties and we demonstrate its use in classification problems.

In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

We develop a theory characterizing optimal stopping times for discrete-time ergodic Markov processes with discounted rewards. The theory differs from prior work by its view of per-stage and terminal reward functions as elements of a certain Hilbert space. In addition to a streamlined analysis establishing existence and uniqueness of a solution to Bellman's equation, this approach provides an elegant framework for the study of approximate solutions. In particular, we propose a stochastic approximation algorithm that tunes weights of a linear combination of basis functions in order to approximate a value function. We prove that this algorithm converges (almost surely) and that the limit of convergence has some desirable properties. We discuss how variations on this line of analysis can be used to develop similar results for other classes of optimal stopping problems, including those involving independent increment processes, finite horizons, and two-player zero-sum games. We illustrate the approximation method with a computational case study involving the pricing of a path-dependent financial derivative security that gives rise to an optimal stopping problem with a one-hundred-dimensional state space.

We study in this paper a class of constrained linear-quadratic (LQ) optimal control problem formulations for the scalar-state stochastic system with multiplicative noise, which has various applications, especially in the financial risk management. The linear constraint on both the control and state variables considered in our model destroys the elegant structure of the conventional LQ formulation and has blocked the derivation of an explicit control policy so far in the literature. We successfully derive in this paper the analytical control policy for such a class of problems by utilizing the state separation property induced from its structure. We reveal that the optimal control policy is a piece-wise affine function of the state and can be computed off-line efficiently by solving two coupled Riccati equations. Under some mild conditions, we also obtain the stationary control policy for infinite time horizon. We demonstrate the implementation of our method via some illustrative examples and show how to calibrate our model to solve dynamic constrained portfolio optimization problems.