arXiv subjects

Deniz Gündüz

Publications and source records attributed to Deniz Gündüz.

At least 163 records · Page 9Linked to original sources

arXiv2019

A Low-Complexity Cache-Aided Multi-antenna Content Delivery Scheme

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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arXiv2019

Content Caching and Delivery in Wireless Radio Access Networks

Today's mobile data traffic is dominated by content-oriented 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.

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arXiv2019

Non-Orthogonal Unicast and Broadcast Transmission via Joint Beamforming and LDM in Cellular Networks

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 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 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 $\rm{S}$-procedure followed by semidefinite relaxation (SDR). Finally, a dual decomposition-based solution is proposed to facilitate an efficient distributed implementation of LDM where the optimal 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.

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arXiv2019

Optimal Utility-Privacy Trade-off with Total Variation Distance as a Privacy Measure

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 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 a bounded cost function.

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arXiv2019

Privacy Against Brute-Force Inference Attacks

Privacy-preserving data release is about disclosing information about useful data while retaining the privacy of sensitive data. Assuming that the sensitive data is threatened by a brute-force adversary, we define Guessing Leakage as a measure of privacy, based on the concept of guessing. After investigating the properties of this measure, we derive the optimal utility-privacy trade-off via a linear program with any $f$-information adopted as the utility measure, and show that the optimal utility is a concave and piece-wise linear function of the privacy-leakage budget.

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arXiv2019

Reinforcement Learning to Minimize Age of Information with an Energy Harvesting Sensor with HARQ and Sensing Cost

The time average expected age of information (AoI) is studied for status updates sent from an energy-harvesting transmitter with a finite-capacity battery. The optimal scheduling policy is first studied under different feedback mechanisms when the channel and energy harvesting statistics are known. For the case of unknown environments, an average-cost reinforcement learning algorithm is proposed that learns the system parameters and the status update policy in real time. The effectiveness of the proposed methods is verified through numerical results.

eess.SP↗

arXiv2019

Coded Caching with Heterogeneous Cache Sizes and Link Qualities: The Two-User Case

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 $N=2$ files. The results are then extended to the caching problem with heterogeneous distortion requirements.

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arXiv2018

SparseCast: Hybrid Digital-Analog Wireless Image Transmission Exploiting Frequency Domain Sparsity

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 (CSNR) 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 BCS-SPL. Our findings show that the proposed algorithm outperforms SoftCast by approximately 3.5 dB and BCS-SPL by 15.2 dB.

eess.IV↗

arXiv2018

Audience-Retention-Rate-Aware Caching and Coded Video Delivery with Asynchronous Demands

Most results on coded caching focus on a static scenario, in which a fixed number of users synchronously place their requests from a content library, and the performance is measured in terms of the latency in satisfying all of these demands. In practice, however, users start watching an online video content asynchronously over time, and often abort watching a video before it is completed. The latter behaviour is captured by the notion of audience retention rate, which measures the portion of a video content watched on average. In order to bring coded caching one step closer to practice, asynchronous user demands are considered in this paper, by allowing user demands to arrive randomly over time, and both the popularity of video files, and the audience retention rates are taken into account. A decentralized partial coded caching (PCC) scheme is proposed, together with two cache allocation schemes; namely the optimal cache allocation (OCA) and the popularity-based cache allocation (PCA), which allocate users' caches among different chunks of the video files in the library. Numerical results validate that the proposed PCC scheme, either with OCA or PCA, outperforms conventional uncoded caching as well as the state-of-the-art decentralized caching schemes, which consider only the file popularities, and are designed for synchronous demand arrivals. An information-theoretical lower bound on the average delivery rate is also presented.

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arXiv2018

A Reinforcement Learning Approach to Age of Information in Multi-User Networks

Scheduling the transmission of time-sensitive data to multiple users over error-prone communication channels is studied with the goal of minimizing the long-term average age of information (AoI) at the users under a constraint on the average number of transmissions at the source node. After each transmission, the source receives an instantaneous ACK/NACK feedback from the intended receiver and decides on what time and to which user to transmit the next update. 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. Then a reinforcement learning (RL) approach is introduced, which does not assume any a priori information on the random processes governing the channel states. Different RL methods are verified and compared through numerical simulations.

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arXiv2018

Centralized Caching and Delivery of Correlated Contents over a Gaussian Broadcast Channel

Content delivery in a multi-user cache-aided broadcast network is studied, where a server holding a database of correlated contents communicates with the users over a Gaussian broadcast channel (BC). The minimum transmission power required to satisfy all possible demand combinations is studied, when the users are equipped with caches of equal size. A lower bound on the required transmit power is derived, assuming uncoded cache placement, as a function of the cache capacity. A centralized joint cache and channel coding scheme is proposed, which not only utilizes the user's local caches, but also exploits the correlation among the contents in the database. This scheme provides an upper bound on the minimum required transmit power for a given cache capacity. Our results indicate that exploiting the correlations among the contents in a cache-aided Gaussian BC can provide significant energy savings.

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arXiv2018

Cache-Aided Interactive Multiview Video Streaming in Small Cell Wireless Networks

The emergence of novel interactive multimedia applications with high rate and low latency requirements has led to a drastic increase in the video data traffic over wireless cellular networks. Endowing the small base stations of a macro-cell with caches that can store some of the content is a promising technology to cope with the increasing pressure on the backhaul connections, and to reduce the delay for demanding video applications. In this work, delivery of an interactive multiview video to a set of wireless users is studied in an heterogeneous cellular network. Differently from existing works that focus on the optimization of the delivery delay and ignore the video characteristics, the caching and scheduling policies are jointly optimized, taking into account the quality of the delivered video and the video delivery time constraints. We formulate our joint caching and scheduling problem as the average expected video distortion minimization, and show that this problem is NP-hard. We then provide an equivalent formulation based on submodular set function maximization and propose a greedy solution with $\frac{1}{2}(1-\mbox{e}^{-1})$ approximation guarantee. The evaluation of the proposed joint caching and scheduling policy shows that it significantly outperforms benchmark algorithms based on popularity caching and independent scheduling. Another important contribution of this paper is a new constant approximation ratio for the greedy submodular set function maximization subject to a $d$-dimensional knapsack constraint.

cs.NI↗

arXiv2018

Coded Caching and Content Delivery with Heterogeneous Distortion Requirements

Cache-aided coded content delivery is studied for devices with diverse quality-of-service (QoS) requirements, specified by a different average distortion target. The network consists of a server holding a database of independent contents, and users equipped with local caches of different capacities. User caches are filled by the server during a low traffic period without the knowledge of particular user demands. As opposed to the current literature, which assumes that the users request files in their entirety, it is assumed that the users in the system have distinct distortion requirements; and therefore, each user requests a single file from the database to be served at a different distortion level. Our goal in this work is to characterize the minimum delivery rate the server needs to transmit over an error-free shared link to satisfy all possible demand combinations at the requested distortion levels, considering both centralized and decentralized cache placement.

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arXiv2018

Storage-Latency Trade-off in Cache-Aided Fog Radio Access Networks

A fog radio access network (F-RAN) is studied, in which $K_T$ edge nodes (ENs) connected to a cloud server via orthogonal fronthaul links, serve $K_R$ users through a wireless Gaussian interference channel. Both the ENs and the users have finite-capacity cache memories, which are filled before the user demands are revealed. While a centralized placement phase is used for the ENs, which model static base stations, a decentralized placement is leveraged for the mobile users. An achievable transmission scheme is presented, which employs a combination of interference alignment, zero-forcing and interference cancellation techniques in the delivery phase, and the \textit{normalized delivery time} (NDT), which captures the worst-case latency, is analyzed.

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arXiv2017

Mobility and Popularity-Aware Coded Small-Cell Caching

In heterogeneous cellular networks with caching capability, due to mobility of users and storage constraints of small-cell base stations (SBSs), users may not be able to download all of their requested content from the SBSs within the delay deadline of the content. In that case, the users are directed to the macro-cell base station (MBS) in order to satisfy the service quality requirement. Coded caching is exploited here to minimize the amount of data downloaded from the MBS taking into account the mobility of the users as well as the popularity of the contents. An optimal distributed caching policy is presented when the delay deadline is below a certain threshold, and a distributed greedy caching policy is proposed when the delay deadline is relaxed.

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arXiv2017

The Multi-layer Information Bottleneck Problem

The muti-layer information bottleneck (IB) problem, where information is propagated (or successively refined) from layer to layer, is considered. Based on information forwarded by the preceding layer, each stage of the network is required to preserve a certain level of relevance with regards to a specific hidden variable, quantified by the mutual information. The hidden variables and the source can be arbitrarily correlated. The optimal trade-off between rates of relevance and compression (or complexity) is obtained through a single-letter characterization, referred to as the rate-relevance region. Conditions of successive refinabilty are given. Binary source with BSC hidden variables and binary source with BSC/BEC mixed hidden variables are both proved to be successively refinable. We further extend our result to Guassian models. A counterexample of successive refinability is also provided.

stat.ML↗

arXiv2017

Centralized Coded Caching of Correlated Contents

Coded caching and delivery is studied taking into account the correlations among the contents in the library. Correlations are modeled as common parts shared by multiple contents; that is, each file in the database is composed of a group of subfiles, where each subfile is shared by a different subset of files. The number of files that include a certain subfile is defined as the level of commonness of this subfile. First, a correlation-aware uncoded caching scheme is proposed, and it is shown that the optimal placement for this scheme gives priority to the subfiles with the highest levels of commonness. Then a correlation-aware coded caching scheme is presented, and the cache capacity allocated to subfiles with different levels of commonness is optimized in order to minimize the delivery rate. The proposed correlation-aware coded caching scheme is shown to remarkably outperform state-of-the-art correlation-ignorant solutions, indicating the benefits of exploiting content correlations in coded caching and delivery in networks.

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arXiv2017

Channel Sensing and Communication over a Time-Correlated Channel with an Energy Harvesting Transmitter

An energy harvesting (EH) transmitter communicating over a time-correlated wireless channel is considered. The transmitter is capable of sensing the current channel state, albeit at the cost of both energy and transmission time. The EH transmitter aims to maximize its long-term throughput by choosing one of the following actions: $i)$ defer its transmission to save energy for future use, $ii)$ transmit reliably at a low rate, $iii)$ transmit at a high rate, and $iv)$ sense the channel to reveal the channel state at a cost of energy and transmission time, and then decide to defer or to transmit. The problem is formulated as a partially observable Markov decision process with a belief on the channel state. The optimal policy is shown to exhibit a threshold behavior on the belief state, with battery-dependent threshold values. The optimal threshold values and performance are characterized numerically via the value iteration algorithm as well as a policy search algorithm that exploits the threshold structure of the optimal policy. Our results demonstrate that, despite the associated time and energy cost, sensing the channel intelligently to track the channel state improves the achievable long-term throughput significantly as compared to the performance of those protocols lacking this ability as well as the one that always senses the channel.

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