In frequency division duplexing systems, downlink massive multiple-input multiple-output (MIMO) precoding algorithms rely on accurate channel state information (CSI) feedback from users. This paper investigates the tradeoff between the CSI feedback overhead and the resulting user performance in terms of achievable sum rate. Our approach consists of determining the precoding directly from the user feedback. We employ a deep learning-based design for an end-to-end precoding-oriented feedback architecture, including learned pilots, user compressors for finite-rate feedback, and base station processing to determine precoding vectors. We propose a novel loss function that maximizes the sum of achievable rates while minimizing the CSI feedback overhead. We consider both single- and multi-cell multi-user MIMO systems, analyzing the impact of intra- and inter-cell interference on the CSI feedback strategy design, as well as robustness. Simulation results demonstrate that our approach outperforms previous precoding-oriented methods and offers greater efficiency than conventional methods that separate CSI compression and precoding.

Dirty paper coding (DPC) is a classical problem in information theory that considers communication in the presence of channel state known only at the transmitter. While the theoretical impact of DPC has been substantial, practical realizations of DPC, such as Tomlinson–Harashima precoding (THP) or lattice-based schemes, often rely on specific modeling assumptions about the input, state and channel. In this work, we explore whether modern learning-based approaches can offer a complementary path forward by revisiting the DPC problem. We propose a data-driven solution in which both the encoder and decoder are parameterized by neural networks. Our proposed model operates without prior knowledge of the state (also referred to as "interference"), channel or input statistics, and recovers nonlinear mappings that yield effective interference pre-cancellation. To the best of our knowledge, this is the first interpretable proof-of-concept demonstrating that learning-based DPC schemes can recover characteristic features of well-established solutions, such as THP and lattice-based precoding, and outperform them in several regimes.

The relay channel, consisting of a source-destination pair along with a relay, is a fundamental component of cooperative communications. While the capacity of a general relay channel remains unknown, various relaying strategies, including compress-and-forward (CF), have been proposed. In CF, the relay forwards a quantized version of its received signal to the destination. Given the correlated signals at the relay and destination, distributed compression techniques, such as Wyner–Ziv coding, can be harnessed to utilize the relay-to-destination link more efficiently. Leveraging recent advances in neural network-based distributed compression, we revisit the relay channel problem and integrate a learned task-aware Wyner–Ziv compressor into a primitive relay channel with a finite-capacity out-of-band relay-to-destination link. The resulting neural CF scheme demonstrates that our compressor recovers binning of the quantized indices at the relay, mimicking the optimal asymptotic CF strategy, although no structure exploiting the knowledge of source statistics was imposed into the design. The proposed neural CF, employing finite order modulation, operates closely to the rate achievable in a primitive relay channel with a Gaussian codebook. We showcase the advantages of exploiting the correlated destination signal for relay compression through various neural CF architectures that involve end-to-end training of the compressor and the demodulator components. Our learned task-oriented compressors provide the first proof-of-concept work toward interpretable and practical neural CF relaying schemes.

Efficient and low-latency wireless connectivity between the base station (BS) and a sparse set of sporadically active devices from a massive number of devices is crucial for emerging massive machine-type communications (mMTC). This paper addresses the challenge of identifying active devices while meeting stringent access delay and reliability constraints in mMTC environments. A novel multi-stage active device identification framework is proposed where we aim to refine a partial estimate of the active device set using feedback and hypothesis testing across multiple stages eventually leading to an exact recovery of active devices after the final stage of processing. In our proposed approach, active devices independently transmit binary preambles during each stage, leveraging feedback signals from the BS, whereas the BS employs a non-coherent binary energy detection. The minimum user identification cost associated with our multi-stage non-coherent active device identification framework with feedback, in terms of the required number of channel-uses, is quantified using information-theoretic techniques in the asymptotic regime of total number of devices ℓ when the number of active deviceskscales ask= Θ(1). Practical implementations of our multi-stage active device identification schemes, leveraging Belief Propagation (BP) techniques, are also presented and evaluated. Simulation results show that our multi-stage BP strategies exhibit superior performance over single-stage strategies, even when considering overhead costs associated with feedback and hypothesis testing.

Database de-anonymization typically involves matching an anonymized database with correlated publicly available data. Existing research focuses either on practical aspects without requiring knowledge of the data distribution yet provides limited guarantees, or on theoretical aspects assuming known distributions. This paper aims to bridge these two approaches, offering theoretical guarantees for database de-anonymization under synchronization errors and obfuscation without prior knowledge of data distribution. Using a modified replica detection algorithm and a new seeded deletion detection algorithm, we establish sufficient conditions on the database growth rate for successful matching, demonstrating a double-logarithmic seed size relative to row size is sufficient for detecting deletions in the database. Importantly, our findings indicate that these sufficient de-anonymization conditions are tight and are the same as in the distribution-aware setting, avoiding asymptotic performance loss due to unknown distributions. Finally, we evaluate the performance of our proposed algorithms through simulations, confirming their effectiveness in more practical, non-asymptotic, scenarios.

Hardware Trojans can inflict harm on wireless networks by exploiting the link margins inherent in communication systems. We investigate a setting in which, alongside a legitimate communication link, a hardware Trojan embedded in the legitimate transmitter attempts to establish communication with its intended rogue receiver. To illustrate the susceptibility of wireless networks against pilot attacks, we examine a two-phased scenario. In the channel estimation phase, the Trojan carries out a covert pilot scaling attack to corrupt the channel estimation of the legitimate receiver. Subsequently, in the communication phase, the Trojan exploits the ensuing imperfect channel estimation to covertly communicate with its receiver. By analyzing the corresponding hypothesis tests conducted by the legitimate receiver in both phases, we establish that the pilot scaling attack allows the Trojan to operate in the so-called "linear regime" i.e., covertly and reliably transmitting at a positive rate to the rogue receiver. Our results highlight the vulnerability of the channel estimation process in wireless communication systems against hardware Trojans.

Efficiently identifying active devices with minimal latency is crucial in massive machine-type communication networks characterized by sparse and sporadic device activity. This paper addresses the above challenge by introducing a novel active device identification strategy which employs a multi-stage framework that iteratively refines partial estimates of active devices through feedback and hypothesis testing, leading to an exact recovery. In our proposed method, active devices transmit binary preambles independently in each stage, utilizing feedback signals from the BS. Meanwhile, the BS utilizes non-coherent binary energy detection. In addition to theoretical bounds, practical implementations of our multi-stage active device identification schemes using Belief Propagation (BP) techniques are presented. Our simulation results demonstrate that the multi-stage strategy is superior to the single-stage one introduced in our earlier work and performs close to the theoretical bound, even when considering overhead costs related to feedback.

In this paper, we consider the one-shot version of the classical Wyner-Ziv problem where a source is compressed in a lossy fashion when only the decoder has access to a correlated side information. Following the entropy-constrained quantization framework, we assume a scalar quantizer followed by variable length entropy coding. We consider compression of a uniform source, motivated by its role in the compression of processes with low-dimensional features embedded within a high-dimensional ambient space. We find upper and lower bounds to the entropy-distortion functions of the uniform source for quantized and noisy side information, and illustrate tightness of the bounds at high compression rates.

High-frequency wideband cellular communications over mmWave and sub-THz offer the opportunity for high data rates. However, it also presents high path loss, resulting in limited coverage. High-gain beamforming from the antenna array is essential to mitigate the coverage limitations. The conventional phased antenna arrays (PAA) cause high scheduling latency owing to analog beam constraints, i.e., only one frequency-flat beam is generated. Recently introduced joint phase-time array (JPTA) architecture, which utilizes both true-time-delay (TTD) units and phase shifters (PSs), alleviates analog beam constraints by creating multiple frequency-dependent beams for scheduling multiple users at different directions in a frequency-division manner. One class of previous studies offered solutions with "rainbow" beams, which tend to allocate a small bandwidth per beam direction. Another class focused on uniform linear array (ULA) antenna architecture, whose frequency-dependent beams were designed along a single axis of either azimuth or elevation direction. This paper presents a novel 3D beamforming design that maximizes beamforming gain toward desired azimuth and elevation directions and across sub-bands partitioned according to scheduled users’ bandwidth requirements. We provide analytical solutions and iterative algorithms to design the PSs and TTD units for a desired subband beam pattern. Through simulations of the beamforming gain, we observe that our proposed solutions outperform the state-of-the-art solutions reported elsewhere.

The re-identification or de-anonymization of users from anonymized data through matching with publicly available correlated user data has raised privacy concerns, leading to the complementary measure of obfuscation in addition to anonymization. Recent research provides a fundamental understanding of the conditions under which privacy attacks, in the form of database matching, are successful in the presence of obfuscation. Motivated by synchronization errors stemming from the sampling of time-indexed databases, this paper presents a unified framework considering both obfuscation and synchronization errors and investigates the matching of databases under noisy entry repetitions. By investigating different structures for the repetition pattern, replica detection and seeded deletion detection algorithms are devised and sufficient and necessary conditions for successful matching are derived. Finally, the impacts of some variations of the underlying assumptions, such as the adversarial deletion model, seedless database matching, and zero-rate regime, on the results are discussed. Overall, our results provide insights into the privacy-preserving publication of anonymized and obfuscated time-indexed data as well as the closely related problem of the capacity of synchronization channels.

Running deep visual analytics models for real-time applications is challenging for mobile devices. Offloading the computation to edge server can mitigate computation bottleneck at the mobile device, but may decrease the analytics performance due to the necessity of compressing the image data. We consider a “split computing” system to offload a part of the deep learning model's computation and introduce a novel learned feature compression approach with lightweight computation. We demonstrate the effectiveness of the split computing pipeline in performing computation offloading for the problems of object detection and image classification. Compared to compressing the raw images at the mobile, and running the analytics model on the decompressed images at the server, the proposed feature-compression approach can achieve significantly higher analytics performance at the same bit rate, while reducing the complexity at the mobile. We further propose a scalable feature compression approach, which facilitates adaptation to network bandwidth dynamics, while having comparable performance to the non-scalable approach.

Downlink massive multiple-input multiple-output (MIMO) precoding algorithms in frequency division duplexing (FDD) systems rely on accurate channel state information (CSI) feedback from users. In this paper, we analyze the tradeoff between the CSI feedback overhead and the performance achieved by the users in systems in terms of achievable rate. The final goal of the proposed system is to determine the beamforming information (i.e., precoding) from channel realizations. We employ a deep learning-based approach to design the end-to-end precoding-oriented feedback architecture, that includes learned pilots, users' compressors, and base station processing. We propose a loss function that maximizes the sum of achievable rates with minimal feedback overhead. Simulation results show that our approach outperforms previous precoding-oriented methods, and provides more efficient solutions with respect to conventional methods that separate the CSI compression blocks from the precoding processing.

In this paper, we study a navigation problem where a mobile robot needs to locate a mmWave wireless signal. Using the directionality properties of the signal, we propose an estimation and path planning algorithm that can efficiently navigate in cluttered indoor environments. We formulate Extended Kalman filters for emitter location estimation in cases where the signal is received in line-of-sight or after reflections. We then propose to plan motion trajectories based on belief-space dynamics in order to minimize the uncertainty of the position estimates. The associated non-linear optimization problem is solved by a state-of-the-art constrained iLQR solver. In particular, we propose a method that can handle a large number of obstacles (∼ 300) with reasonable computation times. We validate the approach in an extensive set of simulations. We show that our estimators can help increase navigation success rate and that planning to reduce estimation uncertainty can improve the overall task completion speed.

We consider lossy compression of an information source when the decoder has lossless access to a correlated one. This setup, also known as the Wyner-Ziv problem, is a special case of distributed source coding. To this day, real-world applications of this problem have neither been fully developed nor heavily investigated. We propose a data-driven method based on machine learning that leverages the universal function approximation capability of artificial neural networks. We find that our neural network-based compression scheme re-discovers some principles of the optimum theoretical solution of the Wyner-Ziv setup, such as binning in the source space as well as linear decoder behavior within each quantization index, for the quadratic-Gaussian case. These behaviors emerge although no structure exploiting knowledge of the source distributions was imposed. Binning is a widely used tool in information theoretic proofs and methods, and to our knowledge, this is the first time it has been explicitly observed to emerge from data-driven learning.

The de-anonymization of users from anonymized microdata through matching or aligning with publicly-available correlated databases has been of scientific interest recently. While most of the rigorous analyses of database matching have focused on random-distortion models, the adversarial-distortion models have been wanting in the relevant literature. In this work, motivated by synchronization errors in the sampling of time-indexed microdata, matching (alignment) of random databases under adversarial column deletions is investigated. It is assumed that a constrained adversary, which observes the anonymized database, can delete up to a δ fraction of the columns (attributes) to hinder matching and preserve privacy. Column histograms of the two databases are utilized as permutation-invariant features to detect the column deletion pattern chosen by the adversary. The detection of the column deletion pattern is then followed by an exact row (user) matching scheme. The worst-case analysis of this two-phase scheme yields a sufficient condition for the successful matching of the two databases, under the near-perfect recovery condition. A more detailed investigation of the error probability leads to a tight necessary condition on the database growth rate, and in turn, to a single-letter characterization of the adversarial matching capacity. This adversarial matching capacity is shown to be significantly lower than the "random" matching capacity, where the column deletions occur randomly. Overall, our results analytically demonstrate the privacy-wise advantages of adversarial mechanisms over random ones during the publication of anonymized time-indexed data.

Reliable and prompt identification of active users is critical for enabling random access in massive machine-to-machine type networks which typically operate within stringent access delay and energy constraints. In this paper, an energy efficient active user identification protocol is envisioned in which the active users simultaneously transmit On-Off Keying (OOK) modulated preambles whereas the base station uses non-coherent detection to avoid the channel estimation overheads. The minimum number of channel-uses required for active user identification in the asymptotic regime of total number of users ℓ when the number of active devices k scales as k = Θ(1) is characterized along with an achievability scheme relying on the equivalence of activity detection to a group testing problem. A practical scheme for active user identification based on a belief propagation strategy is also proposed and its performance is compared against the theoretical bounds.

Rate splitting (RS) and rate splitting multiple access (RSMA) have emerged as a promising and powerful multiple access, interference management, and multi-user strategy for next-generation wireless systems and networks. This Special Issue is entirely dedicated to the theory, design, optimization, and applications of RS and RSMA in various network configurations. It starts with a guest editor-authored tutorial paper [A1] that delineates the basic principles and applications of RS and RSMA. The tutorial paper is then followed by 17 technical papers.

Rate-Splitting Multiple Access (RSMA) has emerged as a powerful multiple access, interference management, and multi-user strategy for next generation communication systems. In this tutorial, we depart from the orthogonal multiple access (OMA) versus non-orthogonal multiple access (NOMA) discussion held in 5G, and the conventional multi-user linear precoding approach used in space-division multiple access (SDMA), multi-user and massive MIMO in 4G and 5G, and show how multi-user communications and multiple access design for 6G and beyond should be intimately related to the fundamental problem of interference management. We start from foundational principles of interference management and rate-splitting, and progressively delineate RSMA frameworks for downlink, uplink, and multi-cell networks. We show that, in contrast to past generations of multiple access techniques (OMA, NOMA, SDMA), RSMA offers numerous benefits: 1) enhanced spectral, energy and computation efficiency; 2) universality by unifying and generalizing OMA, SDMA, NOMA, physical-layer multicasting, multi-user MIMO under a single framework that holds for any number of antennas at each node (SISO, SIMO, MISO, and MIMO settings); 3) flexibility by coping with any interference levels (from very weak to very strong), network loads (underloaded, overloaded), services (unicast, multicast), traffic, user deployments (channel directions and strengths); 4) robustness to inaccurate channel state information (CSI) and resilience to mixed-critical quality of service; 5) reliability under short channel codes and low latency. We then discuss how those benefits translate into numerous opportunities for RSMA in over forty different applications and scenarios of 6G, e.g., multi-user MIMO with statistical/quantized CSI, FDD/TDD/cell-free massive MIMO, millimeter wave and terahertz, cooperative relaying, physical layer security, reconfigurable intelligent surfaces, cloud-radio access network, internet-of-things, massive access, joint communication and jamming, non-orthogonal unicast and multicast, multigroup multicast, multibeam satellite, space-air-ground integrated networks, unmanned aerial vehicles, integrated sensing and communications, grant-free access, network slicing, cognitive radio, optical/visible light communications, mobile edge computing, machine/federated learning, etc. We finally address common myths and answer frequently asked questions, opening the discussions to interesting future research avenues. Supported by the numerous benefits and applications, the tutorial concludes on the underpinning role played by RSMA in next generation networks, which should inspire future research, development, and standardization of RSMA-aided communication for 6G.

Low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) have attracted considerable attention in efforts to reduce power consumption in millimeter wave (mmWave) and massive MIMO systems. This paper presents an information-theoretic analysis with capacity bounds for classes of linear transceivers with finite quantization. The transmitter modulates symbols via a unitary transform followed by a DAC and the receiver employs an ADC followed by the inverse unitary transform. If the unitary transform is set to a discrete Fourier transform (DFT) matrix, the model naturally captures filtering and spectral constraints. In particular, this model allows studying the impact of quantization on out-of-band (OOB) emission constraints. The out-of-band emission constraints are defined using a “spectrum mask” in practical wireless systems. All transmissions need to meet the OOB constraint to allow other services and technologies to operate in adjacent bands.In the limit of a large random unitary transform, it is shown that the effect of quantization can be precisely described via an additive Gaussian noise model. This model in turn leads to simple and intuitive expressions for the power spectrum of the transmitted signal and a lower bound to the capacity with quantization. Comparison with non-quantized capacity and a capacity upper bound that does not make linearity assumptions suggests that while low resolution quantization has minimal impact on the achievable rate at typical parameters in 5G systems, satisfying OOB emissions is potentially much more of a challenge.

Massive Machine-Type Communications (mMTC) is a key service category in the current generation of wireless networks featuring an extremely high density of energy and resource-limited devices with sparse and sporadic activity patterns. In order to enable random access in such mMTC networks, base station needs to identify the active devices while operating within stringent access delay constraints. In this paper, an energy efficient active device identification protocol is proposed in which active devices transmit On-Off Keying (OOK) modulated preambles jointly and base station employs non-coherent energy detection avoiding channel estimation overheads. The minimum number of channel-uses required by the active user identification protocol is characterized in the asymptotic regime of total number of devices$\ell $when the number of active devices$k$scales as$k=\Theta (1)$along with an achievability scheme relying on the equivalence of activity detection to a group testing problem. Several practical schemes based on Belief Propagation (BP) and Combinatorial Orthogonal Matching Pursuit (COMP) are also proposed. Simulation results show that BP strategies outperform COMP significantly and can operate close to the theoretical achievability bounds. In a partial-recovery setting where few misdetections are allowed, BP continues to perform well.

Millimeter wave systems suffer from high power consumption and are constrained to use low resolution quantizers —digital to analog and analog to digital converters (DACs and ADCs). However, low resolution quantization leads to reduced data rate and increased out-of-band emission noise. In this paper, a multiple-input multiple-output (MIMO) system with linear transceivers using low resolution DACs and ADCs is considered. An information-theoretic analysis of the system to model the effect of quantization on spectrospatial power distribution and capacity of the system is provided. It is shown that the impact of quantization can be accurately described via a linear model with additive independent Gaussian noise. This model in turn leads to simple and intuitive expressions for spectrospatial power distribution of the transmitter and a lower bound on the achievable rate of the system. The derived model is validated through simulations and numerical evaluations, where it is shown to accurately predict both spectral and spatial power distributions.

The re-identification or de-anonymization of users from anonymized data through matching with publicly-available correlated user data has raised privacy concerns, leading to the complementary measure of obfuscation in addition to anonymization. Recent research provides a fundamental understanding of the conditions under which privacy attacks are successful, either in the presence of obfuscation or synchronization errors stemming from the sampling of time-indexed databases. This paper presents a unified framework considering both obfuscation and synchronization errors and investigates the matching of databases under noisy column repetitions. By devising replica detection and seeded deletion detection algorithms, and using information-theoretic tools, sufficient conditions for successful matching are derived. It is shown that a seed size logarithmic in the row size is enough to guarantee the detection of all deleted columns. It is also proved that this sufficient condition is necessary, thus characterizing the database matching capacity of database matching under noisy column repetitions and providing insights on privacy-preserving publication of anonymized and obfuscated time-indexed data.

In-band full-duplex (FD) communication has emerged as one of the promising techniques to improve data rates in next generation wireless systems. Typical FD scenarios considered in the literature assume FD base stations (BSs) and half-duplex (HD) users activated either in uplink (UL) or downlink (DL), where inter-user interference (IUI) is treated as noise at the DL user. This paper considers more general FD scenarios where an arbitrary fraction of the users are capable of FD and/or they can perform successive interference cancellation (SIC) to mitigate IUI. Consequently, one user can be activated in either UL or DL (HD-UL and HD-DL modes), or simultaneously in both directions requiring self-interference mitigation (SIM) at that user (FD-SIM mode). Furthermore, two users can be scheduled, one in UL and the other in DL (both operating in HD), where the DL user can treat IUI as noise (FD-IN mode) or perform SIC to mitigate IUI (FD-SIC mode). This paper studies opportunistic mode selection and user scheduling under long-term and short-term temporal fairness in single-carrier and multi-carrier (OFDM) FD systems, with the goal of maximizing system utility (e.g. sum-rate). First, the feasible region of temporal demands is characterized for both long-term and short-term fairness. Subsequently, optimal temporal fair schedulers as well as practical low-complexity online algorithms are devised. Simulation results demonstrate that using SIC to mitigate IUI as well as having FD capability at users can improve FD throughput gains significantly especially, when user distribution is concentrated around a few hotspots.

This work considers active deanonymization of bipartite networks. The scenario arises naturally in evaluating privacy in various applications such as social networks, mobility networks, and medical databases. For instance, in active deanonymization of social networks, an anonymous victim is targeted by an attacker (e.g. the victim visits the attacker’s website), and the attacker queries her group memberships (e.g. by querying the browser history) to deanonymize her. In this work, the fundamental limits of privacy, in terms of the minimum number of queries necessary for deanonymization, is investigated. A stochastic model is considered, where 1) the bipartite network of group memberships is generated randomly; 2) the attacker has partial prior knowledge of the group memberships; and 3) it receives noisy responses to its real-time queries. The bipartite network is generated based on linear and sublinear preferential attachment, and the stochastic block model. The victim’s identity is chosen randomly based on a distribution modeling the users’ risk of being the victim (e.g. probability of visiting the website). An attack algorithm is proposed which builds upon techniques from communication with feedback, and its performance, in terms of expected number of queries, is analyzed. Simulation results are provided to verify the theoretical derivations.

High resolution analog to digital converters (ADCs) are conventionally used at the receiver terminals to store an accurate digital representation of the received signal, thereby allowing for reliable decoding of transmitted messages. However, in a wide range of applications, such as communication over millimeter wave and massive multiple-input multiple-output (MIMO) systems, the use of high resolution ADCs is not feasible due to power budget limitations. In the conventional fully digital receiver design, where each receiver antenna is connected to a distinct ADC, reducing the ADC resolution leads to performance loss in terms of achievable rates. One proposed method to mitigate the rate-loss is to use analog linear combiners leading to design of hybrid receivers. Here, the hybrid framework is augmented by the addition of delay elements to allow for temporal analog processing. Two new classes of receivers consisting of delay elements, analog linear combiners, and one-bit ADCs are proposed. The fundamental limits of communication in single and multi-user (uplink and downlink) MIMO systems employing the proposed receivers are investigated. In the high signal to noise ratio regime, it is shown that the proposed receivers achieve the maximum achievable rates among all receivers with the same number of one-bit ADCs.

Current IoT networks are characterized by an ultra-high density of devices with different energy budget constraints, typically having sparse and sporadic activity patterns. Access points require an efficient strategy to identify the active devices for a timely allocation of resources to enable massive machine-type communication. Recently, group testing based approaches have been studied to handle sparse activity detection in massive random access problems. In this paper, a non-adaptive group testing strategy is proposed which can take into account the energy constraints on different sensor clusters. A theoretical extension of the existing randomized group testing strategies to the case of multiple clusters is presented and the necessary constraints that the optimal sampling parameters should satisfy in order to improve the efficiency of group tests is established. The cases of fixed activity pattern where there is a fixed set of active sensors and random activity pattern where each sensor can be independently active with certain probability are examined. The theoretical results are verified and validated by Monte-Carlo simulations. In massive wireless sensor networks comprising of devices with different energy efficiencies, our proposed low-power-use mode of access can potentially extend the lifetime of battery powered sensors with finite energy budget.

De-anonymizing user identities by matching various forms of user data available on the internet raises privacy concerns. A fundamental understanding of the privacy leakage in such scenarios requires a careful study of conditions under which correlated databases can be matched. Motivated by synchronization errors in time indexed databases, in this work, matching of random databases under random column deletion is investigated. Adapting tools from information theory, in particular ones developed for the deletion channel, conditions for database matching in the absence and presence of deletion location information are derived, showing that partial deletion information significantly increases the achievable database growth rate for successful matching. Furthermore, given a batch of correctly-matched rows, a deletion detection algorithm that provides partial deletion information is proposed and a lower bound on the algorithm's deletion detection probability in terms of the column size and the batch size is derived. The relationship between the database size and the batch size required to guarantee a given deletion detection probability using the proposed algorithm suggests that a batch size growing double-logarithmic with the row size is sufficient for a nonzero detection probability guarantee.

Narrow beams are key to wireless communications in millimeter wave frequency bands. Beam alignment (BA) allows the base station (BS) to adjust the direction and width of the beam used for communication. During BA, the BS transmits a number of scanning beams covering different angular regions. The goal is to minimize the expected width of the uncertainty region (UR) that includes the angle of departure of the user. Conventionally, in interactive BA, it is assumed that the feedback corresponding to each scanning packet is received prior to transmission of the next one. However, in practice, the feedback delay could be larger because of propagation or system constraints. This paper investigates BA strategies that operate under arbitrary fixed feedback delays. This problem is analyzed through a source coding perspective where the feedback sequences are viewed as source codewords. It is shown that these codewords form a codebook with a particular characteristic which is used to define a new class of codes called d—unimodal codes. By analyzing the properties of these codes, a lower bound on the minimum achievable expected beamwidth is provided. The results reveal potential performance improvements in terms of the BA duration it takes to achieve a fixed expected width of the UR over the state-of-the-art BA methods which do not consider the effect of delay.

Many-access channel (MnAC) model allows the number of users in the system and the number of active users to scale as a function of the blocklength and as such is suited for dynamic communication systems with massive number of users such as the Internet of Things. Existing MnAC models assume a priori knowledge of channel gains which is impractical since acquiring Channel State Information (CSI) for massive number of users can overwhelm the available radio resources. This paper incorporates Rayleigh fading effects to the MnAC model and derives an upper bound on the symmetric message-length capacity of the Rayleigh-fading Gaussian MnAC. Furthermore, a lower bound on the minimum number of channel uses for discovering the active users is established. In addition, the performance of Noisy-Combinatorial Orthogonal Matching Pursuit (N-COMP) based group testing (GT) is studied as a practical strategy for active device discovery. Simulations show that, for a given SNR, as the number of users increase, the required number of channel uses for N-COMP GT scales approximately the same way as the lower bound on minimum user identification cost. Moreover, in the low SNR regime, for sufficiently large population sizes, the number of channel uses required by N-COMP GT was observed to be within a factor of two of the lower bound when the expected number of active users scales sub-linearly with the total population size.

In this paper, matching pairs of stocahstically generated graphs in the presence of generalized seed side-information is considered. The graph matching problem emerges naturally in various applications such as social network de-anonymization, image processing, DNA sequencing, and natural language processing. A pair of randomly generated labeled Erdös-Rényi graphs with pairwise correlated edges are considered. It is assumed that the matching strategy has access to the labeling of the vertices in the first graph, as well as a collection of shortlists — called ambiguity sets — of possible labels for the vertices of the second graph. The objective is to leverage the correlation among the edges of the graphs along with the side-information provided in the form of ambiguity sets to recover the labels of the vertices in the second graph. This scenario can be viewed as a generalization of the seeded graph matching problem, where the ambiguity sets take a specific form such that the exact labels for a subset of vertices in the second graph are known prior to matching. A matching strategy is proposed which operates by evaluating the joint typicality of the adjacency matrices of the graphs. Sufficient conditions on the edge statistics as well as ambiguity set statistics are derived under which the proposed matching strategy successfully recovers the labels of the vertices in the second graph. Additionally, Fano-type arguments are used to derive necessary conditions for successful seeded graph matching.

SafePredict is a novel meta-algorithm that works with any base prediction algorithm for online data to guarantee an arbitrarily chosen correctness rate, 1-ϵ, by allowing refusals. Allowing refusals means that the meta-algorithm may refuse to emit a prediction produced by the base algorithm so that the error rate on non-refused predictions does not exceed ϵ. The SafePredict error bound does not rely on any assumptions on the data distribution or the base predictor. When the base predictor happens not to exceed the target error rate ϵ, SafePredict refuses only a finite number of times. When the error rate of the base predictor changes through time SafePredict makes use of a weight-shifting heuristic that adapts to these changes without knowing when the changes occur yet still maintains the correctness guarantee. Empirical results show that (i) SafePredict compares favorably with state-of-the-art confidence-based refusal mechanisms which fail to offer robust error guarantees; and (ii) combining SafePredict with such refusal mechanisms can in many cases further reduce the number of refusals. Our software is included in the supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2019.2932415.

Non-cooperative cellular massive MIMO, combined with power control, is known to lead to significant improvements in per-user throughput compared with conventional LTE technology. In this paper, we investigate further refinements to massive MIMO, first, in the form of three-fold sectorization, and second, coordinated multi-point operation (with and without sectorization), in which the three base stations cooperate in the joint service of their users. For these scenarios, we analyze the downlink performance for both maximum-ratio and zero-forcing precoding and derive closed-form lower-bound expressions on the achievable rate of the users. These expressions are then used to formulate power optimization problems with two throughput fairness criteria:${i}$) network-wide max-min fairness, andii) per-cell max-min fairness. Furthermore, we provide centralized and decentralized power control strategies to optimize the transmit powers in the network. We demonstrate that employing sectorized antenna elements mitigates the detrimental effects of pilot contamination by rejecting a portion of interfering pilots in the spatial domain during channel estimation phase. Simulation results with practical sectorized antennas reveal that sectorization and multi-point coordination combined with sectorization lead to more than$1.7\times $and$2.6\times $improvements in the 95%-likely per-user throughput, respectively.

Use of low resolution analog to digital converters (ADCs) is an effective way to reduce the high power consumption of millimeter wave (mmWave) receivers. In this paper, a receiver with low resolution ADCs based on adaptive thresholds is considered in downlink mmWave communications in which the channel state information is not known a-priori and acquired through channel estimation. A performance comparison of low-complexity algorithms for power and ADC allocation among transmit and receive terminals, respectively, is provided. Through simulation of practical mmWave cellular networks, it is shown that the use of low resolution ADCs does not significantly degrade the system throughput (as compared to a conventional fully digital high resolution receiver) when using the adaptive threshold receiver in conjunction with simple power and ADC allocation strategies.

A resource-constrained system monitors a source of information by requesting a finite number of updates subject to random transmission delays. An a priori fixed update request policy is shown to minimize a polynomial penalty function of the age of information over arbitrary time horizons. Partial updates, compressed updates with reduced transmission and information content, in the presented model are shown to incur an age penalty independent of the compression. Finite horizons are shown to have better performance in terms of second order statistic relative to infinite horizons.

Low-resolution digital-to-analog and analog-to-digital converters (DACs and ADCs) have attracted considerable attention in efforts to reduce power consumption in millimeter wave (mmWave) and massive MIMO systems. This paper presents an information-theoretic analysis with capacity bounds for classes of linear transceivers with quantization. The transmitter modulates symbols via a unitary transform followed by a DAC and the receiver employs an ADC followed by the inverse unitary transform. If the unitary transform is set to an FFT matrix, the model naturally captures filtering and spectral constraints which are essential to model in any practical transceiver. In particular, this model allows studying the impact of quantization on out-of-band emission constraints. In the limit of a large random unitary transform, it is shown that the effect of quantization can be precisely described via an additive Gaussian noise model. This model in turn leads to simple and intuitive expressions for the power spectrum of the transmitted signal and a lower bound to the capacity with quantization. Comparison with non-quantized capacity and a capacity upper bound that does not make linearity assumptions suggests that while low resolution quantization has minimal impact on the achievable rate at typical parameters in 5G systems today, satisfying out-of-band emissions are potentially much more of a challenge.

Semi-supervised classification, one of the most prominent fields in machine learning, studies how to combine the statistical knowledge of the often abundant unlabeled data with the often limited labeled data in order to maximize overall classification accuracy. In this context, the process of actively choosing the data to be labeled is referred to as active learning. In this paper, we initiate the non-asymptotic analysis of the optimal policy for semi-supervised classification with actively obtained labeled data. Considering a general Bayesian classification model, we provide the first characterization of the jointly optimal active learning and semi-supervised classification policy, in terms of the cost-performance tradeoff driven by the label query budget (number of data items to be labeled) and overall classification accuracy. Leveraging recent results on the Rényi Entropy, we derive tight information-theoretic bounds on such active learning cost-performance tradeoff.

Directional transmission patterns (a.k.a. narrow beams) are the key to wireless communications in millimeter wave (mmWave) frequency bands which suffer from high path loss and severe shadowing. In addition, the propagation channel in mmWave frequencies incorporates only a few number of spatial clusters requiring a procedure to align the corresponding narrow beams with the angle of departure (AoD) of the channel clusters. The objective of this procedure, called beam alignment (BA) is to increase the beamforming gain for subsequent data communication. Several prior studies consider optimizing BA procedure to achieve various objectives such as reducing the BA overhead, increasing throughput, and reducing power consumption. While these studies mostly provide optimized BA schemes for scenarios with a single active user, there are often multiple active users in practical networks. Consequently, it is more efficient in terms of BA overhead and delay to design multi-user BA schemes which can perform beam management for multiple users collectively. This paper considers a class of multi-user BA schemes where the base station performs a one shot scan of the angular domain to simultaneously localize multiple users. The objective is to minimize the average of expected width of remaining uncertainty regions (UR) on the AoDs after receiving users' feedbacks. Fundamental bounds on the optimal performance are analyzed using information theoretic tools. Furthermore, a BA optimization problem is formulated and a practical BA scheme, which provides significant gains compared to the beam sweeping used in 5G standard, is proposed.

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. Two centralized caching schemes are proposed, both of which not only utilize the user's local caches, but also exploit the correlation among the contents in the database. The first scheme implements uncoded cache placement and delivers coded contents to users using superposition coding. The second scheme, which is proposed for small cache sizes, places coded contents in users' caches and jointly encodes the cached contents of users and the messages targeted at them. The performance of the proposed schemes, which provide upper bounds on the required transmit power for a given cache capacity, is characterized. The scheme based on coded placement improves upon the first one for small cache sizes, and under certain conditions meets the uncoded placement lower bound. A lower bound on the required transmit power is also presented assuming uncoded cache placement. Our results indicate that exploiting the correlations among the contents in a cache-aided Gaussian BC can provide significant energy savings.

This paper studies the fundamental limits of content delivery in a cache-aided broadcast network for correlated content generated by a discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. A class of achievable correlation-aware schemes based on a two-step source coding approach is proposed. Library files are first compressed, and then cached and delivered using a combination of multiple-request caching schemes that are agnostic to the content correlations. The first step uses Gray-Wyner source coding to represent the library via private descriptions and descriptions that are common to more than one file. The second step then becomes a multiple-request caching problem, where the demand structure is dictated by the configuration of the compressed library, and it is interesting in its own right. The performance of the proposed two-step scheme is evaluated by comparing its achievable rate with a lower bound on the optimal peak and average rate-memory trade-offs in a two-file multiple-receiver network, and in a three-file two-receiver network. Specifically, in a network with two files and two receivers, the achievable rate matches the lower bound for a significant memory regime and it is within half of the conditional entropy of files for all other memory values. In the three-file two-receiver network, the two-step strategy achieves the lower bound for large cache capacities, and it is within half of the joint entropy of two of the sources conditioned on the third one for all other cache sizes.

Caching at the wireless edge can be used to keep up with the increasing demand for high-definition wireless video streaming. By prefetching popular content into memory at wireless access points or end-user devices, requests can be served locally, relieving strain on expensive backhaul. In addition, using network coding allows the simultaneous serving of distinct cache misses via common coded multicast transmissions, resulting in significantly larger load reductions compared to those achieved with traditional delivery schemes. Most prior works simply treat video content as fixed-size files that users would like to fully download. This work is motivated by the fact that video can be coded in a scalable fashion and that the decoded video quality depends on the number of layers a user receives in sequence. Using a Gaussian source model, caching and coded delivery methods are designed to minimize the squared error distortion at end-user devices in a rate-limited caching network. The framework is very general and accounts for heterogeneous cache sizes, video popularities and user-file play-back qualities. As part of the solution, a new decentralized scheme for lossy cache-aided delivery subject to preset user distortion targets is proposed, which further generalizes prior literature to a setting with file heterogeneity.

In this paper, matching of correlated high-dimensional databases is investigated. A stochastic database model is considered where the correlation among the database entries is governed by an arbitrary joint distribution. Concentration of measure theorems such as typicality and laws of large numbers are used to develop a database matching scheme and derive necessary conditions for successful matching. Furthermore, it is shown that these conditions are tight through a converse result which characterizes a set of distributions on the database entries for which reliable matching is not possible. The necessary and sufficient conditions for reliable matching are evaluated in the cases when the database entries are independent and identically distributed as well as under Markovian database models.

Analog-to-digital converters (ADCs) are a major contributor to the power consumption of multiple-input multiple-output (MIMO) communication systems with large number of antennas. Use of low resolution ADCs has been proposed as a means to decrease power consumption in MIMO receivers. However, reducing the ADC resolution leads to performance loss in terms of achievable transmission rates. In order to mitigate the rate-loss, the receiver can perform analog processing of the received signals before quantization. Prior works consider one-shot analog processing where at each channel-use, analog linear combinations of the received signals are fed to a set of one-bit threshold ADCs. In this paper, a receiver architecture is proposed which uses a sequence of delay elements to allow for blockwise linear combining of the received analog signals. In the high signal to noise ratio regime, it is shown that the proposed architecture achieves the maximum achievable transmission rate given a fixed number of one-bit ADCs. Furthermore, a tradeoff between transmission rate and the number of delay elements is identified which quantifies the increase in maximum achievable rate as the number of delay elements is increased.

The fundamental limits of communication over multiple-input multiple-output (MIMO) networks are considered when a limited number of one-bit analog to digital converters (ADC) are used at the receiver terminals. Prior works have mainly focused on point-to-point communications, where receiver architectures consisting of a concatenation of an analog processing module, a limited number of one-bit ADCs with non-adaptive thresholds, and a digital processing module are considered. In this work, a new receiver architecture is proposed which utilizes adaptive threshold one-bit ADCs - where the ADC thresholds at each channel-use are dependent on the channel outputs in the previous channel-uses - to mitigate the quantization rate-loss. Coding schemes are proposed for communication over the point-to-point and broadcast channels, and achievable rate regions are derived. In the high SNR regime, it is shown that using the proposed architectures and coding schemes leads to the largest achievable rate regions among all receiver architectures with the same number of one-bit ADCs.

In the conventional information theoretic analysis of multiterminal communication scenarios, it is often assumed that all of the distributed terminals use the communication channel simultaneously. However, in practical wireless communication systems - due to restricted computation complexity at network terminals - a limited number of users can be activated either in uplink or downlink simultaneously. This necessitates the design of a scheduler which determines the set of active users at each time-slot. A well-designed scheduler maximizes the average system utility subject to a set of fairness criteria, which must be met in a limited window-length to avoid long starvation periods. In this work, scheduling under short-term temporal fairness constraints is considered. The objective is to maximize the average system utility such that the fraction of the time-slots that each user is activated is within desired upper and lower bounds in the fairness window-length. The set of feasible window-lengths is characterized as a function of system parameters. It is shown that the optimal system utility is non-monotonic and super-additive in window-length. Furthermore, a scheduling strategy is proposed which satisfies short-term fairness constraints for arbitrary window-lengths, and achieves optimal average system utility as the window-length is increased asymptotically. Numerical simulations are provided to verify the results.

Millimeter-wave (mmWave) bands have the potential to enable significantly high data rates in wireless systems. In order to overcome intense path loss and severe shadowing in these bands, it is essential to employ directional beams for data transmission. Furthermore, it is known that the mmWave channel incorporates a few number of spatial clusters necessitating additional time to align the corresponding beams with the channel prior to data transmission. This procedure is known as beam training (BT). While a longer BT leads to more directional beams (equivalently higher beamforming gains), there is less time for data communication. In this paper, this trade-off is investigated for a time slotted system under practical constraints such as finite beamwidth resolution and discrete modulation and coding schemes. At each BT time slot, the access point (AP) scans a region of uncertainty by transmitting a probing packet and refines angle of arrival (AoA) estimate based on user equipment (UE) feedback. Given a total number time slots, the objective is to find the optimum allocation between BT and data transmission and a feasible beamwidth for the estimation of AoA at each BT time slot such that the expected throughput is maximized. It is shown that the problem satisfies the optimal substructure property enabling the use of a backward dynamic programming approach to find the optimal solution with polynomial computational complexity. Simulation results reveal that in practical scenarios, the proposed approach outperforms existing techniques such as exhaustive and bisection search.

Millimeter-wave (mmWave) bands have shown the potential to enable high data rates for next generation mobile networks. In order to cope with high path loss and severe shadowing in mmWave frequencies, it is essential to employ massive antenna arrays and generate narrow transmission patterns (beams). When narrow beams are used, mobile user tracking is indispensable for reliable communication. In this paper, a joint beam tracking and data communication strategy is proposed in which, the base station (BS) increases the beamwidth during data transmission to compensate for location uncertainty caused by user mobility. In order to evade low beamforming gains due to widening the beam pattern, a probing scheme is proposed in which the BS transmits a number of probing packets to refine the estimation of angle of arrival based on the user feedback, which enables reliable data transmission through narrow beams again. In the proposed scheme, time is divided into similar frames each consisting of a probing phase followed by a data communication phase. A steady state analysis is provided based on which, the duration of data transmission and probing phases are optimized. Furthermore, the results are generalized to consider practical constraints such as minimum feasible beamwidth. Simulation results reveal that the proposed method outperforms well-known approaches such as optimized beam sweeping.

This letter provides a theoretical analysis for the static beamforming problem when the base station is equipped with two antennas. It is assumed that the base station cannot change the beamforming vector dynamically due to hardware restrictions. The objective is to find the static beamforming vector that maximizes network utility when long-term information about the user location is available. Non-linear optimization techniques are used to find the solution of the problem for two different network utility functions: average SNR and average rate. Numerical results are provided to support the analysis.

The scaling of coherent and non-coherent channel capacity is studied in a single-input multiple-output (SIMO) block Rayleigh fading channel as both the bandwidth and the number of receiver antennas go to infinity jointly with the transmit power fixed. The transmitter has no channel state information (CSI), while the receiver may have genie-provided CSI (coherent receiver), or the channel statistics only (non-coherent receiver). Our results show that if the available bandwidth is smaller than a threshold bandwidth which is proportional (up to leading order terms) to the square root of the number of antennas, there is no gap between the coherent capacity and the non-coherent capacity in terms of capacity scaling behavior. On the other hand, when the bandwidth is larger than this threshold, there is a capacity scaling gap. Since achievable rates using pilot symbols for channel estimation are subject to the non-coherent capacity bound, this work reveals that pilot-assisted coherent receivers in systems with a large number of receive antennas are unable to exploit excess spectrum above a given threshold for capacity gain.

The capacity of a Multiple-Input Multiple-Output (MIMO) channel in which the antenna outputs are processed by an analog linear combining network and quantized by a set of threshold quantizers is studied. The linear combining weights and quantization thresholds are selected from a set of possible configurations as a function of the channel matrix. The possible configurations of the combining network model specific analog receiver architectures, such as single antenna selection, sign quantization of the antenna outputs or linear processing of the outputs. An interesting connection between the capacity of this channel and a constrained sphere packing problem in which unit spheres are packed in a hyperplane arrangement is shown. From a high-level perspective, this follows from the fact that each threshold quantizer can be viewed as a hyperplane partitioning the transmitter signal space. Accordingly, the output of the set of quantizers corresponds to the possible regions induced by the hyperplane arrangement corresponding to the channel realization and receiver configuration. This connection provides a number of important insights into the design of quantization architectures for MIMO receivers; for instance, it shows that for a given number of quantizers, choosing configurations which induce a larger number of partitions can lead to higher rates1.

In this paper, the problem of matching pairs of correlated random graphs with multi-valued edge attributes is considered. Graph matching problems of this nature arise in several settings of practical interest including social network de-anonymization, study of biological data, and web graphs. An achievable region of graph parameters for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching. The algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs. Our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs. The result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest.

In this paper, de-anonymizing internet users by actively querying their group memberships in social networks is considered. An anonymous victim visits the attacker's website, and the attacker uses the victim's browser history to query her social media activity for the purpose of de-anonymization using the minimum number of queries. A stochastic model of the problem is considered where the attacker has partial prior knowledge of the group membership graph and receives noisy responses to its real-time queries. The victim's identity is assumed to be chosen randomly based on a given distribution which models the users' risk of visiting the malicious website. A de-anonymization algorithm is proposed which operates based on information thresholds and its performance both in the finite and asymptotically large social network regimes is analyzed. Furthermore, a converse result is provided which proves the optimality of the proposed attack strategy.

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.

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple unicast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly dynamic, unpredictable, and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to specifically account for the correlation among content files, such as, for example, the one between updated versions of dynamic data. It is shown that: 1) caching content pieces based on their correlation with the rest of the library and 2) jointly compressing requested files using cached information as references during delivery, can provide load reductions that go beyond those achieved with existing schemes. This is accomplished via the design of a class of correlation-aware achievable schemes, shown to significantly outperform the state-of-the-art correlation-unaware solutions. Our results show that as we move towards real-time and/or personalized media dominated services, where exact cache hits are almost non-existent but updates can exhibit high levels of correlation, network cached information can still be useful as references for network compression.

The availability of very wide spectrum in millimeter wave bands combined with large antenna arrays and ultra-dense networks raises two basic questions: What is the true value of overly abundant degrees of freedom and how can networks be designed to fully exploit them? This paper determines the capacity scaling of large cellular networks as a function of bandwidth, area, number of antennas, and base station density. It is found that the network capacity has a fundamental bandwidth scaling limit, beyond which the network becomes power-limited. An infrastructure multi-hop protocol achieves the optimal network capacity scaling for all network parameters. In contrast, current protocols that use only single-hop direct transmissions cannot achieve the capacity scaling in wideband regimes except in the special case when the density of base stations is taken to impractical extremes. This finding suggests that multi-hop communication will be important to fully realize the potential of next-generation cellular networks. Dedicated relays, if sufficiently dense, can also perform this task, relieving user nodes from the battery drain of cooperation. On the other hand, more sophisticated strategies such as hierarchical cooperation, that are essential for achieving capacity scaling in ad hoc networks, are unnecessary in the cellular context.

In-band full-duplex (FD) communication provides a promising alternative to half-duplex (HD) for wireless systems, due to increased spectral efficiency and capacity. In this paper, HD and FD radio implementations of two way, two hop, and two way two hop communication are compared in terms of degrees of freedom (DoF) under a realistic residual self-interference (SI) model. DoF analysis is carried out for each communication scenario for HD, antenna conserved (AC), and RF chain conserved (RC) FD radio implementations. The DoF analysis indicates that for the two way channel, the achievable AC FD with imperfect SI cancellation performs strictly below HD, and RC FD DoF tradeoff is superior when the SI can be sufficiently cancelled. For the two hop channel, FD is better when the relay has a large number of antennas and enough SI cancellation. For the two way two hop channel, when both nodes require similar throughput, the achievable DoF pairs for FD do not outperform HD. FD still can achieve better DoF pairs than HD, provided the relay has sufficient number of antennas and SI suppression.

With the increasing interest in the use of millimeter wave bands for 5G cellular systems comes renewed interest in resource sharing. Properties of millimeter wave bands such as massive bandwidth, highly directional antennas, high penetration loss, and susceptibility to shadowing, suggest technical advantages to spectrum and infrastructure sharing in millimeter wave cellular networks. However, technical advantages do not necessarily translate to increased profit for service providers, or increased consumer surplus. In this paper, detailed network simulations are used to better understand the economic implications of resource sharing in a vertically differentiated duopoly market for cellular service. The results suggest that resource sharing is less often profitable for millimeter wave service providers compared to microwave cellular service providers, and does not necessarily increase consumer surplus.

This paper studies the fundamental limits of caching in a network with two receivers and two files generated by a two-component discrete memoryless source with arbitrary joint distribution. Each receiver is equipped with a cache of equal capacity, and the requested files are delivered over a shared error-free broadcast link. First, a lower bound on the optimal peak rate-memory trade-off is provided. Then, in order to leverage the correlation among the library files to alleviate the load over the shared link, a two-step correlation-aware cache-aided coded multicast (CACM) scheme is proposed. The first step uses Gray-Wyner source coding to represent the library via one common and two private descriptions, such that a second correlation-unaware multiple-request CACM step can exploit the additional coded multicast opportunities that arise. It is shown that the rate achieved by the proposed two-step scheme matches the lower bound for a significant memory regime and it is within half of the conditional entropy for all other memory values.

The capacity of a discrete-time, multi-input multi-output (MIMO) channel with output quantization is investigated for different receiver architectures. A general framework for low-resolution quantization is proposed in which the antenna outputs are processed by analog combiners and sign quantizers are used for analog-to-digital conversion. The configuration of the analog combiners is chosen as a function of the channel realization so that the transmission rate can be maximized over the set of available configurations. To exemplify the proposed approach, four analog receiver architectures are considered: (a) sign quantization of the antenna outputs, (b) single antenna selection, (c) multiple antenna selection, and (d) linear processing of the antenna outputs. In each scenario, capacity is investigated as a function of the transmit power, the number of transmit/receive antennas and sign quantizers. In particular, it is shown that architecture (a) is sufficient to approach the optimal high signal-to-noise ratio (SNR) performance for a MIMO receiver in which the number of receive antennas is larger than the number of sign quantizers. Numerical evaluations of the average performance are presented for the case in which the channel gains are i.i.d. Gaussian distributed.

Consider a multi-user channel, where each user has a large but non-replenishable bit pool to transmit. Completion time refers to the number of channel uses spent by each user to complete its transmission. In this paper, an information-theoretic formulation of completion time is based on the concept of constrained rates, which are defined over possibly different number of channel uses. Analogous to the capacity region, the completion time region characterizes all possible trade-offs among users' completion times. For a two-user multi-access channel, it is shown that the completion time region is achieved by operating the channel in two independent phases: a multi-access phase when both users are transmitting, and a point-to-point phase when one user has finished and the other is still transmitting. Using a similar two-phase approach, the completion time regions (or inner and outer bounds) are established for a two-user Gaussian broadcast channel and a two-user Gaussian interference channel. It is observed that although consisting of two convex subregions, the completion time region may not be convex in general. Finally, optimization problems of minimizing the weighted sum completion time for a Gaussian multi-access channel and a Gaussian broadcast channel are solved, demonstrating the utility of the completion time approach.

Modern compression codes exploit signals' complex structures to encode them very efficiently. On the other hand, compressed sensing algorithms recover “structured” signals from their under-determined set of linear measurements. Currently, there is a noticeable gap between the types of structures used in the area of compressed sensing and those employed by state-of-the-art compression codes. Recent results in the literature on deterministic signals aim at bridging this gap through devising compressed sensing decoders that employ compression codes. This paper focuses on structured stochastic processes and studies application of lossy compression codes to compressed sensing of such signals. The performance of the formerly proposed compressible signal pursuit (CSP) optimization is studied in this stochastic setting. It is proved that in the low-distortion regime, as the blocklength grows to infinity, the CSP optimization reliably and robustly recovers n instances of a stationary process from its random linear measurements as long as n is slightly more than n times the rate-distortion dimension (RDD) of the source. It is also shown that under some regularity conditions, the RDD of a stationary process is equal to its information dimension. This connection establishes the optimality of CSP at least for memoryless stationary sources, which have known fundamental limits. Finally, it is shown that CSP combined by a family of universal variable-length fixed-distortion compression codes yields a family of universal compressed sensing recovery algorithms.

In non-coherent wideband fading channels, where energy rather than spectrum is the limiting resource, peaky and non-peaky signaling schemes have long been considered species apart, as the first approaches asymptotically the capacity of a wideband AWGN channel with the same average SNR, whereas the second reaches a peak rate at some finite critical bandwidth and then falls to zero as bandwidth grows to infinity. In this paper, it is shown that this distinction is in fact an artifact of the limited attention paid in the past to the product between the bandwidth and the fraction of time it is in use. This fundamental quantity, called bandwidth occupancy, measures average bandwidth usage over time. For all signaling schemes with the same bandwidth occupancy, achievable rates approach to the wideband AWGN capacity within the same gap as the bandwidth occupancy approaches its critical value, and decrease to zero as the occupancy goes to infinity. This unified analysis produces quantitative closed-form expressions for the ideal bandwidth occupancy, recovers the existing capacity results for (non-) peaky signaling schemes, and unveils a tradeoff between the accuracy of approximating capacity with a generalized Taylor polynomial and the accuracy with which the optimal bandwidth occupancy can be bounded.

The rate-distortion dimension (RDD) of an analog stationary process is studied as a measure of complexity that captures the amount of information contained in the process. It is shown that the RDD of a process, defined as two times the asymptotic ratio of its rate-distortion function R(D) to log 1/D as the distortion D approaches zero, is equal to its information dimension (ID). This generalizes an earlier result by Kawabata and Dembo and provides an operational approach to evaluate the ID of a process, which previously was shown to be closely related to the effective dimension of the underlying process and also to the fundamental limits of compressed sensing. The relation between RDD and ID is illustrated for a piecewise constant process.

Cache-aided coded multicast leverages side information at wireless edge caches to efficiently serve multiple groupcast demands via common multicast transmissions, leading to load reductions that are proportional to the aggregate cache size. However, the increasingly unpredictable and personalized nature of the content that users consume challenges the efficiency of existing caching-based solutions in which only exact content reuse is explored. This paper generalizes the cache-aided coded multicast problem to a source compression with distributed side information problem that specifically accounts for the correlation among the content files. It is shown how joint file compression during the caching and delivery phases can provide load reductions that go beyond those achieved with existing schemes. This is accomplished through a lower bound on the fundamental rate-memory trade-off as well as a correlation-aware achievable scheme, shown to significantly outperform state-of-the-art correlation-unaware solutions, while approaching the limiting rate-memory trade-off.

Data compression and compressed sensing algorithms exploit the structure present in a signal for its efficient representation and measurement, respectively. While most state-of-the-art data compression codes take advantage of complex patterns present in signals of interest, this is not the case in compressed sensing. This paper explores usage of efficient data compression codes in building compressed sensing recovery methods for stochastic processes. It is proved that for an i.i.d. process, compression-based compressed sensing achieves the fundamental limits in terms of the number of measurements. It is also proved that compressed sensing recovery methods built based on a family of universal compression codes yield a family of universal compressed sensing schemes.

The resource usage model for millimeter wave bands has been the subject of considerable debate. The massive bandwidth, highly directional antennas, high penetration loss and susceptibility to shadowing in these bands suggest certain advantages to spectrum and infrastructure sharing. In particular, resources that are "open", such as unlicensed spectrum or a deployment of base stations open to all service providers, may offer greater gains in mmWave bands than at conventional cellular frequencies. However, even when sharing is technically beneficial (as recent research in this area suggests that it is), it may not be profitable. In this paper, both the technical and economic implications of resource sharing in millimeter wave networks are studied. Millimeter wave service is considered in the economic framework of a network good, and detailed network simulations are used to understand data rates, profit, and demand for millimeter wave service, with and without open resources. The results suggest that "open" deployments of neutral small cells that serve subscribers of any service provider encourage market entry by making it easier for networks to reach critical mass, more than "open" (unlicensed) spectrum would.

In this paper, a class of broadcast interference channels (BIC) is investigated, where one of the two broadcast receivers is subject to interference coming from a point-to-point transmission. For a general discrete memoryless broadcast interference channel (DM-BIC), an achievable scheme based on message splitting, superposition, and binning is proposed and a concise representation of the corresponding achievable rate region ℛ. is obtained. Two partial-order broadcast conditions interference-oblivious less noisy and interference-cognizant less noisy are defined, thereby extending the usual less noisy condition for a regular broadcast channel by taking interference into account. Under these conditions, a reduced form of ℛ is shown to be equivalent to a rate region based on a simpler scheme, where the broadcast transmitter uses only superposition. Furthermore, if interference is strong for the interference-oblivious less noisy DM-BIC, the capacity region is given by the aforementioned two equivalent rate regions. For the interference-cognizant less noisy DM-BIC, it is argued that the strong but not very strong interference condition does not exist, and in this case, the capacity region for the very strong interference is obtained. For a Gaussian broadcast interference channel, channel parameters are categorized into three regimes. For the first two regimes, which are closely related to the two partial-order broadcast conditions, achievable rate regions are derived by specializing the corresponding achievable schemes of DM-BICs with Gaussian input distributions. The entropy power inequality-based outer bounds are obtained by combining bounding techniques for a Gaussian broadcast channel and a Gaussian interference channel. These inner and outer bounds lead to either exact or approximate characterizations of capacity regions and sum capacity under various conditions. For the remaining complementing regime, inner and outer bounds are also provided.

This paper investigates distortion-power tradeoffs in transmission of quasi-stationary sources over delay and buffer limited block fading channels by studying encoder and decoder buffering techniques to smooth out the source and channel variations. Four source and channel coding schemes that consider buffer and power constraints are presented to minimize the reconstructed source distortion. The first one is a high performance scheme, which benefits from optimized source and channel rate adaptation. In the second scheme, the channel coding rate is fixed and optimized along with transmission power with respect to channel and source variations; hence this scheme enjoys simplicity of implementation. The two last schemes have fixed transmission power with optimized adaptive or fixed channel coding rate. For all the proposed schemes, closed form solutions for mean distortion, optimized rate, and power are provided and in the high SNR regime, the mean distortion exponent and the asymptotic mean power gains are derived. The proposed schemes with buffering exploit the diversity due to source and channel variations. Specifically, when the buffer size is limited, fixed channel rate adaptive power scheme outperforms an adaptive rate fixed power scheme. Furthermore, analytical and numerical results demonstrate that with limited buffer size, the system performance in terms of reconstructed signal SNR saturates as transmission power increases, suggesting that appropriate buffer size selection is important to achieve a desired reconstruction quality.

Peaky and non-peaky signaling schemes have long been considered species apart in non-coherent wideband fading channels, as the first approaches asymptotically the linear-in-power capacity of a wideband AWGN channel with the same SNR, whereas the second reaches a nearly power-limited peak rate at some finite critical bandwidth and then falls to zero as bandwidth grows to infinity. In this paper it is shown that this distinction is in fact an artifact of the limited attention paid in the past to the product between the bandwidth and the fraction of time it is in use. This fundamental quantity, that is termed bandwidth occupancy, measures average bandwidth usage over time. The two types of signaling in the literature are harmonized to show that, for any type of signals, there is a fundamental limit-a critical bandwidth occupancy. All signaling schemes with the same bandwidth occupancy approach the capacity of wideband AWGN channels with the same asymptotic behavior as the bandwidth occupancy grows to its critical value. For a bandwidth occupancy above the critical, rate decreases to zero as the bandwidth occupancy goes to infinity.

This paper studies noncoherent wideband systems with a single antenna transmitter and a multiple antenna receiver with many elements, under signaling with peak-to-average power ratio constraints. The analysis considers the scaling behavior of capacity and achievable rates by letting both the number of antennas and the bandwidth go to infinity jointly. In contrast to prior work on wideband single input single output (SISO) channels without a-priori channel state information, it is shown that a sufficiently large number of receive antennas can make up for the vanishingly small SNR at each antenna. In particular, it is shown that when bandwidth grows sufficiently slowly with the number of antennas, the capacity scaling with an increasing number of receive antennas is the same as the optimal coherent capacity scaling. If the bandwidth grows faster than a certain threshold, however, the additional bandwidth does not help because a finite transmit power is spread over an excessively large bandwidth.

Energy harvesting multihop networks allow for perpetual operation of low cost limited range wireless devices. Compared with their battery-operated counterparts, the coupling of energy and data causality constraints with half-duplex relay operation makes it challenging to operate such networks. In this paper, a throughput maximization problem for energy harvesting two-hop networks with decode-and-forward half-duplex relays is investigated. For a system with two parallel relays, various combinations of the following four transmission modes are considered: broadcast from the source, multiaccess from the relays, and successive relaying phases I and II. Optimal transmission policies for one and two parallel relays are studied under the assumption of noncausal knowledge of energy arrivals and finite size relay data buffers. The problem is formulated using a convex optimization framework, which allows for efficient numerical solutions and helps identify important properties of optimal policies. Numerical results are presented to provide throughput comparisons and to investigate the impact of multiple relays, size of relay data buffers, transmission modes, and energy harvesting on the throughput.

The papers in this special issue presents cutting-edge research results in the emerging area of energy harvesting wireless communications and wireless energy transfer. This first issue starts with a review article coauthored by the guest editors that summarizes recent results in the broad area of energy harvesting communications, in particular, in information-theoretic, offline and online schedulingtheoretic, medium access, networking approaches to energy harvesting communications, as well as in energy cooperation and simultaneous wireless energy and information transfer.

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.

This paper introduces a novel and efficient approach for user cooperation in wireless video multicast using randomized distributed space time codes (R-DSTC), in which the sender first transmits the source packets, and the sender and receivers that have received all source packets then generate and send the parity packets simultaneously using R-DSTC. As more parity packets are delivered, more receivers can recover all source packets and join the parity packet transmission. Four variations of the proposed systems are considered. The first one requires complete channel information between the sender and all receivers and between all receivers to derive the optimal transmission rates for sending source and parity packets, and employs receiver feedback to determine when to terminate parity transmission. The other three suboptimal systems do not require full channel information and/or receiver feedback, and hence are more feasible in practice. All four versions can support significantly higher video rates and correspondingly higher quality of decoded video, than prior approaches in the literature, which require full channel information but not feedback.

Source and channel coding for an energy-limited wireless sensor node is investigated. The sensor node observes independent Gaussian source samples with variances changing over time slots. The channel is modeled as a flat fading channel, whose gain remains constant during each time slot, and changes from one time slot to the next. The compressed samples are stored in a finite data buffer, and need to be delivered to the destination in at most d time slots. The objective is to minimize the average squared-error distortion between the source samples and their reconstructions. First, a battery operated system, in which the sensor node has a finite amount of energy at the beginning of transmission, is investigated. Then, the impact of energy harvesting, and the energy cost of processing and sampling are considered. The optimal compression and transmission policy is formulated as the solution of a convex optimization problem, and the properties of the optimal policies are identified. For the strict delay case, d=1, a two-dimensional (2D) waterfilling interpretation is provided. Numerical results are presented to illustrate the structure of the optimal policy, and to analyze the effect of the delay constraints, data buffer size, energy harvesting, and processing and sampling costs.

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. In order to satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e, random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.

With millimeter wave bands emerging as a strong candidate for 5G cellular networks, next-generation systems may be in a unique position where spectrum is plentiful. To assess the potential value of this spectrum, this paper derives scaling laws on the per mobile downlink feasible rate with large bandwidth and number of nodes, for both Infrastructure Single Hop (ISH) and Infrastructure Multi-Hop (IMH) architectures. It is shown that, for both cases, there exist critical bandwidth scalings above which increasing the bandwidth no longer increases the feasible rate per node. These critical thresholds coincide exactly with the bandwidths where, for each architecture, the network transitions from being degrees-of-freedom-limited to power-limited. For ISH, this critical bandwidth threshold is lower than IMH when the number of users per base station grows with network size. This result suggests that multi-hop transmissions may be necessary to fully exploit large bandwidth degrees of freedom in deployments with growing number of users per cell.

With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.

Millimeter-wave (mmW) frequencies between 30 and 300 GHz are a new frontier for cellular communication that offers the promise of orders of magnitude greater bandwidths combined with further gains via beamforming and spatial multiplexing from multielement antenna arrays. This paper surveys measurements and capacity studies to assess this technology with a focus on small cell deployments in urban environments. The conclusions are extremely encouraging; measurements in New York City at 28 and 73 GHz demonstrate that, even in an urban canyon environment, significant non-line-of-sight (NLOS) outdoor, street-level coverage is possible up to approximately 200 m from a potential low-power microcell or picocell base station. In addition, based on statistical channel models from these measurements, it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities. Cellular systems, however, will need to be significantly redesigned to fully achieve these gains. Specifically, the requirement of highly directional and adaptive transmissions, directional isolation between links, and significant possibilities of outage have strong implications on multiple access, channel structure, synchronization, and receiver design. To address these challenges, the paper discusses how various technologies including adaptive beamforming, multihop relaying, heterogeneous network architectures, and carrier aggregation can be leveraged in the mmW context.

In various wireless systems, such as sensor RFID networks and body area networks with implantable devices, the transmitted signals are simultaneously used both for information transmission and for energy transfer. To satisfy the conflicting requirements on information and energy transfer, this paper proposes the use of constrained run-length limited (RLL) codes in lieu of conventional unconstrained (i.e., random-like) capacity-achieving codes. The receiver's energy utilization requirements are modeled stochastically, and constraints are imposed on the probabilities of battery underflow and overflow at the receiver. It is demonstrated that the codewords' structure afforded by the use of constrained codes enables the transmission strategy to be better adjusted to the receiver's energy utilization pattern, as compared to classical unstructured codes. As a result, constrained codes allow a wider range of trade-offs between the rate of information transmission and the performance of energy transfer to be achieved.

Communication over a broadband fading channel powered by an energy harvesting transmitter is studied. Assuming non-causal knowledge of energy/data arrivals and channel gains, optimal transmission schemes are identified by taking into account the energy cost of the processing circuitry as well as the transmission energy. A constant processing cost for each active sub-channel is assumed. Three different system objectives are considered: 1) throughput maximization, in which the total amount of transmitted data by a deadline is maximized for a backlogged transmitter with a finite capacity battery; 2) energy maximization, in which the remaining energy in an infinite capacity battery by a deadline is maximized such that all the arriving data packets are delivered; and 3) transmission completion time minimization, in which the delivery time of all the arriving data packets is minimized assuming infinite size battery. For each objective, a convex optimization problem is formulated, the properties of the optimal transmission policies are identified, and an algorithm which computes an optimal transmission policy is proposed. Finally, based on the insights gained from the offline optimizations, low-complexity online algorithms performing close to the optimal dynamic programming solution for the throughput and energy maximization problems are developed under the assumption that the energy/data arrivals and channel states are known causally at the transmitter.

Energy harvesting (EH) technology enables wireless nodes to operate in a self-powered fashion; however, the stochastic nature of the harvesting process and the limited amount of harvested energy require efficient management of the available resources. In this paper, an EH transmitter communicating over a fading channel is studied considering jointly the energy costs of transmission and processing. In particular, under the assumption of known energy and data arrival profiles and fading states, optimal transmission policies are studied, so that, the remaining energy in the battery of the transmitter is maximized by a given deadline while all the arriving data packets are delivered to the receiver. A "directional glue pouring" interpretation is provided for the algorithm that computes the optimal offline transmission policy. The relation of this problem with the transmission completion time minimization problem is also discussed. Finally, a heuristic algorithm for online optimization, which performs close to the optimal offline transmission policy, is proposed.

In this paper, a class of Gaussian broadcast interference channels is investigated, where one of the two broadcast users is subject to the interference coming from a point-to-point transmission. Channel parameters are categorized into three regimes. For the first two, where an ordering of the decodability of the broadcast users exists, inner bounds based on superposition and rate splitting are obtained. Entropy-power-inequality-based outer bounds are derived by combining bounding techniques for Gaussian broadcast and interference channels. These inner and outer bounds lead to either exact or approximate characterizations of the capacity region and sum capacity under various conditions. For the remaining complementing regime, inner and outer bounds are also provided.

In wireless networks, management of harvested energy is important due to limited and stochastic energy sources. In this paper, throughput maximization for energy harvesting two-hop communication with half-duplex relays is considered. Optimal transmission policies are found for one relay and two parallel relays under the assumption of known energy arrivals at the source and the relays. For each case, a convex optimization problem is formulated to efficiently solve and identify properties of the optimal transmission policies. Performance comparisons are provided to investigate the impact of multiple relays and energy harvesting.

Distortion minimization for an energy harvesting sensor node communicating over a fading channel is studied. Slotted transmission is considered such that, new source samples and energy packets arrive at the beginning of each time slot (TS), and the fading channel state changes from one TS to the next. A delay constraint is imposed requiring each source sample to be reconstructed at the destination d TSs after its arrival. Assuming independent Gaussian samples with variances changing over TSs, total distortion is minimized under the offline optimization framework, i.e., energy arrivals, source variances and channel gains are assumed to be known non-causally. Optimal compression rates and transmission powers are found and some properties of the optimal strategy are discussed. A two-dimensional water-filling interpretation of the optimal solution is provided for a battery-run node with d = 1.

A relay channel with orthogonal components in which the destination is affected by an interference signal that is non-causally available only at the source is studied. The interference signal has structure in that it is produced by another transmitter communicating with its own destination. Moreover, the interferer is not willing to adjust its communication strategy to minimize the interference. Knowledge of the interferer's signal may be acquired by the source, for instance, by exploiting HARQ retransmissions on the interferer's link. The source can then utilize the relay not only for communicating its own message, but also for cooperative interference mitigation at the destination by informing the relay about the interference signal. Proposed transmission strategies are based on partial decode-and-forward (PDF) relaying and leverage the interference structure. Achievable schemes are derived for discrete memoryless models, Gaussian and Ricean fading channels. Furthermore, optimal strategies are identified in some special cases. Finally, numerical results bring insight into the advantages of utilizing the interference structure at the source, relay or destination.

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. The rate-distortion function is characterized for memoryless sources, and then specialized to Gaussian and binary sources for selected functions and with quadratic and Hamming distortion metrics, respectively. The optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source statistics, including the correlation, and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy. Finally, the analysis is extended to the multi-hop set-up with jointly Gaussian sources, where each encoder can observe only one of the sources.

Reliable transmission of a discrete memoryless source to multiple destinations over a relay network is considered. Motivated by sensor network applications, it is assumed that the relays and the destinations all have access to side information correlated with the underlying source signal. Joint source-channel cooperative transmission is studied in which the terminals in the network help the transmission of the source signal to the destinations by using their overheard signals, as in the classical channel cooperation scenario, as well as the available correlated side information. Decode-and-forward-based cooperative transmission is studied in a network of multiple relay terminals and two different achievability schemes are proposed: 1) a regular encoding and sliding-window decoding scheme without explicit source binning at the encoder; and 2) a semiregular encoding and backward decoding scheme with binning based on the side information statistics. It is shown that both of these schemes lead to the same source-channel code rate, which is shown to be the source-channel capacity in the case of 1) a physically degraded relay network with a single destination in which the side information signals are degraded in the same order as the channel; and 2) a relay network with multiple destinations, in which all the terminals want to reconstruct the source reliably, while at most one of them can act as a relay.

This work considers communication over Gaussian interference channels with processing energy cost, which explicitly takes into account the energy expended for processing when transmitters are on. In the presence of processing energy cost, transmitting all the time as in the conventional no-cost case is no longer optimal. For a two-user Gaussian interference channel with processing energy cost, assuming that the on-off states of transmitters are not utilized for signaling, several transmission schemes with varying complexities are proposed and their sum-rates are compared with an interference-free upper bound. Moreover, the very strong interference regime, under which interference does not incur any rate penalty, is identified and shown to be larger than the case of no processing energy cost for certain scenarios of interest. Also, extensions to a three-user cascade Gaussian Z interference channel with processing energy cost are provided, where scheduling of user transmissions based on the channel set-up is investigated.

Many cellular user equipments (UEs) today are able to access both the unlicensed band (e.g., via WiFi) and the cellular licensed band. However, in most regions of the world, they are not allowed to access both bands simultaneously for data communications - users can only choose either WiFi or cellular. In order to allow UEs to enjoy both bands at the same time, in our earlier work a framework for femtocells to simultaneously access both licensed and unlicensed bands was proposed. In this paper, the framework is extended to include strategies for femtocells to balance their traffic in licensed and unlicensed bands. The goal is to maximize the sum utility (i.e., user satisfaction) of femto and WiFi users while keeping the femto-to-macro interference below predefined thresholds. The optimal femto traffic balancing scheme is obtained and implemented in a network simulator which considers the activities and interactions of macro, femto and WiFi networks. Simulation results demonstrate that the proposed scheme significantly increases sum utility of all macro, femto and WiFi users, compared with the current practices where users can choose only one band (licensed or unlicensed).

Two-way real-time video communication in wireless networks requires high bandwidth, low delay and error resiliency. This paper addresses these demands by proposing a system with the integration of Network Coding (NC), user cooperation using Randomized Distributed Space-time Coding (R-DSTC) and packet level Forward Error Correction (FEC) under a one-way delay constraint. Simulation results show that the proposed scheme significantly outperforms both conventional direct transmission as well as R-DSTC based two-way cooperative transmission, and is most effective when the distance between the users is large.

We study a class of discrete memoryless broadcast interference channels (DM-BICs), where one of the broadcast receivers is subject to the interference from a point-to-point transmission. A general achievable rate region R based on rate splitting, superposition coding and binning at the broadcast transmitter and rate splitting at the interfering transmitter is derived. Under two partial order broadcast conditions interference-oblivious less noisy and interference-cognizant less noisy, a reduced form of R is shown to be equivalent to the region based on a simpler scheme that uses only superposition coding at the broadcast transmitter. Furthermore, the capacity regions of a DM-BIC under the two partial order broadcast conditions are characterized respectively for the strong and very strong interference conditions.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise.

This paper considers the problem of lossy compression for the computation of a function of two correlated sources, both of which are observed at the encoder. Due to presence of observation costs, the encoder is allowed to observe only subsets of the samples from both sources, with a fraction of such sample pairs possibly overlapping. For both Gaussian and binary sources, the distortion-rate function, or rate-distortion function, is characterized for selected functions and with quadratic and Hamming distortion metrics, respectively. Based on these results, for both examples, the optimal measurement overlap fraction is shown to depend on the function to be computed by the decoder, on the source correlation and on the link rate. Special cases are discussed in which the optimal overlap fraction is the maximum or minimum possible value given the sampling budget, illustrating non-trivial performance trade-offs in the design of the sampling strategy.

In wireless networks, energy consumed for communication includes both the transmission and the processing energy. In this paper, point-to-point communication over a fading channel with an energy harvesting transmitter is studied considering jointly the energy costs of transmission and processing. Under the assumption of known energy arrival and fading profiles, optimal transmission policy for throughput maximization is investigated. Assuming that the transmitter has sufficient amount of data in its buffer at the beginning of the transmission period, the average throughput by a given deadline is maximized. Furthermore, a “directional glue pouring algorithm” that computes the optimal transmission policy is described.

In cyber-physical systems where sensors measure the temporal evolution of a given phenomenon of interest and radio communication takes place over short distances, the energy spent for source acquisition and compression may be comparable with that used for transmission. Additionally, in order to avoid limited lifetime issues, sensors may be powered via energy harvesting and thus collect all the energy they need from the environment. This work addresses the problem of energy allocation over source acquisition/compression and transmission for energy-harvesting sensors. At first, focusing on a single-sensor, energy management policies are identified that guarantee a minimum average distortion while at the same time ensuring the stability of the queue connecting source and channel encoders. It is shown that the identified class of policies is optimal in the sense that it stabilizes the queue whenever this is feasible by any other technique that satisfies the same average distortion constraint. Moreover, this class of policies performs an independent resource optimization for the source and channel encoders. Suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied for performance comparison. The problem of optimizing the desired trade-off between average distortion and backlog size is then formulated and solved via dynamic programming tools. Finally, a system with multiple sensors is considered and time-division scheduling strategies are derived that are able to maintain the stability of all data queues and to meet the average distortion constraints at all sensors whenever it is feasible.

Energy efficiency is a key requirement in the design of wireless sensor networks. While most theoretical studies only account for the energy requirements of communication, the sensing process, which includes measurements and compression, can also consume comparable energy. In this paper, the problem of sensing and communicating parallel sources is studied by accounting for the cost of both communication and sensing. In the first formulation of the problem, the sensor has a separate energy budget for sensing and a rate budget for communication, while, in the second, it has a single energy budget for both tasks. Furthermore, in the second problem, each source has its own associated channel. Assuming that sources with larger variances have lower sensing costs, the optimal allocation of sensing energy and rate that minimizes the overall distortion is derived for the first problem. Moreover, structural results on the solution of the second problem are derived under the assumption that the sources with larger variances are transmitted on channels with lower noise. Closed-form solutions are also obtained for the case where the energy budget is sufficiently large. For an arbitrary order on the variances and costs, the optimal solution to the first problem is also obtained numerically and compared with several suboptimal strategies.

Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called STiCMAC, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called randomized distributed space-time coding (R-DSTC), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, STiCMAC picks a transmission mode that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it robust to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment.

This paper proposes a novel relaying-based approach for managing mobility in dense cellular networks. Dense deployment of base stations will have small cells, necessitating frequent handovers. Moreover, small cell solutions such as femtocells may lack low-delay backhaul connections to the operator core's network where handover procedures are coordinated. As a result, handover delays can be large, impacting signal quality severely. The proposed relay-based hierarchical mobility scheme forwards data over-the-air to other base stations close to mobile as the mobile moves but before the network point of attachment can be switched. Hybrid ARQ and randomized beamforming are used to opportunistically gain the benefits of relays with no explicit coordination between the relay base stations and the source or mobile. Thus, the protocol is attractive for high-speed dynamic environments and can be implemented with minimal messaging overhead. A simulation of the protocol in a 3GPP Long-Term Evolution (LTE) cellular system with dense cells and high-speed mobiles shows the ability of the protocol to mitigate short-term rate outages during handover with significant improvements for delay sensitive applications.

A K-user cascade Gaussian Z-interference channel is a subclass of the general K-user Gaussian interference channel, where each user, except the first one, experiences interference only from the previous user. Under simple Han-Kobayashi schemes assuming Gaussian inputs and no time sharing, it is shown that the maximum sum rate is achieved by each user transmitting either common or private signals. For K = 3, channel conditions under which the achieved sum rate is either equal to or within 0.5 bits to the sum capacity are identified.

In a multi-access channel, completion time refers to the number of channel uses required for users, each with some given fixed bit pool, to complete the transmission of all their data bits. In this paper, the characterization of the completion time region is based on the concept of constrained rates, where users' rates are defined over possibly different number of channel uses. An information theoretic formulation of completion time is given and the completion time region is then established for two-user Gaussian multi-access channel, which, analogous to capacity region, characterizes all possible trade-offs between users' completion times.

A distributed cooperative forwarding framework based on randomized coding is proposed, where cooperative links are formed and packets are forwarded on the fly, without explicit relay selection, actuation or resource allocation. In this framework, a cooperative flooding method and two cooperative forwarding schemes that actuate the cooperative transmissions of the nodes within an optimally formed progress region are described. It is shown that by assuring packets' progress cooperatively through a region instead of a string of predetermined nodes, progress of the packets towards the final destination is guaranteed even in sparse networks, under severe fading and mobility conditions. The proposed forwarding schemes are shown to provide reductions in the total number of hops, while causing minimal spatial footprint on the network.

This work addresses the problem of energy allocation over source compression and transmission for a single energy-harvesting sensor. An optimal class of policies is identified that simultaneously guarantees a maximal average distortion and the stability of the queue connecting source and channel encoders, whenever this is feasible by any other strategy. This class of policies performs an independent resource optimization for the source and channel encoders. Analog transmission techniques as well as suboptimal strategies that do not use the energy buffer (battery) or use it only for adapting either source or channel encoder energy allocation are also studied.

Cellular operators have been offloading data traffic from their licensed bands to unlicensed bands through a large number of WiFi hotspots over the past years. Although this approach improves the cellular network capacity to some extent, it falls short of getting significant throughput gains. In this paper, it is argued that femtocells, covering a short range, can be a perfect platform to jointly exploit the merits of both licensed and unlicensed frequency bands. In particular, a framework is proposed for a femtocell to simultaneously access both licensed and unlicensed bands. The performance of coexisting femtocell and WiFi networks operating over a fully-utilized unlicensed band are analytically modeled and are verified via simulations. Impact of femtocell channel access parameters on the performance of WiFi and cellular networks is also investigated, shedding light on how a femtocell can best adjust its channel access parameters to coexist with incumbent unlicensed spectrum users like WiFi networks.

A Gaussian Interference Channel (IC) is investigated in which a relay assists two source-destination pairs. The relay is assumed to receive over dedicated orthogonal channels from the sources (e.g., over orthogonal bands or time slots, or over wired links), while it transmits in the same band as the sources. This scenario is referred to as IC assisted by an out-of-band reception/ in-band transmission relay (IC-OIR). An achievable rate region is derived for the IC-OIR that encompasses, besides the standard signal relaying, interference management via interference relaying, cancellation and precoding. The sum-capacity is found in a specific regime defined by the very strong relay-interference conditions. Numerical results validate the performance gains of interference mitigation via the relay.

In this work, a four-terminal complex Gaussian network composed of a source, a destination, an eavesdropper, and a jammer relay is studied under two different set of assumptions: 1) The jammer relay does not hear the source transmission, and 2) The jammer relay is causally given the source message. In both cases, the jammer relay assists the eavesdropper and aims to decrease the achievable secrecy rates. The source, on the other hand, aims to increase it. To help the eavesdropper, the jammer relay can use pure relaying and/or send interference. Each of the problems is formulated as a two-player, noncooperative, zero-sum continuous game. Assuming Gaussian strategies at the source and the jammer relay in the first problem, the Nash equilibrium is found and shown to be achieved with mixed strategies in general. The optimal cumulative distribution functions (cdfs) for the source and the jammer relay that achieve the value of the game, which is the Nash equilibrium secrecy rate, are found. For the second problem, the Nash equilibrium solution is found and the results are compared to the case when the jammer relay is not informed about the source message.

This work considers coordination and bargaining between two selfish users over a Gaussian interference channel. The usual information theoretic approach assumes full cooperation among users for codebook and rate selection. In the scenario investigated here, each user is willing to coordinate its actions only when an incentive exists and benefits of cooperation are fairly allocated. The users are first allowed to negotiate for the use of a simple Han-Kobayashi type scheme with fixed power split. Conditions for which users have incentives to cooperate are identified. Then, two different approaches are used to solve the associated bargaining problem. First, the Nash Bargaining Solution (NBS) is used as a tool to get fair information rates and the operating point is obtained as a result of an optimization problem. Next, a dynamic alternating-offer bargaining game (AOBG) from bargaining theory is introduced to model the bargaining process and the rates resulting from negotiation are characterized. The relationship between the NBS and the equilibrium outcome of the AOBG is studied and factors that may affect the bargaining outcome are discussed. Finally, under certain high signal-to-noise ratio regimes, the bargaining problem for the generalized degrees of freedom is studied.

A Gaussian interference channel (IC) with a relay is considered. The relay is assumed to operate over an orthogonal band with respect to the underlying IC, and the overall system is referred to as IC with an out-of-band relay (IC-OBR). The system can be seen as operating over two parallel interference-limited channels: The first is a standard Gaussian IC and the second is a Gaussian relay channel characterized by two sources and destinations communicating through the relay without direct links. We refer to the second parallel channel as OBR Channel (OBRC). The main aim of this work is to identify conditions under which optimal operation, in terms of the capacity region of the IC-OBR, entails either signal relaying and/or interference forwarding by the relay, with either a separable or nonseparable use of the two parallel channels, IC, and OBRC. Here, “separable” refers to transmission of independent information over the two constituent channels. For a basic model in which the OBRC consists of four orthogonal channels from sources to relay and from relay to destinations (IC-OBR Type-I), a condition is identified under which signal relaying and separable operation is optimal. This condition entails the presence of a relay-to-destinations capacity bottleneck on the OBRC and holds irrespective of the IC. When this condition is not satisfied, various scenarios, which depend on the IC channel gains, are identified in which interference forwarding and nonseparable operation are necessary to achieve optimal performance. In these scenarios, the system exploits the “excess capacity” on the OBRC via interference forwarding to drive the IC-OBR system in specific interference regimes (strong or mixed). The analysis is then turned to a more complex IC-OBR, in which the OBRC consists of only two orthogonal channels, one from sources to relay and one from relay to destinations (IC-OBR Type-II). For this channel, some capacity resuls are derived that parallel the conclusions for IC-OBR Type-I and point to the additional analytical challenges.

The sum-capacity for specific sub-classes of ergodic fading Gaussian two-user interference channels (IFCs) is developed under the assumption of perfect channel state information at all transmitters and receivers. For the sub-classes of uniformly strong (every fading state is strong) and ergodic very strong two-sided IFCs (a mix of strong and weak fading states satisfying specific fading averaged conditions) the optimality of completely decoding the interference, i.e., converting the IFC to a compound multiple access channel (C-MAC), is proved. It is also shown that this capacity-achieving scheme requires encoding and decoding jointly across all fading states. As an achievable scheme and also as a topic of independent interest, the capacity region and the corresponding optimal power policies for an ergodic fading C-MAC are developed. For the sub-class of uniformly weak IFCs (every fading state is weak), genie-aided outer bounds are developed. The bounds are shown to be achieved by treating interference as noise and by separable coding for one-sided fading IFCs. Finally, for the sub-class of one-sided hybrid IFCs (a mix of weak and strong states that do not satisfy ergodic very strong conditions), an achievable scheme involving rate splitting and joint coding across all fading states is developed and is shown to perform at least as well as a separable coding scheme.

A standard assumption in network information theory is that all nodes are informed at all times of the operations carried out (e.g., of the codebooks used) by any other terminal in the network. In this paper, information theoretic limits are sought under the assumption that, instead, some nodes are not informed about the codebooks used by other terminals. Specifically, capacity results are derived for a relay channel in which the relay is oblivious to the codebook used by the source (oblivious relaying), and an interference relay channel with oblivious relaying and in which each destination is possibly unaware of the codebook used by the interfering source (interference-oblivious decoding). Extensions are also discussed for a related scenario with standard codebook-aware relaying but interference-oblivious decoding. The class of channels under study is limited to out-of-band (or “primitive”) relaying: Relay-to-destinations links use orthogonal resources with respect to the transmission from the source encoders. Conclusions are obtained under a rigorous definition of oblivious processing that is related to the idea of randomized encoding. The framework and results discussed in this paper suggest that imperfect codebook information can be included as a source of uncertainty in network design along with, e.g., imperfect channel and topology information.

A source communicates with a remote destination via a number of distributed relays. Communication from source to relays takes place over a (discrete or Gaussian) broadcast channel, while the relays are connected to the receiver via orthogonal finite-capacity links. Unknowns to the source and relays, link failures may occur between any subset of relays and the destination in a nonergodic fashion. Upper and lower bounds are derived on average achievable rates with respect to the prior distribution of the link failures, assuming the relays to be oblivious to the source codebook. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach, previously investigated for quasi-static fading channels, and different robust distributed compression techniques. Numerical results show that lower and upper bounds are quite close over most operating regimes, and provide insight into optimal transmission design choices for the scenario at hand. Extension to the case of nonoblivious relays is also discussed.

With the increased popularity of mobile multimedia services, efficient and robust video multicast strategies are of critical importance. Cooperative communications has been shown to improve the robustness and the data rates for point-to-point transmission. In this paper, a two-hop cooperative transmission scheme for multicast in infrastructure-based networks is used, where multiple relays forward the data simultaneously using randomized distributed space time codes (RDSTC). This randomized cooperative transmission is further integrated with layered video coding and packet level forward error correction (FEC) to enable efficient and robust video multicast. Three different schemes are proposed to find the system operating parameters based on the availability of the channel information at the source station: RDSTC with full channel information, RDSTC with limited channel information, and RDSTC with node count. The performance of these three schemes are compared with rate adaptive direct transmission and conventional multicast that does not use rate adaptation. The results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, all three proposed randomized cooperative schemes outperform both strategies significantly as long as the network has enough nodes. Furthermore, the performance gap between RDSTC with full channel information and RDSTC with limited channel information or node count is relatively small, indicating the robustness of the proposed cooperative multicast system using RDSTC.

A cooperative network where the transmission between two nodes is assisted by many half-duplex relays over parallel Gaussian channels is considered. The parallel channel model is suitable for a broadband system, such as orthogonal frequency division multiplexing or a block fading channel. For the decode-and-forward protocol, an optimization problem for joint power, time and subchannel allocation under per-node power constraints is formulated to maximize the total transmission rate between the source and the destination. To solve this optimization problem, first the optimal power allocation for a given subchannel allocation is found. Then a greedy algorithm that jointly allocates subchannels and power is described. Finally, the time allocation is optimized by a numerical search procedure. The limiting case where the number of subchannels goes to infinity is also studied. Numerical results reveal that the achieved rate for the infinite number of subchannels is an upper bound for the finite subchannel case and the proposed greedy algorithm results in rates close to those for infinite number of subchannels when the number of subchannels is sufficiently large. Furthermore, most of the cooperative gains can be achieved by the use of a small number of relays.

In this paper the fading multiple antenna (MIMO) wire-tap channel is investigated under short term power constraints. The secret diversity gain and the secret multiplexing gain are defined. Using these definitions, the secret diversity-multiplexing tradeoff (DMT) is calculated analytically for no transmitter side channel state information (CSI) and for full CSI. When there is no CSI at the transmitter, under the assumption of Gaussian codebooks, it is shown that the eavesdropper steals both transmitter and receiver antennas, and the secret DMT depends on the remaining degrees of freedom. When CSI is available at the transmitter (CSIT), the eavesdropper steals only transmitter antennas. This dependence on the availability of CSI is unlike the DMT results without secrecy constraints, where the DMT remains the same for no CSI and full CSI at the transmitter under short term power constraints. A zero-forcing type scheme is shown to achieve the secret DMT when CSIT is available.

In this paper we study a two-hop cooperative transmission scheme where multiple relays forward the data simultaneously using Randomized Distributed Space Time Codes (R-DSTC). We propose to integrate this randomized cooperative transmission with layered video coding and packet level Forward Error Correction (FEC) to enable error resilient video multicast. Data rates in both hops as well as the FEC rate are adopted to maximize the video quality. Our results show that while rate-adaptive direct transmission provides better video quality than conventional multicast, randomized cooperative scheme outperforms both strategies significantly.

Cooperative communications leverages the spatial diversity available in a wireless network enabling multiple radio nodes work together to improve the overall system performance. When a destination receiver combines the signal from an originating source with the associated signals from relay nodes, significant improvements in the bit error rate performance can be achieved. This paper details the measured bit error rate performance of a three-node cooperative communication system operating in a software defined radio testbed. The measured performances of several types of cooperative physical layer protocols are compared to similar systems operating over a single wireless link. The measured results include cooperative systems operating with a maximum ratio combining technique and two cooperative coded systems using hard decision decoding.

The performance unfairness problem in a single cell IEEE 802.11 wireless local area network (WLAN) is considered. While existing research is based on the assumption that all nodes have the same transmission success probability and per-node throughput, this fairness exists only if all nodes within range of the access point can sense each other. Recent measurements suggest that this is not necessarily true and terminals can be hidden from each other. In this paper, the impact of hidden terminals on the performance unfairness among individual nodes is investigated via analysis, simulation and experimental measurements in a real network. In the presence of hidden terminals, it is observed that the widely accepted conclusion of equal performance among nodes does not hold any more. Instead, nodes far from the access point (AP) see more hidden terminals than those close to the AP, so they get more packet losses and lower throughput. This phenomenon is not due to inter-cell interference, or channel disparities among nodes, and is significant even when the RTS/CTS mechanism designed to mitigate the impact of hidden terminals is turned on. The simulation results show that for a 16-node WLAN with a fixed data rate of 6 Mbps, the throughput of a node close to the AP is more than twice that of an edge node, due to hidden terminals.

This work considers coordination and bargaining between two selfish users over a Gaussian interference channel using game theory. The usual information theoretic approach assumes full cooperation among users for codebook and rate selection. In the scenario investigated here, each selfish user is willing to coordinate its actions only when an incentive exists and benefits of cooperation are fairly allocated. To improve communication rates, the two users are allowed to negotiate for the use of a simple Han-Kobayashi type scheme with fixed power split and conditions for which users have incentives to cooperate are identified. The Nash bargaining solution (NBS) is used as a tool to get fair information rates. The operating point is obtained as a result of an optimization problem and compared with a TDM-based one in the literature.

A Gaussian interference channel (IC) aided by a half-duplex relay is considered, in which the relay receives and transmits in an orthogonal band with respect to the IC. The system thus consists of two parallel channels, the IC and the channel over which the relay is active, which is referred to as Out-of-Band Relay Channel (OBRC). The OBRC is operated by separating a multiple access phase from the sources to the relay and a broadcast phase from the relay to the destinations. Conditions under which the optimal operation, in terms of the sum-capacity, entails either signal relaying and/or interference forwarding by the relay are identified. These conditions also assess the optimality of either separable or non-separable transmission over the IC and OBRC. Specifically, the optimality of signal relaying and separable coding is established for scenarios where the relay-to-destination channels set the performance bottleneck with respect to the source-to-relay channels on the OBRC. Optimality of interference forwarding and non-separable operation is also established in special cases.

In this paper, a cooperative multicast scheme that uses Randomized Distributed Space Time Codes (R-DSTC), along with packet level Forward Error Correction (FEC), is studied. For the source packets, two-hop transmission is considered, where a packet is transmitted first by the access point (AP), and then forwarded using R-DSTC by the nodes that receive the packet. On the other hand, parity packets are generated by the nodes that receive all the source packets correctly and are transmitted using R-DSTC. The optimum transmission rates for source and parity packets, as well as the number of parity packets required, are determined such that the video quality at all nodes is maximized. It is shown that this scheme can support a higher video rate than a previously developed R-DSTC based scheme where both source and parity packets go through a two-hop transmission, as well as non-cooperative direct transmission.

The exact expressions for symbol error probability and outage probability of randomized distributed space-time codes (RDSTC) under Rayleigh fading are derived. The diversity gain derived in the literature uses the Chernoff bound which may not be achievable in general. Utilizing the exact expression, the achievable diversity gain of RDSTCs is validated. The analytical expressions are verified by Monte-Carlo simulations.

A four terminal Gaussian network composed of a source, a destination, an eavesdropper and a jammer relay is investigated when the jammer relay is causally given the source message. The source aims to increase the achievable secrecy rates, whereas the jammer relay aims to decrease it. To help the eavesdropper and to decrease achievable perfect secrecy rates, the jammer relay can use pure relaying and/or send interference to assist eavesdropping. The problem is formulated as a zero-sum game and the saddle point solutions are found. The results are compared to the case when the jammer relay is not informed about the source message.

A cellular system where macrocells are overlaid with femtocells is studied. Each femtocell is served by a home base station (HBS) that is connected to the macrocell base station (BS) via an unreliable network access link, such as DSL followed by the Internet. A scenario with a single macrocell and a single femtocell is considered first, and is then extended to include multiple macrocells and femtocells, both with standard single-cell processing and with multicell processing (or network MIMO). Two main issues are addressed for the uplink channel: ({i}) Interference management between femto and macrocells; ({ii}) Robustness to uncertainties on the quality of the femtocell (HBS-to-BS) access link. The problem is formulated in information-theoretic terms, and inner and outer bounds are derived to achievable per-cell sum-rates for outdoor and home users. Expected sum-rates with respect to the distribution of the femtocells access link states are studied as well. Overall, the analysis lends evidence to the performance advantages of sophisticated interference management techniques, based on joint decoding and relaying, and of robust coding strategies via the broadcast coding approach (i.e., unequal error protection).

Physical (PHY) layer cooperation in a wireless network allows neighboring nodes to share their communication resources in order to create a virtual antenna array by means of distributed transmission and signal processing. A novel medium access control (MAC) protocol, called CoopMAC, has been recently proposed to integrate cooperation at the PHY layer with the MAC sublayer, thereby achieving substantial throughput and delay performance improvements. CoopMAC capitalizes on the broadcast nature of the wireless channel and rate adaptation, recruiting a single relay on the fly to support the communication of a particular source-destination pair. In this paper, we propose a cross-layer rate-adaptive design that opportunistically combines the recruitment of multiple cooperative nodes and carrier sensing multiple access with collision avoidance. We focus on a single-source single-destination setup, and develop a randomized cooperative framework, which is referred to as randomized CoopMAC (RCoopMAC). Thanks to the randomization of the coding rule, the RCoopMAC approach enables the blind participation of multiple relays at unison relying only on the mean channel state information (CSI) of the potential cooperating nodes, without introducing additional signaling overhead to coordinate the relaying process. The proposed RCoopMAC scheme is not only beneficial in substantially improving the link quality and therefore the sustainable data rates but, thanks to the decentralized and agnostic coding rule, it also allows to effectively recruit multiple relays in a robust fashion, i.e., even when the required mean CSI is partially outdated.

Wireless video multicast enables delivery of popular events to many mobile users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. In this paper, an integration of layered video coding, packet level forward error correction, and two-hop relaying is proposed to enable efficient and robust video multicast in infrastructure-based wireless networks. First, transmission with conventional omni-directional antennas is considered where relays have to transmit in non-overlapping time slots in order to avoid collision. In order to improve system efficiency, we next investigate a system in which relays transmit simultaneously using directional antennas. In both systems, we consider a non-layered configuration, where the relays forward all received video packets and all users receive the same video quality, as well as a layered setup, where the relays forward only the base-layer video. For each system setup, we consider optimization of the relay placement, user partition, transmission rates of each hop, and time scheduling between source and relay transmissions. Our analysis shows that the non-layered system can provide better video quality to all users than the conventional direct transmission system, and the layered system enables some users to enjoy significantly better quality, while guaranteeing other users the same or better quality than direct transmission. The directional relay system can provide substantial improvements over the omni-directional relay system. To support our results, a prototype is implemented using open source drivers and socket programming, and the system performance is validated with real-world experiments.

The successive refinement problem is extended to vector sources where individual distortion constraints are posed on each vector component. For vector Gaussian sources with squared-error distortion, a single-letter rate-distortion characterization is inherited from the previously studied Gaussian multiple descriptions problem with covariance distortion constraints. Though this characterization is amenable to well-known numerical convex optimization techniques, an analytical solution is difficult to obtain in full generality even for 2-D sources. In this work, the special case of successive refinability is addressed analytically. Specifically, vector Gaussian sources are shown to benotsuccessively refinable everywhere unlike scalar Gaussian sources. It is also shown that, for 2-D Gaussian sources, the rate loss at the second stage can be as high as 0.5 b/sample in a ¿degenerate¿ scenario corresponding to what is known as sequential coding of correlated sources. Finally, analysis of 2-D binary symmetric sources with Hamming distortion reveals that the behavior of these sources with respect to successive refinability exhibits remarkable similarity to their 2-D Gaussian counterparts.

In this letter the performance limits and design principles of rateless codes over fading channels are studied. The diversity-multiplexing tradeoff (DMT)is used to analyze the system performance for all possible transmission rates. It is revealed from the analysis that the design of such rateless codes follows the design principle of approximately universal codes for multiple-input multiple-output (MIMO) channels. It is also shown that for a single-input single-output (SISO) channel, simple permutation codes of unit length for parallel channels can be transformed directly into rateless codes that achieve the DMT performance limit of the channel.

A key technology in cooperative communications is distributed space-time coding (DSTC) which achieves spatial diversity gain from multiple relays. A novel DSTC, called randomized distributed space-time coding (R-DSTC), shows considerable advantages over a regular DSTC in terms of system complexity. In this paper, we exploit the benefits of R-DSTC physical (PHY) layer and develop a distributed and opportunistic medium access control (MAC) layer protocol for R-DSTC deployment in an IEEE 802.11 wireless local area network (WLAN). Unlike other cooperative MAC designs, in our proposed PHY-MAC cross-layer framework, there is no need to decide which stations will serve as relays before each packet transmission. Instead, the MAC layer opportunistically recruits relay stations on the fly; any station that receives a packet from the source correctly forwards it to the destination. Through extensive simulations, we validate the efficiency of our MAC layer protocol and demonstrate that network capacity and delay performance is considerably improved with respect to legacy IEEE 802.11g network.

Cooperative communication is a technique that can be employed to meet the increased throughput needs of next-generation WiMAX systems. In a cooperative scenario, multiple stations can jointly emulate the antenna elements of a multi-input multi-output (MIMO) system in a distributed fashion. Although distributed space-time coding (DSTC) is being considered by the IEEE 802.16j/16 m standards for spatial diversity gain, it has several inherent drawbacks. These are addressed in the recently invented randomized distributed space-time coding, called R-DSTC. In this paper, we present the framework for the R-DSTC technique in the emerging relay-assisted WiMAX network, and develop a cooperative medium access control (MAC) layer protocol, called CoopMAX, for R-DSTC deployment in an IEEE 802.16 system. Our scheme couples the MAC layer with the physical (PHY) layer for performance optimization. The PHY layer yields significant diversity gain, while the MAC layer achieves a substantial end-to-end throughput gain. Through extensive simulations, we evaluate the performance of CoopMAX and show that it can generate capacity gains of up to about 77% for an IEEE 802.16 network.

A Gaussian interference channel with an infrastructure relay (ICIR) is investigated. The relay has finite-capacity links to both sources and destinations that are orthogonal to each other and to the underlying interference channel. A general achievable rate region is presented by using the relay both to convey additional information from the sources (signal relaying) and to ease interference cancellation (interference forwarding). Outer bounds to the capacity region are also derived, and used to determine a number of regimes of interest where either signal relaying only or both signal relaying and interference forwarding are optimal.

A multi-relay network is considered in which communication from source to relays takes place over a (discrete or Gaussian) broadcast channel, while the relays are connected to the receiver via orthogonal finite-capacity links. Unbeknownst to the source and relays, link failures may take place between any subset of relays and destination in a non-ergodic fashion. Upper and lower bounds are derived on average achievable rates with respect to the prior distribution of the link failures, assuming the relays to be oblivious to the source codebook. The lower bounds are obtained via strategies that combine the broadcast coding approach, previously investigated for quasi-static fading channels, and various robust distributed compression techniques.

Source and channel coding over multiuser channels in which receivers have access to correlated source side information are considered. For several multiuser channel models necessary and sufficient conditions for optimal separation of the source and channel codes are obtained. In particular, the multiple-access channel, the compound multiple-access channel, the interference channel, and the two-way channel with correlated sources and correlated receiver side information are considered, and the optimality of separation is shown to hold for certain source and side information structures. Interestingly, the optimal separate source and channel codes identified for these models are not necessarily the optimal codes for the underlying source coding or the channel coding problems. In other words, while separation of the source and channel codes is optimal, the nature of these optimal codes is impacted by the joint design criterion.

A transmitter without channel state information wishes to send a delay-limited Gaussian source over a slowly fading channel. The source is coded in superimposed layers, with each layer successively refining the description in the previous one. The receiver decodes the layers that are supported by the channel realization and reconstructs the source up to a distortion. The expected distortion is minimized by optimally allocating the transmit power among the source layers. For two source layers, the allocation is optimal when power is first assigned to the higher layer up to a power ceiling that depends only on the channel fading distribution; all remaining power, if any, is allocated to the lower layer. For convex distortion cost functions with convex constraints, the minimization is formulated as a convex optimization problem. In the limit of a continuum of infinite layers, the minimum expected distortion is given by the solution to a set of linear differential equations in terms of the density of the fading distribution. As the number of channel uses per source symbol tends to zero, the power distribution that minimizes expected distortion converges to the one that maximizes expected capacity.

In a distributed wireless network, it is possible to employ several relays and mimic a multiple antenna transmission system. In this paper we propose a MAC layer solution that allows multiple relays to send information to the receiver at unison, using a randomized distributed space time code. The randomized space-time coding can recruit relays on the fly, thus significantly reducing signaling overhead. The cross-layer design between physical layer and MAC layer involves relay discovery and rate adaptation, and results in improvements in throughput and delay performance. The design is dynamic and can be adapted to changing network conditions. The proposed MAC scheme can be integrated into various wireless technologies such as distributed contention based networks (e.g., IEEE 802.11 BSS and ad hoc mode) as well as centralized multiple access networks (e.g., IEEE 802.16).

Wireless video multicast enables delivery of popular events to many wireless users in a bandwidth efficient manner. However, providing good and stable video quality to a large number of users with varying channel conditions remains elusive. We propose to integrate layered video coding with cooperative communication to enable efficient and robust video multicast in infrastructure-based wireless networks. We determine the user partition and transmission time scheduling that can optimize a multicast performance criterion.

In this paper the performance limits and design principles of rateless codes over fading channels are studied. The diversity-multiplexing tradeoff (DMT) is used to analyze the system performance for all possible transmission rates. It is revealed from the analysis that the design of such rateless codes follows the design principle of approximately universal codes for parallel multiple-input multiple-output (MIMO) channels, in which each sub-channel is a MIMO channel. More specifically, it is shown that for a single-input single-output (SISO) channel, the previously developed permutation codes of unit length for parallel channels having rate LR can be transformed directly into rateless codes of length L having multiple rate levels (R, 2R, …, LR), to achieve the DMT performance limit.

Lossy transmission over a relay channel in which the relay has access to correlated side information is considered. First, a joint source-channel decode-and-forward scheme is proposed for general discrete memoryless sources and channels. Then the Gaussian relay channel where the source and the side information are jointly Gaussian is analyzed. For this Gaussian model, several new source-channel cooperation schemes are introduced and analyzed in terms of the squared-error distortion at the destination. A comparison of the proposed upper bounds with the cut-set lower bound is given, and it is seen that joint source-channel cooperation improves the reconstruction quality significantly. Moreover, the performance of the joint code is close to the lower bound on distortion for a wide range of source and channel parameters.

Secure distributed data compression in the presence of an eavesdropper is explored. Two correlated sources that need to be reliably transmitted to a legitimate receiver are available at separate encoders. Noise-free, limited rate links from the encoders to the legitimate receiver, one of which can also be perfectly observed by the eavesdropper, are considered. The eavesdropper also has its own correlated observation. Inner and outer bounds on the achievable compression-equivocation rate region are given. Several different scenarios involving the side information at the transmitters as well as multiple receivers/eavesdroppers are also considered.

The problem of resource allocation is studied for two-sender two-receiver fading Gaussian interference channels (IPCs) and compound multiaccess channels (C-MACs). The senders in an IFC communicate with their own receiver (unicast) while those in a C-MAC communicate with both receivers (multicast). The instantaneous fading state between every transmit- receive pair in this network is assumed to be known at all transmitters and receivers. Under an average power constraint at each source, the sum-capacity of the C-MAC and the power policy that achieves this capacity is developed. The conditions defining the classes of strong and very strong ergodic IPCs are presented and the multicast sum-capacity is shown to be tight for both classes.

Secure data compression in the presence of side information at both a legitimate receiver and an eavesdropper is explored. A noise-free, limited rate link between the source and the receiver, whose output can be perfectly observed by the eavesdropper, is assumed. As opposed to the wiretap channel model, in which secure communication can be established by exploiting the noise in the channel, here the existence of side information at the receiver is used. Both coded and uncoded side information are considered. In the coded side information scenario, inner and outer bounds on the compression-equivocation rate region are given. In the uncoded side information scenario, the availability of the legitimate receiverpsilas and the eavesdropperpsilas side information at the encoder is considered, and the compression-equivocation rate region is characterized for these cases. It is shown that the side information at the encoder can increase the equivocation rate at the eavesdropper. Hence, the side information at the encoder is shown to be useful in terms of security; this is in contrast with the pure lossless data compression case where side information at the encoder would not help.

We consider transmission of a continuous amplitude source over an$L$-block Rayleigh-fading$M_t \times M_r$multiple-input multiple-output (MIMO) channel when the channel state information is only available at the receiver. Since the channel is not ergodic, Shannon's source–channel separation theorem becomes obsolete and the optimal performance requires a joint source–channel approach. Our goal is to minimize the expected end-to-end distortion, particularly in the high signal-to-noise ratio (SNR) regime. The figure of merit is the distortion exponent, defined as the exponential decay rate of the expected distortion with increasing SNR. We provide an upper bound and lower bounds for the distortion exponent with respect to the bandwidth ratio among the channel and source bandwidths. For the lower bounds, we analyze three different strategies based on layered source coding concatenated with progressive superposition or hybrid digital/analog transmission. In each case, by adjusting the system parameters we optimize the distortion exponent as a function of the bandwidth ratio. We prove that the distortion exponent upper bound can be achieved when the channel has only one degree of freedom, that is$L=1$, and$\min\{M_t,M_r\}=1$. When we have more degrees of freedom, our achievable distortion exponents meet the upper bound for only certain ranges of the bandwidth ratio. We demonstrate that our results, which were derived for a complex Gaussian source, can be extended to more general source distributions as well.

In this paper, we consider lossless transmission of arbitrarily correlated sources over a multiple access channel. Characterization of the achievable rates in the most general setting is one of the long-standing open problems of information theory. We consider a special case of this problem where the receiver has access to correlated side information given which the sources are independent. We prove a source channel separation theorem for this system, that is, we show that there is no loss in performance in first applying distributed source coding where each encoder compresses its source conditioned on the side information at the receiver, and then applying an optimal multiple access channel code with independent codebooks. We also give necessary and sufficient conditions for source and channel separability in the above problem if there is a perfect two-sided feedback from the receiver to the transmitters. These two communication scenarios constitute examples of few non-trivial multi-user scenarios for which separation holds

In this paper, an interference relay channel where two independent sources communicate with two destinations by the help of a relay is studied. The relay is full-duplex and employs decode-forward type strategy. Using Carleial's rate splitting where each source transmits common messages to be decoded at both destinations and private messages for the desired destination, an achievable rate region is obtained. The relay decodes both common and private messages and transmits them cooperatively with the sources to the destinations. For the symmetric Gaussian interference relay channel, the results show that the maximum rate sum is achieved when the relay helps the sources in the transmission of common messages only. Moreover, unlike the regular interference channel, when the relay is present, the sources continue to transmit common information even when the interfering links can support much less rate than the direct links.

A transmitter without channel state information wishes to send a delay-limited Gaussian source over a slowly fading channel that has a finite number of discrete fading states. The source is coded in layers, with each layer successively refining the description in the previous one. These coded source layers are then superimposed and simultaneously transmitted to the receiver. The receiver decodes the layers that are supported by the realization of the channel, and combines the descriptions in the decoded layers to reconstruct the source up to a distortion. The expected distortion is minimized by optimally allocating the transmit power among the given number of source layers. For two layers, the allocation is optimal when power is first assigned to the higher layer up to a power ceiling that depends only on the channel fading distribution; all remaining power, if any, is allocated to the lower layer. For multiple layers, the overall expected distortion can be written as a set of recurrence relations, and the minimum expected distortion is found by recursively applying the two-layer optimization procedure at each recurrence step.

We study the multiple antenna half-duplex relay channel from the diversity-multiplexing tradeoff (DMT) perspective. We find performance upper bounds and show that compress-and-forward (CF) protocol achieves the upper bound. We argue that although it is hard to find the exact DMT expressions for decode-and-forward (DF) type protocols, they would be suboptimal in the multiple antenna case. We also study the multiple-access relay channel (MARC), and evaluate how CF works in this system. Our results show that CF is a robust strategy, which performs well in different relay networks and multiple antenna scenarios.

We consider discrete memoryless compound multiple access and interference channels with correlated sources and correlated side information at the receivers, and investigate necessary and sufficient conditions for lossless transmission. We first give sufficient conditions for the most general setting, and then show that these conditions are also necessary for both channels under certain assumptions on the side information and the interference. In particular, we generalize the notion of strong interference to take into account the correlation among the sources and side information. We prove the optimality of 'informational' or 'operational' source-channel separation for certain special cases. While informational separation results in independent source and channel encoding and decoding; operational separation corresponds to separation at the encoder, while decoding is done jointly. To our knowledge, these results constitute the first source-channel separation results for interference and compound multiple access channels with correlated sources and side information.

We consider transmission of a Gaussian source over a Gaussian relay channel, where the relay terminal has access to correlated side information. We propose several cooperative joint source-channel coding strategies that utilize both the broadcast nature of the wireless transmission and/or the availability of the correlated side information at the relay, and compare these to distortion lower bounds obtained by the cut-set arguments. In general, the best performing scheme depends on the correlation among the source and the relay signals, and the average link qualities. We illustrate that the strategies introduced in this paper perform very close to the lower bound in most cases.

A transmitter without channel state information (CSI) wishes to send a delay-limited Gaussian source over a slowly fading channel. The source is coded in superimposed layers, with each layer successively refining the description in the previous one. The receiver decodes the layers that are supported by the channel realization and reconstructs the source up to a distortion. In the limit of a continuum of infinite layers, the optimal power distribution that minimizes the expected distortion is given by the solution to a set of linear differential equations in terms of the density of the fading distribution. In the optimal power distribution, as SNR increases, the allocation over the higher layers remains unchanged; rather the extra power is allocated towards the lower layers. On the other hand, as the bandwidth ratio b (channel uses per source symbol) tends to zero, the power distribution that minimizes expected distortion converges to the power distribution that maximizes expected capacity. While expected distortion can be improved by acquiring CSI at the transmitter (CSIT) or by increasing diversity from the realization of independent fading paths, at high SNR the performance benefit from diversity exceeds that from CSIT, especially when b is large.

We consider reliable transmission of a discrete memoryless source over a cooperative relay broadcast channel, where both the relay and the destination terminals want to reconstruct the source; and over a relay channel, where only the destination terminal wishes to obtain a lossless reconstruction. We assume that both the relay and the destination have correlated side information. We find the necessary and sufficient conditions for a general cooperative relay broadcast channel, and for a physically degraded relay channel when the side information at the destination is a degraded version of the relay side information. Our achievability results are based on operational source-channel separation. We utilize source and channel codes that interact only by passing along decoded source codewords from one block to another.

In this work, we consider a CDMA cell with multiple terminals transmitting video signals. We adapt the system parameters to minimize the sum of compression powers and transmitter powers of all users while guaranteeing the received video quality at each terminal. The adjustable parameters at user i include the transmitter power Pt,i, the video coding bit rate Rs,i, and video encoder parameters that control the complexity and hence power consumption of the video coder (referred simply as complexity betai). Instead of determining Pt,idirectly, we first determine the desired signal to interference-noise ratio (SINR) gammai. Based on the optimal gammaiand Rs,i, we then determine Pt,i. Our analysis shows that the product of Rs,iand gammaiis an important quantity. Given the complexity betai(i.e., given the compression power) and quality constraint, in order to reduce the transmission power, one should choose Rs,iand gammaito minimize their product. When only the total transmission power is concerned, the optimal operating points can be determined at individual users separately: each user should run the encoder to minimize the product of Rs,iand gammai. When the objective is to minimize the sum of compression and transmission powers of all users, the optimal solution can be found in two steps. The first step searches the optimal Rs,iand gammaithat minimize Rs,itimesgammaifor each video category and each possible betaiwhile satisfying the quality constraint at user i. The second step searches the optimal {betai}i=1,...,Nfor all users jointly, that minimizes the sum of transmission and compression powers of all users. The first step can be completed offline in advance, only the second step needs to be computed in real time based on channel conditions of the users. Our results indicate that for the same class of video users, the one who is closer to the base station compresses at a lower complexity. Simulation results show that significant power savings are obtained by our adaptive algorithms over nonadaptive approaches, where {Rs,i,betai,gammai} are fixed regardless the channel conditions

User cooperation is a powerful tool to combat fading and increase robustness for communication over wireless channels. Although it is doubtless a promising technique for enhancing channel reliability, its performance in terms of average source distortion is not clear since source–channel separation theorem fails under the most common nonergodic slow-fading channel assumption, when channel state information (CSI) is only available at the receiving terminals. This work sheds some light on the end-to-end performance of joint source–channel coding for cooperative relay systems in the high signal-to-noise ratio (SNR) regime. Considering distortion exponent as a figure of merit, we propose various strategies for cooperative source and channel coding that significantly improve the performance compared to the conventional scheme of source coding followed by cooperative channel coding. We characterize the optimal distortion exponent of a full-duplex relay channel for all bandwidth ratios. For the half-duplex relay channel, we provide an upper bound which is tight for small and large bandwidth ratios. We consider the effect of correlated side information on the distortion exponent as well.

We consider a general multiple-antenna network with multiple sources, multiple destinations, and multiple relays in terms of the diversity–multiplexing tradeoff (DMT). We examine several subcases of this most general problem taking into account the processing capability of the relays (half-duplex or full-duplex), and the network geometry (clustered or nonclustered). We first study the multiple-antenna relay channel with a full-duplex relay to understand the effect of increased degrees of freedom in the direct link. We find DMT upper bounds and investigate the achievable performance of decode-and-forward (DF), and compress-and-forward (CF) protocols. Our results suggest that while DF is DMT optimal when all terminals have one antenna each, it may not maintain its good performance when the degrees of freedom in the direct link are increased, whereas CF continues to perform optimally. We also study the multiple-antenna relay channel with a half-duplex relay. We show that the half-duplex DMT behavior can significantly be different from the full-duplex case. We find that CF is DMT optimal for half-duplex relaying as well, and is the first protocol known to achieve the half-duplex relay DMT. We next study the multiple-access relay channel (MARC) DMT. Finally, we investigate a system with a single source–destination pair and multiple relays, each node with a single antenna, and show that even under the ideal assumption of full-duplex relays and a clustered network, this virtual multiple-input multiple-output (MIMO) system can never fully mimic a real MIMO DMT. For cooperative systems with multiple sources and multiple destinations the same limitation remains in effect.

We consider a Rayleigh fading wireless relay channel where communication is constrained by delay and average power limitations. Assuming partial channel state information at the transmitters and perfect channel state information at the receivers, we first study the delay-limited capacity of this system and show that, contrary to a single source-single destination case, a non-zero delay-limited capacity is achievable. We introduce opportunistic decode-and-forward (ODF) protocol which utilizes the relay depending on the channel state. Opportunistic cooperation significantly improves the delay-limited capacity of the system and performs very close to the cut-set bound. We also consider the system performance in terms of minimum outage probability. We show that ODF provides performance close to the cut-set bound from the outage probability perspective as well. Our results emphasize the importance of feedback for cooperative systems that have delay sensitive applications

Throughput is an important performance measure for data communications over wireless links. In this work, we consider joint adaptation of coding rates, modulation modes and level of cooperation for cooperative relaying to maximize the data throughput. We consider five different schemes with joint adaptation based on channel statistics: Direct transmission, conventional multihop, stationary and dynamic coded cooperation, cooperative multihop. We first obtain closed form expressions for the throughputs achieved by these schemes. Considering channel coding and modulation schemes used in wireless local area standard (WLAN) IEEE 802.11, we then compute the best throughputs and provide performance comparisons. Our results show that coded cooperation with adaptation provides substantial improvement over direct transmission and conventional multi- hop. Cooperative multihop enables further improvements over coded cooperation by fully exploiting cooperative diversity as well as rate adaptation and achieves the highest throughput performance among these five transmission schemes. We also observe that to maximize the data throughput in direct transmission and conventional multihop, adaptation for each hop only depends on the channel quality from the transmitter to the receiver. However, in cooperative strategies a system wide optimization is necessary.

This paper addresses uplink power control for terminals that transmit multimedia signals in a CDMA cell. The aim of the power control is to minimize total power consumed by the terminals due to signal compression and transmission while the end-to-end distortion for each terminal is kept at a predetermined value. We propose a distributed iterative power control algorithm and prove convergence. The simulations for Gauss-Markov source with transform coder and H.263 encoded video signal show that the proposed algorithm achieves the jointly optimal power values for most of the channel conditions. It is also observed that the algorithm outperforms non-adaptive multimedia transmission in terms of power consumption

Past work on cooperative communications has indicated substantial improvement in channel reliability through cooperative transmission strategies. To exploit this benefit for video transmission, we propose to jointly allocate bits among video coding, channel coding and cooperation to optimize the decoded video quality. Recognizing that not all source bits are equal, we further propose to protect the more important bits through user cooperation. We simulate and compare four modes of video transmission that differ in their error protection strategy (equal vs. layered, with vs. without cooperation). Our simulation uses the H.263+ video codec and the RCPC channel code over quasi-static Rayleigh fading channels. We show that cooperation can provide significant improvement over no-cooperation when the average channel SNR is in the low to medium range, and that layered cooperation can extend this benefit to the entire range of channel quality.

We consider the end-to-end distortion achieved by transmitting a continuous amplitude source over M parallel, independent quasi-static fading channels. We analyze the high SNR expected distortion behavior characterized by the distortion exponent. We first give an upper bound for the distortion exponent in terms of the bandwidth ratio between the channel and the source assuming the availability of the channel state information at the transmitter. Then we propose joint source-channel coding schemes based on layered source coding and multiple rate channel coding. We show that the upper bound is tight for large and small bandwidth ratios. For the rest, we provide the best known distortion exponents in the literature. By suitably scaling the bandwidth ratio, our results would also apply to block fading channels

We study the diversity-multiplexing tradeoff (DMT) for the full-duplex relay channel when the source and the destination have multiple antennas, and the relay has 1 or more. We find DMT upper bounds and investigate the achievable performance of decode-and-forward (DF), partial decode-and-forward (PDF), and compress-and-forward (CF) protocols. We study the effect of increased degrees of freedom in the direct link and the source-relay channel when multiple antennas are introduced. Our results suggest that while DF is DMT optimal when all terminals have one antenna each, it cannot maintain its good performance when the degrees of freedom in the direct link is increased. CF proves to be a more robust strategy, which works well in multi-antenna scenarios studied in this paper. We also extend our results for clustered relay networks to find DMT upper bounds and achievable performances

In this paper, we consider transmission of a continuous amplitude source over a quasi-static MIMO Rayleigh fading channel. The performance metric is end-to-end distortion of the source caused both by the lossy compression and the channel errors. We are interested in the high SNR behavior expressed in the distortion exponent, which is the exponential decay rate of the average end-to-end distortion as a function of SNR. Our goal is to maximize this distortion exponent by considering joint source and channel coding techniques. We provide digital strategies that utilize layered source coding coupled with multi-rate channel coding either by progressive or by superposition transmission, as well as a hybrid digital-analog scheme. When either the transmitter or the receiver has one antenna, we show that we are able to achieve the optimal distortion exponent.

User cooperation is an efficient approach to obtain diversity in both centralized and distributed wireless networks. In this paper, we consider a coded cooperative system under quasi-static Rayleigh fading and investigate the partner-choice problem. We find conditions on the interuser and user-to-destination channel qualities for cooperation to be beneficial. Using frame-error rate (FER) as a metric, we define the user cooperation gain (G) for evaluating the relative performance improvement of cooperative over direct transmissions when a particular channel code is used. We introduce the cooperation decision parameter (CDP), which is a function of user-to-destination average received signal-to-noise ratios (SNRs), and demonstrate that whether cooperation is useful or not ($G≫1$or$G≪1$) depends only on the CDP, not the interuser link quality. We use an analytical formulation of the CDP to investigate user cooperation gain and provide insights on how a user can choose among possible partners to maximize cooperation gain. We first consider the asymptotic performance when one or both partners have high average received SNR at the destination. We then provide conditions on user and destination locations for cooperation to be beneficial for arbitrary SNRs. We illustrate these cooperative regions and study geometric conditions for the best partner choice. We also define the system cooperation gain and illustrate cooperation benefit for both users. All of our theoretical results are verified through numerical examples.

User cooperation is an efficient approach to obtain diversity in both centralized and distributed wireless networks. In this paper, we consider a coded cooperative system under quasi-static Rayleigh fading and investigate the partner-choice problem. We find conditions on the interuser and user-to-destination channel qualities for cooperation to be beneficial. Using frame-error rate as a metric, we define the user cooperation gain (G) for evaluating the relative performance improvement of cooperative over direct transmission when a particular channel code is used. We introduce the cooperation decision parameter (CDP), which is a function of user-to-destination average received signal-to-noise ratios (SNRs), and demonstrate that whether cooperation is useful or not (G>1 or G<1) depends only on the CDP, not the interuser link quality. We use an analytical formulation of the CDP to investigate user cooperation gain and provide insights on how a user can choose among possible partners to maximize cooperation gain. We first consider the asymptotic performance when one or both partners have high average received SNR at the destination. We then provide conditions on user and destination locations for cooperation to be beneficial for arbitrary SNRs. We illustrate these cooperative regions, and study geometric conditions for the best partner choice. We also define the system cooperation gain and illustrate cooperation benefits for both users. All of our theoretical results are verified through numerical examples

Cooperation provides an efficient form of diversity in wireless communications. In this paper we consider a coded cooperative system where the source and the relays may have multiple antennas. We describe two simple algorithms for choosing a good relay: blind-selection-algorithm and informed-selection-algorithm. These algorithms only require the knowledge of average received signal to noise ratios at the destination. Simulation results, carried out for a cellular system, show that both algorithms result in substantial improvement over direct transmission and random choice of relay in the cell and provide error rates close to best relay performance.

We consider a CDMA cell with multiple terminals transmitting video signals. We minimize the sum of signal processing and transmitter power while the received quality at each terminal is guaranteed. The system parameters to be adjusted include video coding bit rate, video compression complexity and transmitter power. Instead of full search in the space of {bit rate, complexity, transmitter power} for all users, we design a two-step fast algorithm to reduce the computation burden in the base station. In our algorithm, the search in the base station is over the space of complexity only. Our results indicate that, for the same class of video users, the one who is closest to the base station compresses at least complexity. This is used to further reduce the computation required by our algorithm.

Cooperative techniques form a new wireless communication paradigm in which terminals help each other in relaying information to combat the random fading and to provide diversity in radio channels. Past work has focused on improving channel reliability through cooperation. We propose to jointly allocate bits among source coding, channel coding and cooperation to minimize the expected source distortion. Recognizing that not all source bits are equal, we further propose to protect the more important bits through user cooperation. To evaluate the gain of layered cooperation, we simulate four modes of communications that differ in their error protection strategy (equal vs. layered, with vs. without cooperation) with a practical channel coder, and show that, for i.i.d. Gaussian sources, layered cooperation can achieve significant performance gains over non-layered/non-cooperative communication. We also carry out an information theoretic analysis illustrating fundamental benefits of layered cooperation.

We consider a cooperative transmission scheme in which the collaborating nodes may have multiple antennas. We present the performance analysis and design of space-time codes that are capable of achieving the full diversity provided by user cooperation. Our codes use the principle of overlays in time and space, and ensure that cooperation takes place as often as possible. We show how cooperation among nodes with different numbers of antennas can be accomplished, and how the quality of the interuser link affects the cooperative performance. We illustrate that space-time cooperation can greatly reduce the error rates of all the nodes involved, even for poor interuser channel quality.

This paper presents a low-complexity multiuser decoding technique that can be implemented in real time for a convolutionally coded direct sequence code division multiple access (DS-CDMA) system. The main contribution, denoted here as the iterative prior update (IPU), consists of iterative interference cancellation and prior updates on sequences of coded bits combined with M-algorithm and list decoding. We illustrate performance gains over other low-complexity sequence detection and decoding strategies and argue that the algorithm converges within a few iterations and requires only a small size buffer for keeping track of the priors along iterations. The fact that the we can use existing available architectures for Viterbi decoding with slight modifications and can meet the real-time processing constraints makes the IPU algorithm an attractive alternative for cellular systems.

Cooperation of mobiles provides signal diversity in wireless networks. We consider a coded cooperative system under Rayleigh fading and path loss. For an arbitrary signal to noise ratio, we find conditions on the user geometry for two-user cooperation to be beneficial. We find that whether cooperation is useful or not is only determined by the cooperation decision parameter (CDP). We illustrate regions for which cooperation results in a gain in frame error rate (FER) for individual users and use the analytical formulation of the CDP to investigate how locations of the user and the partner affect the user cooperation gain. Our analytical results also provide insights on how a user can choose among many partners to maximize cooperation gain. All of our theoretical results are verified by numerical simulations of the FER.

In this work, we consider a CDMA cell with multiple terminals transmitting video signals. The concept of a utility function is used to maximize the number of received picture frames with adequate quality per Joule of energy. For a reconstructed signal at the video decoder, the quality is controlled by the encoded bit rate, compression complexity as well as received signal-to-interference-noise ratio (SINR). In this work, video quality is measured in peak signal-to-noise ratio (PSNR) rather than SINR. We find that for a given compression complexity, maximum utility is achieved when the product of bit rate and required SINR is minimized. This maximum usually occurs at maximum video coding complexity. We also investigate the capacity in terms of the number of users that can be supported simultaneously for this system, and how the total utility varies with the number of users in this system. This can be used as an admission policy by the central station.

We investigate the power consumed by two mobile terminals transmitting compressed source signals to a base station in a CDMA cellular system. The aim is to minimize total power consumption at the terminals by simultaneously adjusting the complexity of source compression and the transmitter power. We find that in general the complexity of source compression should increase with increasing distance between a terminal and the base station. However, the exact configuration of compression and transmitter power depends on the interaction of the two terminals. The optimum operating points of the two terminals are contained in a pair of nonlinear equations. We use the example of a transform coder processing signals from a Gauss-Markov source to explore the advantages of an adaptive system over a system with fixed compression. The analysis can be applied to a variety of source coders and be extended to a system with more than two users per cell.

User cooperation is a spatial diversity technique where multiple terminals form a virtual antenna array to combat fading. We incorporate source coding into the cooperation scenario and analyze cooperation protocols with respect to the average distortion they achieve. We first compare the amplify-and-forward (AF) protocol to direct transmission (DT) and show that it does not increase the performance in the average distortion sense. Then we propose two new cooperation protocols which achieve better performance by increasing the spectral efficiency while still providing diversity, yet maintaining the simple nature of the previous protocols

We consider a wireless system consisting of one source, one destination and M relays. Assuming path loss and Rayleigh fading, we use the cutset upper bound to show that no matter where the relays are located, the maximum diversity one can obtain is M+1. However, one can achieve a higher diversity gain, namely /spl lfloor/(M+2/2)/sup 2//spl rfloor/, if /spl lfloor/M/2/spl rfloor/ of the relays are clustered with the source and /spl lceil/M/2/spl rceil/ with the destination. This result utilizes the observation that if two wireless nodes are very close, Rayleigh assumption breaks and the proper channel model is additive white Gaussian noise (AWGN). Hence to realize a virtual multiinput multioutput (MIMO) system, clustering is essential.

User cooperation represents an effective way of introducing diversity in wireless networks. Spatial diversity gains are obtained through the cooperative use of antennas belonging to several nodes. We design and analyze the performance of channel codes that are capable of achieving the full diversity provided by user cooperation, with the constraint that they also provide the best possible performance in the interuser link. We show that even though the interuser channel is noisy, the codes provide substantial diversity and coding gains over the noncooperative case.

We examine a network consisting of one source, one destination and two amplifying and forwarding relays and consider a scenario in which destination and relays can have various processing limitations. For all possible diversity combining schemes at the relays and at the destination, we find diversity order results analytically and confirm our findings through numerical calculations of bit error rate (BER) versus signal-to-noise-ratio (SNR) curves. We compare our results with direct transmission, well known transmit diversity methods and traditional multihop transmission and conclude that diversity reception in multihop networks provides the lowest error rate.

We consider a cooperative system in which the partnering mobiles are equipped with multiple antennas. We present space-time codes that have the capability of providing full cooperation diversity, while achieving maximum possible diversity and best performance in the inter-user channel. Our codes also perform well when cooperation does not take place. We illustrate that cooperative space-time coding offers significant performance improvement over direct transmission even when the inter-user channel is noisy. We also consider code design for users with different numbers of antennas.

Cooperative coding is a way to exploit the diversity provided by user cooperation in wireless networks. In this paper, we provide performance analysis of cooperative space-time coding, which arises when mobiles have multiple antennas. We perform an asymptotic analysis in order to determine the achieved diversity order through cooperative space-time coding for various inter-user channel qualities. In addition, we derive tight bounds on the performance of cooperative space-time codes. Numerical examples are presented to demonstrate the results.

In this work, we introduce an approach for minimizing the total power consumption of a mobile transmitter due to source compression, channel coding and transmission subject to a fixed end-to-end source distortion. We illustrate our approach both on an abstract class of sources and channels and on a realistic H.263 video transmission system through a wireless channel. Performance under different channel environments and implementation schemes are investigated. Our numerical analysis shows that optimized settings can reduce the total power consumption by a significant factor and prolong battery life considerably compared with fixed parameter settings.

Mobile users' data rate and quality of service are limited by the fact that, within the duration of any given call, they experience severe variations in signal attenuation, thereby necessitating the use of some type of diversity. In this two-part paper, we propose a new form of spatial diversity, in which diversity gains are achieved via the cooperation of mobile users. Part I describes the user cooperation strategy, while Part II (see ibid., p.1939-48) focuses on implementation issues and performance analysis. Results show that, even though the interuser channel is noisy, cooperation leads not only to an increase in capacity for both users but also to a more robust system, where users' achievable rates are less susceptible to channel variations.

For pt.I see ibid., p.1927-38. This is the second of a two-part paper on a new form of spatial diversity, where diversity gains are achieved through the cooperation of mobile users. Part I described the user cooperation concept and proposed a cooperation strategy for a conventional code-division multiple-access (CDMA) system. Part II investigates the cooperation concept further and considers practical issues related to its implementation. In particular, we investigate the optimal and suboptimal receiver design, and present performance analysis for the conventional CDMA implementation proposed in Part I. We also consider a high-rate CDMA implementation and a cooperation strategy when assumptions about the channel state information at the transmitters are relaxed. We illustrate that, under all scenarios studied, cooperation is beneficial in terms of increasing system throughput and cell coverage, as well as decreasing sensitivity to channel variations.

We study a multiple-antenna system where the transmitter is equipped with quantized information about instantaneous channel realizations. Assuming that the transmitter uses the quantized information for beamforming, we derive a universal lower bound on the outage probability for any finite set of beamformers. The universal lower bound provides a concise characterization of the gain with each additional bit of feedback information regarding the channel. Using the bound, it is shown that finite information systems approach the perfect information case as (t-1)2/sup -B/t-1/, where B is the number of feedback bits and t is the number of transmit antennas. The geometrical bounding technique, used in the proof of the lower bound, also leads to a design criterion for good beamformers, whose outage performance approaches the lower bound. The design criterion minimizes the maximum inner product between any two beamforming vectors in the beamformer codebook, and is equivalent to the problem of designing unitary space-time codes under certain conditions. Finally, we show that good beamformers are good packings of two-dimensional subspaces in a 2t-dimensional real Grassmannian manifold with chordal distance as the metric.

We propose a new scheme for multiple antenna transmission in the context of spread-spectrum signaling. The new scheme consists of using shifted Gold sequences to modulate independent information on the multiple antennas. We show that this strategy of using multiphase spreading (MPS) on different antennas greatly improves the throughput over currently known spread-spectrum multiple-antenna methods. We also find the optimal power allocation strategy among multiple transmit antennas for a fixed rate of channel state information, which might be provided via a feedback link, at the transmitter. We demonstrate the differences in optimal power distribution for maximizing capacity and minimizing probability of outage. When the transmission from the two antennas uses orthogonal spreading, we find that optimizing the power does not give much gain over the equal power transmission. However, when the transmissions are not orthogonal as in the case of MPS, then allocating power to maximize throughput gives considerable gain over equal power transmission. We also consider the effect of imperfections in the feedback channel on the optimal power allocation and show that our power allocation scheme is robust to feedback errors.

We introduce an approach for adaptive minimization of the total power consumption of wireless video communications subject to a given level of quality of service. Our approach exploits tradeoffs between the power consumption of the H.263 encoder, the Reed-Solomon channel encoder and the transmitter. Simulation results show that source and channel coding parameters and transmit energy per bit should vary based on channel conditions. Optimized settings can reduce the total power consumption by a significant factor compared to fixed parameter settings which do not match with the channel conditions.

We describe a new suboptimal decoding technique for linear codes based on the calculation of maximum weight basis of the code. The idea is based on estimating the maximum number locations in a codeword which have the least probability of estimation error without violating the codeword structure. In this paper we discuss the details of the algorithm for a convolutional code. The error correcting capability of the convolutional code increases with the constraint length of the code. Unfortunately the decoding complexity of Viterbi (1967) algorithm grows exponentially with the constraint length. We also augment the maximal weight basis algorithm by incorporating the ideas of list decoding technique. The complexity of the algorithm grows only quadratically with the constraint length and the performance of the algorithm is comparable to the optimal Viterbi decoding method.

We investigate how the description of a correlated information V improves the investment in the stock market X. The objective is to maximize the growth rate of wealth in repeated investments, We find a single-letter characterization of the incremental growth rate /spl Delta/(R), the maximum increase in growth rate when V is described to the investor at rate R. The incremental growth rate specialized to the horse race market is related to source coding with side information of Wyner and Ahlswede-Kormer. We provide two horse race examples: jointly binary and jointly Gaussian. The initial efficiency /spl Delta/'(0) is the maximum possible increase in the growth rate per bit of description, We show that the initial efficiency is related to the dependency between V and the market. In particular, for the horse race market, the initial efficiency is the square of the Hirschfeld-Gebelein-Renyi maximal correlation between V and X. This provides a connection with the hypercontraction of the Markov operator of Ahlswede and Gacs. For the general market the initial efficiency is 1 when the side information V is equal to the stock market outcome X.