208 results found
Yang Q, Mohammadi Amiri M, Gunduz D, Audience-retention-rate-aware caching and coded video delivery with asynchronous demands, IEEE Transactions on Communications, 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.
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, 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, 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.
Tao M, Gunduz D, Xu F, et al., 2019, Content caching and delivery in wireless radio access networks, IEEE Transactions on Communications, ISSN: 0090-6778
Today’s mobile data traffic is dominated by contentoriented traffic. Caching popular contents at the network edge can alleviate network congestion and reduce content delivery latency. This paper provides a comprehensive and unified study of caching and delivery techniques in wireless radio access networks (RANs) with caches at all edge nodes (ENs) and user equipments (UEs). Three cache-aided RAN architectures are considered: RANs without fronthaul, with dedicated fronthaul, and with wireless fronthaul. It first reviews in a tutorial nature how caching facilitates interference management in these networks by enabling interference cancellation (IC), zero-forcing (ZF), and interference alignment (IA). Then, two new delivery schemes are presented. One is for RANs with dedicated fronthaul, which considers centralized cache placement at the ENs but both centralized and decentralized placement at the UEs. This scheme combines IA, ZF, and IC together with soft-transfer fronthauling. The other is for RANs with wireless fronthaul, which considers decentralized cache placement at all nodes. It leverages the broadcast nature of wireless fronthaul to fetch not only uncached but also cached contents to boost transmission cooperation among the ENs. Numerical results show that both schemes outperform existing results for a wide range of system parameters, thanks to the various caching gains obtained opportunistically.
Rassouliy B, Gunduz D, 2019, Optimal utility-privacy trade-off with total variation distance as a privacy measure, IEEE Transactions on Information Forensics and Security, ISSN: 1556-6013
The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satis?es the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.
Ceran ET, Gunduz D, Gyorgy A, 2019, Average age of information with hybrid ARQ under a resource constraint, IEEE Transactions on Wireless Communications, Vol: 18, Pages: 1900-1913, ISSN: 1536-1276
Scheduling the transmission of status updates over an error-prone communication channel is studied in order to minimize the long-term average age of information at the destination under a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK/NACK feedback, and decides on the next update without prior knowledge on the success of future transmissions. The optimal scheduling policy is first studied under different feedback mechanisms when the channel statistics are known; in particular, the standard automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols are considered. The structural results are derived for the optimal policy under HARQ, while the optimal policy is determined analytically for ARQ. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the transmission policy in real time. The effectiveness of the proposed methods is verified through the numerical results.
Mohammadi Amiri M, Gunduz D, Computation scheduling for distributed machine learning with straggling workers, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: Institute of Electrical and Electronics Engineers
We study scheduling of computation tasks acrossnworkers in a large scale distributed learning problem. Computa-tion speeds of the workers are assumed to be heterogeneous andunknown to the master, and redundant computations are assignedto the workers in order to tolerate straggling workers. We con-sider sequential computation and instantaneous communicationfrom each worker to the master, and each computation round,which can model a single iteration of the stochastic gradientdescent (SGD) algorithm, iscompletedonce the master receivesk≤ndistinct computations, referred to as thecomputationtarget. Our goal is to characterize theaverage completion timeas a function of thecomputation load, which denotes the portionof the dataset available at each worker, and the computationtarget. We propose two computation scheduling schemes thatspecify the computation tasks assigned to each worker, as wellas their order of execution. We also establish a lower bound onthe minimum average completion time. Numerical results showa significant reduction in the average computation time over theexisting coded and uncoded computing schemes.
Rassouli B, Rosas F, Gunduz D, 2019, Latent Feature Disclosure under Perfect Sample Privacy, 10th IEEE International Workshop on Information Forensics and Security (WIFS), Publisher: IEEE, ISSN: 2157-4766
Rassouli B, Gündüz D, 2019, Optimal utility-privacy trade-off with total variation distance as a privacy measure, 2018 IEEE Information Theory Workshop, ITW 2018
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved. The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in order to receive utility, while preserving the privacy of sensitive data from the legitimate receiver. The total variation distance is motivated as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with an arbitrary bounded cost function.
Mital N, Kralevska K, Ling C, et al., 2019, Storage-repair bandwidth trade-off for wireless caching with partial failure and broadcast repair, IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 41-45
Repair of multiple partially failed cache nodes is studied in a distributed wireless content caching system, where r out of a total of n cache nodes lose part of their cached data. Broadcast repair of failed cache contents at the network edge is studied; that is, the surviving cache nodes transmit broadcast messages to the failed ones, which are then used, together with the surviving data in their local cache memories, to recover the lost content. The trade-off between the storage capacity and the repair bandwidth is derived. It is shown that utilizing the broadcast nature of the wireless medium and the surviving cache contents at partially failed nodes significantly reduces the required repair bandwidth per node.
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
Ozfatura E, Rarris T, Gunduz D, et al., 2018, Delay-Aware Coded Caching for Mobile Users, 29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Publisher: IEEE
Bourtsoulatze E, Gunduz D, 2018, Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks, 2018 IEEE 29TH ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC)
Ceran ET, Gunduz D, Gyorgy A, 2018, A Reinforcement Learning Approach to Age of Information in Multi-User Networks, 29th IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Publisher: IEEE, Pages: 1967-1971
Tung T-Y, Gunduz D, 2018, SparseCast: Hybrid digital-analog wireless image transmission exploiting frequency domain sparsity, IEEE Communications Letters, Vol: 22, Pages: 2451-2454, ISSN: 1089-7798
A hybrid digital-analog wireless image transmission scheme, called SparseCast, is introduced, which provides graceful degradation with channel quality. SparseCast achieves improved end-to-end reconstruction quality while reducing the bandwidth requirement by exploiting frequency-domain sparsity through compressed sensing. The proposed algorithm produces a linear relationship between the channel signal-to-noise ratio and peak signal-to-noise ratio (PSNR) without requiring the channel state knowledge at the transmitter. This is particularly attractive when transmitting to multiple receivers or over unknown time-varying channels, as the receiver PSNR depends on the experienced channel quality and is not bottlenecked by the worst channel. SparseCast is benchmarked against two alternative algorithms: SoftCast and block CS-smooth projected Landweber (BCS-SPL). Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.5 dB and BCS-SPL by 15.2 dB.
Sreekumar S, Gunduz D, Cohen A, 2018, Distributed Hypothesis Testing Under Privacy Constraints, IEEE Information Theory Workshop (ITW), Publisher: IEEE, Pages: 470-474, ISSN: 2475-420X
Li Z, Oechtering TJ, Gunduz D, 2018, Privacy against a hypothesis testing adversary, IEEE Transactions on Information Forensics and Security, ISSN: 1556-6013
Privacy against an adversary (AD) that tries to detect the underlying privacy-sensitive data distribution is studied. The original data sequence is assumed to come from one of the two known distributions, and the privacy leakage is measured by the probability of error of the binary hypothesis test carried out by the AD. A management unit (MU) is allowed to manipulate the original data sequence in an online fashion, while satisfying an average distortion constraint. The goal of the MU is to maximize the minimal type II probability of error subject to a constraint on the type I probability of error assuming an adversarial Neyman-Pearson test, or to maximize the minimal error probability assuming an adversarial Bayesian test. The asymptotic exponents of the maximum minimal type II probability of error and the maximum minimal error probability are shown to be characterized by a Kullback-Leibler divergence rate and a Chernoff information rate, respectively. Privacy performances of particular management policies, the memoryless hypothesis-aware policy and the hypothesis-unaware policy with memory, are compared. The proposed formulation can also model adversarial example generation with minimal data manipulation to fool classifiers. Lastly, the results are applied to a smart meter privacy problem, where the user’s energy consumption is manipulated by adaptively using a renewable energy source in order to hide user’s activity from the energy provider.
Faqir OJ, Kerrigan EC, Gunduz D, 2018, Information transmission bounds in mobile communication networks, UKACC 12th International Conference on Control (CONTROL), Publisher: IEEE, Pages: 99-99
Giaconi G, Gunduz D, Poor HV, 2018, Privacy-aware smart metering progress and challenges, IEEE Signal Processing Magazine, Vol: 35, Pages: 59-78, ISSN: 1053-5888
The next-generation energy network, the so-called smart grid (SG), promises tremendous increases in efficiency, safety, and flexibility in managing the electricity grid as compared to the legacy energy network. This is needed today more than ever, as global energy consumption is growing at an unprecedented rate and renewable energy sources (RESs) must be seamlessly integrated into the grid to assure a sustainable human development.
Faqir OJ, Kerrigan EC, Gunduz D, 2018, Energy-optimal control in mobile aerial relay-assisted networks, UKACC 12th International Conference on Control (CONTROL), Publisher: IEEE, Pages: 100-100
Rosas De Andraca F, Chen K-C, Gunduz D, 2018, Social learning for resilient data fusion against data falsification attacks, Computational Social Networks, Vol: 5, ISSN: 2197-4314
BackgroundInternet of Things (IoT) suffers from vulnerable sensor nodes, which are likely to endure data falsification attacks following physical or cyber capture. Moreover, centralized decision-making and data fusion turn decision points into single points of failure, which are likely to be exploited by smart attackers.MethodsTo tackle this serious security threat, we propose a novel scheme for enabling distributed decision-making and data aggregation through the whole network. Sensor nodes in our scheme act following social learning principles, resembling agents within a social network.ResultsWe analytically examine under which conditions local actions of individual agents can propagate through the network, clarifying the effect of Byzantine nodes that inject false information. Moreover, we show how our proposed algorithm can guarantee high network performance, even for cases when a significant portion of the nodes have been compromised by an adversary.ConclusionsOur results suggest that social learning principles are well suited for designing robust IoT sensor networks and enabling resilience against data falsification attacks.
Somuyiwa SO, Gunduz D, Gÿorgy A, 2018, Reinforcement Learning for Proactive Caching of Contents with Different Demand Probabilities, ISSN: 2154-0217
© 2018 IEEE. A mobile user randomly accessing a dynamic content library over a wireless channel is considered. At each time instant, a random number of contents are added to the library and each content remains relevant to the user for a random period of time. Contents are classified into finitely many classes such that whenever the user accesses the system, he requests each content randomly with a class-specific demand probability. Contents are downloaded to the user equipment (UE) through a wireless link whose quality also varies randomly with time. The UE has a cache memory of finite capacity, which can be used to proactively store contents before they are requested by the user. Any time contents are downloaded, the system incurs a cost (energy, bandwidth, etc.) that depends on the channel state at the time of download, and scales linearly with the number of contents downloaded. Our goal is to minimize the expected long-term average cost. The problem is modeled as a Markov decision process, and the optimal policy is shown to exhibit a threshold structure; however, since finding the optimal policy is computationally infeasible, parametric approximations to the optimal policy are considered, whose parameters are optimized using the policy gradient method. Numerical simulations show that the performance gain of the resulting scheme over traditional reactive content delivery is significant, and increases with the cache capacity. Comparisons with two performance lower bounds, one computed based on infinite cache capacity and another based on non-casual knowledge of the user access times and content requests, demonstrate that our scheme can perform close to the theoretical optimum.
Rassouli B, Varasteh M, Gunduz D, 2018, Gaussian multiple access channels with one-bit quantizer at the receiver, Entropy, Vol: 20, ISSN: 1099-4300
The capacity region of a two-transmitter Gaussian multiple access channel(MAC) under average input power constraints is studied, when the receiveremploys a zero-threshold one-bit analog-to-digital converter (ADC). It isproved that the input distributions of the two transmitters that achieve theboundary points of the capacity region are discrete. Based on the position of aboundary point, upper bounds on the number of the mass points of thecorresponding distributions are derived.
Guler B, Gunduz D, Yener A, 2018, Lossy coding of correlated sources over a multiple access channel: necessary conditions and separation results, IEEE Transactions on Information Theory, Vol: 64, Pages: 6081-6097, ISSN: 0018-9448
Lossy coding of correlated sources over a multiple access channel (MAC) is studied. First, a joint source-channel coding scheme is presented when the decoder has correlated side information. Next, the optimality of separate source and channel coding, that emerges from the availability of a common observation at the encoders, or side information at the encoders and the decoder, is investigated. It is shown that separation is optimal when the encoders have access to a common observation whose lossless recovery is required at the decoder, and the two sources are independent conditioned on this common observation. Optimality of separation is also proved when the encoder and the decoder have access to shared side information conditioned on which the two sources are independent. These separation results obtained in the presence of side information are then utilized to provide a set of necessary conditions for the transmission of correlated sources over a MAC without side information. Finally, by specializing the obtained necessary conditions to the transmission of binary and Gaussian sources over a MAC, it is shown that they can potentially be tighter than the existing results in the literature, providing a novel converse for this fundamental problem.
Shi J, Liu L, Gunduz D, et al., 2018, Polar codes and polar lattices for the Heegard-Berger problem, IEEE Transactions on Communications, Vol: 66, Pages: 3760-3771, ISSN: 0090-6778
Explicit coding schemes are proposed to achieve the rate-distortion function of the Heegard-Berger problem using polar codes. Specifically, a nested polar code construction is employed to achieve the rate-distortion function for doublysymmetric binary sources when the side information may be absent. The nested structure contains two optimal polar codes for lossy source coding and channel coding, respectively. Moreover, a similar nested polar lattice construction is employed when the source and the side information are jointly Gaussian. The proposed polar lattice is constructed by nesting a quantization polar lattice and a capacity-achieving polar lattice for the additive white Gaussian noise channel.
Chin J-X, Giaconi G, De Rubira TT, et al., 2018, Considering Time Correlation in the Estimation of Privacy Loss for Consumers with Smart Meters, Power Systems Computation Conference (PSCC), Publisher: IEEE
Rassouli B, Gunduz D, 2018, On perfect privacy, 2018 IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 2551-2555, ISSN: 2157-8117
For a pair of (dependent) random variables (X, Y), the following problem is addressed: What is the maximum information that can be revealed about Y, while disclosing no information about X? Assuming that a Markov kernel maps Y to the revealed information U, it is shown that the maximum mutual information between Y and U, i.e., I(Y; U), can be obtained as the solution of a standard linear program, when X and U are required to be independent, called perfect privacy. The resulting quantity is shown to be greater than or equal to the non-private information about X carried by Y. For jointly Gaussian (X, Y), it is shown that perfect privacy is not possible if the kernel is applied to only Y; whereas perfect privacy can be achieved if the mapping is from both X and Y; that is, if the private variables can also be observed at the encoder. Finally, it is shown that when Y is not a deterministic function of X, perfect privacy is always feasible when the mapping has access to both X and Y.1
Rassouli B, Gunduz D, 2018, On Perfect Privacy, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 2555-2559
Sreekumar S, Gunduz D, 2018, Testing against conditional independence under security constraints, International Symposium on Information Theory, Publisher: IEEE, Pages: 181-185, ISSN: 2157-8117
© 2018 IEEE. A distributed binary hypothesis testing problem involving three parties, a remote node, called the observer, a legitimate decoder, called the detector, and an adversary, is studied. The remote node observes a discrete memoryless source, and communicates its observations over a rate-limited noiseless public channel to the detector, which tests for the conditional independence of its own observations from that of the remote node, conditioned on some additional side information. The adversary, in addition to observing the public message, has access to its own correlated side-information. Considering the type 2 error exponent for a given type 1 error probability constraint as the performance measure for the hypothesis test at the detector, and equivocation of the source at the adversary as the secrecy measure, a single-letter characterization of the rate-error exponent-equivocation trade-off is established. Additionally, for a general distortion measure, imposing the average distortion at the adversary as the measure of secrecy achieved, an inner bound on the trade-off between the rate, error exponent and average distortion is obtained. This bound is shown to be tight under the less noisy condition on the adversary's side information.
Cao D, Zhang D, Chen P, et al., 2018, Coded Caching with Heterogeneous Cache Sizes and Link Qualities: The Two-User Case, IEEE International Symposium on Information Theory (ISIT), Publisher: IEEE, Pages: 1545-1549
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