248 results found
Popovski P, Simeone O, Boccardi F, et al., 2020, Semantic-effectiveness filtering and control for post-5G wireless connectivity, Journal of the Indian Institute of Science, ISSN: 0970-4140
The traditional role of a communication engineer is to address the technical problem of transporting bits reliably over a noisy channel. With the emergence of 5G, and the availability of a variety of competing and coexisting wireless systems, wireless connectivity is becoming a commodity. This article argues that communication engineers in the post-5G era should extend the scope of their activity in terms of design objectives and constraints beyond connectivity to encompass the semantics of the transferred bits within the given applications and use cases. To provide a platform for semantic-aware connectivity solutions, this paper introduces the concept of a semantic-effectiveness (SE) plane as a core part of future communication architectures. The SE plane augments the protocol stack by providing standardized interfaces that enable information filtering and direct control of functionalities at all layers of the protocol stack. The advantages of the SE plane are described in the perspective of recent developments in 5G, and illustrated through a number of example applications. The introduction of a SE plane may help replacing the current “next-G paradigm” in wireless evolution with a framework based on continuous improvements and extensions of the systems and standards.
Boloursaz Mashhadi M, Gunduz D, 2020, Deep learning for massive MIMO channel state acquisition and feedback, Journal of the Indian Institute of Science, ISSN: 0970-4140
Massive multiple-input multiple-output (MIMO) systems are a main enabler of the excessive throughput requirements in 5G and future generation wireless networks as they can serve many users simultaneously with high spectral and energy efficiency. To achieve this massive MIMO systems require accurate and timely channel state information (CSI), which is acquired by a training process that involves pilot transmission, CSI estimation, and feedback. This training process incurs a training overhead, which scales with the number of antennas, users, and subcarriers. Reducing the training overhead in massive MIMO systems has been a major topic of research since the emergence of the concept. Recently, deep learning (DL)-based approaches have been proposed and shown to provide significant reduction in the CSI acquisition and feedback overhead in massive MIMO systems compared to traditional techniques. In this paper, we present an overview of the state-of-the-art DL architectures and algorithms used for CSI acquisition and feedback, and provide further research directions.
Mital N, Gunduz D, Ling C, 2020, Coded caching in a multi-server system with random topology, IEEE Transactions on Communications, Pages: 1-1, ISSN: 0090-6778
Cache-aided content delivery is studied in a multi-server system with P servers and K users, each equipped with a local cache memory. In the delivery phase, each user connects randomly to any ρ out of P servers. Thanks to the availability of multiple servers, which model small-cell base stations (SBSs), demands can be satisfied with reduced storage capacity at each server and reduced delivery rate per server; however, this also leads to reduced multicasting opportunities compared to the single-server scenario. A joint storage and proactive caching scheme is proposed, which exploits coded storage across the servers, uncoded cache placement at the users, and coded delivery. The delivery latency is studied for both successive and parallel transmissions from the servers. It is shown that, with successive transmissions the achievable average delivery latency is comparable to the one achieved in the single-server scenario, while the gap between the two depends on ρ, the available redundancy across the servers, and can be reduced by increasing the storage capacity at the SBSs. The optimality of the proposed scheme with uncoded cache placement and MDS-coded server storage is also proved for successive transmissions.
Kurka DB, Gunduz D, 2020, DeepJSCC-f: deep joint source-channel coding of images with feedback, IEEE Journal on Selected Areas in Information Theory, Pages: 1-1, ISSN: 2641-8770
We consider wireless transmission of images in the presence of channel output feedback. From a Shannon theoretic perspective feedback does not improve the asymptotic end-to-end performance, and separate source coding followed by capacity-achieving channel coding, which ignores the feedback signal, achieves the optimal performance. It is well known that separation is not optimal in the practical finite blocklength regime; however, there are no known practical joint source-channel coding (JSCC) schemes that can exploit the feedback signal and surpass the performance of separation-based schemes. Inspired by the recent success of deep learning methods for JSCC, we investigate how noiseless or noisy channel output feedback can be incorporated into the transmission system to improve the reconstruction quality at the receiver. We introduce an autoencoder-based JSCC scheme, which we call DeepJSCC-f, that exploits the channel output feedback, and provides considerable improvements in terms of the end-to-end reconstruction quality for fixed-length transmission, or in terms of the average delay for variable-length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding-based designs.
Ozfatura M, Gunduz D, 2020, Mobility-aware coded storage and delivery, IEEE Transactions on Communications, ISSN: 0090-6778
We consider a cache-enabled heterogeneous cellular network, where mobile users (MUs) connect to multiple cache-enabled small-cell base stations (SBSs) during a video downloading session. SBSs can deliver these requests using their local cache contents as well as by downloading them from a macro-cell base station (MBS), which has access to the file library. We introduce a novel mobility-aware content storage and delivery scheme, which jointly exploits coded storage at the SBSs and coded delivery from the MBS to reduce the backhaul load from the MBS to the SBSs. We show that the proposed scheme provides a significant reduction both in the backhaul load when the cache capacity is sufficiently large, and in the number of sub-files required. Overall, for practical scenarios, in which the number of sub-files that can be created is limited either by the size of the files, or by the protocol overhead, the proposed coded caching and delivery scheme decidedly outperforms state-of-the-art alternatives. Finally, we show that the benefits of the proposed scheme also extends to scenarios with non-uniform file popularities and arbitrary mobility patterns.
Mohammadi Amiri M, Gunduz D, Federated learning over wireless fading channels, IEEE Transactions on Wireless Communications, ISSN: 1536-1276
We study federated machine learning at the wirelessnetwork edge, where limited power wireless devices, each withits own dataset, build a joint model with the help of a remoteparameter server (PS). We consider a bandwidth-limited fadingmultiple access channel (MAC) from the wireless devices to thePS, and propose various techniques to implement distributedstochastic gradient descent (DSGD) over this shared noisywireless channel. We first propose a digital DSGD (D-DSGD)scheme, in which one device is selected opportunistically fortransmission at each iteration based on the channel conditions;the scheduled device quantizes its gradient estimate to a finitenumber of bits imposed by the channel condition, and transmitsthese bits to the PS in a reliable manner. Next, motivated bythe additive nature of the wireless MAC, we propose a novelanalog communication scheme, referred to as thecompressedanalogDSGD (CA-DSGD), where the devices first sparsifytheir gradient estimates while accumulating error from previousiterations, and project the resultant sparse vector into a low-dimensional vector for bandwidth reduction. We also design apower allocation scheme to align the received gradient vectorsat the PS in an efficient manner. Numerical results show thatD-DSGD outperforms other digital approaches in the literature;however, in general the proposed CA-DSGD algorithm convergesfaster than the D-DSGD scheme, and reaches a higher level ofaccuracy. We have observed that the gap between the analogand digital schemes increases when the datasets of devices arenot independent and identically distributed (i.i.d.). Furthermore,the performance of the CA-DSGD scheme is shown to be robustagainst imperfect channel state information (CSI) at the devices.Overall these results show clear advantages for
Pujol Roig J, Gutierrez-Estevez DM, Gunduz D, 2020, Management and orchestration of virtual network functions via deep reinforcement learning, IEEE Journal on Selected Areas in Communications, Vol: 38, Pages: 304-317, ISSN: 0733-8716
Management and orchestration (MANO) of re-sources by virtual network functions (VNFs) represents one of thekey challenges towards a fully virtualized network architectureas envisaged by 5G standards. Current threshold-based policiesinefficiently over-provision network resources and under-utilizeavailable hardware, incurring high cost for network operators,and consequently, the users. In this work, we present a MANOalgorithm for VNFs allowing a central unit (CU) to learnto autonomously re-configure resources (processing power andstorage), deploy new VNF instances, or offload them to the cloud,depending on the network conditions, available pool of resources,and the VNF requirements, with the goal of minimizing a costfunction that takes into account the economical cost as wellas latency and the quality-of-service (QoS) experienced by theusers. First, we formulate the stochastic resource optimizationproblem as a parameterized action Markov decision process(PAMDP). Then, we propose a solution based on deep reinforce-ment learning (DRL). More precisely, we present a novel RLapproach, called parameterized action twin (PAT) deterministicpolicy gradient, which leverages anactor-critic architecturetolearn to provision resources to the VNFs in an online manner.Finally, we present numerical performance results, and map themto 5G key performance indicators (KPIs). To the best of ourknowledge, this is the first work that considers DRL for MANOof VNFs’ physical resources.
Faqir OJ, Kerrigan EC, Gunduz D, 2020, Information transmission bounds between moving terminals, IEEE Communications Letters, Pages: 1-1, ISSN: 1089-7798
In networks of mobile autonomous agents, e.g. for data acquisition, we may wish to maximize data transfer or to reliably transfer a minimum amount of data, subject to quality of service or energy constraints. These requirements can be guaranteed through both offline node design/specifications and online trajectory/communications design. Regardless of the distance between them, for a stationary point-to-point transmitter-receiver pair communicating across a single link under average power constraints, the total data transfer is unbounded as time tends to infinity. In contrast, we show that if the transmitter/receiver is moving at any constant speed away from each other, then the maximum transmittable data is bounded. Although general closed-form expressions as a function of communication and mobility profile parameters do not yet exist, we provide closed-form expressions for particular cases, such as ideal free space path loss. Under more general scenarios we instead give lower bounds on the total transmittable information across a single link between mobile nodes.
Mohammadi Amiri M, Gunduz D, 2020, Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air, IEEE Transactions on Signal Processing, Vol: 68, Pages: 2155-2169, ISSN: 1053-587X
© 1991-2012 IEEE. We study federated machine learning (ML) at the wireless edge, where power- and bandwidth-limited wireless devices with local datasets carry out distributed stochastic gradient descent (DSGD) with the help of a parameter server (PS). Standard approaches assume separate computation and communication, where local gradient estimates are compressed and transmitted to the PS over orthogonal links. Following this digital approach, we introduce D-DSGD, in which the wireless devices employ gradient quantization and error accumulation, and transmit their gradient estimates to the PS over a multiple access channel (MAC). We then introduce a novel analog scheme, called A-DSGD, which exploits the additive nature of the wireless MAC for over-the-air gradient computation, and provide convergence analysis for this approach. In A-DSGD, the devices first sparsify their gradient estimates, and then project them to a lower dimensional space imposed by the available channel bandwidth. These projections are sent directly over the MAC without employing any digital code. Numerical results show that A-DSGD converges faster than D-DSGD thanks to its more efficient use of the limited bandwidth and the natural alignment of the gradient estimates over the channel. The improvement is particularly compelling at low power and low bandwidth regimes. We also illustrate for a classification problem that, A-DSGD is more robust to bias in data distribution across devices, while D-DSGD significantly outperforms other digital schemes in the literature. We also observe that both D-DSGD and A-DSGD perform better with the number of devices, showing their ability in harnessing the computation power of edge devices.
Yang Q, Hassanzadeh P, Gunduz D, et al., 2020, Centralized Caching and Delivery of Correlated Contents Over Gaussian Broadcast Channels, IEEE TRANSACTIONS ON COMMUNICATIONS, Vol: 68, Pages: 122-136, ISSN: 0090-6778
Roushdy A, Seyed Motahari A, Nafie M, et al., 2020, Cache-aided combination networks with interference, IEEE Transactions on Wireless Communications, Vol: 19, Pages: 148-161, ISSN: 1536-1276
Centralized coded caching and delivery isstudied for a radio access combination network (RACN),whereby a set ofHedge nodes (ENs), connected to acloud server via orthogonal fronthaul links with limitedcapacity, serve a total ofKuser equipments (UEs) overwireless links. The cloud server is assumed to hold alibrary ofNfiles, each of sizeFbits; and each user,equipped with a cache of sizeμRNFbits, is connectedto a distinct set ofrENs each of which equipped witha cache of sizeμTNFbits, whereμT,μR∈[0,1]arethe fractional cache capacities of the UEs and the ENs,respectively. The objective is to minimize the normalizeddelivery time (NDT), which refers to the worst case deliverylatency when each user requests a single distinct file fromthe library. Three coded caching and transmission schemesare considered, namely theMDS-IA,soft-transferandzero-forcing (ZF)schemes. MDS-IA utilizes maximum distanceseparable (MDS) codes in the placement phase and realinterference alignment (IA) in the delivery phase. Theachievable NDT for this scheme is presented forr= 2and arbitrary fractional cache sizesμTandμR, and alsofor arbitrary value ofrand fractional cache sizeμTwhen the cache capacity of the UE is above a certainthreshold. The soft-transfer scheme utilizes soft-transferof coded symbols to ENs that implement ZF over the edgelinks. The achievable NDT for this scheme is presentedfor arbitraryrand arbitrary fractional cache sizesμTandμR. The last scheme utilizes ZF between the ENs andthe UEs without the participation of the cloud server inthe delivery phase. The achievable NDT for this scheme is presented for an arbitrary value ofrwhen the totalcache size at a pair of UE and EN is sufficient to store thewhole library, i.e.,μT+μR≥1. The results indicate thatthe fronthaul c
Amiri MM, Gunduz D, 2019, Computation Scheduling for Distributed Machine Learning With Straggling Workers, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 67, Pages: 6270-6284, ISSN: 1053-587X
Sun Y, Zhao J, Zhou S, et al., 2019, Heterogeneous coded computation across heterogeneous workers
© 2019 IEEE. Coded distributed computing framework enables large-scale machine learning (ML) models to be trained efficiently in a distributed manner, while mitigating the straggler effect. In this work, we consider a multi-task assignment problem in a coded distributed computing system, where multiple masters, each with a different matrix multiplication task, assign computation tasks to workers with heterogeneous computing capabilities. Both dedicated and probabilistic worker assignment models are considered, with the objective of minimizing the average completion time of all tasks. For dedicated worker assignment, greedy algorithms are proposed and the corresponding optimal load allocation is derived based on the Lagrange multiplier method. For probabilistic assignment, successive convex approximation method is used to solve the non-convex optimization problem. Simulation results show that the proposed algorithms reduce the completion time by 80% over uncoded scheme, and 49% over an unbalanced coded scheme.
Erdemir E, Dragotti PL, Gunduz D, 2019, Privacy-Aware Location Sharing with Deep Reinforcement Learning
© 2019 IEEE. Location-based services (LBSs) have become widely popular. Despite their utility, these services raise concerns for privacy since they require sharing location information with untrusted third parties. In this work, we study privacy-utility trade-off in location sharing mechanisms. Existing approaches are mainly focused on privacy of sharing a single location or myopic location trace privacy; neither of them taking into account the temporal correlations between the past and current locations. Although these methods preserve the privacy for the current time, they may leak significant amount of information at the trace level as the adversary can exploit temporal correlations in a trace. We propose an information theoretically optimal privacy preserving location release mechanism that takes temporal correlations into account. We measure the privacy leakage by the mutual information between the user's true and released location traces. To tackle the history-dependent mutual information minimization, we reformulate the problem as a Markov decision process (MDP), and solve it using asynchronous actor-critic deep reinforcement learning (RL).
Sreekumar S, Gunduz D, 2019, Distributed hypothesis testing over discrete memoryless channels, IEEE Transactions on Information Theory, Vol: 66, Pages: 2044-2066, ISSN: 0018-9448
A distributed binary hypothesis testing (HT) problem involving two parties, one referred to as the observer and the other as the detector is studied. The observer observes a discrete memoryless source (DMS) and communicates its observations to the detector over a discrete memoryless channel (DMC). The detector observes another DMS correlated with that at the observer, and performs a binary HT on the joint distribution of the two DMS’s using its own observed data and the information received from the observer. The trade-off between the type I error probability and the type II error-exponent of the HT is explored. Single-letter lower bounds on the optimal type II errorexponent are obtained by using two different coding schemes, a separate HT and channel coding scheme and a joint HT and channel coding scheme based on hybrid coding for the matched bandwidth case. Exact single-letter characterization of the same is established for the special case of testing against conditional independence, and it is shown to be achieved by the separate HT and channel coding scheme. An example is provided where the joint scheme achieves a strictly better performance than the separation based scheme.
Hameed MZ, Gyorgy A, Gunduz D, 2019, Communication without interception: Defense against modulation detection
© 2019 IEEE. We consider a communication scenario, in which an intruder tries to determine the modulation scheme of the intercepted signal. Our aim is to minimize the accuracy of the intruder, while guaranteeing that the intended receiver can still recover the underlying message with the highest reliability. This is achieved by constellation perturbation at the encoder, similarly to adversarial attacks against classifiers in machine learning. In image classification, the perturbation is limited to be imperceptible to a human observer, while in our case the perturbation is constrained so that the message can still be reliably decoded by a legitimate receiver that is oblivious to the perturbation. Simulation results demonstrate the viability of our approach to make wireless communication secure against both state-of-the-art deep-learning- and decision-tree-based intruders with minimal sacrifice in the communication performance.
Amiri MM, Duman TM, Gunduz D, 2019, Collaborative machine learning at the wireless edge with blind transmitters
© 2019 IEEE. We study wireless collaborative machine learning (ML), where mobile edge devices, each with its own dataset, carry out distributed stochastic gradient descent (DSGD) over-the-air with the help of a wireless access point acting as the parameter server (PS). At each iteration of the DSGD algorithm wireless devices compute gradient estimates with their local datasets, and send them to the PS over a wireless fading multiple access channel (MAC). Motivated by the additive nature of the wireless MAC, we propose an analog DSGD scheme, in which the devices transmit scaled versions of their gradient estimates in an uncoded fashion. We assume that the channel state information (CSI) is available only at the PS. We instead allow the PS to employ multiple antennas to alleviate the destructive fading effect, which cannot be cancelled by the transmitters due to the lack of CSI. Theoretical analysis indicates that, with the proposed DSGD scheme, increasing the number of PS antennas mitigates the fading effect, and, in the limit, the effects of fading and noise disappear, and the PS receives aligned signals used to update the model parameter. The theoretical results are then corroborated with the experimental ones.
Gesbert D, Gunduz D, de Kerret P, et al., 2019, Guest Editorial Special Issue on Machine Learning in Wireless Communication-Part 2, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 37, Pages: 2409-2412, ISSN: 0733-8716
Gesbert D, Gunduz D, de Kerret P, et al., 2019, Special Issue on Machine Learning in Wireless Communication-Part I, IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, Vol: 37, Pages: 2181-2183, ISSN: 0733-8716
Yang Q, Mohammadi Amiri M, Gunduz D, 2019, Audience-retention-rate-aware caching and coded video delivery with asynchronous demands, IEEE Transactions on Communications, Vol: 67, Pages: 7088-7102, ISSN: 0090-6778
Most of the current literature on coded cachingfocus on a static scenario, in which a fixed number of userssynchronously place their requests from a content library, andthe performance is measured in terms of the latency in satisfyingall of these requests. In practice, however, users start watching anonline video content asynchronously over time, and often abortwatching a video before it is completed. The latter behaviour iscaptured by the notion of audience retention rate, which measuresthe portion of a video content watched on average. In order tobring coded caching one step closer to practice, asynchronoususer demands are considered in this paper, by allowing userdemands to arrive randomly over time, and both the popularityof video files, and the audience retention rates are taken intoaccount. A decentralized partial coded delivery (PCD) schemeis proposed, and two cache allocation schemes are employed;namely homogeneous cache allocation (HoCA) and heterogeneouscache allocation (HeCA), which allocate users’ caches amongdifferent chunks of the video files in the library. Numerical resultsvalidate that the proposed PCD scheme, either with HoCA orHeCA, outperforms conventional uncoded caching as well as thestate-of-the-art decentralized caching schemes, which consideronly the file popularities, and are designed for synchronousdemand arrivals. An information-theoretical lower bound on theaverage delivery rate is also presented.
Thanks to the recent advances in processing speed, data acquisition and storage, machine learning (ML) is penetrating every facet of our lives, and transforming research in many areas in a fundamental manner. Wireless communications is another success story - ubiquitous in our lives, from handheld devices to wearables, smart homes, and automobiles. While recent years have seen a flurry of research activity in exploiting ML tools for various wireless communication problems, the impact of these techniques in practical communication systems and standards is yet to be seen. In this paper, we review some of the major promises and challenges of ML in wireless communication systems, focusing mainly on the physical layer. We present some of the most striking recent accomplishments that ML techniques have achieved with respect to classical approaches, and point to promising research directions where ML is likely to make the biggest impact in the near future. We also highlight the complementary problem of designing physical layer techniques to enable distributed ML at the wireless network edge, which further emphasizes the need to understand and connect ML with fundamental concepts in wireless communications.
Yang Q, Mashhadi MB, Gunduz D, 2019, Deep Convolutional Compression For Massive MIMO CSI Feedback, 2019 IEEE 29th International Workshop on Machine Learning for Signal Processing (MLSP), Publisher: IEEE
Gindullina E, Badia L, Gunduz D, 2019, Average Age-of-Information with a Backup Information Source
© 2019 IEEE. Data collected and transmitted by Internet of things (IoT) devices are typically used for control and monitoring purposes; and hence, their timely delivery is of utmost importance for the underlying applications. However, IoT devices operate with very limited energy sources, severely reducing their ability for timely collection and processing of status updates. IoT systems make up for these limitations by employing multiple low-power low-complexity devices that can monitor the same signal, possibly with different quality observations and different energy costs, to create diversity against the limitations of individual nodes. We investigate policies to minimize the average age of information (AoI) in a monitoring system that collects data from two sources of information denoted as primary and backup sources, respectively. We assume that each source offers a different trade-off between the AoI and the energy cost. The monitoring node is equipped with a finite size battery and harvests ambient energy. For this setup, we formulate the scheduling of status updates from the two sources as a Markov decision process (MDP), and obtain a policy that decides on the optimal action to take (i.e., which source to query or remain idle) depending on the current energy level and AoI. The performance of the obtained policy is compared with an aggressive policy for different system parameters. We identify few types of optimal solution structures and discuss the benefits of having a backup source of information in the system.
Cao D, Zhang D, Chen P, et al., 2019, Coded caching with asymmetric cache sizes and link qualities: The two-user case, IEEE Transactions on Communications, Vol: 67, Pages: 6112-6126, ISSN: 0090-6778
Centralized coded caching problem is studied for the two-user scenario, considering heterogeneous cache capacities at the users and private channels from the server to the users, in addition to a shared channel. Optimal caching and delivery strategies that minimize the worst-case delivery latency are presented for an arbitrary number of files. The converse proof follows from the sufficiency of file-index-symmetric caching and delivery codes, while the achievability is obtained through memory-sharing among a number of special memory–capacity pairs. The optimal scheme is shown to exploit the private link capacities by transmitting part of the corresponding user‘s request in an uncoded fashion. When there are no private links, the results presented here improve upon the two known results in the literature, namely, i) equal cache capacities and arbitrary number of files; and ii) unequal cache capacities and two files. The results are then extended to the caching problem with heterogeneous distortion requirements.
Bourtsoulatze E, Kurka DB, Gunduz D, 2019, Deep joint source-channel coding for wireless image transmission, IEEE Transactions on Cognitive Communications and Networking, Vol: 5, Pages: 567-579, ISSN: 2332-7731
We propose a joint source and channel coding (JSCC) technique for wireless image transmission that does not rely on explicit codes for either compression or error correction; instead, it directly maps the image pixel values to the complex-valued channel input symbols. We parameterize the encoder and decoder functions by two convolutional neural networks (CNNs), which are trained jointly, and can be considered as an autoencoder with a non-trainable layer in the middle that represents the noisy communication channel. Our results show that the proposed deep JSCC scheme outperforms digital transmission concatenating JPEG or JPEG2000 compression with a capacity achieving channel code at low signal-to-noise ratio (SNR) and channel bandwidth values in the presence of additive white Gaussian noise (AWGN). More strikingly, deep JSCC does not suffer from the “cliff effect”, and it provides a graceful performance degradation as the channel SNR varies with respect to the SNR value assumed during training. In the case of a slow Rayleigh fading channel, deep JSCC learns noise resilient coded representations and significantly outperforms separation-based digital communication at all SNR and channel bandwidth values.
Zhao J, Gunduz D, Simeone O, et al., 2019, Non-orthogonal unicast and broadcast transmission via joint beamforming and LDM in cellular networks, IEEE Transactions on Broadcasting, Pages: 1-13, ISSN: 0018-9316
Limited bandwidth resources and higher energy efficiency requirements motivate incorporating multicast and broadcast transmission into the next-generation cellular network architectures, particularly for multimedia streaming applications. Layered division multiplexing (LDM), a form of non-orthogonal multiple access (NOMA), can potentially improve unicast throughput and broadcast coverage with respect to traditional orthogonal frequency division multiplexing (FDM) or time division multiplexing (TDM), by simultaneously using the same frequency and time resources for multiple unicast or broadcast transmissions. In this paper, the performance of LDM-based unicast and broadcast transmission in a cellular network is studied by assuming a single frequency network (SFN) operation for the broadcast layer, while allowing arbitrarily clustered cooperation among the base stations (BSs) for the transmission of unicast data streams. Beamforming and power allocation between unicast and broadcast layers, the so-called injection level in the LDM literature, are optimized with the aim of minimizing the sum-power under constraints on the user-specific unicast rates and on the common broadcast rate. The effects of imperfect channel coding and imperfect channel state information (CSI) are also studied to gain insights into robust implementation in practical systems. The non-convex optimization problem is tackled by means of successive convex approximation (SCA) techniques. Performance upper bounds are also presented by means of the S-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM in each of the SCA subproblems, where the unicast beamforming vectors can be obtained locally by the cooperating BSs. Numerical results are presented, which show the tightness of the proposed bounds and hence the near-optimality of the proposed solutions.
Ozfatura E, Gunduz D, Ulukus S, 2019, Speeding Up Distributed Gradient Descent by Utilizing Non-persistent Stragglers, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 2729-2733
Mital N, Kralevska K, Ling C, et al., 2019, Practical Functional Regenerating Codes for Broadcast Repair of Multiple Nodes, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 221-225
Amiri MM, Gunduz D, 2019, Over-the-Air Machine Learning at the Wireless Edge
© 2019 IEEE. We study distributed machine learning at the wireless edge, where limited power devices (workers) with local datasets implement distributed stochastic gradient descent (DSGD) over-the-air with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the workers to the PS for communicating the local gradient estimates. Motivated by the additive nature of the wireless MAC, we study analog transmission of low-dimensional gradient estimates while accumulating error from previous iterations. We also design an opportunistic worker scheduling scheme to align the received gradient vectors at the PS in an efficient manner. Numerical results show that the proposed DSGD algorithm converges much faster than the state-of-the-art, while also providing a significantly higher accuracy.
Zhao J, Amiri MM, Gunduz D, 2019, A Low-Complexity Cache-Aided Multi-Antenna Content Delivery Scheme
© 2019 IEEE. We study downlink beamforming in a single-cell network with a multi-antenna base station (BS) serving cache-enabled users. For a given common rate of the files in the system, we first formulate the minimum transmit power with beamforming at the BS as a non-convex optimization problem. This corresponds to a multiple multicast problem, to which a stationary solution can be efficiently obtained through successive convex approximation (SCA). It is observed that the complexity of the problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users in the system. Therefore, we introduce a low-complexity alternative through time-sharing that limits the number of subfiles that can be received by a user in each time slot. It is shown through numerical simulations that, the reduced-complexity beamforming scheme has minimal performance gap compared to transmitting all the subfiles jointly, and outperforms the state-of-the-art low-complexity scheme at all SNR and rate values with sufficient spatial degrees of freedom, and in the high SNR/high rate regime when the number of spatial degrees of freedom is limited.
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