Interactive statistical decision making (ISDM) features algorithm-dependent data generated through interaction. Existing information-theoretic lower bounds in ISDM largely target expected risk, while tail-sensitive objectives are less developed. We generalize the interactive Fano framework of Chen et al. by replacing the hard success event with a randomized one-bit statistic representing an arbitrary bounded transform of the loss. This yields a Bernoulli f-divergence inequality, which we invert to obtain a two-sided interval for the transform, recovering the previous result as a special case. Instantiating the transform with a bounded hinge and using the Rockafellar-Uryasev representation, we derive lower bounds on the prior-predictive (Bayesian) CVaR of bounded losses. For KL divergence with the mixture reference distribution, the bound becomes explicit in terms of mutual information via Pinsker's inequality.

We investigate Dobrushin coefficients of discrete Markov kernels that have bounded pointwise maximal leakage (PML) with respect to all distributions with a minimum probability mass bounded away from zero by a constant $c>0$. This definition recovers local differential privacy (LDP) for $c\to 0$. We derive achievable bounds on contraction in terms of a kernels PML guarantees, and provide mechanism constructions that achieve the presented bounds. Further, we extend the results to general $f$-divergences by an application of Binette's inequality. Our analysis yields tighter bounds for mechanisms satisfying LDP and extends beyond the LDP regime to any discrete kernel.

We study an information-theoretic privacy mechanism design problem, where an agent observes useful data $Y$ that is arbitrarily correlated with sensitive data $X$, and design disclosed data $U$ generated from $Y$ (the agent has no direct access to $X$). We introduce \emph{sparse point-wise privacy leakage}, a worst-case privacy criterion that enforces two simultaneous constraints for every disclosed symbol $u\in\mathcal{U}$: (i) $u$ may be correlated with at most $N$ realizations of $X$, and (ii) the total leakage toward those realizations is bounded. In the high-privacy regime, we use concepts from information geometry to obtain a local quadratic approximation of mutual information which measures utility between $U$ and $Y$. When the leakage matrix $P_{X|Y}$ is invertible, this approximation reduces the design problem to a sparse quadratic maximization, known as the Rayleigh-quotient problem, with an $\ell_0$ constraint. We further show that, for the approximated problem, one can without loss of optimality restrict attention to a binary released variable $U$ with a uniform distribution. For small alphabet sizes, the exact sparsity-constrained optimum can be computed via combinatorial support enumeration, which quickly becomes intractable as the dimension grows. For general dimensions, the resulting sparse Rayleigh-quotient maximization is NP-hard and closely related to sparse principal component analysis (PCA). We propose a convex semidefinite programming (SDP) relaxation that is solvable in polynomial time and provides a tractable surrogate for the NP-hard design, together with a simple rounding procedure to recover a feasible leakage direction. We also identify a sparsity threshold beyond which the sparse optimum saturates at the unconstrained spectral value and the SDP relaxation becomes tight.

An information-theoretic privacy mechanism design is studied, where an agent observes useful data $Y$ which is correlated with the private data $X$. The agent wants to reveal the information to a user, hence, the agent utilizes a privacy mechanism to produce disclosed data $U$ that can be revealed. We assume that the agent has no direct access to $X$, i.e., the private data is hidden. We study privacy mechanism design that maximizes the disclosed information about $Y$, measured by the mutual information between $Y$ and $U$, while satisfying a point-wise constraint with different privacy leakage budgets. We introduce a new measure, called the \emph{multi-level point-wise leakage}, which allows us to impose different leakage levels for different realizations of $U$. In contrast to previous studies on point-wise measures, which use the same leakage level for each realization, we consider a more general scenario in which each data point can leak information up to a different threshold. As a result, this concept also covers cases in which some data points should not leak any information about the private data, i.e., they must satisfy perfect privacy. In other words, a combination of perfect privacy and non-zero leakage can be considered. When the leakage is sufficiently small, concepts from information geometry allow us to locally approximate the mutual information. We show that when the leakage matrix $P_{X|Y}$ is invertible, utilizing this approximation leads to a quadratic optimization problem that has closed-form solution under some constraints. In particular, we show that it is sufficient to consider only binary $U$ to attain the optimal utility. This leads to simple privacy designs with low complexity which are based on finding the maximum singular value and singular vector of a matrix.

The growing demand for large artificial intelligence model (LAIM) services is driving a paradigm shift from traditional cloud-based inference to edge-based inference for low-latency, privacy-preserving applications. In particular, edge-device co-inference, which jointly executes LAIM inference across edge devices and servers, has emerged as a promising strategy for resource-efficient LAIM execution in wireless networks. In this paper, we investigate a pruning-aware LAIM co-inference scheme, where a pre-trained LAIM is pruned and partitioned into on-device and on-server sub-models for deployment. For analysis, we first prove that the LAIM output distortion is upper bounded by its parameter distortion. Then, we derive a lower bound on the parameter distortion via rate-distortion theory, analytically capturing the relationship between pruning ratio and co-inference performance. Next, based on the analytical results, we formulate an LAIM co-inference distortion bound minimization problem by jointly optimizing the pruning ratio, split point, transmit power, and computation frequency under system latency, energy, and available resource constraints. Moreover, we propose an efficient algorithm to tackle the considered highly non-convex problem. Finally, extensive experimental results demonstrate the effectiveness of the proposed design. In particular, model parameter distortion is shown to provide a reliable bound on output distortion. Also, the proposed joint design achieves superior performance in balancing trade-offs among inference performance, system latency, and energy consumption compared with various benchmark schemes.

A cache-aided compression problem with perfect privacy is studied, where a server has access to a database ofNfiles, (Y1, ...,YN), each of sizeFbits. The server is connected toKusers through a shared link, where each user has access to a local cache of sizeMFbits. In the placement phase, the server fills the users’ caches without prior knowledge of their future demands, while the delivery phase takes place after the users send their demands to the server. We assume that each fileYiis arbitrarily correlated with a private attributeX, and an adversary is assumed to have access to the shared link. The users and the server have access to a shared secret keyW. The goal is to design the cache contents and the delivered messageCsuch that the average length ofCis minimized, while satisfying: i. The responseCdoes not disclose any information aboutX, i.e.,XandCare statistically independent yieldingI(X;C) = 0, which corresponds to the perfect privacy constraint; ii. Useriis able to decode its demand,Ydi, by using its local cacheZi, delivered messageC, and the shared secret keyW. Due to the correlation of database with the private attribute, existing codes for cache-aided delivery do not fulfill the perfect privacy constraint. Indeed, in this work, we propose a lossless variable-length coding scheme that combines privacy-aware compression with coded caching techniques. In particular, we use two-part code construction and Functional Representation Lemma. Furthermore, we propose an alternative coding scheme based on the minimum entropy coupling concept and a greedy entropy-based algorithm. We show that the proposed scheme improves the previous results obtained by Functional Representation Lemma. Considering two special cases we improve both coding schemes using the common information concept. Finally, we compare the proposed schemes in numerical examples and provide an application considering an encoder with limited buffer size.

Dynamic Security Assessment (DSA) is critical for maintaining stability in large-scale smart grids, especially with the growing integration of renewable energy sources and the inherent uncertainties. Traditional model-based analytical methods are increasingly inadequate under these complex conditions. To address these challenges, we propose a pioneering Quantum Federated Learning-based DSA (QFLDSA) method by combining hybrid quantum-classical machine learning and federated learning. QFLDSA offers an effective way to deal with high-dimensional data and uncertainties inherent in the grid. Moreover, QFLDSA leverages the unique capabilities of quantum computing to enhance the processing of differential-algebraic equations that underpin grid stability. This paper demonstrates through extensive simulations that QFLDSA significantly outperforms traditional methods, achieving the highest average F1-score performance at 97.94%, while maintaining 97.67$\pm$0.17% prediction accuracy on both classical and quantum computing devices only with fewer transmitted model parameters (reducing up to$\sim$1000X). These enhancements enable more reliable and rapid deployment of preventive stability control measures across smart grids. Our results underscore QFLDSA’s potential as a robust solution for the dynamic security challenges of modern smart grids, paving the way for future innovations in grid management technology.Note to Practitioners—In the rapidly evolving world of smart cyber-physical grids, ensuring the stability of electric power systems is paramount. Failures in these systems can lead to catastrophic blackouts, affecting countless homes and businesses. Traditional DSA methods to assess and ensure this stability, while effective, are becoming increasingly complex and vulnerable to single points of failure or cyberattacks. Enter the QFLDSA method, a novel approach we introduce in this paper. In simple terms, this method combines the strengths of quantum machine learning and federated learning to analyze data efficiently across a distributed system. Here’s why these matters: 1) Localized Analysis: Instead of relying on a central hub to analyze all data, QFLDSA allows for localized data analysis. This means that if one part of the system fails, it does not bring down the entire grid’s analysis capabilities. It is akin to having multiple control rooms instead of one, ensuring that a problem in one room does not halt the entire operation. 2) Future-Ready: As we move towards a future where quantum computing becomes more prevalent, QFLDSA is designed to work seamlessly with both today’s classical devices and tomorrow’s quantum devices. This ensures that as technology evolves, our method remains relevant and efficient. 3) Proven Performance: We have not just introduced a new method; we have rigorously tested it. Our theoretical proofs and practical tests confirm that QFLDSA offers accurate and efficient data analysis for smart grids. For industry professionals, the takeaway is clear: if looking for a resilient, future-ready, and proven method to ensure the stability of smart grid, QFLDSA offers a compelling solution.

Over-the-air computation (AirComp) enables swift wireless data aggregation by leveraging the superposition property of multiple-access channels (MAC), making it essential for the seamless integration of communication and computing in future networks. Meanwhile, fluid antenna systems (FAS) offer dynamic spatial degrees of freedom (DoF) by reconfiguring antenna positions, thus enhancing adaptability under varying channel conditions. This paper investigates the integration of FAS into a communication and AirComp coexistence framework. We aim to jointly optimize the transceiver beamforming vectors and the antenna positioning vector (APV) to minimize the computation distortion while ensuring reliable cellular communication performance. To tackle this highly non-convex problem, we develop an efficient joint learning-optimization framework. Specifically, we propose a neural network (NN) framework with a dedicated surrogate loss function design to infer optimal APV based on multi-path channel conditions, while an alternating optimization (AO) method is developed to find a locally optimal solution of transceivers by iteratively optimizing each variables with the others being fixed. Besides, to provide analytical tractability and benchmark insight, the APV design problem is relaxed and transformed into a tractable quadratically constrained quadratic program (QCQP) by successive convex approximation (SCA) as a special case under line-of-sight (LoS) channels, which reveals the performance bounds and convergence properties of the system. Numerical results show that proposed method significantly improves the system performance compared with traditional fixed-position antenna (FPA) as well as various benchmark schemes with remarkable generalization capabilities across diverse channel conditions.

This paper explores the implications of guaranteeing privacy by imposing a lower bound on the information density between the private and the public data. We introduce a novel and operationally meaningful privacy measure calledpointwise maximal cost(PMC) and demonstrate that imposing an upper bound on PMC is equivalent to enforcing a lower bound on the information density. PMC quantifies the information leakage about a secret to adversaries who aim to minimize non-negative cost functions after observing the outcome of a privacy mechanism. When restricted to finite alphabets, PMC can equivalently be defined as the information leakage to adversaries aiming to minimize the probability of incorrectly guessing randomized functions of the secret. We study the properties of PMC and apply it to standard privacy mechanisms to demonstrate its practical relevance. Through a detailed examination, we connect PMC with other privacy measures that impose upper or lower bounds on the information density. These are pointwise maximal leakage (PML), local differential privacy (LDP), and (asymmetric) local information privacy. In particular, we show that a mechanism satisfies LDP if and only if it has both bounded PMC and bounded PML. Overall, our work fills a conceptual and operational gap in the taxonomy of privacy measures, bridges existing disconnects between different frameworks, and offers insights for selecting a suitable notion of privacy in a given application.

In this paper, we study the fundamental limits in the design of fair representations under different demographic (statistical) parity constraints through the lens of information theory. We first consider the design problem achieving perfect demographic parity. More specifically, an agent uses some useful dataXto solve a taskT. Since bothXandTare correlated with some sensitive attribute or secretS, the agent designs a representationYthat has no information about the sensitive attributeS, i.e., satisfying perfect demographic parity constraint, implying thatI(Y ; S)= 0. We then relax the perfect demographic parity and consider a bounded-parity constraint, implying thatI(Y ; S)≤ ϵ. Under perfect demographic parity, we consider two scenarios. First, we consider a design problem where we want to maximize the informationI(Y ; T)that the representation contains about the task, while constraining the level of compression (or encoding rate), that is, ensuring thatI(Y ;X)≤r. Second, inspired by the Conditional Fairness Bottleneck problem, we consider a design problem where we want to maximize the informationI(Y ; T|S)that the representation contains about the task which is not shared by the sensitive attribute, while constraining the amount of irrelevant information, that is, ensuring thatI(Y ;X|T, S)≤r. Under bounded demographic parity, we designYthat maximizes the mutual informationI(Y ; T)about the task while satisfying a bounded compression (or encoding rate) constraint, that is, ensuring thatI(Y ;X)≤r. Simultaneously,Ysatisfies the bounded demographic parity constraintI(Y ; S)≤ ϵ. To designY, we use extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma which are based on randomization techniques and study the tightness of the obtained bounds in special cases. Every design problem studied in this paper can also be interpreted as a code design problem with either perfect privacy or a bounded leakage constraint and a limited rate, considering the sensitive attribute as a secret.

We study two semantic communication problems with privacy constraints considering single-task and multi-task scenarios. In both scenarios, an encoder has access to an information source arbitrarily correlated with some latent private information. In the single-task scenario, a user has a task, and the encoder designs a message to be revealed, which is called the semantic of the information source. Due to the privacy constraints, the semantic cannot be disclosed directly, so the encoder adds noise to produce data that can be disclosed. The goal is to design the disclosed message that maximizes the utility attained by the user while satisfying a privacy constraint. In the multi-task scenario, the user hasLtasks with priorities. Similarly, the encoder designs the disclosed message by adding noise to the semantic, which is optimized for the intended tasks. The goal is to design a mechanism to produce the disclosed message that maximizes the weighted sum of the utilities achieved by the user while satisfying a privacy constraint on the private data. In this work, we first consider the single-task scenario and design the added noise utilizing various methods, including the extended versions of the Functional Representation Lemma, Strong Functional Representation Lemma, and the separation technique. By designing the added noise, we obtain lower bounds with constructive proofs. We then study the multi-task scenario and derive a simple privacy mechanism design considering the source semantics. We show that in the multi-task scenario the main problem can be divided into multiple parallel single-task problems. In both scenarios, the obtained lower and upper bounds are studied considering different cases to study their tightness. We show that under some assumptions our proposed designs are optimal. We provide a few numerical experiments based on the MNIST dataset and medical applications to illustrate the designs and evaluate the bounds, considering both single and multi-task scenarios. Finally, we study an application where a semantic communication with two separate blind encoders is considered.

This work investigates a multi-user, multi-antenna uplink wireless system, in which multiple users transmit signals to a base station. Prior research has explored the potential for linear growth in spectral efficiency by employing multiple transmit and receive antennas. This gain depends heavily on the quality of channel state information and the number of uncorrelated antennas. However, spatial correlations, arising from closely-spaced antennas and channel aging effects, stemming from the difference between the channel state at pilot and data time instances, can substantially counteract these benefits, and degrade the transmission rate, especially in non-stationary environments. To address these challenges, this work introduces a real-time beamforming framework to compensate for the spatial correlation and channel aging effects. First, a channel estimation scheme leveraging temporal channel correlations and considering mobile device velocity and antenna spacing is developed. Subsequently, an expression approximating the average spectral efficiency, which depends on pilot spacing, pilot and data powers, and beamforming vectors, is obtained. By maximizing this expression, optimal parameters are identified. Numerical results demonstrate the effectiveness of the proposed approach compared to prior works. Interestingly, the optimal pilot spacing remains unaffected by large-scale channel parameters and the velocities of interfering users. The impact of interference components also diminishes with an increasing number of transmit antennas.

The transition toward 6G is pushing wireless communication into a regime where the classical plane-wave assumption no longer holds. Millimeter-wave and sub-THz frequencies shrink wavelengths to millimeters, while meter-scale arrays featuring hundreds of antenna elements dramatically enlarge the aperture. Together, these trends collapse the classical Rayleigh far-field boundary from kilometers to mere single-digit meters. Consequently, most practical 6G indoor, vehicular, and industrial deployments will inherently operate within the radiating near-field, where reliance on the plane-wave approximation leads to severe array-gain losses, degraded localization accuracy, and excessive pilot overhead. This paper re-examines the fundamental question: "Where does the far-field truly begin?" Rather than adopting purely geometric definitions, we introduce an application-oriented approach based on user-defined error budgets and a rigorous Fresnel-zone analysis that fully accounts for both amplitude and phase curvature. We propose three practical mismatch metrics: worst-case element mismatch, worst-case normalized mean square error, and spectral efficiency loss. For each metric, we derive a provably optimal transition distance–the minimal range beyond which mismatch permanently remains below a given tolerance–and provide closed-form solutions. Extensive numerical evaluations across diverse frequencies and antenna-array dimensions show that our proposed thresholds can exceed the Rayleigh distance by more than an order of magnitude. By transforming the near-field from a design nuisance into a precise, quantifiable tool, our results provide a clear roadmap for enabling reliable and resource-efficient near-field communications and sensing in emerging 6G systems.

We study agents acting in an unknown environment where the agent’s goal is to find a robust policy. We consider robust policies as policies that achieve high cumulative rewards for all possible environments. To this end, we consider agents minimizing the maximum regret over different environment parameters, leading to the study of minimax regret. This research focuses on deriving information-theoretic bounds for minimax regret in Markov Decision Processes (MDPs) with a finite time horizon. Building on concepts from supervised learning, such as minimum excess risk (MER) and minimax excess risk, we use recent bounds on the Bayesian regret to derive minimax regret bounds. Specifically, we establish minimax theorems and use bounds on the Bayesian regret to perform minimax regret analysis using these minimax theorems. Our contributions include defining a suitable minimax regret in the context of MDPs, finding information-theoretic bounds for it, and applying these bounds in various scenarios.

Integrated sensing and communications (ISAC) is a promising component of 6G networks, fusing communication and radar technologies to facilitate new services. Additionally, the use of extremely large-scale antenna arrays (ELAA) at the ISAC common receiver not only facilitates terahertz-rate communication links but also significantly enhances the accuracy of target detection in radar applications. In practical scenarios, communication scatterers and radar targets often reside in close proximity to the ISAC receiver. This, combined with the use of ELAA, fundamentally alters the electromagnetic characteristics of wireless and radar channels, shifting from far-field planar-wave propagation to near-field spherical wave propagation. Under the far-field planar-wave model, the phase of the array response vector varies linearly with the antenna index. In contrast, in the near-field spherical wave model, this phase relationship becomes nonlinear. This shift presents a fundamental challenge: the widely-used Fourier analysis can no longer be directly applied for target detection and communication channel estimation at the ISAC common receiver. In this work, we propose a feasible solution to address this fundamental issue. Specifically, we demonstrate that there exists a high-dimensional space in which the phase nonlinearity can be expressed as linear. Leveraging this insight, we develop a lifted super-resolution framework that simultaneously performs communication channel estimation and extracts target parameters with high precision.

This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy [1]. Specifically, it extends the rate-distortion analysis of Dong and Van Roy [2], which provides near-optimal bounds for linear bandits. A key limitation of these results is the assumption of a finite action space. We address this by extending the analysis to settings with infinite and continuous action spaces. Additionally, we specialize our results to bandit problems with expected rewards that are Lipschitz continuous with respect to the action space, deriving a regret bound that explicitly accounts for the complexity of the action space.

We study a semantic communication problem with a privacy constraint where an encoder consists of two separate parts, e.g., encoder 1 and encoder 2. The first encoder has access to information source X = (X1,…,XN) which is arbitrarily correlated with private data S. The private data is not accessible by encoder 1, however, the second encoder has access to it and the output of encoder 1. A user asks for a task h(X) and the first encoder designs the semantic of the information source f(X) to disclose. Due to the privacy constraints f(X) can not be revealed directly to the user and the second encoder applies a statistical privacy mechanism to produce disclosed data U. Here, we assume that encoder 2 has no access to the task and the design of the disclosed data is based on the semantic and the private data.In this work, we propose a novel approach where U is produced by solving a privacy-utility trade-off based on the semantic and the private data. We design U utilizing different methods such as using extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma. We evaluate our design by computing the utility attained by the user. Finally, we study and compare the obtained bounds in a numerical example.

This paper studies integrated sensing and communication (ISAC) systems with two rate-limited helpers who observe the channel state sequence and the feedback sequence, respectively. Depending on the timing to compress and use the state information, our proposed coding scheme gives an inner bound of the capacity-compression-distortion tradeoff region. The tradeoff is realized by sending part of the state information at the beginning of the transmission to facilitate the communication and compressing the remaining part together with the feedback signal. A special case with a tight bound is also provided.

Integrated sensing and communication (ISAC) has already established itself as a promising solution to the spectrum scarcity problem, even more so when paired with a reconfigurable intelligent surface (RIS), as RISs can shape the propagation environment by adjusting their phase-shift coefficients. Albeit the potential performance gain, a RIS is also a potential security threat to the system. In this paper, we explore both the positive and negative sides of having a RIS in a multi-user multiple-input multiple-output (MIMO) ISAC network. We first develop an alternating optimization algorithm, obtaining the active and passive beamforming vectors that maximize the sensing signal-to-noise ratio (SNR) under minimum signal-to-interference-plus-noise ratio (SINR) constraints for the communication users and finite power budget. We also investigate the destructive potential of the RIS by devising a RIS phase-shift optimization algorithm that minimizes the sensing SNR while preserving the same minimum communication SINR previously guaranteed by the system. We further investigate the impact of the RIS's individual element failures on the system performance. The simulation results show that the RIS performance-boosting potential is as good as its destructive one and that both of our optimization strategies are hindered by the investigated impairments.

We investigate the performance of primitive rateless (PR) codes and introduce an enhanced ordered statistics decoder (OSD) designed for decoding them. We demonstrate that constructing PR codes using linear Boolean functions simplifies decoding to identifying dual bases over$\text{G F}\left(2^{k}\right)$. By leveraging self-dual bases over$\text{G F}\left(2^{k}\right)$, the decoding process for high-rate PR codes is simplified, contributing to a reduced complexity OSD algorithm. Through simulations, we establish that high-rate PR codes can achieve block error rates comparable to their BCH counterparts across various signal-to-noise ratios (SNRs) and code rates. The PR code can be tailored to any rate and block length, and the proposed OSD algorithm makes it well-suited for low-latency applications.

We study different classes of reinforcement learning problems using the minimax regret framework. We formalize a finite-horizon reinforcement learning problem setting that is suitable for the information-theoretic analysis of minimax regret which encompasses linear bandits, Markov decision processes, linear Markov decision processes, and other reinforcement learning problems. We derive a minimax theorem applicable to this setting that does not require any finiteness or deterministic policy constraints. Using this theorem, we show that any Bayesian regret bound can be used to bound the minimax regret within our framework. We then apply the minimax theorem to obtain an information-theoretic upper bound for the minimax regret, leveraging a general Bayesian regret bound. The derived minimax regret bound inherits key properties of the Bayesian regret bound, including its ability to isolate factors such as the information ratio, the mutual information between the learning target and the environment, and the Bayesian regret of the target policy. Finally, we demonstrate the applicability of our bounds in various settings, including linear bandits, episodic reinforcement learning, and linear Markov decision processes, recovering known results for the minimax regret.

We study an information-theoretic privacy mechanism design, where an agent observes useful data Y and wants to reveal the information to a user. Since the useful data is correlated with the private data X, the agent uses a privacy mechanism to produce disclosed data U that can be released. We assume that the agent observes Y and has no direct access to X, i.e., the private data is hidden. We study the privacy mechanism design that maximizes the revealed information about Y while satisfying a bounded Local Information Privacy (LIP) criterion. When the leakage is sufficiently small, concepts from information geometry allow us to locally approximate the mutual information. By utilizing this approximation the main privacy-utility trade-off problem can be rewritten as a quadratic optimization problem that has closed-form solution under some constraints. For the cases where the closed-form solution is not obtained we provide lower bounds on it. In contrast to the previous works that have complexity issues, here, we provide simple privacy designs with low complexity which are based on finding the maximum singular value and singular vector of a matrix. To do so, we follow two approaches where in the first one we find a lower bound on the main problem and then approximate it, however, in the second approach we approximate the main problem directly.In this work, we present geometrical studies of the proposed methods and in a numerical example we compare our results considering both approaches with the optimal solution and the previous methods. Finally, we discuss how the proposed methods can be applied to deal with differential privacy.

In the context of the joint radar and communications (JRC) framework, reconfigurable intelligent surfaces (RISs) emerged as a promising technology for their ability to shape the propagation environment by adjusting their phase-shift coefficients. However, achieving perfect synchronization and effective collaboration between access points (APs) and RISs is crucial to successful operation. This paper investigates the performance of a bistatic JRC network operating in the millimeter-wave (mmWave) frequency band, where the receiving AP is equipped with an RIS-integrated array. This system simultaneously serves multiple UEs while estimating the position of a target with limited prior knowledge of its position. To achieve this, we optimize both the power allocation of the transmitted waveform and the RIS phase-shift matrix to minimize the position error bound (PEB) of the target. At the same time, we ensure that the UEs achieve an acceptable level of spectral efficiency. The numerical results show that an RIS-integrated array, even with a small number of receiving antennas, can achieve high localization accuracy. Additionally, optimized phase-shifts significantly improve the localization accuracy in comparison to a random phase-shift configuration.

A multi-user private data compression problem is studied. A server has access to a database of$N$files,$(Y_{1},\ldots,\ Y_{N})$, each of size$F$bits and is connected to an encoder. The encoder is connected through an unsecured link to a user. We assume that each file$Y_{i}$is arbitrarily correlated with a private attribute$X$, which is assumed to be accessible by the encoder. Moreover, an adversary is assumed to have access to the link. The users and the encoder have access to a shared secret key$W$. We assume that at each time the user asks for a file$Y_{d_{i}}$, where$(d_{1},\ \ldots,\ d_{K})$corresponds to the demand vector. The goal is to design the delivered message$\mathcal{C}=(\mathcal{C}_{1},\ \ldots,\mathcal{C}_{K})$after the user send his demands to the encoder such that the average length of$\mathcal{C}$is minimized, while satisfying:$\mathbf{i}$. The message$\mathcal{C}$does not reveal any information about$X$, i.e.,$X$and$\mathcal{C}$are independent, which corresponds to the perfect privacy constraint; ii. The user is able to decode its demands,$Y_{d_{i}}$, by using$\mathcal{C}$, and the shared key$W$. Here, the encoder sequentially encode each demand$Y_{d_{i}}$at time$i$, using the shared key and previous encoded messages. We propose a variable-length coding scheme that uses privacy-aware compression techniques. We study proposed upper and lower bounds on the average length of$\mathcal{C}$in an example. Finally, we study an application considering cache-aided networks.

We propose a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks, where single-antenna users transmit orthogonal frequency division multiplexing signals without dedicated pilots. The base station exploits the spatial and velocity diversities of users to simultaneously decode messages and detect targets, transforming user transmissions into a powerful sensing tool. Each user's signal, structured by a known codebook, propagates through a sparse multi-path channel with shared moving targets and user-specific scatterers. Notably, common targets induce distinct delay-Doppler-angle signatures, while stationary scatterers cluster in parameter space. We formulate the joint multi-path parameter estimation and data decoding as a 3D super-resolution problem, extracting delays, Doppler shifts, and angles-of-arrival via atomic norm minimization, efficiently solved using semidefinite programming. A core innovation is multi-user fusion, where diverse user observations are collaboratively combined to enhance sensing and decoding. This approach improves robustness and integrates multi-user perspectives into a unified estimation framework, enabling high-resolution sensing and reliable communication. Numerical results show that the proposed framework significantly enhances both target estimation and communication performance, highlighting its potential for next-generation ISAC systems.

In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data$X$to solve a task$T$. Since both$X$and$T$are correlated with some sensitive attribute or secret$S$, the agent designs a representation$Y$that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that$I(Y; S) \leq \epsilon$. Here, we relax the perfect demographic (statistical) parity and consider a bounded-parity constraint. In this work, we design the representation$Y$that maximizes the mutual information$I(Y; T)$about the task while satisfying a bounded compression (or encoding rate) constraint, that is, ensuring that$I(Y; X) \leq r$. Simultaneously,$Y$satisfies the bounded statistical parity constraint$I(Y; S) \leq \epsilon$. To design$Y$, we use extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma which are based on randomization techniques and study the tightness of the obtained bounds in special cases. The main idea to derive the lower bounds is to use randomization over useful data$X$or sensitive data$S$. Considering perfect demographic parity, i.e.,$\epsilon=0$, we improve the existing results (lower bounds) by using a tighter version of the Strong Functional Representation Lemma and propose new upper bounds. We then propose upper and lower bounds for the main problem and show that allowing non-zero leakage can improve the attained utility. Finally, we study the bounds and compare them in a numerical example. The problem studied in this paper can also be interpreted as one of code design with bounded leakage and bounded rate privacy considering the sensitive attribute as a secret.

In the era of smart cyber-physical grid, dynamic insecurity risk has become a significant concern due to the increasing integration of renewable energy sources and the inherent uncertainties in smart grid. Dynamic security assessment (DSA) has been adopted to hedge against such risks by estimating the stability of large-scale smart grids. Existing DSA approaches often involve complex high dimensional models which incur high communication and computational costs, hindering their practical adoption. In this paper, we address these limitations with the Quantum Federated Ensembled Variational Adaptive Learning (QFEVAL) approach for smart grid DSA. QFEVAL is designed to combine quantum machine learning and federated learning to handle the differential-algebraic equations that describe smart grid stability, providing an efficient way to deal with high-dimensional data and uncertainties. QFEVAL enables the training of the hybrid quantum-classical neural networks on distributed DSA datasets located at different nodes in smart grids, without requiring large numbers of parameters to be transmitted. QFEVAL accurately predicts the stability of the smart grid under various conditions, enabling the implementation of preventive stability control measures. Through extensive experiments, we demonstrate that QFEVAL achieves comparable performance to 9 state-of-the-art DSA approaches with more than 2 orders of magnitude fewer model parameter transmissions. QFEVAL paves the way for reliable, secure, and continuous electricity supply, offering a robust solution to the challenges of DSA in smart grids.

Several previous works have addressed the inherent trade-off between allocating resources in the power and time domains to pilot and data signals in multiple input multiple output systems over block-fading channels. In particular, when the channel changes rapidly in time, channel aging degrades the performance in terms of spectral efficiency without proper pilot spacing and power control. Despite recognizing non-stationary stochastic processes as more accurate models for time-varying wireless channels, the problem of pilot spacing and power control in multi-antenna systems operating over non-stationary channels is not addressed in the literature. In this paper, we address this gap by introducing a refined first-order autoregressive model that exploits the inherent temporal correlations over non-stationary Rician aging channels. We design a multi-frame structure for data transmission that better reflects the non-stationary fading environment than previously developed single-frame structures. Subsequently, to determine the optimal pilot spacing and power control within this multi-frame structure, we develop an optimization framework and an efficient algorithm based on maximizing a deterministic equivalent expression for the spectral efficiency, demonstrating its generality by encompassing previous channel aging results. Our numerical results indicate the efficacy of the proposed method in terms of spectral efficiency gains over the single frame structure.

The rapid development of deep-learning enabled task-oriented communications (TOC) significantly shifts the paradigm of wireless communications. However, the high computation demands, particularly in resource-constrained systems e.g., mobile phones and UAVs, make TOC challenging for many tasks. To address the problem, we propose a novel TOC method with two models: a static and a dynamic model. In the static model, we apply a neural network (NN) as a task-oriented encoder (TOE) when there is no computation budget constraint. The dynamic model is used when device computation resources are limited, and it uses dynamic NNs with multiple exits as the TOE. The dynamic model sorts input data by complexity with thresholds, allowing the efficient allocation of computation resources. Furthermore, we analyze the convergence of the proposed TOC methods and show that the model converges at rate$O\left ({{\frac {1}{\sqrt {T}}}}\right)$with an epoch of lengthT. Experimental results demonstrate that the static model outperforms baseline models in terms of transmitted dimensions, floating-point operations (FLOPs), and accuracy simultaneously. The dynamic model can further improve accuracy and computational demand, providing an improved solution for resource-constrained systems.

In this article, we address the problem of federated learning in the presence of stragglers. For this problem, a coded federated learning framework has been proposed, where the central server aggregates gradients received from the non-stragglers and gradient computed from a privacy-preservation global coded dataset to mitigate the negative impact of the stragglers. However, when aggregating these gradients, fixed weights are consistently applied across iterations, neglecting the generation of the global coded dataset and the dynamic nature of the trained model over iterations. This oversight may result in diminished learning performance. To overcome this drawback, we propose a new method named adaptive coded federated learning (ACFL). In ACFL, before the training, each device uploads a local coded dataset with additive noise to the central server to generate a global coded dataset under privacy-preservation requirements. During each iteration of the training, the central server aggregates the gradients received from the non-stragglers and the gradient computed from the global coded dataset, where an adaptive policy for varying the aggregation weights is designed. Under this policy, we optimize the performance in terms of privacy and learning, where the learning performance is analyzed through convergence analysis and the privacy performance in sharing local coded datasets with the server is characterized via mutual information differential privacy. Finally, we perform simulations to demonstrate the superiority of ACFL compared with the baseline methods.

In this paper, we consider the problem of federated learning (FL) with devices that have intermittent connectivity to the central server. For this problem, the concept of semi-decentralized FL has been proposed in the literature. This paradigm allows non-straggler devices to relay the gradients computed by the stragglers to the server, and enables realization of gradient coding (GC) to mitigate the negative impact of the stragglers that fail to communicate directly to the central server. However, for GC in semi-decentralized FL, the communication overhead caused by information transmission among the devices is significant. To overcome this shortcoming, inspired by the existing communication-optimal exact consensus algorithm (CECA), we propose a new communication-efficient semi-decentralized FL method (COFFEE). In each round, the devices exchange information by taking a certain number of steps towards communication-optimal exact consensus, ensuring that each device obtains the average of the gradients computed by both its previous neighbors and itself. Afterwards, the non-stragglers transmit the local average result to the server for global aggregation to update the global model. We analyze the convergence performance and the communication overhead of COFFEE analytically. Building on this, to further enhance learning performance under a specific communication overhead, we propose an enhanced version of COFFEE with an adaptive aggregation rule at the central server, referred to as A-COFFEE, which adjusts to the straggler pattern of the devices over training rounds. Experiments are conducted to verify that the proposed methods outperform the baseline methods.

This work studies gradient coding (GC) in the context of distributed training problems with unreliable communication. We propose cooperative GC (CoGC), a novel gradient-sharing-based GC framework that leverages cooperative communication among clients. This approach eliminates the need for dataset replication, making it communication- and computation-efficient and suitable for federated learning (FL). By employing the standard GC decoding mechanism, CoGC yields strictly binary outcomes: the global model is either recovered exactly or the recovery is meaningless, with no intermediate outcomes. This characteristic ensures the optimality of the training and demonstrates strong resilience to client-to-server communication failures. However, due to the limited flexibility of the recovery outcomes, the decoding mechanism may also result in communication inefficiency and hinder convergence, especially when communication channels among clients are in poor condition. To overcome this limitation and further exploit the potential of GC matrices, we propose a complementary decoding mechanism, termed GC+, which leverages information that would otherwise be discarded during GC decoding failures. This approach significantly improves system reliability against unreliable communication, as the full recovery1of the global model dominates in GC+. To conclude, this work establishes solid theoretical frameworks for both CoGC and GC+. We assess the system reliability by outage analyses and convergence analyses for each decoding mechanism, along with a rigorous investigation of how outages affect the structure and performance of GC matrices. Finally, the effectiveness of CoGC and GC+is validated through extensive simulations.

Over-the-air computation (AirComp) is emerging as a promising technology for wireless data aggregation. However, its performance is hampered by users with poor channel conditions. To mitigate such a performance bottleneck, this paper introduces an active reconfigurable intelligence surface (RIS) into the AirComp system. We begin by exploring the ideal active RIS model and propose a joint optimization of the transceiver and RIS configuration to minimize the mean squared error (MSE) between the target and estimated function values. To manage the resulting tri-convex optimization problem, we employ the alternating optimization (AO) framework to decompose it into three convex subproblems, each of which can be solved optimally. We then investigate two specific cases and analyze their respective asymptotic performance to reveal the superiority of the active RIS in mitigating the MSE relative to its passive counterpart. Lastly, we adapt our transceiver and RIS configuration optimization approach to account for the self-interference of the active RIS. To handle the resulting highly non-convex problem, we further develop a two-layer AO framework. Simulation results confirm the superiority of the active RIS in enhancing AirComp performance compared to its passive counterpart.

This paper characterizes the trade-offs between information and energy transmission over an additive white Gaussian noise channel in the finite block-length regime with finite channel input symbols. These trade-offs are characterized in the form of inequalities involving the information transmission rate, energy transmission rate, decoding error probability (DEP) and energy outage probability (EOP) for a given finite block-length code. The first set of results identify a set of necessary conditions that a given code must satisfy for simultaneous information and energy transmission. Following this, a novel method for constructing a family of codes that can satisfy a target information rate, energy rate, DEP and EOP is proposed. Finally, achievability results identify the set of tuples of information rate, energy rate, DEP and EOP that can be simultaneously achieved by the constructed family of codes.

In this paper, we study the problem of distributed learning (DL) with devices transmitting sign information of the local gradients to the server under communication constraints, where the devices are susceptible to Byzantine attacks. For this problem, a sign-based gradient descent method with majority vote and stochastic 1-bit quantization (Sign-M-stochastic) has been proposed very recently. However, the Byzantine resilience of Sign-M-stochastic is inherently limited, based on the fact that all Byzantine devices and honest devices participate equally in the training process. To overcome this drawback and enhance the resilience to Byzantine attacks, inspired by the audit-based distributed detection systems, we propose a novel DL method with an audit mechanism (DL-AM). In each iteration, the sign information of the local gradients are obtained by the devices from stochastic 1-bit quantization. All devices, partitioned into groups, send the sign information to the server through multiple paths, both directly and via other devices in the same group. This approach provides the server with additional information about the identities of the devices, which enables the server to form the global model update by aggregating the sign information of different devices with varying weights. We analyze the convergence performance of the proposed method from a theoretical perspective. Finally, numerical results demonstrate the superiority of DL-AM over the baseline methods.

Distribution-preserving integrated sensing and communication is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. Furthermore, we consider the case where the system aims to keep the reconstructed sequence secret from an eavesdropper who also observes the channel output and receives rate-limited side information about the estimator. An inner bound of the tradeoff region and a capacity-achieving special case are presented. In addition, we provide some numerical examples to illustrate the tradeoff between the communication rate, distortion, and the preservation of the distribution.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states, and the channel state information (CSI) is revealed to the encoders in a causal manner. The main results of this paper are inner and outer bounds of the capacity region, for which we investigate coding schemes incorporating wiretap coding and secret key agreements between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are proposed. The first is a new coding scheme that uses backward decoding and the Wyner-Ziv coding, and the secret key is constructed from a lossy description of the CSI. The other is an extended version of an existing coding scheme for point-to-point wiretap channels with causal CSI. A numerical example shows that the achievable region given by the first coding scheme can be strictly larger than the second one. However, these two schemes do not outperform each other in general, and there exist some numerical examples in which each coding scheme achieves some rate pairs that cannot be achieved by another scheme. Our established inner bound reduces to some best-known results in the literature as special cases. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

The Internet of Things interconnects billions of devices and forms a vast network where users sporadically transmit short messages through multi-path wireless channels. These channels are characterized by the superposition of a small number of scaled and delayed copies of Dirac spikes. At the receiver, the observed signal is a sum of these convolved signals, and the task is to find the amplitudes, continuous-indexed delays, and transmitted messages from a single signal. This task is inherently ill-posed without additional assumptions on the channel or messages. In this work, we assume the channel exhibits sparsity in the delay domain and that independent and identically distributed random linear encoding is applied to the messages at the devices. Leveraging these assumptions, we propose a semidefinite programming optimization capable of simultaneously recovering both messages and the delay parameters of the channels from only a single received signal. Our theoretical analysis establishes that the required number of samples at the receiver scales proportionally to the sum-product of sparsity and message length of all users, aligning with the degrees of freedom in the lifting-type optimization frameworks. Numerical experiments confirm the efficacy of the proposed method in accurately estimating closely-spaced delay parameters and recovering messages.

Quantum federated learning (QFL) is an emerging interdisciplinary field that merges the principles of quantum computing (QC) and federated learning (FL), with the goal of leveraging quantum technologies to enhance privacy, security, and efficiency in the learning process. Currently, there is no comprehensive survey for this interdisciplinary field. This review offers a thorough, holistic examination of QFL. We aim to provide a comprehensive understanding of the principles, techniques, and emerging applications of QFL. We discuss the current state of research in this rapidly evolving field, identify challenges and opportunities associated with integrating these technologies, and outline future directions and open research questions. We propose a unique taxonomy of QFL techniques, categorized according to their characteristics and the quantum techniques employed. As the field of QFL continues to progress, we can anticipate further breakthroughs and applications across various industries, driving innovation and addressing challenges related to data privacy, security, and resource optimization. This review serves as a first-of-its-kind comprehensive guide for researchers and practitioners interested in understanding and advancing the field of QFL.

Stragglers’ effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local model updates to the central parameter server (PS) through fading channels. To tackle communication stragglers without dataset sharing or prior information about the network at PS, we propose cooperative gradient coding (CoGC) for semi-decentralized FL to enable the exact global model recovery at PS. Furthermore, we conduct a thorough theoretical analysis of the proposed approach. Namely, an outage analysis of the proposed approach is provided, followed by a convergence analysis based on the failure probability of the global model recovery at PS. Nevertheless, simulation results reveal the superiority of the proposed approach in the presence of stragglers under imbalanced data distribution.

Distribution-preserving integrated sensing and communication with secure reconstruction is investigated in this paper. In addition to the distortion constraint, we impose another constraint on the distance between the reconstructed sequence distribution and the original state distribution to force the system to preserve the statistical property of the channel states. An inner bound of the distribution-preserving capacity-distortion region is provided with some capacity region results under special cases. A numerical example demonstrates the tradeoff between the communication rate, reconstruction distortion and distribution preservation. Furthermore, we consider the case that the reconstructed sequence should be kept secret from an eavesdropper who also observes the channel output. An inner bound of the tradeoff region and a capacity-achieving special case are presented.

This paper studies the truncation method from Alquier [1] to derive high-probability PAC-Bayes bounds for unbounded losses with heavy tails. Assuming that the p-th moment is bounded, the resulting bounds interpolate between a slow rate$1/\sqrt{n}$when$p=2$, and a fast rate$1/n$when$p\rightarrow\infty$and the loss is essentially bounded. Moreover, the paper derives a high-probability PAC-Bayes bound for losses with a bounded variance. This bound has an exponentially better dependence on the confidence parameter and the dependency measure than previous bounds in the literature. Finally, the paper extends all results to guarantees in expectation and single-draw PAC-Bayes. In order to so, it obtains analogues of the PAC-Bayes fast rate bound for bounded losses from [2] in these settings. The full version of the paper can be found in https://arxiv.org/abs/2403.16681.

We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we prove that bounding the information density from above or below in turn implies a lower or upper bound on the information density, respectively. Using this result, we establish new relationships between local information privacy, asymmetric local information privacy, pointwise maximal leakage and local differential privacy. We further provide applications of these relations to privacy mechanism design. Secondly, we provide equivalence statements of lower bounds on information density and risk-averse adversaries. More specifically, we prove an equivalence between a guessing framework and a cost-function framework that both result in the same lower bound on the information density.

We investigate the problem of secure multi-terminal strong coordination aided by a multiple-access wiretap channel (MAC-WT). In this setup, independent and identically distributed (i.i.d.) copies of correlated sources are observed by two transmitters who encode the channel inputs to the MAC-WT. The legitimate receiver on observing the channel output must produce approximately i.i.d. copies of an output random variable jointly distributed with the two sources. Furthermore, we demand that an external eavesdropper learns essentially nothing about the sources and the simulated output sequence by observing its corresponding MAC-WT output. This is aided by the presence of independent pairwise shared randomness between each encoder and the legitimate decoder. The shared randomness rate tuples which permit such channel simulation with strong secrecy are of interest. We derive an achievable rate region based on a combination of coordination coding and wiretap coding, along with an outer bound. The inner bound is shown to be tight and a complete characterization is derived for the special case when the sources are independent and the legitimate receiver's channel is composed of deterministic links.

We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has$L$tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to the privacy constraints the semantic can not be disclosed directly and the encoder adds noise to produce disclosed data. The goal is to design the disclosed data that maximizes the weighted sum of the utilities achieved by the user while satisfying a privacy constraint on the private data. In this work, we first consider a single-task scenario and design the added noise utilizing various methods including the extended versions of the Functional Representation Lemma, Strong Functional Representation Lemma, and separation technique. We then study the multi-task scenario and derive a simple design of the source semantics. We show that in the multi-task scenario the main problem can be divided into multiple parallel single-task problems.

We study the problem of federated learning (FL) in the presence of stragglers, the devices that are intermittently connected to the central server. Although under the newly developed semi-decentralized federated learning (SFL) framework, gradient coding (GC) can be applied to evade the stragglers by letting them relay their locally computed gradients to the central server via non-stragglers, the communication burden of GC in SFL is very heavy. To overcome this drawback, motivated by the communication-optimal exact consensus algorithm (CECA) proposed in the literature, we propose a new communicationefficient semi-decentralized method (COFFEE) in SFL. In each round of COFFEE, the devices take a certain number of steps towards consensus in a decentralized manner with high communication efficiency, and each of them acquires the average of its own gradient and the gradients of its previous neighbors. After that, the non-straggler devices send the obtained average results to the server, which aggregates the received vectors to yield the global model update. The learning performance of the proposed method is analyzed through convergence analysis. Finally, we run simulations to show the superiority of COFFEE over the baseline method, i.e., GC in SFL.

We investigate the problem of multi-terminal strong coordination over a network of noiseless links with degraded source observations. In this setup, independent and identically distributed (i.i.d.) copies of correlated sources are observed by two transmitters, with one of the source observations being common while the other one is private. The transmitters communicate their source descriptions over noiseless links to the receiver, which must produce approximately i.i.d. copies of an output random variable jointly distributed with the two sources. This is aided by the presence of common randomness shared between all three parties. The communication and common randomness rate tuples which permit such channel simulation are of interest. We derive a complete characterization for this multi-terminal strong coordination problem. It is observed that the optimal scheme is based on a superposition structure, where the common source description forms the base layer and the private source description forms the top layer.

In this article, we study the fundamental limits in the design of fair and/or private representations achieving perfect demographic parity and/or perfect privacy through the lens of information theory. More precisely, given some useful data$X$that we wish to employ to solve a task$T$, we consider the design of a representation$Y$that has no information of some sensitive attribute or secret$s$, that is, such that$I(Y;S)=0$. We consider two scenarios. First, we consider a design desiderata where we want to maximize the information$I(Y;T)$that the representation contains about the task, while constraining the level of compression (or encoding rate), that is, ensuring that$I(Y;X)\leq r$. Second, inspired by the Conditional Fairness Bottleneck problem, we consider a design desiderata where we want to maximize the information$I(Y,\ T\vert S)$that the representation contains about the task which is not shared by the sensitive attribute or secret, while constraining the amount of irrelevant information, that is, ensuring that$I(Y;X\vert T,\ S)\leq r$. In both cases, we employ extended versions of the Functional Representation Lemma and the Strong Functional Representation Lemma and study the tightness of the obtained bounds. Every result here can also be interpreted as a coding with perfect privacy problem by considering the sensitive attribute as a secret.

In this paper, we study a cell-free multiple-input multiple-output network equipped with integrated sensing and communication (ISAC) access points (APs). The distributed APs are used to jointly serve the communication needs of user equipments (UEs) while sensing a target, assumed to be an eavesdropper (Eve). To increase the system's robustness towards said Eve, we develop an ISAC waveform model that includes artificial noise (AN) aimed at degrading the Eve channel quality. The central processing unit receives the observations from each AP and calculates the optimal precoding and AN covariance matrices by solving a semi-definite relaxation of a constrained Cramer-Rao bound (CRB) minimization problem. Simulation results highlight an underlying trade-off between sensing and communication performances: in particular, the UEs signal-to-noise and interference ratio and the maximum Eve's signal to noise ratio are directly proportional to the CRB. Furthermore, the optimal AN covariance matrix is rank-1 and has a peak in the eve's direction, leading to a surprising inverse-proportionality between the UEs-Eve distance and optimal-CRB magnitude.

In this paper, we present new high-probability PAC-Bayes bounds for different types of losses. Firstly, for losses with a bounded range, we recover a strengthened version of Catoni's bound that holds uniformly for all parameter values. This leads to new fast-rate and mixed-rate bounds that are interpretable and tighter than previous bounds in the literature. In particular, the fast-rate bound is equivalent to the Seeger--Langford bound. Secondly, for losses with more general tail behaviors, we introduce two new parameter-free bounds: a PAC-Bayes Chernoff analogue when the loss' cumulative generating function is bounded, and a bound when the loss' second moment is bounded. These two bounds are obtained using a new technique based on a discretization of the space of possible events for the "in probability" parameter optimization problem. This technique is both simpler and more general than previous approaches optimizing over a grid on the parameters' space. Finally, using a simple technique that is applicable to any existing bound, we extend all previous results to anytime-valid bounds.

This paper considers the problem of distributed learning (DL) in the presence of stragglers. For this problem, DL methods based on gradient coding have been widely investigated, which redundantly distribute the training data to the workers to guarantee convergence when some workers are stragglers. However, these methods require the workers to transmit real-valued vectors during the process of learning, which induces very high communication burden. To overcome this drawback, we propose a novel DL method based on 1-bit gradient coding (1-bit GC-DL), where 1-bit data encoded from the locally computed gradients are transmitted by the workers to reduce the communication overhead. We theoretically provide the convergence guarantees of the proposed method for both the convex loss functions and non-convex loss functions. It is shown empirically that 1-bit GC-DL outperforms the baseline methods, which attains better learning performance under the same communication overhead.

We study an information theoretic privacy mechanism design problem for two scenarios where the private data is either observable or hidden. In the hidden private data scenario, an agent observes useful dataYthat is correlated with private dataX, and generate disclosed dataUwhich maximizes the revealed information aboutYwhile satisfying a bounded privacy leakage constraint. Considering the other scenario, the agent has additional access toX. To design the privacy mechanism, we first extend the Functional Representation Lemma and Strong Functional Representation Lemma by relaxing the independence condition and thereby allowing a certain leakage. We then find lower and upper bounds on the privacy-utility trade-offs in both scenarios. In particular, for the case where no leakage is allowed andXis observable, our upper and lower bounds improve previous bounds. Considering bounded mutual information as privacy constraint and the observable private data scenario we show that if the common information and mutual information betweenXandYare equal, then the attained upper bound is tight. Finally, the privacy-utility trade-off with prioritized private data is studied where part ofXis more private than the remaining part.

To date, no “information-theoretic” frameworks for reasoning about generalization error have been shown to establish minimax rates for gradient descent in the setting of stochastic convex optimization. In this work, we consider the prospect of establishing such rates via several existing information-theoretic frameworks: input-output mutual information bounds, conditional mutual information bounds and variants, PAC-Bayes bounds, and recent conditional variants thereof. We prove that none of these bounds are able to establish minimax rates. We then consider a common tactic employed in studying gradient methods, whereby the final iterate is corrupted by Gaussian noise, producing a noisy “surrogate” algorithm. We prove that minimax rates cannot be established via the analysis of such surrogates. Our results suggest that new ideas are required to analyze gradient descent using information-theoretic techniques.

We consider the problem of gridless blind deconvolution and demixing (GB2D) in scenarios where multiple users communicate messages through multiple unknown channels, and a single base station (BS) collects their contributions. This scenario arises in various communication fields, including wireless communications, the Internet of Things, over-the-air computation, and integrated sensing and communications. In this setup, each user's message is convolved with a multi-path channel formed by several scaled and delayed copies of Dirac spikes. The BS receives a linear combination of the convolved signals, and the goal is to recover the unknown amplitudes, continuous-indexed delays, and transmitted waveforms from a compressed vector of measurements at the BS. However, without prior knowledge of the transmitted messages and channels, GB2D is highly challenging and intractable in general. To address this issue, we assume that each user's message follows a distinct modulation scheme living in a known low-dimensional subspace. By exploiting these subspace assumptions and the sparsity of the multipath channels for different users, we transform the nonlinear GB2D problem into a matrix tuple recovery problem from a few linear measurements. To achieve this, we propose a semidefinite programming optimization that exploits the specific low-dimensional structure of the matrix tuple to recover the messages and continuous delays of different communication paths from a single received signal at the BS. Finally, our numerical experiments show that our proposed method effectively recovers all transmitted messages and the continuous delay parameters of the channels with sufficient samples.

Strong secrecy communication over a discrete memoryless state-dependent multiple access channel (SD-MAC) with an external eavesdropper is investigated. The channel is governed by discrete memoryless and i.i.d. channel states and the channel state information (CSI) is revealed to the encoders in a causal manner. An inner bound of the capacity is provided. To establish the inner bound, we investigate coding schemes incorporating wiretap coding and secret key agreement between the sender and the legitimate receiver. Two kinds of block Markov coding schemes are studied. The first one uses backward decoding and Wyner-Ziv coding and the secret key is constructed from a lossy reproduction of the CSI. The other one is an extended version of the existing coding scheme for point-to-point wiretap channels with causal CSI. We further investigate some capacity-achieving cases for state-dependent multiple access wiretap channels (SD-MAWCs) with degraded message sets. It turns out that the two coding schemes are both optimal in these cases.

We extend the results of Ghourchian et al. [1] to joint source-channel coding with eavesdropping. Our work characterizes the sequential encoding process using the cumulative rate distribution functions (CRDF) and includes a security constraint using the cumulative leakage distribution functions (CLF). The information leakage is defined based on the mutual information between the source and the output of the wiretap channel to the eavesdropper. We derive inner and outer bounds on the achievable CRDF for a given source and CLF, and show that the bounds are tight when the distribution achieving the capacity of the wiretap channel is the same as the one achieving the capacity of the channel.

In this work, we study the performance of the Thompson Sampling algorithm for Contextual Bandit problems based on the framework introduced by [1] and their concept of lifted information ratio. First, we prove a comprehensive bound on the Thompson Sampling expected cumulative regret that depends on the mutual information of the environment parameters and the history. Then, we introduce new bounds on the lifted information ratio that hold for sub-Gaussian rewards, thus generalizing the results from [1] which analysis requires binary rewards. Finally, we provide explicit regret bounds for the special cases of unstructured bounded contextual bandits, structured bounded contextual bandits with Laplace likelihood, structured Bernoulli bandits, and bounded linear contextual bandits.

Pointwise maximal leakage (PML) is an operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X to a single outcome of a related random variable Y. In this paper, we extend the notion of PML to random variables on arbitrary probability spaces. We develop two new definitions: First, we extend PML to countably infinite random variables by considering adversaries who aim to guess the value of discrete (finite or countably infinite) functions of X. Then, we consider adversaries who construct estimates of X that maximize the expected value of their corresponding gain functions. We use this latter setup to introduce a highly versatile form of PML that captures many scenarios of practical interest whose definition requires no assumptions about the underlying probability spaces.

A privacy mechanism design problem is studied through the lens of information theory. In this work, an agent observes useful data Y = (Y1,…,YN) that is correlated with private data X = (X1,…,XN) which is assumed to be also accessible by the agent. Here, we consider K users where user i demands a sub-vector of Y, denoted by Ci. The agent wishes to disclose Cito user i. A privacy mechanism is designed to generate disclosed data U which maximizes a linear combinations of the users utilities while satisfying a bounded privacy constraint in terms of mutual information. In a similar work it has been assumed that Xiis a deterministic function of Yi, however in this work we let Xiand Yibe arbitrarily correlated.First, an upper bound on the privacy-utility trade-off is obtained by using a specific transformation, Functional Representation Lemma and Strong Functional Representation Lemma, then we show that the upper bound can be decomposed into N parallel problems. Next, lower bounds on privacy-utility tradeoff are derived using Functional Representation Lemma and Strong Functional Representation Lemma. The upper bound is tight within a constant and the lower bounds assert that the disclosed data is independent of all $\left\{ {{X_j}} \right\}_{i = 1}^N$ except one which we allocate the maximum allowed leakage to it. Finally, the obtained bounds are studied in special cases.

In this paper, we investigate the communication designs of over-the-air computation (AirComp) empowered federated learning (FL) systems considering uplink model aggregation and downlink model dissemination jointly. We first derive an upper bound on the expected difference between the training loss and the optimal loss, which reveals that optimizing the FL performance is equivalent to minimizing the distortion in the received global gradient vector at each edge node. As such, we jointly optimize each edge node transmit and receive equalization coefficients along with the edge server forwarding matrix to minimize the maximum gradient distortion across all edge nodes. We further utilize the MNIST dataset to evaluate the performance of the considered FL system in the context of the handwritten digit recognition task. Experiment results show that deploying multiple antennas at the edge server significantly reduces the distortion in the received global gradient vector, leading to a notable improvement in recognition accuracy compared to the single antenna case.

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

A private cache-aided compression problem is studied, where a server has access to a database of$N$files,$(Y_{1},\ldots,Y_{N})$, each of size$F$bits and is connected through a shared link to$K$users, each equipped with a local cache of size$MF$bits. In the placement phase, the server fills the users' caches without knowing their demands, while the delivery phase takes place after the users send their demands to the server. We assume that each file$Y_{i}$is arbitrarily correlated with a private attribute$X$, and an adversary is assumed to have access to the shared link. The users and the server have access to a shared key$W$. The goal is to design the cache contents and the delivered message$\mathcal{C}$such that the average length of$\mathcal{C}$is minimized, while satisfying:$\mathbf{i}$. The response$\mathcal{C}$does not reveal any information about$X$, i.e.,$X$and$\mathcal{C}$are independent, which corresponds to the perfect privacy constraint;$\mathbf{ii}$. User$i$is able to decode its demand,$Y_{d_{i}}$, by using$\mathcal{C}$, its local cache$Z_{i}$, and the shared key$W$. Since the database is correlated with$X$, existing codes for cache-aided delivery do not satisfy the perfect privacy condition. Indeed, we propose a variable-length coding scheme that combines privacy-aware compression with coded caching techniques. In particular, we use two-part code construction and Functional Representation Lemma. Finally, we extend the results to the case, where$X$and$\mathcal{C}$can be correlated, i.e., non-zero leakage is allowed.

Machine learning (ML) is a key technique for big-data-driven modelling and analysis of massive Internet of Things (IoT) based intelligent and ubiquitous computing. For fast-increasing applications and data amounts, distributed learning is a promising emerging paradigm since it is often impractical or inefficient to share/aggregate data to a centralized location from distinct ones. This paper studies the problem of training an ML model over decentralized systems, where data are distributed over many user devices and the learning algorithm run on-device, with the aim of relaxing the burden at a central entity/server. Although gossip-based approaches have been used for this purpose in different use cases, they suffer from high communication costs, especially when the number of devices is large. To mitigate this, incremental-based methods are proposed. We first introduce incremental block-coordinate descent (I-BCD) for the decentralized ML, which can reduce communication costs at the expense of running time. To accelerate the convergence speed, an asynchronous parallel incremental BCD (API-BCD) method is proposed, where multiple devices/agents are active in an asynchronous fashion. We derive convergence properties for the proposed methods. Simulation results also show that our API-BCD method outperforms state of the art in terms of running time and communication costs.

We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-existing notion of maximal leakage, which quantifies the amount of information leaking about a secret$X$by disclosing a single outcome of a (randomized) function calculated on$X$. Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about$X$to adversaries seeking to guess arbitrary (possibly randomized) functions of$X$, or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre- and post-processing, etc. Furthermore, we propose to view information leakage as a random variable which, in turn, allows us to regard privacy guarantees as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy,$f$-information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.

Pointwise maximal leakage (PML) is a robust and operationally meaningful privacy measure that quantifies the amount of information leaking about a secret X by disclosing a single outcome of a (randomized) function calculated on X. In this paper, we define a new privacy measure called event maximal leakage (EML), which generalizes PML by quantifying the amount of information leaking about X to arbitrary events. Then, we use our new privacy measure to define a new probabilistic privacy guarantee called (ϵ, δ)-EML. We study the data-processing and composition properties of (ϵ, δ)-EML and other privacy guarantees, where our goal is to understand whether or not they are closed under pre- and post-processing, and how they change as a result of adaptively composing privacy mechanisms.

The design of privacy mechanisms for two scenarios is studied where the private data is hidden or observable. In the first scenario, an agent observes useful data Y , which is correlated with private data X, and wants to disclose the useful information to a user. A privacy mechanism is employed to generate data U that maximizes the revealed information about Y while satisfying a privacy criterion. In the second scenario, the agent has additionally access to the private data. To this end, the Functional Representation Lemma and Strong Functional Representation Lemma are extended relaxing the independence condition and thereby allowing a certain leakage. Lower bounds on privacy-utility trade-off are derived for the second scenario as well as upper bounds for both scenarios. In particular, for the case where no leakage is allowed, our upper and lower bounds improve previous bounds.

An information theoretic privacy mechanism design problem for two scenarios is studied where the private data is either hidden or observable. In each scenario, privacy leakage constraints are considered using two different measures. In these scenarios the private data is hidden or observable. In the first scenario, an agent observes useful data Y that is correlated with private data X, and wishes to disclose the useful information to a user. A privacy mechanism is designed to generate disclosed data U which maximizes the revealed information about Y while satisfying a per-letter privacy constraint. In the second scenario, the agent has additionally access to the private data. First, the Functional Representation Lemma and Strong Functional Representation Lemma are extended by relaxing the independence condition to find a lower bound considering the second scenario. Next, lower bounds as well as upper bounds on privacy-utility trade-off are derived for both scenarios. In particular, for the case where X is deterministic function of Y, we show that our upper and lower bounds are asymptotically optimal considering the first scenario.

This paper characterizes the trade-offs between the information and energy transmission rates, the decoding error probability, and the energy outage probability in simultaneous information and energy transmission over an additive white Gaussian noise channel. The results in this paper take into account the impact of energy harvester (EH) non-linearities on the harvested energy. The analysis is carried out in the finite block-length regime with finite constellations. Improved converse and achievability bounds that account for the EH non-linearities are presented.

In this paper, we develop an efficient training beam sequence design approach for millimeter wave MISO tracking systems. We impose a discrete state Markov process assumption on the evolution of the angle of departure and introduce the maximum a posteriori criterion to track it in each beam training period. Since it is infeasible to derive an explicit expression for the resultant tracking error probability, we turn to its upper bound, which possesses a closed-form expression and is therefore leveraged as the objective function to optimize the training beam sequence. Considering the complicated objective function and the unit modulus constraints imposed by analog phase shifters, we resort to the particle swarm algorithm to solve the formulated optimization problem. Numerical results validate the superiority of the proposed training beam sequence design approach.

To leverage massive distributed data and computation resources, machine learning in the network edge is considered to be a promising technique, especially for large-scale model training. Federated learning (FL), as a paradigm of collaborative learning techniques, has obtained increasing research attention with the benefits of communication efficiency and improved data privacy. Due to the lossy communication channels and limited communication resources (e.g., bandwidth and power), it is of interest to investigate fast responding and accurate FL schemes over wireless systems. Hence, we investigate the problem of jointly optimized communication efficiency and resources for FL over wireless Internet of Things (IoT) networks. To reduce complexity, we divide the overall optimization problem into two subproblems, i.e., the client scheduling problem and the resource allocation problem. To reduce the communication costs for FL in wireless IoT networks, a new client scheduling policy is proposed by reusing stale local model parameters. To maximize successful information exchange over networks, a Lagrange multiplier method is first leveraged by decoupling variables, including power variables, bandwidth variables, and transmission indicators. Then, a linear-search-based power and bandwidth allocation method is developed. Given appropriate hyperparameters, we show that the proposed communication-efficient FL (CEFL) framework converges at a strong linear rate. Through extensive experiments, it is revealed that the proposed CEFL framework substantially boosts both the communication efficiency and learning performance of both training loss and test accuracy for FL over wireless IoT networks compared to a basic FL approach with uniform resource allocation.

Edge computing provides a promising paradigm to support the implementation of Internet of Things (IoT) by offloading tasks to nearby edge nodes. Meanwhile, the increasing network size makes it impractical for centralized data processing due to limited bandwidth, and consequently a decentralized learning scheme is preferable. Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes. For RL in a decentralized setup, edge nodes (agents) connected through a communication network aim to work collaboratively to find a policy to optimize the global reward as the sum of local rewards. However, communication costs, scalability, and adaptation in complex environments with heterogeneous agents may significantly limit the performance of decentralized RL. Alternating direction method of multipliers (ADMM) has a structure that allows for decentralized implementation and has shown faster convergence than gradient descent-based methods. Therefore, we propose an adaptive stochastic incremental ADMM (asI-ADMM) algorithm and apply the asI-ADMM to decentralized RL with edge-computing-empowered IoT networks. We provide convergence properties for the proposed algorithms by designing a Lyapunov function and prove that the asI-ADMM has$\mathcal {O}(1/k) + \mathcal {O}(1/M)$convergence rate, where$k$and$M$are the number of iterations and batch samples, respectively. Then, we test our algorithm with two supervised learning problems. For performance evaluation, we simulate two applications in decentralized RL settings with homogeneous and heterogeneous agents. The experimental results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability and can well adapt to complex IoT environments.

In this paper, we study a quantized feedback scheme to maximize the goodput of a finite blocklength communication scenario over a quasi-static fading channel. It is assumed that the receiver has perfect channel state information (CSI) and sends back the CSI to the transmitter over a resolution-limited error-free feedback channel. With this partial CSI, the transmitter is supposed to select the optimum transmission rate, such that it maximizes the overall goodput of the communication system. This problem has been studied for the asymptotic blocklength regime, however, no solution has so far been presented for finite blocklength. Here, we study this problem in two cases: with and without constraint on reliability. We first formulate the optimization problems and analytically solve them. Iterative algorithms that successfully exploit the system parameters for both cases are presented. It is shown that although the achievable maximum goodput decreases with shorter blocklengths and higher reliability requirements, significant improvement can be achieved even with coarsely quantized feedback schemes.

We consider a finite-state Discrete-Time Markov Chain (DTMC) source that can be sampled for detecting the events when the DTMC transits to a new state. Our goal is to study the trade-off between sampling frequency and staleness in detecting the events. We argue that, for the problem at hand, using Age of Information (AoI) for quantifying the staleness of a sample is conservative and therefore, study another freshness metricage penalty, which is defined as the time elapsed since the first transition out of the most recently observed state. We study two optimization problems: minimize average age penalty subject to an average sampling frequency constraint, and minimize average sampling frequency subject to an average age penalty constraint; both are Constrained Markov Decision Problems. We solve them using the Lagrangian MDP approach, where we also provide structural results that reduce the search space. Our numerical results demonstrate that the computed Markov policies not only outperform optimal periodic sampling policies, but also achieve sampling frequencies close to or lower than that of an optimal clairvoyant (non-causal) sampling policy, if a small age penalty is allowed.

We study a statistical signal processing privacy problem, where an agent observes useful data$Y$and wants to reveal the information to a user. Since the useful data is correlated with the private data$X$, the agent employs a privacy mechanism to generate data$U$that can be released. We study the privacy mechanism design that maximizes the revealed information about$Y$while satisfying a strong$\ell _{1}$-privacy criterion. When a sufficiently small leakage is allowed, we show that the optimizer distributions of the privacy mechanism design problem have a specific geometry, i.e., they are perturbations of fixed vector distributions. This geometrical structure allows us to use a local approximation of the conditional entropy. By using this approximation the original optimization problem can be reduced to a linear program so that an approximate solution for the optimal privacy mechanism can be easily obtained. The main contribution of this work is to consider a non-invertible leakage matrix with non-zero leakage. In our first example, inspired by a watermark application, we first demonstrate the accuracy of the approximation. Then, we employ different measures for utility and privacy leakage to compare the privacy-utility trade-off using our approach with other methods. In particular, we show that by allowing small leakage, significant utility can be achieved using our method compared to the case where no leakage is allowed. In the second and third examples which are based on the MNIST data set and medical applications, we illustrate the suggested design for disclosed data$U$. It has been shown that the letters of$Y$which are disclosing more information about$X$are combined (randomized) to produce a new letter of$U$.

In this work, we present polar code designs that offer a provably optimal solution for biometric identification and authentication systems under noisy enrollment for certain sources and observation channels. We consider a discrete memoryless biometric source and discrete symmetric memoryless observation channels. It is shown that the proposed polar code designs achieve the fundamental limits with privacy and secrecy constraints. Depending on how the secret keys are extracted and whether the privacy leakage rate should be close to zero, we consider four related setups, which are (i) the generated secret key system, (ii) the chosen secret key system, (iii) the generated secret key system with zero leakage, and (iv) the chosen secret key system with zero leakage. For the first two setups, (i) and (ii), the privacy level is characterized by the privacy leakage rate. For the last two setups (iii) and (iv), private keys are additionally employed to achieve close to zero privacy leakage rate. In setups (i) and (iii), it is assumed that the secret keys are generated, i.e., extracted from biometric information. While in setups (ii) and (iv), secret keys provided to the system are chosen uniformly at random from some trustful source. This work provides the first examples of fundamental limits-achieving code designs for identification and authentication. Moreover, since the code designs are based on polar codes and many existing works study low-complexity and short block-length polar coding, the proposed code designs in this work provide the code design structure and a framework for the application of biometric identification and authentication.

We propose ReDense as a simple and low complexity way to improve the performance of trained neural networks. We use a combination of random weights and rectified linear unit (ReLU) activation function to add a ReLU dense (ReDense) layer to the trained neural network such that it can achieve a lower training loss. The lossless flow property (LFP) of ReLU is the key to achieve the lower training loss while keeping the generalization error small. ReDense does not suffer from vanishing gradient problem in the training due to having a shallow structure. We experimentally show that ReDense can improve the training and testing performance of various neural network architectures with different optimization loss and activation functions. Finally, we test ReDense on some of the state-of-the-art architectures and show the performance improvement on benchmark datasets.

We propose a feature reuse approach for an existing multi-layer randomization based feedforward neural network. The feature representation is directly linked among all the necessary hidden layers. For the feature reuse at a particular layer, we concatenate features from the previous layers to construct a large-dimensional feature for the layer. The large-dimensional concatenated feature is then efficiently used to learn a limited number of parameters by solving a convex optimization problem. Experiments show that the proposed model improves the performance in comparison with the original neural network without a significant increase in computational complexity.

In a communication network, decentralized learning refers to the knowledge collaboration between the different local agents (processing nodes) to improve the local estimation performance without sharing private data. The ideal case is that the decentralized solution approximates the centralized solution, as if all the data are available at a single node, and requires low computational power and communication overhead. In this work, we propose a decentralized learning of randomization-based neural networks with asynchronous communication and achieve centralized equivalent performance. We propose an ARock-based alternating-direction-method-of-multipliers (ADMM) algorithm that enables individual node activation and one-sided communication in an undirected connected network, characterized by a doubly-stochastic network policy matrix. Besides, the proposed algorithm reduces the computational cost and communication overhead due to its asynchronous nature. We study the proposed algorithm on different randomization-based neural networks, including ELM, SSFN, RVFL, and its variants, to achieve the centralized equivalent performance under efficient computation and communication costs. We also show that the proposed asynchronous decentralized learning algorithm can outperform a synchronous learning algorithm regarding computational complexity” especially when the network connections are sparse.

In this study, Nash and Stackelberg equilibria of single-stage and multi-stage quadratic signaling games between an encoder and a decoder are investigated. In the considered setup, the objective functions of the encoder and the decoder are misaligned, there is a noisy channel between the encoder and the decoder, the encoder has a soft power constraint, and the decoder has also noisy observation of the source to be estimated. We show that there exist only linear encoding and decoding strategies at the Stackelberg equilibrium, and derive the equilibrium strategies and costs. Regarding the Nash equilibrium, we explicitly characterize affine equilibria for the single-stage setup and show that the optimal encoder (resp. decoder) is affine for an affine decoder (resp. encoder) for the multi-stage setup. On the decoder side, between the information coming from the encoder and noisy observation of the source, our results describe what should be the combining ratio of these two channels. Regarding the encoder, we derive the conditions under which it is meaningful to transmit a message.

We develop a new formulation of nonanticipative rate distortion function (NRDF) to characterize and compute multidimensional partially observable Gauss-Markov processes with MSE distortion. The key result to obtain this new formulation is a “genie-aided” design of our decoder that encapsulates both its previous decoding symbols and the past observation symbols. The new formulation is applied to a system modeled by jointly Gaussian processes to obtain the following new results. (i) An optimal characterization of a new finite dimensional optimization problem and its corresponding optimal realization. Surprisingly, the information structure of the optimal realization reveals that the decoder is in fact independent of all the previous observations symbols. (ii) For time-invariant processes, we convexify our characterization under the assumption that all matrices commute by pairs and derive strong structural properties for the involved matrices for which our assumption is valid. (iii) We solve the convex program using KKT conditions to obtain a solution via a general reverse-waterfilling algorithm which demonstrates that the distortion allocation at each dimension can be computed by a third-degree polynomial equation.

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In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the $\beta$-VAE, the VIB, or the nonlinear IB.

We study the problem of rate-distortion equivocation with side-information only available at the decoder when an independent private random key is shared between the sender and the receiver. The sender compresses the sequence, and the receiver reconstructs it such that the average distortion between the source and the output is limited. The equivocation is measured at an eavesdropper that intercepts the source encoded message, utilizing side-information correlated with the source and the side-information at the decoder. We have derived the entire achievable rate-distortion-equivocation region for this problem.

We generalize the problem of controlling the interference created to an external observer while communicating over a discrete memoryless channel (DMC) which was studied in [1]. In particular, we consider the scenario where the transmission is established over a compound DMC channel with unknown state at both the encoder and the decoder. Depending on the exact state s of the channel, we ask for a different level of average precision Δson the establishment of the interference coordination with the external observer. For this setup, we fully characterize the capacity region.

Most methods for publishing data with privacy guarantees introduce randomness into datasets which reduces the utility of the published data. In this paper, we study the privacy-utility tradeoff by taking maximal leakage as the privacy measure and the expected Hamming distortion as the utility measure. We study three different but related problems. First, we assume that the data-generating distribution (i.e., the prior) is known, and we find the optimal privacy mechanism that achieves the smallest distortion subject to a constraint on maximal leakage. Then, we assume that the prior belongs to some set of distributions, and we formulate a min-max problem for finding the smallest distortion achievable for the worst-case prior in the set, subject to a maximal leakage constraint. Lastly, we define a partial order on privacy mechanisms based on the largest distortion they generate. Our results show that when the prior distribution is known, the optimal privacy mechanism fully discloses symbols with the largest prior probabilities, and suppresses symbols with the smallest prior probabilities. Furthermore, we show that sets of priors that contain more uniform distributions lead to larger distortion, while privacy mechanisms that distribute the privacy budget more uniformly over the symbols create smaller worst-case distortion. A full version of this paper is accessible at: https://arxiv.org/pdf/2105.01033.pdf

In this paper, we study the connection between entropic optimal transport and entropy power inequality (EPI). First, we prove an HWI-type inequality making use of the infinitesimal displacement convexity of optimal transport map. Second, we derive two Talagrand-type inequalities using the saturation of EPI that corresponds to a numerical term in our expression. We evaluate for a wide variety of distributions this term whereas for Gaussian and i.i.d. Cauchy distributions this term is found in explicit form. We show that our results extend previous results of Gaussian Talagrand inequality for Sinkhorn distance to the strongly log-concave case.

In this work, we present a polar code design that offers a provably optimal solution for biometric identification systems allowing authentication under noisy enrollment with secrecy and privacy constraints. Binary symmetric memoryless source and channels are considered. It is shown that the proposed polar code design achieves the fundamental limits and satisfies more stringent secrecy constraints than previously in the literature. The proposed polar code design provides the first example of a code design that achieves the fundamental limits involving both identification and authentication.

This work presents several expected generalization error bounds based on the Wasserstein distance. More specifically, it introduces full-dataset, single-letter, and random-subset bounds, and their analogous in the randomized subsample setting from Steinke and Zakynthinou [1]. Moreover, when the loss function is bounded and the geometry of the space is ignored by the choice of the metric in the Wasserstein distance, these bounds recover from below (and thus, are tighter than) current bounds based on the relative entropy. In particular, they generate new, non-vacuous bounds based on the relative entropy. Therefore, these results can be seen as a bridge between works that account for the geometry of the hypothesis space and those based on the relative entropy, which is agnostic to such geometry. Furthermore, it is shown how to produce various new bounds based on different information measures (e.g., the lautum information or several $f$-divergences) based on these bounds and how to derive similar bounds with respect to the backward channel using the presented proof techniques.

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones, and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to analyze information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multiagent system with data locally processed via distributed edge nodes. A class of minibatch stochastic alternating direction method of multipliers (ADMMs) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed learning systems, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding-based stochastic incremental ADMM is investigated. Given an appropriate minibatch size, we show that the minibatch stochastic ADMM-based method converges in a rate of O(1/√k), where k denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication efficient, rapidly responding, and robust in the presence of straggler nodes compared with state-of-the-art algorithms.

In this article, we study the problem of latency and reliability trade-off in ultra-reliable low-latency communication (URLLC) in the presence of decoding complexity constraints. We consider linear block encoded codewords transmitted over a binary-input AWGN channel and decoded with order-statistic (OS) decoder. We first investigate the performance of OS decoders as a function of decoding complexity and propose an empirical model that accurately quantifies the corresponding trade-off. Next, a consistent way to compute the aggregate latency for complexity constrained receivers is presented, where the latency due to decoding is also included. It is shown that, with strict latency requirements, decoding latency cannot be neglected in complexity constrained receivers. Next, based on the proposed model, several optimization problems, relevant to the design of URLLC systems, are introduced and solved. It is shown that the decoding time has a drastic effect on the design of URLLC systems when constraints on decoding complexity are considered. Finally, it is also illustrated that the proposed model can closely describe the performance versus complexity trade-off for other candidate coding solutions for URLLC such as tail-biting convolutional codes, polar codes, and low-density parity-check codes.

Full-duplex communications have the potential to almost double the spectral efficiency. To realize such a potentiality, the signal separation at base station’s antennas plays an essential role. This article addresses the fundamentals of such separation by proposing a new smart antenna architecture that allows every antenna to be either shared or separated between uplink and downlink transmissions. The benefits of such architecture are investigated by an assignment problem to optimally assign antennas, beamforming and power to maximize the weighted sum spectral efficiency. We propose a near-to-optimal solution using block coordinate descent that divides the problem into assignment problems, which are NP-hard, a beamforming and power allocation problems. The optimal solutions for the beamforming and power allocation are established while near-to-optimal solutions to the assignment problems are derived by semidefinite relaxation. Numerical results indicate that the proposed solution is close to the optimum, and it maintains a similar performance for high and low residual self-interference powers. With respect to the usually assumed antenna separation technique and half-duplex transmission, the sum spectral efficiency gains increase with the number of antennas. We conclude that our proposed smart antenna assignment for signal separation is essential to realize the benefits of multiple antenna full-duplex communications.

Machine learning models are known to memorize the unique properties of individual data points in a training set. This memorization capability can be exploited by several types of attacks to infer information about the training data, most notably, membership inference attacks. In this paper, we propose an approach based on information leakage for guaranteeing membership privacy. Specifically, we propose to use a conditional form of the notion of maximal leakage to quantify the information leaking about individual data entries in a dataset, i.e., the entrywise information leakage. We apply our privacy analysis to the Private Aggregation of Teacher Ensembles (PATE) framework for privacy-preserving classification of sensitive data and prove that the entrywise information leakage of its aggregation mechanism is Schur-concave when the injected noise has a log-concave probability density. The Schur-concavity of this leakage implies that increased consensus among teachers in labeling a query reduces its associated privacy cost. Finally, we derive upper bounds on the entrywise information leakage when the aggregation mechanism uses Laplace distributed noise.

In this paper, we study a stochastic disclosure control problem using information-theoretic methods. The useful data to be disclosed depend on private data that should be protected. Thus, we design a privacy mechanism to produce new data which maximizes the disclosed information about the useful data under a strong χ2-privacy criterion. For sufficiently small leakage, the privacy mechanism design problem can be geometrically studied in the space of probability distributions by a local approximation of the mutual information. By using methods from Euclidean information geometry, the original highly challenging optimization problem can be reduced to a problem of finding the principal right-singular vector of a matrix, which characterizes the optimal privacy mechanism. In two extensions we first consider a scenario where an adversary receives a noisy version of the user's message and then we look for a mechanism which finds U based on observing X, maximizing the mutual information between U and Y while satisfying the privacy criterion on U and Z under the Markov chain (Z, Y)-X-U.

In this paper, we study fundamental trade-offs in privacy-preserving biometric identification systems with noisy enrollment. The proposed identification systems include helper data, secret keys, and private keys. Helper data are stored in a public database and used for identification. Secret keys are either stored in a secure database or provided to the user, and can be used in a next step, e.g. for authentication. Private keys are provided by users, and are also used for identification. In this paper, we impose a noisy enrollment channel and an arbitrarily small privacy and secrecy leakage rate. We characterize the optimal trade-off among the identification, secret key, private key, and helper data rates. Depending on how secret keys are produced, we study two cases of the proposed privacy-preserving identification systems, where the secret keys are generated and chosen respectively. By introducing private keys, it is shown that the identification system achieves close to zero privacy leakage rate in both generated and chosen secret key settings. The results also show that the identification rate and the secret key rate can be enlarged by increasing the private key rate. This work provides a framework for analyzing privacy-preserving identification systems and an insight on the design of optimal systems.

In this work, we study the generalization capability of algorithms from an information-theoretic perspective. It has been shown that the expected generalization error of an algorithm is bounded from above by a function of the relative entropy between the conditional probability distribution of the algorithm’s output hypothesis, given the dataset with which it was trained, and its marginal probability distribution. We build upon this fact and introduce a mathematical formulation to obtain upper bounds on this relative entropy. Assuming that the data is discrete, we then develop a strategy using this formulation, based on the method of types and typicality, to find explicit upper bounds on the generalization error of stable algorithms, i.e., algorithms that produce similar output hypotheses given similar input datasets. In particular, we show the bounds obtained with this strategy for the case of$\epsilon $-DP and$\mu $-GDP algorithms.

We study hypothesis testing problems with fixed compression mappings and with user-dependent compression mappings to decide whether or not an observation sequence is related to one of the users in a database, which contains compressed versions of previously enrolled users' data. We first provide the optimal characterization of the exponent of the probability of the second type of error for the fixed compression mappings scenario when the number of users in the database grows exponentially. We then establish operational equivalence relations between the Wyner-Ahlswede-Körner network, the single-user hypothesis testing problem, the multi-user hypothesis testing problem with user-dependent compression mappings and the identification systems with user-dependent compression mappings. These equivalence relations imply the strong converse and exponentially strong converse for the multi-user hypothesis testing and the identification systems both with user-dependent compression mappings. Finally they also show how an identification scheme can be turned into a multi-user hypothesis testing scheme with an explicit transfer of rate and error probability conditions and vice versa.

High-dimensional identification systems consisting of two groups of users in the presence of statistical uncertainties are considered in this work. The task is to design enrollment mappings to compress users' information and an identification mapping that combines the stored information in the database and an observation to estimate the underlying user index. The compression-identification trade-off regions are established for the compound, extended compound, general and mixture settings. It is shown that several settings admit the same compression-identification trade-offs. We then study a connection between the Wyner-Ahlswede-Körner network and the identification setting. It indicates that a strong converse for the WAK network is equivalent to a strong converse for the identification setting. Finally, we present strong converse arguments for the discrete identification setting that are extensible to the Gaussian scenario.

We explore the use of traditional and contemporary hidden Markov models (HMMs) for sequential physiological data analysis and sepsis prediction in preterm infants. We investigate the use of classical Gaussian mixture model based HMM, and a recently proposed neural network based HMM. To improve the neural network based HMM, we propose a discriminative training approach. Experimental results show the potential of HMMs over logistic regression, support vector machine and extreme learning machine.

We design a ReLU-based multilayer neural network to generate a rich high-dimensional feature vector. The feature guarantees a monotonically decreasing training cost as the number of layers increases. We design the weight matrix in each layer to extend the feature vectors to a higher dimensional space while providing a richer representation in the sense of training cost. Linear projection to the target in the higher dimensional space leads to a lower training cost if a convex cost is minimized. An ℓ2-norm convex constraint is used in the minimization to improve the generalization error and avoid overfitting. The regularization hyperparameters of the network are derived analytically to guarantee a monotonic decrement of the training cost and therefore, it eliminates the need for cross-validation to find the regularization hyperparameter in each layer.

In this work, we exploit an asynchronous computing framework namely ARock to learn a deep neural network called self-size estimating feedforward neural network (SSFN) in a decentralized scenario. Using this algorithm namely asynchronous decentralized SSFN (dSSFN), we provide the centralized equivalent solution under certain technical assumptions. Asynchronous dSSFN relaxes the communication bottleneck by allowing one node activation and one side communication, which reduces the communication overhead significantly, consequently increasing the learning speed. We compare asynchronous dSSFN with traditional synchronous dSSFN in the experimental results, which shows the competitive performance of asynchronous dSSFN, especially when the communication network is sparse.

Several recent works in communication systems have proposed to leverage the power of neural networks in the design of encoders and decoders. In this approach, these blocks can be tailored to maximize the transmission rate based on aggregated samples from the channel. Motivated by the fact that, in many communication schemes, the achievable transmission rate is determined by a conditional mutual information term, this paper focuses on neural-based estimators for this information-theoretic quantity. Our results are based on variational bounds for the KL-divergence and, in contrast to some previous works, we provide a mathematically rigorous lower bound. However, additional challenges with respect to the un-conditional mutual information emerge due to the presence of a conditional density function which we address here.

The problem of communication in Rayleigh fading channels with estimated channel state information at the receiver (CSIR) is investigated. Based on a related hypothesis testing problem in the Neyman-Pearson formulation, a non-asymptotic- in the codeword block-length-converse on the maximal coding rate is derived. The bound summarizes succinctly the effect of various system parameters that include the length of channel coherence interval, the length of the training and data intervals and the power allocated to training and data transmission. The bound is also studied in the asymptotic-in the codeword blocklength-regime and a particularly simple, non-trivial upper bound on the ergodic capacity of Raleigh fading channels with estimated CSIR is obtained. Finally, a second-order asymptotic expansion of the non-asymptotic converse is provided, which can be very useful in the study of latency-constrained communication systems.

We design a low complexity decentralized learning algorithm to train a recently proposed large neural network in distributed processing nodes (workers). We assume the communication network between the workers is synchronized and can be modeled as a doubly-stochastic mixing matrix without having any master node. In our setup, the training data is distributed among the workers but is not shared in the training process due to privacy and security concerns. Using alternating-direction-method-of-multipliers (ADMM) along with a layer-wise convex optimization approach, we propose a decentralized learning algorithm which enjoys low computational complexity and communication cost among the workers. We show that it is possible to achieve equivalent learning performance as if the data is available in a single place. Finally, we experimentally illustrate the time complexity and convergence behavior of the algorithm.

We consider a three-node network, in which two agents wish to communicate over a noisy channel, while controlling the distribution observed by a third external agent. We use strong coordination to constrain the distribution, and we provide a complete characterization of the "remote strong coordination and reliable communication" region.

The alternating direction method of multipliers (ADMM) has recently been recognized as a promising approach for large-scale machine learning models. However, very few results study ADMM from the aspect of communication costs, especially jointly with privacy preservation. We investigate the communication efficiency and privacy of ADMM in solving the consensus optimization problem over decentralized networks. We first propose incremental ADMM (I-ADMM), the updating order of which follows a Hamiltonian cycle. To protect privacy for agents against external eavesdroppers, we investigate I-ADMM with privacy preservation, where randomized initialization and step size perturbation are adopted. Using numerical results from simulations, we demonstrate that the proposed I-ADMM with step size perturbation can be both communication efficient and privacy preserving.

In this work, we unify several expected generalization error bounds based on random subsets using the framework developed by Hellström and Durisi. First, we recover the bounds based on the individual sample mutual information from Bu et al. and on a random subset of the dataset from Negrea et al. Then, we introduce their new, analogous bounds in the randomized subsample setting from Steinke and Zakynthinou, and we identify some limitations of the framework. Finally, we extend the bounds from Haghifam et al. for Langevin dynamics to stochastic gradient Langevin dynamics and we refine them for loss functions with potentially large gradient norms.

We study an information-theoretic privacy problem, where an agent observes useful data Y and wants to reveal the information to a user. Since the useful data is correlated with sensitive data X, the agent employs a privacy mechanism to produce data U that can be disclosed. Thus, we study the privacy mechanism design that maximizes the revealed information about Y while satisfying an ℓ1-privacy criterion under the Markov chain X-Y -U. When a sufficiently small leakage is allowed, we show that the optimizer of the design problem has a specific structure which allows us to use a local approximation of mutual information. More specifically, we show that the optimizer vectors are perturbations of fixed distributions. By using this approximation the original optimization problem can be reduced to a linear programming problem and an approximate solution for privacy mechanism design can be obtained.

Wireless content caching in cellular networks is an efficient way to reduce the service delay and alleviate backhaul pressure. For the benefits of sharing spectral and storage resources, clustering in cached networks has recently attracted significant research interests. Meanwhile, since the multimedia content (e.g., video) of caching networks may require a huge transmission rates, millimeter wave (mmWave) communication is considered to be an efficient transmission scheme for cache-enabled networks. We investigate the ergodic rate and average service delay for typical user terminal (UT) in the clustered cache-enabled small cell networks (SCN) and ultra dense networks (UDN) with mmWave channels. In SCN, each cluster consists of cache-enabled UTs, and in the UDN a cluster is formed by cache-enabled UTs and small base stations (SBSs) with non-uniform caching capacity. The clusters are assumed to be discs and content sharing is only possible within clusters through mmWave device-to-device (D2D) tier and SBS tier communications. With stochastic geometry methods, the distributions of content sharing distance and signal-to-interference-noise-ratio (SINR) of typical UT in a cluster are derived for both SCN and UDN scenarios. To minimize the average service delay in high SINR region, we provide an algorithm to jointly optimize caching scheme for SBSs and UTs. By simulations, we validate our theoretical analysis and the performance of proposed caching scheme. The numerical results also show that there exists best radius in the design of cluster for UDNs.

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. In the enrollment phase, users' data are stored into the database in two layers. In the identification phase an observer obtains an observation, which originates from an unknown user in the enrolled database through a memoryless channel. The observation is sent for processing in two stages. In the first stage, the observation is pre-processed, and the result is then used in combination with the stored first layer information in the database to output a list of compatible users to the second stage. Then the second step uses the information of users contained in the list from both layers and the original observation sequence to return the exact user identity and a corresponding reconstruction sequence. The rate-distortion regions are characterized for both discrete and Gaussian scenarios. Specifically, for a fixed list size and distortion level, the compression-identification trade-off in the Gaussian scenario results in three different operating cases characterized by three auxiliary functions. While the choice of the auxiliary random variable for the first layer information is essentially unchanged when the identification rate is varied, the second one is selected based on the dominant function within those three. Due to the presence of a mixture of discrete and continuous random variables, the proof for the Gaussian case is highly non-trivial, which makes a careful measure theoretic analysis necessary. In addition, we study a connection of the previous setting to a two observer identification and a related problem with a lower bound for the list size, where the latter is motivated from privacy concerns.

We study whether using non-orthogonal multiple access (NOMA) in the uplink of a mobile network can reduce the queueing delay compared to orthogonal multiple access (OMA) when the system requires communications at very low latency and high reliability. We first consider an ideal system model with perfect channel state information (CSI) at the transmitter and long codewords, where we determine the optimal decoding orders when the decoder uses successive interference cancellation (SIC) and derive closed-form expressions for the optimal rate when joint decoding is used. While joint decoding performs well even under tight delay constraints, NOMA with SIC decoding often performs worse than OMA. For low-latency systems, we must also consider the impact of finite-length channel coding, as well as rate adaptation based imperfect CSI. We derive closed-form approximations for the corresponding outage or error probabilities and find that those effects create a larger performance penalty for NOMA than for OMA. Thus, NOMA with SIC decoding may often be unsuitable for low-latency systems.

To make the system available at low-cost, millimeter-wave (mmWave) multiple-input multiple-output (MIMO) architectures employ analog arrays, which are driven by a limited number of radio frequency (RF) chains. One primary challenge of using large hybrid analog-digital arrays is that the digital baseband cannot directly access the signal to/from each antenna. To address this limitation, recent research has focused on retransmissions, iterative precoding, and subspace decomposition methods. Unlike these approaches that exploited the channel's low-rank, in this work we exploit the sparsity of the received signal at both the transmit/receive antennas. While the signal itself is de facto dense, it is well-known that most signals are sparse under an appropriate choice of basis. By delving into the structured compressive sensing (CS) framework and adapting them to variants of the mmWave hybrid architectures, we provide methodologies to recover the analog signal at each antenna from the (low-dimensional) digital signal. Moreover, we characterizes the minimal numbers of measurement and RF chains to provide this recovery, with high probability. We discuss their applications to common variants of the hybrid architecture. By leveraging the inherent sparsity of the received signal, our analysis reveals that a hybrid MIMO system can be "turned into" a fully digital one: the number of needed RF chains increases logarithmically with the number of antennas.

Finding a dataset of minimal cardinality to characterize the optimal parameters of a model is of paramount importance in machine learning and distributed optimization over a network. This paper investigates the compressibility of large datasets. More specifically, we propose a framework that jointly learns the input-output mapping as well as the most representative samples of the dataset (sufficient dataset). Our analytical results show that the cardinality of the sufficient dataset increases sub-linearly with respect to the original dataset size. Numerical evaluations of real datasets reveal a large compressibility, up to 95%, without a noticeable drop in the learnability performance, measured by the generalization error.

We investigate the behavior of the mutual information between two Boolean functions of correlated binary strings. The covariance of these functions is found to be a crucial parameter in the aforementioned mutual information. We then apply this result in the analysis of a specific privacy problem where a user observes a random binary string. Under particular conditions, we characterize the optimal strategy for communicating the outcomes of a function of said string while preventing to leak any information about a different function.

In this paper we revisit the connections of the distributed hypothesis testing against independence (HT) problem with the Wyner-Ahlswede-Korner (WAK) problem and thë identification systems (ID). We show that the strong converse for the WAK problem is equivalent to the strong converse for the HT problem via constructive and nonconstructive transformations of codes. As another consequence of the transformation we provide a new exponentially strong converse equivalence statement. Applying the same idea, we prove a new result that the -identification capacity of the ID problem is equal to the maximum ε-exponent of type II of error in the HT problem when both side compression is allowed.

The capacity of symmetric private information retrieval (PIR) with N servers and K messages, each coded by an (N, M)-MDS code has been characterized as CMDS-SPIR= 1- M/N . A critical assumption for this result is that the randomness is similarly coded by an (N, M)-MDS code, i.e., the code parameters of the messages and randomness are matched. In this work, we are interested in the mismatched case, and as a preliminary result, we establish the capacity of the mismatched MDS coded symmetric PIR (SPIR) problem under an extreme setting, where the messages are coded by an (N, M)-MDS code and the randomness is replicated (i.e., coded by an (N,1)MDS code). The capacity is shown to be Cmis-MDS-SPIR= (1 - 1/N) · (1+M-1/N (1 + M/N + ⋯ + (M/N)K-2))-1. Interestingly, Cmis-MDS-SPIR> CMDS-SPIR, so mismatched coded randomness (with more redundancy) is strictly beneficial. Further, mismatched SPIR exhibits properties that are similar to PIR.

We consider the problem of synthesizing joint distributions of signals and actions over noisy channels in the finite length regime. For a fixed blocklength n and an upper bound on the distance ε, a coding scheme is proposed such that the induced joint distribution is ε-close in L1distance to a target i.i.d. distribution. The set of achievable target distributions and rate for asymptotic strong coordination can be recovered from the main result of this paper by having n that tends to infinity.

In this paper, we obtain generic bounds on the variances of estimation and prediction errors in time series analysis via an information-theoretic approach. It is seen in general that the error bounds are determined by the conditional entropy of the data point to be estimated or predicted given the side information or past observations. Additionally, we discover that in order to achieve the prediction error bounds asymptotically, the necessary and sufficient condition is that the “innovation” is asymptotically white Gaussian. When restricted to Gaussian processes and 1-step prediction, our bounds are shown to reduce to the Kolmogorov-Szegö formula and Wiener-Masani formula known from linear prediction theory.

We introduce the concept of achievable cumulative rate distribution functions (CRDF) to characterize sequentially encoding processes that ensure a lossless or lossy reconstruction subject to an average distortion using a non-causal decoder. Utilizing tools from majorization theory, we derive necessary and sufficient conditions on the CRDF for a given IID source. It turns out that the optimal achievable distortion level can be adequately characterized by the concave-hull of the CRDF.

We study the problem of empirical coordination subject to a fidelity criterion for a general set-up. We prove a result which indicates a strong connection between our frame-work and the framework of empirical coordination developed in [1]. It turns out that when we design codes that achieve empirical coordination according to a given distribution and subject to the fidelity criterion, it is sufficient to consider codes that produce actions of the same joint type for a class of types which is close enough to our desired distribution is some sense.

We use stochastic network calculus to investigate the delay performance of a multiuser MISO system with zero-forcing beamforming. First, we consider ideal assumptions with long codewords and perfect CSI at the transmitter, where we observe a strong channel hardening effect that results in very high reliability with respect to the maximum delay of the application. We then study the system under more realistic assumptions with imperfect CSI and finite blocklength channel coding. These effects lead to interference and to transmission errors, and we derive closed-form approximations for the resulting error probability. Compared to the ideal case, imperfect CSI and finite length coding cause massive degradations in the average transmission rate. Surprisingly, the system nevertheless maintains the same qualitative behavior as in the ideal case: as long as the average transmission rate is higher than the arrival rate, the system can still achieve very high reliability with respect to the maximum delay.

We consider the problem of private information retrieval (FIR) and symmetric private information retrieval (SFIR) from replicated databases with colluding servers, in the presence of Byzantine adversaries and eavesdroppers. Specifically, there are K messages replicatively stored at N databases. A user wants to retrieve one message by communicating with the databases, without revealing the identity of the message retrieved. For T-colluding databases, any T out of N databases may communicate their interactions with the user to guess the identity of the requested message. We consider the situation where the communication system can be vulnerable to attachers, namely, there is an adversary in the system that can tap in on or even try to corrupt the communication. The capacity is defined as the maximum number of information bits of the desired message retrieved per downloaded bit. For SPIR, it is further required that the user learns nothing about the other K - 1 messages in the database. Three types of adversaries are considered: a Byzantine adversary who can overwrite the transmission of any B servers to the user; a passive eavesdropper who can tap in on the incoming and outgoing transmissions of any E servers; and a combination of both -an adversary who can tap in on a set of any E nodes, and overwrite the transmission of a set of any B nodes. The problems of SPIR with colluding servers and the three types of adversaries are named T-BSPIR, T-ESPIR and T-BESPIR, respectively. We derive the capacities of the three secure SPIR problems. The results resemble those of secure network coding problems with adversaries and eavesdroppers. The capacity of T-colluding PIR with Byzantine adversaries is characterized in [1]. In this work, we consider T-colluding PIR with an eavedropper (named T-EPIR). We derive the T-EPIR capacity when E ≥ T; for the case where E ≤ T, we find an outer bound (converse bound) and an inner bound (achievability) on the optimal achievable rate.

A user wants to retrieve a file from a database without revealing the identity of the file retrieved to the operator of the database (server), which is known as the problem of private information retrieval (PIR). If it is further required that the user obtains no information about the other files in the database, the concept of symmetric PIR (SPIR) is introduced to guarantee privacy for both parties. For SPIR, the server(s) need to access some randomness independent of the database, to protect the content of undesired files from the user. The information-theoretic capacity of SPIR is defined as the maximum number of information bits of the desired file retrieved per downloaded bit. In this paper, the problem of SPIR is studied for a distributed storage system with N servers (nodes), where all data (including the files and the randomness) are stored in a distributed way. Specifically, the files are stored by an (N, KC)-MDS storage code. The randomness is distributedly stored such that any KCservers store independent randomness information. We consider two scenarios regarding to the ability of the storage nodes to cooperate. In the first scenario considered, the storage nodes do not communicate or collude. It is shown that the SPIR capacity for MDS-coded storage (hence called MDS-SPIR) is 1 - KC/N, when the amount of the total randomness of distributed nodes (unavailable at the user) is at least KC/N-KCtimes the file size. Otherwise, the MDS-SPIR capacity equals zero. The second scenario considered is the T-colluding SPIR problem (hence called TSPIR). Specifically, any T out of N servers may collude, that is, they may communicate their interactions with the user to guess the identity of the requested file. In the special case with KC= 1, i.e., the database is replicated at each node, the capacity of TSPIR is shown to be 1 - T/N, with the ratio of the total randomness size relative to the file size be at least T/N-T. For TSPIR with MDS-coded storage (called MDS-TSPIR for short), when restricted to schemes with additive randomness where the servers add the randomness to the answers regardless of the queries received, the capacity is proved to equal 1 - KC+T-1/N, with total randomness at least KC+T-1/N-KC-T+1 times the file size. The MDS-TSPIR capacity for general schemes remains an open problem.

We consider the problem of private information retrieval (PIR) with colluding servers and eavesdroppers (abbreviated as ETPIR). The ETPIR problem is comprised of K messages and N servers where each server stores all K messages, a user who wants to retrieve one of the K messages without revealing the desired message index to any set of T colluding servers, and an eavesdropper who can listen to the queries and answers of any E servers but is prevented from learning any information about the messages. The information theoretic capacity of ETPIR is defined to be the maximum number of desired message symbols retrieved privately per information symbol downloaded. We show that the capacity of ETPIR is C = (1 - (E/N))(1 + (T - E/N - E) + · · · + ((T - E/N - E))K-1)-1 when E <; T, and C = (1 - (E/N)) when E ≥ T. To achieve the capacity, the servers need to share a common random variable (independent of the messages), and its size must be at least (E/N) · (1/C) symbols per message symbol. Otherwise, with less amount of shared common randomness, ETPIR is not feasible and the capacity reduces to zero. An interesting observation is that the ETPIR capacity expression takes different forms in two regimes. When E <; T, the capacity equals the inverse of a sum of a geometric series with K terms and decreases with K; this form is typical for capacity expressions of PIR. When E ≥ T, the capacity does not depend on K, a typical form for capacity expressions of SPIR (symmetric PIR, which further requires data-privacy, i.e., the user learns no information about other undesired messages); the capacity does not depend on T either. In addition, the ETPIR capacity result includes multiple previous PIR and SPIR capacity results as special cases.

With large available bandwidth, millimeter wave (mm-wave) communications have attracted considerable research interests because of their potential to achieve multi-giga bps rates. However, one of the main challenges for mm-wave is high pathloss. To address this problem, full-duplex (FD) relaying can be used to increase the effective transmission distance and the spectral efficiency. Thus, studying the application of FD relaying in mm-wave communications will be of value. However, one of the main challenges in FD mm-wave relaying is the residual self-interference (SI), which includes line-of-sight (LOS) and non-LOS parts. To eliminate the SI and improve the spectral efficiency, we propose an orthogonal matching pursuit-based SI-cancellation precoding algorithm. Then, we propose an energy consumption model and analyze the energy efficiency performance. We formulate the joint spectral efficiency and energy efficiency optimization problem, which can be transformed into a convex problem. The numerical results show that the FD precoding scheme can effectively eliminate the residual SI and achieve approximately twice the spectral efficiency of the conventional half-duplex system. We also show that in low-spectral-efficiency regions, the optimal energy efficiency can be achieved, but the achievable energy efficiency will decrease in high-spectral-efficiency regions.

In this work we consider a two-stage identification problem with pre-processing where the users' data and observation are Gaussian distributed. In the first stage the processing unit returns a list of compatible users using the information from the first storage layer and the pre-processed observation. Then, the refined search is performed in the second stage where the processing unit returns the exact user's identity and a corresponding reconstruction sequence. We provide a complete rate-distortion trade-off for the Gaussian setting.

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.

In this paper, we present some contributions from our recent investigation. We address the open issue of interference coordination for sub-28 GHz millimeter-wave communication, by proposing fast- converging coordination algorithms, for dense multi-user multi-cell networks. We propose to optimize a lower bound on the network sum-rate, after investigating its tightness. The bound in question results in distributed optimization, requiring local information at each base station and user. We derive the optimal solution to the transmit and receive filter updates, that we dub non-homogeneous waterfilling, and show its convergence to a stationary point of the bound. We also underline a built-in mechanism to turn-off data streams with low SINR, and allocate power to high-SNR streams. This `stream control' is a at the root of the fast-converging nature of the algorithm. Our numerical result conclude that low- overhead coordination offers large gains, for dense sub-28 GHz systems. These findings bear direct relevance to the ongoing discussions around 5G New Radio.

We investigate the problem of sharing (communi-cating) the outcomes of a memoryless source when some of its statistical parameters must be kept private. Privacy is measured in terms of the Bayesian statistical risk according to a desired loss function while the quality of the reconstruction is measured by the average per-letter distortion. We first bound -uniformly over all possible estimators- the expected risk from below. This information-theoretic bound depends on the mutual information between the parameters and the disclosed (noisy) samples. We then present an achievable scheme that guarantees an upper bound on the average distortion while keeping the risk above a desired threshold, even when the length of the sample increases.

We study the high-dimensional identification systems under the presence of statistical uncertainties. The task is to design mappings for enrollment and identification purposes. The identification mapping compresses users' information then stores the index in the corresponding position in a database. The identification mapping combines the information in the database and the observation which originates randomly from an enrolled user to produce an estimate of the underlying user index. We study two scenarios. Users' data are generated from the same unknown distribution while the observation channel is also subjected to uncertainty. Each user's data are generated iid from the distribution corresponding to its own state, while the observation channel is known. We provide an achievable compression-identification trade-off for the first and second settings considering both discrete and continuous cases. In the discrete scenario, the described regions are also the correspondingly complete characterizations.

The problem of private information retrieval (PIR) is to retrieve one message out of K messages replicated at N databases, without revealing the identity of the desired message to the databases. We consider the problem of PIR with colluding databases and eavesdroppers, named ETPIR. Specifically, any T out of N databases may collude, that is, they may communicate their interactions with the user to guess the identity of the requested message. An eavesdropper is curious to know the content of the messages and can tap in on the incoming and outgoing transmissions of any E databases with the user. The databases share some common randomness unknown to the eavesdropper and the user, and use the common randomness to generate the answers, such that the eavesdropper can learn no information about the K messages. The capacity is defined as the maximum retrieval rate, i.e. the number of information bits of the desired message retrieved per downloaded bit. In our previous work [1], we found that when E ≥ T, the capacity equals 1-[E/N]. In this work, we focus on the case when E ≤ T. We find an outer bound (converse bound) and an inner bound (achievability) on the optimal achievable rate. The gap between the derived inner and outer bounds vanishes as the number of messages K tends to infinity.

We study a hypothesis testing problem to decide whether or not an observ!ation sequence is related to one of users in a database which contains compressed versions of users' data. Our main interest lies on the characterization of the exponent of the probability of the second kind of error when the number of users in the database grows exponentially. We show a lower bound on the error exponent and identify special cases where the bound is tight. Next, we study the ε-achievable error exponent and show a sub-region where the lower bound is tight.

The capacity of symmetric private information retrieval with K messages, N servers (out of which any T may collude) and an omniscient Byzantine adversary (who can corrupt any B answers) is shown to be (1-) T+2B/N [1], under the requirement of zero probability of error. In this work, we show that by weakening the adversary slightly (either providing secret low rate channels between the servers and the user, or limiting the observation of the adversary), and allowing vanishing probability of error, the capacity increases to (1-) T+2B/N.

With the rise of critical machine-to-machine applications, next generation wireless communication systems must meet challenging requirements with respect to latency and reliability. A key question in this context relates to channel state estimation, which allows the transmitter to adapt the code rate to the channel state. In this paper, we characterize the tradeoff between the training sequence length and data codeword length: shorter channel estimation leaves more time for the payload transmission but reduces the estimation accuracy and causes more decoding errors. Using lower coding rates can mitigate this effect, but may result in a higher backlog of data at the transmitter. In order to optimize the training sequence length and the rate adaptation scheme with respect to the delay performance, we employ queuing analysis on top of accurate models of the physical layer. We obtain an analytically tractable solution to the problem by deriving a closed-form approximation for the decoding error probability due to imperfect channel knowledge and finite-blocklength channel coding. The optimized training sequence length and rate adaptation strategy can reduce the delay violation probability by an order of magnitude, compared with suboptimal strategies that do not consider the delay constraints.

Interference channels with confidential messages are studied under strong secrecy constraints, based on the framework of channel resolvability theory. It is shown that if the random binning rate for securing a confidential message is above the resolution of its corresponding wiretapped channel, strong secrecy can be guaranteed. The information-spectrum method introduced by Han and Verdú is generalized to an arbitrary interference channel to obtain a direct channel resolvability result as a first step. For stationary and memoryless channels with discrete output alphabets, the results show that the achievable rates under weak and strong secrecy constraints are the same. This result is then generalized to channels with continuous output alphabets by deriving a reverse direction of Pinsker's inequality to bound the secrecy measure from above by a function of the variational distance of relevant distributions. As an application, Gaussian interference channels are studied in which the agreement between the best known weak and strong secrecy rate regions also appear. Following the footsteps of Csiszár, Hayashi and of Bloch and Laneman, these results provide further evidence that channel resolvability is a powerful and general framework for strong secrecy analysis in multiuser networks.

In this paper, we investigate the problem of mitigating interference between so-called antenna domains of a cloud radio access network (C-RAN). In contrast to previous work, we turn to an approach utilizing primarily the optimal assignment of users to central processors in a C-RAN deployment. We formulate this user assignment problem as an integer optimization problem and propose an iterative algorithm for obtaining a solution. Motivated by the lack of optimality guarantees on such solutions, we opt to find lower bounds on the problem and the resulting interference leakage in the network. We thus derive the corresponding Dantzig-Wolfe decomposition, formulate the dual problem, and show that the former offers a tighter bound than the latter. We highlight the fact that the bounds in question consist of linear problems with an exponential number of variables and adapt the column generation method for solving them. In addition to shedding light on the tightness of the bounds in question, our numerical results show significant sum-rate gains over several comparison schemes. Moreover, the proposed scheme delivers similar performance as weighted minimum mean squared-error (MMSE) with a significantly lower complexity (around 10 times less).

In this paper, a multi-user multi-relay network with wireless energy harvesting (EH) and transferring (ET) is studied. In our system, a simultaneous two-level cooperation, i.e., information-level and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. The energy minimization problem that takes into account the energy causality and outage probability constraints is formulated. However, the optimization problem is non-convex and hard to be solved directly. Alternatively, an approximation technique is adopted to convert it into a convex one. By solving the convex problem, efficient power allocation and ET policies are designed. Numerical results show that the proposed algorithm is able to achieve a near-optimal performance and outperforms the state of arts.

A user wants to retrieve a file from a database without revealing the identity of the file retrieved at the database, which is known as the problem of private information retrieval (PIR). If it is further required that the user obtains no information about the database other than the desired file, the concept of symmetric private information retrieval (SPIR) is introduced to guarantee privacy for both parties. In this paper, the problem of SPIR is studied for a database stored among N nodes in a distributed way, by using an (N, M)-MDS storage code. The information-theoretic capacity of SPIR, defined as the maximum number of information bits of the desired file retrieved per downloaded bit, for the coded database is derived. It is shown that the SPIR capacity for coded database is 1-M/N, when the amount of the shared common randomness of distributed nodes (unavailable at the user) is at least M/N-M times the file size. Otherwise, the SPIR capacity for the coded database equals zero.

We study a two-stage identification problem with pre-processing to enable efficient data retrieval and reconstruction. The first stage outputs a list of compatible users to the second stage which uses it to return the exact user identity with a corresponding reconstruction sequence. The rate-distortion region is characterized. A connection to a two observer identification problem is also studied.

The problem of symmetric private information retrieval (SPIR) from a coded database which is distributively stored among colluding servers is studied. Specifically, the database comprises K files, which are stored among N servers using an (N, M)-MDS storage code. A user wants to retrieve one file from the database by communicating with the N servers, without revealing the identity of the desired file to any server. Furthermore, the user shall learn nothing about the other K - 1 files in the database. In the T-colluding SPIR problem (hence called TSPIR), any T out of N servers may collude, that is, they may communicate their interactions with the user to guess the identity of the requested file. We show that for linear schemes, the information-theoretic capacity of the MDS-TSPIR problem, defined as the maximum number of information bits of the desired file retrieved per downloaded bit, equals 1 - (M+T-1)/N, if the servers share common randomness (unavailable at the user) with amount at least (M+T-1)/(N-M-T+1) times the file size. Otherwise, the capacity equals zero.

In this paper, we extend our previous work on user assignment in Cloud-RAN, where we proposed an algorithm for user assignment (UA). We motivate the inherent fairness issue that is present in the latter UA scheme, since some users in the system will never get served. For that purpose, we propose two schemes to be used in conjunction with aforementioned UA scheme, to improve its fairness. The first scheme aims at improving the minimum throughput (MT), by selecting users with lowest throughput, as input to the UA algorithm, and to be (potentially) scheduled in the next time slot. The second scheme is based on round-robin (RR) scheduling, where the set of potentially scheduled users (for the next slot), is done by excluding all the previously served users, in that round. Moreover, the subset of actual users to be served, is determined using the UA algorithm. We evaluate their fairness and sum-rate performance, via extensive simulations. While one might have expected a tradeoff between the sum-rate performance and fairness, our results showed that MT improves both metric, when compared to the original UA algorithm (without fairness), for some choice of parameter values.

MIMO systems in the lower part of the millimetre-wave (mmWave) spectrum band (i.e., below 28 GHz) do not exhibit enough directivity and selectively, as compared to their counterparts in higher bands of the spectrum (i.e., above 60 GHz), and thus still suffer from the detrimental effect of interference, on the system sum rate. As such systems exhibit large numbers of antennas and short coherence times for the channel, traditional methods of distributed coordination are ill-suited, and the resulting communication overhead would offset the gains of coordination. In this paper, we propose algorithms for tackling the sum-rate maximization problem that are designed to address the above-mentioned limitations. We derive a lower bound on the sum rate, a so-called difference of log and trace (DLT) bound, shed light on its tightness, and highlight its decoupled nature at both the transmitters and receivers. Moreover, we derive the solution to each of the subproblems that we dub non-homogeneous waterfilling (a variation on the MIMO waterfilling solution), and underline an inherent desirable feature: its ability to turn-OFF streams exhibiting low SINR, and contribute to greatly speeding up the convergence of the proposed algorithm. We then show the convergence of the resulting algorithm, max-DLT, to a stationary point of the DLT bound. Finally, we rely on extensive simulations of various network configurations, to establish the fast-converging nature of our proposed schemes, and thus their suitability for addressing the short coherence interval, as well as the increased system dimensions, arising when managing interference in lower bands of the mmWave spectrum. Moreover, our results suggest that interference management still brings about significant performance gains, especially in dense deployments.

This paper investigates the capacity limit of an uplink waveform channel assuming imperfect channel state information at the receiver (CSIR). Various realistic assumptions are incorporated into the problem, which make the study valuable for performance assessment of real cellular networks to identify potentials for performance improvements in practical receiver designs. We assume that the continuous-time received signal is first discretized by mismatched filtering based on the imperfect CSIR. The resulting discrete-time signals are then decoded considering two different decoding strategies, i.e., an optimal decoding strategy based on specific statistics of channel estimation errors and a sub-optimal decoding strategy treating the estimation error signal as additive Gaussian noise. Motivated by the proposed decoding strategies, we study the performance of the decision feedback equalizer for finite constellation inputs, in which inter-stream interferences are treated either using their true statistics or as Gaussian noise. Numerical results are provided to exemplify the benefit of exploiting the knowledge on the statistics of the channel estimation errors and inter-stream interferences. Simulations also assess the effect of the CSI imperfectness on the achievable rate, which reveal that finite constellation inputs are less sensitive to the estimation accuracy than Gaussian input, especially in the high SNR regime.

In this paper, a multi-user multi-relay network with integrated energy harvesting and transferring (IEHT) strategy is studied. In our system, a simultaneous two-level cooperation, i.e., information- and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. For generality purposes, the Nakagami-m fading channels that are independent but not necessarily identically distributed (i.n.i.d.) are considered. The problem of energy efficiency maximization under constraints of the energy causality and a predefined outage probability threshold is formulated and shown to be non-convex. By exploiting fractional and geometric programming, a convex form-based iterative algorithm is developed to solve the problem efficiently. Close-to-optimal power allocation and energy cooperation policies across consecutive transmissions are found. Moreover, the effects of relay locations, wireless energy transmission efficiency, battery capacity as well as the existence of direct links are investigated. The performance comparison with the current state of solutions demonstrates that the proposed policies can manage the harvested energy more efficiently.

In this paper, an energy flow control strategy to reduce the smart meter privacy leakage is studied. The considered smart grid is equipped with an energy storage device. The privacy leakage is modeled as optimal Bayesian detections on the behaviors of the consumer made by an authorized adversary. To evaluate the privacy risk, a Bayesian detection-operational privacy leakage metric is proposed. The design of an optimal privacy-preserving energy control strategy can be formulated as a belief state MDP problem. Therefore, standard methods and algorithms can be utilized to obtain or to approximate the optimal control strategy. A simplified problem to design an instantaneous optimal privacy-preserving control strategy is also considered. It is shown that the problem of the instantaneous optimal control strategy design can be formulated as a set of linear programmings.

In this paper, we consider a discrete time linear quadratic Gaussian (LQG) control problem in which state information of the plant is encoded in a variable-length binary codeword at every time step, and a control input is determined based on the codewords generated in the past. We derive a lower bound of the rate achievable by the class of prefix-free codes attaining the required LQG control performance. This lower bound coincides with the infimum of a certain directed information expression, and is computable by semidefinite programming (SDP). Based on a technique by Silva et al., we also provide an upper bound of the best achievable rate by constructing a controller equipped with a uniform quantizer with subtractive dither and Shannon-Fano coding. The gap between the obtained lower and upper bounds is less than 0:754r + 1 bits per time step regardless of the required LQG control performance, where r is the rank of a signal-to-noise ratio matrix obtained by SDP, which is no greater than the dimension of the state.

The zero-error secrecy capacity of uncertain wiretap channels is defined. If the sensor-estimator channel is perfect, it is also calculated. Further properties are discussed. The problem of estimating a dynamical system with nonstochastic disturbances is studied where the sensor is connected to the estimator and an eavesdropper via an uncertain wiretap channel. The estimator should obtain a uniformly bounded estimation error whereas the eavesdropper's error should tend to infinity. It is proved that the system can be estimated securely if the zero-error capacity of the sensor-estimator channel is strictly larger than the logarithm of the system's unstable pole and the zero-error secrecy capacity of the uncertain wiretap channel is positive.

The problem of one-way file synchronization from the client to the data-center, namely, file updates, is studied. The problem is investigated in particular when an old version of a file which can be available at both client and data-center, is edited by the client to a new version. The edits are modeled as random insertions and deletions (InDels). Based on the updated and the previous version of the file, the client transmits a message to the data-center via a noiseless link, such that the data-center can update the file. A dynamic-programming-run-length-coding (DP-RLC) scheme is proposed for the message encoding in this paper. The lower order terms of the achievable rate are computed explicitly. It is worth noting that these terms match the information-theoretic lower bound derived in our previous work [1]. Therefore, when the insertion and deletion probabilities are small, the achievable rate is nearly optimal.

A generalization of the ensemble of non-terminated systematic LDPC convolutional codes developed in our previous work is proposed that allows us to design codes with lower rates than the original structure. We show that over the BEC, the modified codes have improved asymptotic and finite-length behavior and we determine the operational anytime exponent. Having shown the advantages of lowering the rate of the code, we propose a feedback protocol that permits encoder and decoder to operate at a variable rate. The rate is set on-the-fly and depends on the decoding success of the decoder. We describe the construction of the variable rate code structure and demonstrate by simulations the superiority of the variable rate scheme as compared to a scheme using a fixed rate.

We consider the effective capacity performance measure of persistent- and truncated-retransmission schemes that can involve any combination of multiple transmissions per packet, multiple communication modes, or multiple packet communication. We present a structured unified analytical approach, based on a random walk model and recurrence relation formulation, and give exact effective capacity expressions for persistent hybrid automatic repeat request (HARQ) and for truncated-retransmission schemes. For the latter, effective capacity expressions are given for systems with finite (infinite) time horizon on an algebraic (spectral radius-based) form of a special block companion matrix. In contrast to prior HARQ models, assuming infinite time horizon, the proposed method does not involve a non-trivial per case modeling step. We give effective capacity expressions for several important cases that have not been addressed before, e.g., persistent-HARQ, truncated-HARQ, network-coded ARQ, two-mode-ARQ, and multilayer-ARQ. We propose an alternative quality-of-service-parameter (instead of the commonly used moment generating function parameter) that represents explicitly the target delay and the delay violation probability. This also enables the closed-form expressions for many of the studied systems. Moreover, we use the recently proposed matrix-exponential distributed modeling of wireless fading channels to provide the basis for numerous new effective capacity results for HARQ.

We propose a novel performance analysis framework for lossless- and truncated-hybrid automatic repeat request (HARQ) that enables neat, general, closed-form throughput expressions in a matrix exponential (ME) distribution form. This approach is applicable to all HARQ schemes for which the probability density function of the effective channel can be characterized by a rational Laplace transform, or equivalently, an ME-distribution. This includes, for example, repetition redundancy HARQ in ME distributed channels. Throughput expressions are also given for the K-truncated-HARQ N-fold diversity, ARQ N-fold diversity, and lossless-HARQ 2-fold diversity cases in the ME distributed channel. Schemes with effective channels of non-rational Laplace transforms, such as IR-HARQ, are explored using truncated continued fractions. A novel integration trick is developed for the integration of ME distributions with singular matrices and yields the simple throughput expression of lossless-HARQ. We also give general analytical expressions for the optimal throughput and optimal rate point that benefit from the compact ME-distribution form proposed.

The energy efficiency (EE) of a multi-user multi-relay system with the maximum diversity network coding (MDNC) is studied. We explicitly find the connection among the outage probability, energy consumption, and EE, and formulate the maximizing EE problem under the outage probability constraint. Relay scheduling (RS) and power allocation (PA) are applied to schedule the relay states (transmitting, sleeping, and so on) and optimize the transmitting power under the practical channel and power consumption models. Since the optimization problem is NP hard, to reduce computational complexity, the outage probability is first tightly approximated to a log-convex form. Furthermore, the EE is converted into a subtractive form based on the fractional programming. Then, a convex mixed-integer nonlinear problem is eventually obtained. With a generalized outer approximation algorithm, RS and PA are solved in an iterative manner. The Pareto-optimal curves between the EE and the target outage probability show the EE gains from PA and RS. Moreover, by comparing with the no network coding (NoNC) scenario, we conclude that with the same number of relays, MDNC can lead to EE gains. However, if RS is implemented, NoNC can outperform MDNC in terms of the EE when more relays are needed in the MDNC scheme.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper (respectively, lower) bounding models, which can be used to calculate the outer (respectively, inner) bounds for the capacity region of the original noisy network. Based on the network equivalence framework, this paper presents scalable upper and lower bounding models for wireless networks with potentially many nodes. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels and broadcast channels. The upper bounding model, consisting of only point-to-point bit pipes, is constructed by first extending the one-shot upper bounding models developed by Calmon et al. and then integrating them with network equivalence tools. The lower bounding model, consisting of both point-to-point and point-to-points bit pipes, is constructed based on a two-step update of the lower bounding models to incorporate the broadcast nature of wireless transmission. The main advantages of the proposed methods are their simplicity and the fact that they can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper and lower bounds can approach the capacity in some setups.

We consider secure multi-terminal source coding problems in the presence of a public helper. Two main scenarios are studied: 1) source coding with a helper where the coded side information from the helper is eavesdropped by an external eavesdropper, 2) triangular source coding with a helper where the helper is considered as a public terminal. We are interested in how the helper can support the source transmission subject to a constraint on the amount of information leaked due to its public nature. We characterize the tradeoff between transmission rate, incurred distortion, and information leakage rate at the helper/eavesdropper in the form of a rate-distortion-leakage region for various classes of problems.

We consider multiple-input single-output (MISO) Gaussian channels with joint sum and per-antenna power constraints. A closed-form solution of the optimal beamforming vector is derived which achieves the maximal transmission rate. The result shows that if the sum power constraint only optimal power allocation violates a per-antenna power constraint then the joint power constraint optimal power allocation is at the intersection of the sum power constraint and the per-antenna power constraints.

The problem of transmitting confidential messages in the M × K wireless X network is considered, in which each transmitter intends to send one confidential message to every receiver. In particular, the secrecy degrees of freedom (SDOF) of the considered network are studied by an artificial noise alignment (ANA) approach, which integrates interference alignment and artificial noise transmission. At first, an SDOF upper bound is derived for the M × K X network with confidential messages (XNCM) K(M-1)/K+M-2 to be equation. By proposing an ANA approach, it is shown that the SDOF upper bound is tight when either K = 2 or M = 2 for the considered XNCM with time/frequency varying channels. For K, M ≥ 3, it is shown that an SDOF of K(M-1)/K+M1 equation can be achieved, even when an external eavesdropper appears. The key idea of the proposed scheme is to inject artificial noise into the network, which can be aligned in the interference space at receivers for confidentiality. The proposed method provides a linear approach for secure interference alignment.

The secrecy degrees of freedom (SDOF) of the multiple-input single-output (MISO) broadcast channel with confidential messages (BCC) is studied. The network consists of a two-antenna transmitter and two single-antenna receivers, each demanding a confidential message from the transmitter. The problem is investigated with mixed channel state information at transmitter (CSIT), which is a combination of perfect delayed CSIT and inaccurate current CSIT. When the variance of the estimation error for the current CSIT scales with O(P-α), with α ∈ [0, 1], it is shown that the optimal sum SDOF of the considered BCC is 1+α. Furthermore, the optimal SDOF region of the considered MISO BCC is shown to be a polygon scaling with α. The proposed scheme is based on an artificial noise alignment that can combine the benefits of both types of delayed and current CSIT. These results can be seen as an extension of results of Yang et al. and Gou-Jafar to multiuser networks with secrecy constraints.

We consider an interference network with independent links, whose multi-antenna transmitters have access to an imperfect analog estimate of their local channels. Assuming that the receivers treat the interference as noise, we define the outage rate region as the set of rate-tuples that are achievable with a given probability and we characterize the boundary of the region for transmit beamforming. Our study shows that the Pareto-optimal beamforming vectors judiciously balance the desired signal power and the interference power based on the quality of the estimated channel state. Our analysis further reveals that, in contrast to the well-known results by Jorswieck, for the perfect channel side-information case, transmission at full power is not necessarily Pareto-optimal.

This paper investigates transmission schemes for fixed-rate communications over a Rayleigh block-fading interference channel. There are two source-destination pairs where each source, in the presence of a short-term power constraint, intends to communicate with its dedicated destination at a fixed data rate. It encodes its messages using a point-to-point Gaussian codebook. The two users' transmissions can be conducted orthogonally or non-orthogonally. In the latter case, each destination performs either direct decoding by treating the interference as noise, or successive interference cancellation (SIC) to recover its desired message. For each scheme, we seek solutions of a power control problem to efficiently assign power to the sources such that the codewords can be successfully decoded at destinations. However, because of the random nature of fading, the power control problem for some channel realizations may not have any feasible solution and the transmission will be in outage. Thus, for each transmission scheme, we first compute a lower bound and an upper bound on the outage probability. Next, we use these results to find an outer bound and an inner bound on the ϵ -outage achievable rate region, i.e., the rate region in which the outage probability is below a certain value ϵ.

In this paper, nonlinear distributed joint source-channel coding (JSCC) schemes for transmission of multivariate Gaussian sources over a Gaussian multiple access channel are proposed and analyzed. The main contribution is a zero-delay JSCC named Distributed Quantizer Linear Coder (DQLC), which performs relatively close the information theoretical bounds, improves when the correlation among the sources increases, and does not level off as the signal-to-noise ratio (SNR) becomes large. Therefore it outperforms any linear solution for sufficiently large SNR. Further an extension of DQLC to an arbitrary code length named Vector Quantizer Linear Coder (VQLC) is analyzed. The VQLC closes in on the performance upper bound as the code length increases and can potentially achieve the bound for any number of independent sources. The VQLC leaves a gap to the bound whenever the sources are correlated, however. JSCC achieving the bound for arbitrary correlation has been found for the bivariate case, but that solution is significantly outperformed by the DQLC/VQLC when there is a low delay constraint. This indicates that different approaches are needed to perform close to the bounds when the code length is high and low. The VQLC/DQLC also apply for bandwidth compression of a multivariate Gaussian transmitted on point-to-point links.

We study a multinode network, where a multi-antenna transmitter Tx communicates with its desired receiver Rx, whereas a cluster P ≡̂ {Px,n, n = 1, . .. ,N} of unintended nodes is disturbed by the Tx-Rx (TR) communication. To prevent severe performance degradation, we impose a constraint on the total interference that is inflicted at the nodes of P. The TR link contains a line-of-sight component, whereas the propagation environment for each Tx-Px,n link is shadowed. The Tx node is preprocessing the information sequence by means of a precoding matrix that is optimized to achieve the ergodic capacity under a constraint on the maximum admissible ergodic interference power, arriving on P. In this paper, we show that the optimum precoding strategy involves the transmission of a single stream over the precoding direction, i.e., the eigenvector of the precoding matrix, which corresponds to beamforming along the instantaneous direction of the TR-link channel. The solution of the remaining power allocation problem yields the optimal precoding matrix. For this setup, we provide an efficient stochastic characterization of the network, which allows us to obtain an analytical expression for the TR-link ergodic capacity; this problem has been previously open, even for the case of a single-antenna node Tx and a single-element set P. We complement the analysis by deriving the TR-link signal-to-noise ratio and the average bit error rate, which are associated with our transmission scheme. Numerical results corroborate the theoretical analysis and reveal an interplay between the network parameters and their impact on the TR-link performance.

The problem of transmitting confidential messages in M x K wireless X networks is considered in which each transmitter intends to send one confidential message to every receiver. In particular, the secure degrees of freedom (SDOF) of the considered network are studied based on an artificial noise alignment (ANA) approach, which integrates interference alignment and artificial noise transmission. At first, an SDOF upper bound is derived for the M x K X network with confidential messages (XNCM) to be K(M-1)/K+M-2. By proposing an ANA approach, it is shown that the SDOF upper bound is tight when K = 2 for the considered XNCM with time-/frequency-varying channels. For K ≥ 3, it is shown that SDOF of K(M-1)/K+M-1 can be achieved, even when an external eavesdropper is present. The key idea of the proposed scheme is to inject artificial noise into the network, which can be aligned in the interference space at receivers for confidentiality. Moreover, for the network with no channel state information at transmitters, a blind ANA scheme is proposed to achieve SDOF of K(M-1)/K+M-1 for K, M ≥ 2, with reconfigurable antennas at receivers. The proposed method provides a linear approach to secrecy coding and interference alignment.

We study a lossy source coding problem with secrecy constraints in which a remote information source should be transmitted to a single destination via multiple agents in the presence of a passive eavesdropper. The agents observe noisy versions of the source and independently encode and transmit their observations to the destination via noiseless rate-limited links. The destination should estimate the remote source based on the information received from the agents within a certain mean distortion threshold. The eavesdropper, with access to side information correlated to the source, is able to listen in on one of the links from the agents to the destination in order to obtain as much information as possible about the source. This problem can be viewed as the so-called CEO problem with additional secrecy constraints. We establish inner and outer bounds on the rate-distortion-equivocation region of this problem. We also obtain the region in special cases where the bounds are tight. Furthermore, we study the quadratic Gaussian case and provide the optimal rate-distortion-equivocation region when the eavesdropper has no side information and an achievable region for a more general setup with side information at the eavesdropper.

Two classes of source/channel coding problems, namely, coding with action-dependent side information and coding with additional signal reconstruction are considered in a unified fashion. In the source coding setting, a decoder wishes to reconstruct the source subject to a distortion constraint, while an encoder is required to estimate the decoder's reconstruction reliably. Side information is action-dependent in the sense that its quality and/or availability at the encoder or decoder can be influenced by a cost-constrained action sequence. In the channel coding dual, the decoder wishes to decode both the message and the channel input sequence reliably, and the channel state information available at the encoder or decoder is assumed to depend on the action sequence. We consider discrete memoryless systems and characterize single letter expressions for the rate-distortion-cost function and channel capacity for the respective source and channel coding problems.

We consider the problems of secure lossy source coding with side information in the presence of a passive eavesdropper who has access to the source description. The encoder wishes to compress the source sequence in order to satisfy a distortion criterion at the decoder, while revealing only limited knowledge about the source to the eavesdropper. The side information available to the encoder, the legitimate decoder, or the eavesdropper can be influenced by a cost-constrained action sequence. Three different settings are studied. In the first two settings, we are interested in understanding the influence of the action sequence on the rate-distortion-leakage tradeoff where the action is taken either by the decoder or by the encoder to influence side information at the decoder and eavesdropper. Next, we consider a setting where common action-dependent side information is available securely to both encoder and decoder, and thus can be used for secret key generation. We characterize the optimal rate-distortion-cost-leakage region or the corresponding inner bounds for a discrete memoryless source for above settings. The results are useful in characterizing fundamental limits for example in secure sensor networking and future cyber physical systems.

We consider a Gaussian relay network, which has to protect the message from a powerful eavesdropper, which has the possibility of listening to the transmitted signals on orthogonal channels. The secrecy capacity of the Gaussian network is approximated by considering the corresponding network in the deterministic discrete superposition model. Upper and lower bounds on the secrecy capacity are found in terms of the deterministic model, which are potentially easier to evaluate. We perform this evaluation for the particular topology of the parallel relay network, where the relays form a parallel layer between the source and the destination. By analyzing the deterministic model, we approximate the secrecy capacity within a constant gap. The approximation can be expressed just in terms of the channel gains of the network. It is therefore possible to deduct that the simple scheme of hiding the transmitted signals in noise is approximately optimal.

This paper considers distributed transceiver design and power control for K-user multiple-input-multiple-output interference networks. Each source intends to send multiple independent data streams to its corresponding destination where the number of data streams coincides with the degrees of freedom of the network. Each data stream is encoded at a fixed data rate, whereas different streams can be encoded at possibly different rates. We assume that only local channel side information (i.e., knowledge related to channels directly connected to a terminal) can be acquired by each terminal. We propose iterative algorithms to perform both power control and transceiver design. Transmitter beamforming matrices and receiver filtering matrices are designed to maximize signal-to-interference-plus-noise ratio corresponding to each stream, and a power control scheme is performed to assign the minimum power to each encoded data stream such that successful communication can be guaranteed. The proposed algorithms exhibit a substantial performance improvement compared with the conventional orthogonal transmission schemes.

Considering the time-varying K-user single-antenna interference channel (IC), it has been shown that, when terminals have perfect global channel state information (CSI) and they can tolerate asymptotically long delay, applying an ergodic interference alignment (EIA) scheme can achieve half of the interference-free achievable rate. However, in practice, obtaining such CSI is challenging, and only a limited delay is acceptable. This paper addresses data transmission over the IC by taking these concerns into account. Specifically, we consider the case that each transmitter attains only quantized CSI via limited feedback signals. This causes imperfect interference alignment and a degraded performance. We propose adaptive schemes to compensate the impact of the CSI uncertainties. We first study a power control problem which is concerned with communicating at fixed rates using minimum transmit powers. A power control algorithm is used to reach the solution. Next, we address a throughput maximization problem when the transmit powers are fixed. Through the analysis of system outage probability, we propose a rate adaptation scheme to maximize throughput. Finally, we quantify the throughput loss in delay-limited systems. Our results show that, even with limited feedback, performing the EIA scheme with proper power control or rate adaptation strategies can still outperform conventional orthogonal transmission approaches.

Low power sensor networks with communication enabled by WiFi are expected to be widely deployed. A major challenge is collecting event-driven uplink data from a large number of low-power sensors with low latency. In WiFi, the access point (AP) typically polls nodes individually to schedule uplink transmission times, resulting in a large latency. In this paper, we present a physical (PHY) layer-aided medium access control (MAC) framework to enhance the uplink throughput of sensor data traffic. In the approach, the acknowledgements from the sensor nodes to the poll message are parallelized. By detecting the parallel acknowledgement, the AP knows which nodes have data to send and allocates channel resources by sending a pull message. This approach is referred to as the probe and pull MAC (PPMAC) mechanism. Our scheme is based on maximizing the achievable throughput of PPMAC by optimizing the PHY layer components. More precisely, we investigate the parallel acknowledgement detector design problem and develop a non-convex optimization framework that maximizes the PPMAC throughput by optimizing the parallel acknowledgement detection statistics. Numerical examples illustrate that PPMAC outperforms the point coordination function (PCF) and distributed coordination function (DCF) mechanisms, standardized in IEEE 802.11, in terms of the achievable throughput and the overhead.

We study the achievable sum degrees of freedom (DoF) in wireless single-antenna multi-user multi-hop relay networks. In most existing works targeting this problem, it is assumed that relays operate in perfect full-duplex fashion and can also shield their receptions from the transmissions of other relays in the same and rear layers. In practice, such ideal assumptions are normally hard to realize. And a naive adoption of half-duplex operation in relays may result in significantly inefficient use of available radio resources. We propose spectrally-efficient transmission schemes to address this issue. A lower-bound to the networks' available DoF (i.e., optimally achievable sum DoF) hence can be attained. Our results indicate that besides providing information delivery paths, relays can also bring DoF gain over single-hop networks, even when relay interference issues are taken into consideration. This lower-bound would approach the upper-bound of the available DoF, if each relay layer deploys more nodes. When the number of relays in every layer is infinitely large, the exact available DoF is identified, which shows that handling interference issues through distributed signal processing and half-duplex operation across multiple layers of terminals may not negatively affect DoF performance, compared with ideal full-duplex and multi-antenna networks.

In this paper, we consider throughput and rate optimized throughput for large-scale MIMO-(H)ARQ systems in i.i.d. complex Gaussian block fading channels. Exact analysis of large-scale MIMO is generally intractable, yet the field of random matrix theory has provided asymptotic expressions for the instantaneous channel capacity, which we propose for studying MIMO-(H)ARQ systems. Even with those expressions, closed-form results for the maximum throughput and optimal rate are not known. We therefore provide i) tight asymptotic lower and upper bounds, with a diminishing gap, for the optimal rate that is useful for the design of practical link adaptation algorithms, and ii) a tight asymptotic lower bound for the maximized throughput, suggesting the practical sufficiency of MIMO-ARQ, without any packet combining, over more complex MIMO-HARQ methods.

This paper considers the ergodic block fading multi-user Gaussian interference channel (IC) in which each source desires to communicate to an intended destination. We assume that there is no CSI a priori available at terminals. We develop achievable rate results and compute the associated degrees of freedom by using a pilot-assisted interference alignment scheme. In this scheme, each source first sends known pilot symbols via which the destinations estimate channel gains, and the destinations then broadcast the estimated channel gains via orthogonal feedback channels. The estimated channel gains are used to perform interference alignment for data transmission. The pilot transmission power can be different from the data transmission power. By allocating more power to pilot transmission, channel gains can be estimated more accurately which implies less power left for data transmission. We find the optimum power allocation to pilot symbols and data symbols. Our study recommends, in large networks, to allocate more power to channel training instead of data transmission. In addition, our results reveal that for a K-user ergodic IC with a coherence time T, the total degrees of freedom 1/2 Kopt(1-Kopt/T) is achievable, where Kopt= min {K, T/2} is the optimum number of users selected to be active in the network. This recommends to perform a user selection in large networks (K > T/2), and apply channel training and interference alignment within the set of selected users.

We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.

We consider the throughput performance of ARQ in interfering channels, where the signal of interest as well as the interferers are subject to independent distributed Nakagami-m block fading. The key contribution is the derivation of closed-form expressions for the rate-maximized throughput. For this purpose, we employ the powerful parameterization approach from [1], allowing the problem to be solved exactly in a closed-form. We also consider the scaled-power, and the interference-limited, case.

A cognitive radio network with a multiple-input single-output secondary link and a multi-antenna primary receiver is considered. The secondary transmitter steers its transmission into the direction of its intended destination in order to maximize the received signal-to-noise ratio. Under this beam-forming strategy, the power allocation is optimized to achieve the ergodic capacity under the constraint that the long-term interference, caused at the primary receiver, will not exceed a predefined threshold. Assuming line-of-sight communication (Rician fading) for the secondary link and Rayleigh fading for the secondary-to-primary link channel, we derive the exact closed-form expression for the ergodic capacity. Numerical results corroborate theoretical findings and illustrate the manifold impact of the system parameters into its performance.

The framework of network equivalence theory developed by Koetter et al. introduces a notion of channel emulation to construct noiseless networks as upper/lower bounding models for the original noisy network. This paper presents scalable upper bounding models for wireless networks, by firstly extending the “one-shot” bounding models developed by Calmon et al. and then integrating them with network equivalence tools. A channel decoupling method is proposed to decompose wireless networks into decoupled multiple-access channels (MACs) and broadcast channels (BCs). The main advantages of the proposed method is its simplicity and the fact that it can be extended easily to large networks with a complexity that grows linearly with the number of nodes. It is demonstrated that the resulting upper bounds can approach the capacity in some setups.

This paper investigates a new type of typicality for sequences, termed Supremus typical sequences, in both the strong and the weak senses. It is seen that Supremus typicality is a condition stronger than classic typicality in both the strong and the weak senses. Even though Supremus typical sequences form a (often strictly smaller) subset of classic typical sequences, the Asymptotic Equipartion Property is still valid for Supremus typical sequences. Furthermore, Supremus typicality leads to a generalized typicality lemma that is more accessible and easier to analyze than its classic counterpart.

We consider the problem of lossy source coding with side information under a privacy constraint that the reconstruction sequence at a decoder should be kept secret to a certain extent from another terminal such as an eavesdropper, a sender, or a helper. We are interested in how the reconstruction privacy constraint at a particular terminal affects the rate-distortion tradeoff. In this work, we allow the decoder to use a random mapping, and give inner and outer bounds to the rate-distortion-equivocation region for different cases. In the case where each reconstruction symbol depends only on the source description and current side information symbol, the complete rate-distortion-equivocation region is provided. A binary example illustrating a new tradeoff due to the new privacy constraint, and a gain from the use of randomized decoder is given.

A lossy source coding problem with secrecy constraints is considered where a remote information source should be transmitted to a single destination via multiple agents in the presence of an eavesdropper. The agents observe noisy versions of the source and independently encode and transmit their observations to the destination via noiseless rate-limited links. Unbeknownst to the agents, an eavesdropper intercepts one of the links from the agents to the destination to learn as much as possible about the source. The destination should estimate the remote source subject to a mean distortion threshold. This problem can be viewed as the CEO problem with addition of secrecy constraints. We establish inner and outer bounds on the rate-distortion-equivocation region. In addition, we provide the optimal rate-distortion-equivocation region for the quadratic Gaussian case when the eavesdropper has no side information.

In this paper, we investigate the problem of successive refinement with side information (SI) under secrecy constraint. In particular, under classical successive refinement coding scheme, there are degraded SI sequences Ynand Znat two decoders and Enat the eavesdropper. Based on the status of two switches, three different cases are investigated. In case 1 and 3, the eavesdropper only observes output of encoder 1 and 2, respectively, while in case 2, the eavesdropper observes outputs of both encoder 1 and 2. The Markov chain X - Y - (Z, E) holds in all cases. The equivocation is measured by the normalized entropy of source sequence conditioned on the observation of eavesdropper. We completely characterize the rate-distortion-equivocation regions for all three cases, and show that layered coding is optimal. Finally, a binary source example is given.

MOTIVATION: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. As the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically time-consuming in a desktop computing environment. RESULTS: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the community composition estimation problem by a simultaneous assignment of all sample reads to a pre-processed reference database. A general statistical model based on kernel density estimation techniques is introduced for the assignment task, and the model solution is obtained using convex optimization tools. Further, we design a greedy algorithm solution for a fast solution. Our approach offers a reasonably fast community composition estimation method, which is shown to be more robust to input data variation than a recently introduced related method. AVAILABILITY AND IMPLEMENTATION: A platform-independent Matlab implementation of the method is freely available at http://www.ee.kth.se/ctsoftware; source code that does not require access to Matlab is currently being tested and will be made available later through the above Web site.

The authors study a two‐user state‐dependent generalised multiple‐access channel (GMAC) with correlated states. It is assumed that each encoder has ‘non‐causal’ access to channel state information (CSI). They develop an achievable rate region by employing rate‐splitting, block Markov encoding, Gelfand–Pinsker multicoding, superposition coding and joint typicality decoding. In the proposed scheme, the encoders use a partial decoding strategy to collaborate in the next block, and the receiver uses a backward decoding strategy with joint unique decoding at each stage. The author's achievable rate region includes several previously known regions proposed in the literature for different scenarios of multiple‐access and relay channels. Then, they consider two Gaussian GMACs with additive interference. In the first model, they assume that the interference is known non‐causally at both of the encoders and construct a multi‐layer Costa precoding scheme that removes ‘completely’ the effect of the interference. In the second model, they consider a doubly dirty Gaussian GMAC in which each of interferences is known non‐causally only at one encoder. They derive an inner bound and analyse the achievable rate region for the latter model and interestingly prove that if one of the encoders knows the full CSI, there exists an achievable rate region which is ‘independent’ of the power of interference.

This paper investigates cooperation for secrecy in cognitive radio networks. In particular, we consider a four-node cognitive scenario where the secondary receiver is treated as a potential eavesdropper with respect to the primary transmission. The cognitive transmitter can help the primary transmission, and it should also ensure that the primary message is not leaked to the secondary user. We consider two cognitive scenarios depending on whether the secondary transmitter knows the primary message or not. In the first case, the secondary transmitter is unaware of the primary transmitter's message and acts as a helping interferer to enhance the secrecy of the primary transmission, whereas in the second case, relaying of the primary message is also within its capabilities. First, we find achievable rate regions for these two scenarios in the case of AWGN channels. We then investigate three different optimization problems: the maximization of the primary rate, the maximization of the secondary rate and the minimization of the secondary transmit power. For these optimization problems, we find closed-form expressions in important special cases. Furthermore, we analyze the cooperation between the primary and secondary transmitters from a game-theoretic perspective. We model their interaction as a Stackelberg game, for which we define and find the Stackelberg equilibrium. Finally, we use numerical examples to illustrate the rate regions, the three optimizations, and the impact of the Stackelberg game on the achievable rates and on the transmission strategies of the secondary transmitter.

Here we propose an ensemble of non-terminated systematic LDPC convolutional codes with increasing memory, and show that, over the binary erasure channel (BEC), these codes achieve anytime reliability asymptotically when decoded with an expanding-window message-passing decoder. The corresponding anytime exponents are determined through protograph-based extrinsic information transfer charts. Fundamental complications arising when transmitting with finite block lengths are identified and a combinatorial performance analysis, when transmitting over a static BEC with a fixed number of erasures per codeword block, is developed. Based on the performance analysis, we explore the use of feedback for achieving anytime behavior with constraints on block length. To meet complexity constraints, with or without feedback, the code memory can be limited at the cost of an error floor emerging with a delay proportional to the memory constraint. Although the analysis is developed for a static BEC we show numerically that we can design efficient low-complexity finite-length codes with anytime properties even for the conventional BEC.

This paper examines throughput performance, and its optimization, for lossless and truncated automatic repeat request (ARQ) schemes in Gaussian block fading channels. Specifically, ARQ, repetition redundancy, and in part also incremental redundancy-hybrid ARQ, are considered with various diversity schemes. We propose a parameterization-based method that allows (semi-)closed-form expressions, linking optimized throughput, optimal rate, and mean SNR, to be derived for any ARQ and repetition redundancy-HARQ method even when a non-parameterized closed-form does not exist. We derive numerous throughput and optimal throughput expressions for various ARQ schemes and diversity scenarios, potentially useful for benchmarking purposes or as design guidelines.

This paper characterizes achievable rate regions for the Gaussian partially cognitive interference channel with multiple cognitive pairs (K-PCIFC), where the cognitive transmitters know one part of the primary transmitter's message. We explore a novel methodology using the deterministic discrete superposition model (DSM). We find codes and their achievable rate regions in the DSM, and we show that they yield achievable rate regions in the Gaussian model. Our coding scheme in the DSM can be applied in general scenarios. We devise an achievable rate region for the K-PCIFC, which is within a constant gap to known outer bounds for K = 2 in the weak interference regime. From our expressions we derive guidelines for designing the cognitive link between the primary transmitter and the cognitive transmitters. Furthermore, we show that the gain from cognition diminishes for increasing K.

This paper studies and derives new results for the interference channel with a relay (ICR). Three inner bounds for the discrete memoryless ICR are proposed, based on three coding strategies that employ layered code at the relay. The first scheme is inspired by layered noisy network coding, proposed by Lim et al. for the two-way relay channel, the second and the third schemes rely on simpler encoding and decoding processes, dubbed layered quantize-forward. Performance of the proposed schemes is investigated for two classes of channels with Gaussian noise: the interference channel with in-band relay reception/out-of-band relay transmission and the interference with in-band relay reception/in-band relay transmission. For the former class of channels, it is shown that the first proposed scheme achieves the same inner bound as the generalized hash-forward scheme with incremental binning. In addition, the inner bound is within 0.5 bit of the capacity region under certain conditions on the channel parameters. For the latter class of channels, new upper bounds on sum-rate are established by extending known upper bounds for symmetric channels. The first inner bound is shown to be within 0.5 bit of the capacity region if the relay's power exceeds a certain threshold, which depends on channel parameters. Numerical examples show that the proposed schemes can achieve significantly higher sum-rates when compared with other compress-forward schemes. Analysis also reveals a tradeoff between achievable rates, coding delay, and complexity of the proposed schemes. Results in this paper provide a better understanding of coding for the ICR, in particular, they show that layered coding is a beneficial element in multiuser networks with relays.

A downlink two-hop MISO broadcast network is considered, with a two-antenna source communicating to 2 single-antenna destinations, via multiple single-antenna relays in between. The sum degrees of freedom (DOF) of the network with mixed channel state information at the transmitter (CSIT) is investigated. The mixed CSIT consists of accurate delayed CSIT and inaccurate instantaneous CSIT, and its availability is limited within each hop, i.e., the source is oblivious to the channels of the second hop. Given a transmission power P and a real value α ∈ [0, 1], if the variance of the error for instantaneous CSIT decreases as O(P-α), it is shown that the sum optimal DOF of the considered network is d = 4+2α/3 when there exist at least 3 intermediate relays. The result can be extended to the MIMO and multiple-hop cases. The proposed achievable schemes essentially combine the concept of retrospective interference alignment based on delayed CSIT and linear beamforming based on inaccurate instantaneous CSIT into an integrated form. Our results show that, in multi-hop MISO broadcast networks, delayed CSIT and inaccurate instantaneous CSIT can be exploited simultaneously to benefit network DOF.

In this work, we study the so-called leakage minimization problem, within the context of interference alignment (IA). For that purpose, we propose a novel approach based on controlled perturbations of the leakage function, and show how the latter can be used as a mechanism to control the algorithm's convergence (and thus tradeoff convergence speed for reliability). Although the proposed scheme falls under the broad category of stochastic optimization, we show through simulations that it has a quasi-deterministic convergence that we exploit to improve on the worst case performance of its predecessor, resulting in significantly better sum-rate capacity and average cost function value.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, via 2 single-antenna relays. We investigate spectrally efficient transmission and the associated achievable sum degrees of freedom (DoF) with mixed channel state information at the transmitter (CSIT), which consists of perfect delayed CSIT and imperfect instantaneous CSIT. When the variance of the estimation error of the instantaneous CSIT lies on level of O(P-α) for the transmission power P and some α ∈ [0, 1], we show that the sum DoF 4-2α/3-2α ∈ [4/3, 2] can be achieved by a novel interference alignment (IA) scheme. The result shows that rather than exploiting only delayed or imperfect instantaneous CSIT, the transmission design taking advantages of both can achieve higher sum DoF.

For compressive sensing, we derive achievable performance guarantees for recovering partial support sets of sparse vectors. The guarantees are determined in terms of the fraction of signal power to be detected and the measurement rate, defined as a relation between the dimensions of the measurement matrix. Based on this result we derive a tradeoff between the measurement rate and the mean square error, and illustrate it by a numerical example.

We consider vector-quantized (VQ) transmission of compressed sensing (CS) measurements over noisy channels. Adopting mean-square error (MSE) criterion to measure the distortion between a sparse vector and its reconstruction, we derive channel-optimized quantization principles for encoding CS measurement vector and reconstructing sparse source vector. The resulting necessary optimal conditions are used to develop an algorithm for training channel-optimized vector quantization (COVQ) of CS measurements by taking the end-to-end distortion measure into account.

We consider a resource-constrained scenario where a compressed sensing- (CS) based sensor has a low number of measurements which are quantized at a low rate followed by transmission or storage. Applying this scenario, we develop a new quantizer design which aims to attain a high-quality reconstruction performance of a sparse source signal based on analysis-by-synthesis framework. Through simulations, we compare the performance of the proposed quantization algorithm vis-a-vis existing quantization methods.

In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an iterative linear minimum mean squared estimator. Through simulations we show that the algorithm provides better performance than current state-of-the-art algorithms.

There have been emerging lots of applications for distributed storage systems e.g., those in wireless sensor networks or cloud storage. Since storage nodes in wireless sensor networks have limited battery, it is valuable to find a repair scheme with optimal transmission costs (e.g., energy). The optimal-cost repair has been recently investigated in a centralized way. However a centralized control mechanism may not be available or is very expensive. For the scenarios, it is interesting to study optimal-cost repair in a decentralized setup. We formulate the optimal-cost repair as convex optimization problems for the network with convex transmission costs. Then we use primal and dual decomposition approaches to decouple the problem into subproblems to be solved locally. Thus, each surviving node, collaborating with other nodes, can minimize its transmission cost such that the global cost is minimized. We further study the optimality and convergence of the algorithms. Finally, we discuss the code construction and determine the field size for finding feasible network codes in our approaches.

Motivated by the framework of network equivalence theory [1], [2], we present capacity lower bounding models for wireless networks by construction of noiseless networks which can be used to calculate an inner bound for the corresponding wireless network. We first extend the “one-shot” lower bounding model [6] to many-user scenarios, and then propose a two-step update of the one-shot models to incorporate the broadcast nature of wireless transmission. The main advantage of the proposed lower bounding method is its simplicity and the fact that it can be easily extended to larger networks. We demonstrate by examples that the resulting lower bounds can even approach the capacity in some setups.

We investigate the time-varying Gaussian interference channel (IC) in which each source desires to communicate to an intended destination. For the ergodic time-varying IC with global perfect CSI at all terminals, it has been known that with an interference alignment technique each source-destination pair can communicate at half of the interference-free achievable rate. In practice, the channel gains are estimated by transmitting known pilot symbols from the sources, and the channel estimation procedure is hence prone to errors. In this paper, we model the channel estimation error at the destinations by an independent additive Gaussian noise and study the behavior of the ergodic interference alignment scheme with the global noisy CSI at all terminals. Toward this end, we present a closed-form inner bound on the achievable rate region by which we conclude that the achievable degrees of freedom with global perfect CSI can be preserved, if the variance of channel estimation error is proportional to the inverse of the transmitted power.

We propose using linear mappings over finite rings as encoders in the Slepian-Wolf and the source coding for computing problems. It is known that the arithmetic of many finite rings is substantially easier to implement than the one of finite fields. Hence, one of the advantages of using linear mappings over rings, instead of its field counterparts, is reducing implementation complexity. More importantly, the ring version dominates the field version in terms of achieving strictly better coding rates with strictly smaller alphabet size in the source coding for computing problem [1]. This paper is dedicated to proving an achievability theorem of linear source coding over finite rings in the Slepian-Wolf problem. This result includes those given by Elias [2] and Csiszár [3] saying that linear coding over finite fields is optimal, i.e. achieves the Slepian-Wolf region. Although the optimality issue remains open, it has been verified in various scenarios including particularly many cases use non-field rings [1], [4].

We consider secure multi-terminal source coding problems in the presence of a public helper. Two main scenarios are studied: 1) source coding with a helper where the coded side information from the helper is eavesdropped by an external eavesdropper, 2) triangular source coding with a helper where the helper is considered as a public terminal. We are interested in how the helper can support the source transmission subject to a constraint on the amount of information leaked due to its public nature. We characterize the tradeoff between transmission rate, incurred distortion, and information leakage rate at the helper/eavesdropper in the form of a rate-distortion-leakage region for various classes of problems.

This paper studies a new model for cooperative communication, the interfering relay channels. We show that the hash-forward scheme introduced by Kim for the primitive relay channel is capacity achieving for a class of semideterministic interfering relay channels. The obtained capacity result generalizes and unifies earlier capacity results for a class of primitive relay channels and a class of deterministic interference channels.

This note proves that, for any finite set of correlated discrete i.i.d. sources, there always exists a sequence of linear encoders over some finite non-field rings which achieves the data compression limit, the Slepian-Wolf region. Based on this, we address a variation of the data compression problem which considers recovering some discrete function of the data. It is demonstrated that linear encoder over non-field ring strictly outperforms its field counterpart for encoding some function in terms of achieving strictly larger achievable region with strictly smaller alphabet size.

Collecting sensory data at access point (AP) from large number of sensor nodes with low latency is a critical issue. In Wi-Fi, prior to uplink data delivery, AP typically needs to poll large number of sensor nodes sequentially and allocate channel resources to individual node resulting in large latency. An efficient method to reduce the latency and power consumption in wireless sensor networks is to parallelize the polling operation so that multiple nodes can concurrently respond to the poll request of an AP by sending orthogonal sequences with uplink power control. In this paper, we present a conceptually simple uplink power control scheme for the parallel polling operation between AP and low power sensor nodes. We formulate the uplink power control problem as a sequence design problem and show that uplink channel state information (CSI) required to achieve a given target receive SNR can be significantly reduced by carefully designing sequences. We further develop a low complexity instantaneous (fast) power control scheme in order to reduce the number of computations required by the low power sensor node. We also analyze and compare the detection performance of the instantaneous (fast) and average (slow) power control schemes in terms of diversity gain.

This paper considers joint transceiver design and power control for K-user multiple-input multiple-output (MIMO) interference networks. Each source intends to communicate with its corresponding destination at a fixed data rate. Only local channel side information (i.e. knowledge related to the channels directly connected to a terminal) is available at each terminal. We propose iterative algorithms to perform power control to guarantee successful communication while designing transmitter beamforming matrices and receiver filtering matrices according to the interference alignment concept. The proposed algorithms can exhibit a substantial performance improvement compared to the conventional orthogonal transmission schemes.

We study the achievable sum degrees of freedom (DoF) in a class of wireless single-antenna multi-hop half-duplex relay networks. The networks contain Msinformation sources, Md information destinations, and arbitrary layers of relays, each with 2K (K ≥ max{Ms,Md}) half-duplex relays, in between. A cluster successive relaying transmission scheme is applied to conduct the communication: We divide the relays in each layer into two equal-size clusters and activate them successively to efficiently use the available channel. It is shown that in a time-varying fading environment this scheme asymptotically achieves the sum DoF min { MsK/Ms+K, MdK/Md+K-1}, which is irrelevant to the number of hops the source messages have to pass through. This result also implies that, when the number of relays in each layer is infinitely large, the available DoF (i.e. the optimally achievable sum DoF) of the considered networks is min{Ms,Md}. Neither distributed signal processing nor multiple layers of half-duplex relay operation negatively affects the system DoF performance.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, assisted by 2 single-antenna intermediate relays. We investigate spectrally efficient transmission schemes and their achieved sum degrees of freedom (DoF), with quantized delayed channel state information (CSI) feedback. Assuming Grassmannian vector quantization, we study two feedback scenarios according to the feedback range limit, namely global-range feedback, i.e., the source can receive the feedback signals from both the relays and the destinations, and one-hop-range feedback, i.e., each node can only attain the feedback information of its upcoming hop. We establish a sum DoF lower bound for each case. Our results reveal that when the quantization rate at relays BR= α1log2(SNR) and at destinations BD= α2log2(SNR) for min{α1, α2} ≥ 1, the optimal sum DoF 4 over 3 can be achieved with finite-rate delayed feedback.

The integration of multiple services such as the transmission of private, common, and confidential messages at the physical layer is becoming important for future wireless networks in order to increase spectral efficiency. In this paper, bidirectional relay networks are considered, in which a relay node establishes bidirectional communication between two other nodes using a decode-and-forward protocol. In the broadcast phase, the relay transmits additional common and confidential messages, which then requires the study of the bidirectional broadcast channel (BBC) with common and confidential messages. This channel generalizes the broadcast channel with receiver side information considered by Kramer and Shamai. Low complexity polar codes are constructed that achieve the capacity region of both the degraded symmetric BBC, and the BBC with common and confidential messages. The use of polar codes allows an intuitive interpretation of how to incorporate receiver side information and secrecy constraints as different sets of frozen bits at the different receivers for an optimal code design. In order to show that the constructed codes achieve capacity, a tighter bound on the cardinality of an auxiliary random variable used in the converse is found using a method by Salehi.

We investigate capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations with the help of a full-duplex relay node. The two sources and the relay use the same channel resources (i.e. co-channel transmission). We assume Gaussian channels with time-invariant channel gains which are known by all nodes. The two source nodes are connected by orthogonal limited-rate error-free conferencing links. By extending the proof of the converse for the Gaussian relay channel and introducing two lemmas on conditional (co-)variance, we present two genie-aided outer bounds of the capacity region for this multicast relay network. We extend noisy network coding to use source cooperation with the help of the theory of network equivalence. We also propose a new coding scheme, partial-decode-and-forward based linear network coding, which is essentially a hybrid scheme utilizing rate-splitting and messages conferencing at the source nodes, partial decoding and linear network coding at the relay, and joint decoding at each destination. A low-complexity alternative scheme, analog network coding based on amplify-and-forward relaying, is also investigated and shown to benefit greatly from the help of the conferencing links and can even outperform noisy network coding when the coherent combining gain is dominant.

A secret key agreement framework involving three users is considered in which each of the users 1 and 2 intends to share a secret key with user 3 and users 1 and 2 are eavesdroppers with respect to each other. There is a generalized discrete memoryless multiple access channel (GDMMAC) from users 1 and 2 to user 3 where the three users receive outputs from the channel. Furthermore, there is a feedback channel from user 3 to users 1 and 2 through which user 3 sends information extracted from the received output from the GDMMAC to increase the key rates. We consider both noiseless and noisy feedback. In the case of noiseless feedback, a public channel of unlimited capacity from user 3 to users 1 and 2 is used only once. In the case of noisy feedback, a noisy broadcast channel (BC) from user 3 to users 1 and 2 can be repeatedly used, like GDMMAC. In both setups, inner bounds of the secret key capacity region are derived. The secret key capacity region is derived in some special cases where the channel inputs and outputs form Markov chains in certain orders. For illustration, the corresponding results are also derived and discussed for Gaussian channels. The cases with noiseless feedback, noisy feedback, and no feedback at all are compared with each other.

This paper studies a model for communications in wireless networks supported by designated cooperation links. In particular, a 2-user Gaussian one-sided interference channel with two rate-limited and orthogonal communication links between the receivers is considered. A communication protocol for the channel is proposed, which combines rate-splitting and superposition encoding techniques with the conventional decode-forward and compress-forward strategies. It is shown that a careful design of codebooks and coding scheme, which is obtained from intuition based on superposition coding, can greatly reduce the complexity of the strategy. Analytical and numerical results show that the proposed scheme, although not universally optimal, can achieve the capacity region or sum capacity exactly or asymptotically in certain scenarios. Various limits of sum capacity gain due to cooperation are also discussed.

In this paper we present bilayer LDPC convolutional codes for half-duplex relay channels. Two types of codes, bilayer expurgated LDPC convolutional codes and bilayer lengthened LDPC convolutional codes, are proposed for decode-and-forward (DF) relaying. In the case of the binary erasure relay channel, we prove analytically that both code constructions achieve the capacities of the source-relay link and the source-destination link simultaneously, provided that the channel conditions are known when designing the codes. Meanwhile, both codes enable the highest transmission rate possible with DF relaying for a wide range of channel parameters. In addition, the regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. The code construction and performance analysis are extended to the general binary memoryless symmetric channel, where a capacity-achieving performance is conjectured. Numerical results are provided for both types of codes with finite node degrees over binary erasure channels and binary-input additive white Gaussian noise channels, which verify the aforementioned theoretical analysis.

We consider a pair of secondary users that coexist, in a cognitive network, with multiple primary user pairs. The secondary link is supplied with partial network side information (NSI), which comprises message side information and partial channel side information (CSI), available in different levels at both the transmitter and the receiver of the cognitive link. The cognitive transceiver design has to obey predefined quality-of-service (QoS) criteria, that need to be maintained at the primary receivers, and at the same time properly handle the incoming interference from each primary transmitter in order to establish reliable communication. In this framework, we investigate the design and performance of the combined beamforming and orthogonal space-time block coding (BOSTBC) strategy, whose merits are well-documented, as a candidate transmission scheme for the secondary link. We study both aspects, QoS and interference, of the composite problem and characterize how they affect the beamformer design and the cognitive link performance, in the presence of partial NSI. Further, we propose a CSI quality-dependent model for the QoS criteria which yields an interesting trade-off between the cognitive link design and the primary QoS. Numerical results illustrate the system performance in this framework.

This paper considers the problem of communicating over a relay channel with state when noncausal state information is partially available at the nodes. We first establish a lower bound on the achievable rates based on noisy network coding and Gelfand-Pinsker coding, and show that it provides an alternative characterization of a previously known bound. We then introduce the class of state-decoupled relay channels and show that our lower bound is tight for a subclass of semideterministic channels. We also compute the capacity for two specific examples of this subclass - a channel with multiplicative binary fading and a channel with additive Gaussian interference. These examples are not special cases of previous classes of semideterministic relay channels with known capacity.

In this work, coding for a discrete memoryless broadcast channel with random states and two receivers is studied. Each receiver knows one of the two information messages at the sender and wants to know the other one. Assuming the channel state sequence is noncausally known at the sender, an achievable rate region based on the Gel'fand–Pinsker coding strategy is derived and an outer bound to the capacity region is presented. Further, the capacity region for the special case where in addition one receiver knows the channel state is established. An equivalent characterization of an achievable rate region characterizing convex set is derived using Shannon's concept of transmit strategies. This characterization is used to derive an Arimoto–Blahut-like algorithm including a stopping criterion to compute the weighted rate-sum maxima, which can be used to characterize the whole achievable rate region. The tradeoff between the input distribution and the impact of the channel state, the necessity of the time-sharing operation, and the additive Gaussian channel case assuming Costa's choice of auxiliary random variables are discussed by examples.

We consider transmission over a wiretap channel where both the main channel and the wiretapper's channel are binary erasure channels (BEC). A code construction method is proposed using two edge-type low-density parity-check (LDPC) codes based on the coset encoding scheme. Using a single edge-type LDPC ensemble with a given threshold over the BEC, we give a construction for a two edge-type LDPC ensemble with the same threshold. If the given single edge-type LDPC ensemble has degree two variable nodes, our construction gives rise to degree one variable nodes in the code used over the main channel. This results in zero threshold over the main channel. In order to circumvent this problem, the degree distribution of the two edge-type LDPC ensemble is numerically optimized. We find that the resulting ensembles are able to perform close to the boundary of the rate-equivocation region of the wiretap channel. Further, a method to compute the ensemble average equivocation of two edge-type LDPC ensembles is provided by generalizing a recently published approach to measure the equivocation of single edge-type ensembles for transmission over the BEC in the point-to-point setting. From this analysis, we find that relatively simple constructions give very good secrecy performance.

The focus of this paper is to study the performance of the quantize-and-forward (QF) scheme over a half-duplex relay channel that is slowly fading, with the assumption that the channel state information (CSI) is available only at the receiver side. In order to do so, three steps are taken. The first step is to characterize the achievable rate of the QF scheme over a discrete memoryless half-duplex relay channel. Then, the achievable rate over a corresponding additive white Gaussian noise channel is obtained (the specific assumption regarding the CSI in this paper makes this step nontrivial). With the results from the first two steps, performance measures such as outage probability, expected rate, and diversity-multiplexing tradeoff (DMT) over slow fading channels are evaluated. It is shown that the QF scheme can significantly outperform the compress-and-forward scheme at finite signal-to-noise ratio (SNR) and it can achieve the optimal DMT at asymptotically high SNR. Moreover, it is shown that simple feedback from the destination node to the relay node can further improve the performance of the QF scheme.

A multi-antenna cognitive radio network, with a single pair of primary users and a secondary broadcast channel, is considered. Under perfect channel state information (CSI), the rate-optimal strategy for the primary link is waterfilling, resulting in possibly unused dimensions. The secondary base station, supplied with perfect and global CSI, employs block diagonalization for interference-free message precoding and opportunistic interference alignment to avoid disturbing primary communication. In the absence of sufficient resources to serve all secondary users, a low complexity adaptive antenna selection strategy is proposed. This scheme constructs, in each time-slot, the set of active antennas by opportunistically reusing information and resources from the preceding selection round. Numerical results indicate a complexity reduction at a small rateloss penalty, in comparison with existing selection methods.

We investigate the achievable degrees of freedom (DoF) in K-user X networks (K×K X networks) with delayed channel state information at transmitters (CSIT), where partial cooperation (i.e. message sharing) is potentially allowed among transmitters. We consider two possible cooperation scenarios. In the first scenario one of the transmitters serves as a super node which can obtain the messages of the other transmitters. By proper interference alignment (IA) design, we prove that a DoF 2K/K+1 can be achieved almost surely. In the second scenario, there is no super node but each transmitter shares its message to its left-side neighbor. We show that when K = 3, DoF 7/5 is achievable. In both cases, the achieved DoF are shown to be improved compared with non-cooperative X networks. Moreover, we use a simple example to show that sharing a subset of messages may also improve DoF.

We develop a greedy pursuit algorithm for solving the distributed compressed sensing problem in a connected network. This algorithm is based on subspace pursuit and uses the mixed support-set signal model. Through experimental evaluation, we show that the distributed algorithm performs significantly better than the standalone (disconnected) solution and close to a centralized (fully connected to a central point) solution.

We propose a LDPC Convolutional Code ensemble together with an expanding-window message-passing decoder that asymptotically have anytime properties when used for streaming transmission on the binary erasure channel. We show analytically that the decoding erasure probability of these codes decays exponentially over decoding delay and determine the corresponding anytime exponents.

We consider a pair of secondary nodes (SU) coupled, in Z-topology, with multiple pairs of primary nodes (PU). The secondary (cognitive) transmitter is combining beamforming with orthogonal space-time block coding (BOSTBC) and operates under Quality-of-Service (QoS) constraints that must be guaranteed for the primary receivers (PURx). The cognitive link is designed assuming imperfect channel state information (CSI) for all links, available at the SU transmitter (SUTx). Under this premise we characterize the optimal design in terms of CSI quality and interference and evaluate their impact on the performance of BOSTBC transmission in underlay cognitive networks.

This paper proposes two new coding schemes for the discrete memoryless two-way relay channel. The main target is to show the benefits of compress-forward without Wyner-Ziv binning and of layered relaying in networks wherein a relay is to help multiple destinations, that may have unequal channel quality and/or have access to different side information. Numerical results for a Gaussian channel show that the new coding schemes outperform variants of compress-forward relaying and offer a good trade-off between achievable rates and complexity and decoding delay. The idea can also be applied to other relay networks.

We consider the source coding problem of computing functions of correlated sources, which is an extension of the Slepian-Wolf coding problem. We observe that all the discrete functions are in fact restrictions of polynomial functions over some finite field. Based on this observation, we demonstrate how to use Elias' Lemma to enlarge the coding rate region (compared to the Slepian-Wolf region) for a certain class of polynomial functions. We present a classification result about polynomial functions regarding this coding problem. The result is conclusive in the two-sources scenario and, in fact, gives another interpretation of a result by Han and Kobayashi [1, Theorem 1].

We consider a problem of constrained multi-stage coding for channels with a rewrite option. It is a natural extension of Weissman's channels with action-dependent states to the multistage coding case where an encoder in each stage observes its own message as well as all previous-stage messages, inputs, and outputs. In addition to decoding all messages at the final stage, the new reconstruction constraint introduced in Sumszyk and Steinberg's information embedding with reversible stegotext is imposed on the problem such that the decoder is required to be able to reconstruct all channel input sequences reliably. The complete characterization of the channel capacity region is given for the two-stage case, while the inner and outer bounds to the capacity regions for the cases of three or more stages are provided. For the two-stage case, a discussion regarding the rate constraint of the message in the second stage is also given in which we can draw a connection to the two-stage coding condition which appears in our previous study on channel with action-dependent state and reversible input.

We study secret-key agreement over a non-coherent block-fading multiple input multiple output (MIMO) wiretap channel. We give an achievable scheme based on training and source emulation and analyze the rate in the high SNR regime. Based on this analysis we find the optimal number of antennas to use for training. Our main result is that if the sum of the number of antennas at Alice and Bob is larger than the coherence time of the channel, the achievable rate does not depend on the number of antennas at Eve. In this case source emulation is not needed, and using only training is optimal. We also consider the case when there is no public channel available. In this case we show that secret-key agreement is still possible by using the wireless channel for discussion, giving the same number of secure degrees of freedom as in the case with a public channel.

Noisy network coding (NNC) has been shown to outperform standard compress-and-forward (CF) in networks with multiple relays and/or multiple destinations. Recently, short-message noisy network coding (SNNC) has been proved to achieve the same rate region as NNC for independent sources but with significantly reduced encoding delay and decoding complexity. In this paper, we show that when partial cooperation between source nodes is possible, by performing rate-splitting, message exchange, and superposition coding with proper power allocation at the source nodes, SNNC can achieve a strictly larger rate region than NNC. The gain comes from coherent combining at all the receiving nodes.

A secret key agreement setup between three users is considered in which each of the users 1 and 2 intends to share a secret key with user 3 and users 1 and 2 are eavesdroppers with respect to each other. The three users observe i.i.d. outputs of correlated sources and there is a generalized discrete memoryless multiple access channel (GDMMAC) from users 1 and 2 to user 3 for communication between the users. The secret key agreement is established using the correlated sources and the GDMMAC. In this setup, inner and outer bounds of the secret key capacity region are investigated. Moreover, for a special case where the channel inputs and outputs and the sources form Markov chains in some order, the secret key capacity region is derived. Also a Gaussian case is considered in this setup.

The sparse representation problem of recovering an N dimensional sparse vector x from M1-norm of x under the constraint y = Dx. In this paper, the performance of l1-reconstruction is analyzed, when the dictionary is bi-orthogonal D = [O1O2], where O1, O2are independent and drawn uniformly according to the Haar measure on the group of orthogonal M × M matrices. By an application of the replica method, we obtain the critical conditions under which perfect l1-recovery is possible with bi-orthogonal dictionaries.

We consider the symmetric discrete memoryless relay channel with orthogonal receiver components and show that polar codes are suitable for decode-and-forward and compress-and-forward relaying. In the first case we prove that polar codes are capacity achieving for the physically degraded relay channel; for stochastically degraded relay channels our construction provides an achievable rate. In the second case we construct sequences of polar codes that achieve the compress-and-forward rate by nesting polar codes for source compression into polar codes for channel coding. In both cases our constructions inherit most of the properties of polar codes. In particular, the encoding and decoding algorithms and the bound on the block error probability O(2-Nβ) which holds for any 0<;β<;1/2.

In this paper, delay-free, low complexity, joint source-channel coding (JSCC) for transmission of two correlated Gaussian memoryless sources over a Gaussian Multiple Access Channel (GMAC) is considered. The main contributions of the paper are two distributed JSCC schemes: one discrete scheme based on nested scalar quantization, and one hybrid discrete-analog scheme based on a scalar quantizer and a linear continuous mapping. The proposed schemes show promising performance which improves with increasing correlation and are robust against variations in noise level. Both schemes also exhibit a constant gap to the performance upper bound when the channel signal-to-noise ratio gets large.

In this paper we show how the recently proposed linear finite-field deterministic model (LFFM) can be used to design transmission strategies for the corresponding AWGN model of wireless relay networks. The transmission scheme in the AWGN model uses hierarchical modulation to transmit information on multiple layers. We show that a transmission strategy in the LFFM can be translated to the AWGN model if it is coordinated, that is, if the amount of interference is limited in a certain way. We consider two types of coordination, full and partial, with different restrictions on the transmission scheme. In both cases we show that the rate in the AWGN model is at most a constant gap below the rate in the LFFM, which provides a link between the models. Closed-form upper bounds on the gap are derived based on the analysis of noise and interference forwarding. The bounds are evaluated numerically, and the dependency on the system parameters and the parameters of coordination are discussed. The trade-off between full and partial coordination and the corresponding parameters are illustrated.

We study the available degrees of freedom (DOF) of a class of wireless single-antenna multi-user relay networks. In these networks the communications between M unconnected source-destination pairs are provided by a large number of half-duplex relays. To conduct the communications we propose a cluster successive relaying protocol that divides the relays into two equal-size clusters. Unlike the conventional orthogonal relaying protocol that demands all the relays to simultaneously assist the sources, we require the two relay clusters to take turns forwarding the source messages to more efficiently use the channel. In a time-varying fading environment, through appropriate interference alignment the negative impact of inter-user interference can be effectively minimized. Thus the two clusters of half-duplex relays can mimic a cluster of full-duplex relays. When the number of relays is infinitely large, we show that the M-user half-duplex relay networks have M DOF, i.e. their sum capacity can be characterized as CΣ(SNR) = M log (SNR) + o(log (SNR)). This result implies that allowing only distributed processing and half-duplex operation is able to provide the same DOF performance as permitting joint processing and full-duplex operation in wireless relay networks.

We investigate the design of network codes for multiple-user multiple-relay (MUMR) wireless networks with slow fading (quasi-static) channels. In these networks, M users have independent information to be transmitted to a common base station (BS) with the help of N relays, where M ≥ 2 and N ≥ 1 are arbitrary integers. We investigate such networks in terms of diversity order to measure asymptotic performance. For networks with orthogonal channels, we show that network codes based on maximum distance separable (MDS) codes can achieve the maximum diversity order of N+1. We further show that the MDS coding construction of network codes is also necessary to obtain full diversity for linear finite field network coding (FFNC). Then, we compare the performance of the FFNC approach with superposition coding (SC) at the relays. The results show that the FFNC based on MDS codes has better performance than SC in both the high rate and the high SNR regime. Further, we discuss networks without direct source-to-BS channels for N ≥ M. We show that the proposed FFNC can obtain the diversity order N-M+1, which is equivalent to achieving the Singleton bound for network error-correction codes. Finally, we study the network with nonorthogonal channels and show our codes can still achieve a diversity order of N+1, which cannot be achieved by a scheme based on SC.

In this paper, we propose a new family of rate-compatible regular low-density parity-check (LDPC) convolutional codes. The construction is based on graph extension, i.e., the codes of lower rates are generated by successively extending the graph of the base code with the highest rate. Theoretically, the proposed rate-compatible family can cover all the rational rates from 0 to 1. In addition, the regularity of degree distributions simplifies the code optimization. We prove analytically that all the LDPC convolutional codes of different rates in the family are capable of achieving the capacity of the binary erasure channel (BEC). The analysis is extended to the general binary memoryless symmetric channel, for which a capacity-approaching performance can be achieved. Analytical thresholds and simulation results for finite check and variable node degrees are provided for both BECs and binary-input additive white Gaussian noise channels. The results confirm that the decoding thresholds of the rate-compatible codes approach the corresponding Shannon limits over both channels.

For compressive sensing, we endeavor to improve the recovery performance of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a look ahead strategy. The choice of an atom in the current iteration is performed by checking its effect on the future iterations (look ahead strategy). Through experimental evaluations, the effect of look ahead strategy is shown to provide a significant improvement in performance.

This paper proposes a method to optimize the performance of tandem source-channel coding with respect to the mean-squared error by exploiting the unequal error protection coding. More specifically, we formulate a combination of linear programming and grid search to optimize degree distributions for unequal error protected rateless channel codes. An asymptotic upper bound for the mean-squared error of the cascaded system is also derived. By optimizing the corresponding degree distributions of the rateless codes using unequal error protection principles, the proposed scheme has shown promising performance at high resolution region of source coding.

We investigate the capacity bounds for a wireless multicast relay network where two sources simultaneously multicast to two destinations through Gaussian channels with the help of a full-duplex relay node. All the individual channel gains are assumed to be time-invariant and known to every nodes in the network. The transmissions from two sources and from the relay use the same channel resource (i.e. co-channel transmission) and the two source nodes are connected with an orthogonal error-free backhaul. This multicast relay network is generic in the sense that it can be extended to more general networks by tuning the channel gains within the range [0, ∞). By extending the proof of the converse developed by Cover and El Gamal for the Gaussian relay channel, we characterize the cut-set bound for this multicast relay network. We also present a lower bound by using decoding-and-forward relaying combined with network beam-forming.

We consider the problem of gathering measurements in a wireless sensor network consisting of a large number of sensor nodes. A practical joint source-channel coding scheme is proposed and evaluated. The scheme uses lattices to extend a previously proposed scheme to higher dimensions. The key idea is to use conventional point-to-point communication for a subset of the sensor nodes and side-information aware transmission for the remaining sensor nodes. The selection of sensors is based on their instantaneous channel quality. It is shown that by expanding from one to eight dimensions, a gain of about 1 dB is achievable. The overall transmission delay of the scheme is still very low and it is therefore suitable to use in delay-sensitive applications.

Cooperative communication has been proposed as a means to increase the capacity of a wireless link by mitigating the path-loss, fading and shadowing effects of radio propagation. In this paper, we evaluate the efficiency of cooperative communication in large scale wireless networks under interference from simultaneous transmissions. Specifically, we consider tunable spatial reuse time division multiplexing and half-duplex decode-and-forward cooperative relaying on a hop-by-hop basis. We show that hop-by-hop cooperation improves the reliability of the transmissions particularly in the low-SINR or in the low-coding rate regimes. Moreover, hop-by-hop cooperative relaying gains 15 - 20% more throughput compared to simple multihopping in the interference-limited regime, if the relay location and the reuse distance are jointly optimized.

We study the impact of multiple access protocols on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. In the networks K half-duplex decode-and-forward (DF) relays employ a class of finite field network codes to assist in the communication between M independent sources and a common destination. The sources and relays are divided into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the sources and the relays.

Considering a single-antenna M-user interference channel with symmetrically distributed channel gains, when the channel state information (CSI) is globally available, applying the ergodic interference alignment scheme, each transmitter-receiver pair achieves a rate proportional to ½ of a single user's interference-free achievable rate. This is substantially higher than the achievable rate of the conventional orthogonal transmission schemes such as TDMA. Since the rigid requirement on the CSI may be difficult to realize in practice, in this paper we investigate the performance of applying the ergodic interference alignment scheme when the estimation of each channel gain is made globally known through exploiting only a limited feedback signal from the associated receiver of that channel. Under a block fading environment, we provide a lower bound on the achievable average throughput of the network. Our results imply that the better performance of interference alignment over TDMA may still exist even without the assumption of perfect CSI. Also, the trade off between allocating feedback rate of each receiver to the desired channel or the interference channels at deferent SNR region investigated.

In distributed storage systems reliability is achieved through redundant storage nodes distributed in the network. Then a data collector can recover source information even if some nodes fail. To maintain reliability, an autonomous and efficient protocol should be used to reconstruct the failed node. The repair process causes traffic in the network. Recent results in e.g., [1], [2] found the optimal traffic-storage tradeoff, and proposed regenerating codes to achieve the optimality. We investigate the link costs and the impact of network topologies during the repair process. We formulate the minimum cost repair problem in joint and decoupled methods. We investigate the required field size for the joint method. For the decoupled method, we show that the optimization problem is linear for the linear cost. We further show that the cooperation of surviving nodes could efficiently exploit the network topology and reduce the repair cost. The numerical results in tandem, star and grid networks show the benefits of our methods in term of the repair cost.

An achievable rate region based on Shannon's inner bound is given for the two-way relay channel. The relaying scheme operates on noisy received signals and generates new analog values to be transmitted to a destination. The scheme is therefore referred to as noisy analog network coding. The achievable rates are then optimized for a Gaussian two-way relay channel, when the relay is memoryless (this type of relaying is also known as instantaneous relaying). For one particular instance of the channel when the received signal at the relay is noiseless, it is shown that instantaneous noisy analog network coding can be optimal. For the noisy case, a numerical optimization algorithm is presented in order to optimize the instantaneous coding strategy. The optimized analog mapping turns out to be nonlinear and periodic. Finally, it is demonstrated that the achievable rates associated with optimized mappings can outperform those achieved by linear relaying, compress-and-forward, and can operate close to the recently proposed noisy network coding scheme.

We consider a problem of coding for channels with action-dependent states available noncausally to the encoder where the decoder is additionally required to be able to decode the channel input reliably. Lower and upper bounds on the channel capacity are derived. It is shown that the capacity is determined if there exists a maximizing joint probability distribution in the upper bound which satisfies the two-stage coding condition, and it, in turn, reveals the formula duality between this problem and that of source coding with common reconstruction and action-dependent side information. We also state two simple coding schemes and the corresponding achievable rates for the cases where the two-stage coding condition is not fulfilled.

We consider a secure lossy source coding problem with the presence of an eavesdropper who has access to the source description. An encoder wants to compress the source in such a way that the intended decoder can reconstruct the source sequence and satisfy a distortion criterion, while revealing only limited knowledge about the source to the eavesdropper. In our system an action sequence is generated based on the source description with some costs to influence the side information available to the legitimate decoder and the eavesdropper. We provide a complete characterization of the rate-distortion-cost-equivocation region for a discrete source with correlated action-dependent side information at the decoders. The result serves as a fundamental limit for example in secure sensor networking.

We consider transmission over a wiretap channel where both the main channel and the wiretapper's channel are Binary Erasure Channels (BEC). We use regular convolutional LDPC ensembles, introduced by Felström and Zigangirov, together with Wyner's coset encoding scheme. We show that such a construction achieves the whole rate-equivocation region of the BEC wiretap channel. This result is based on the recent observation by Kudekar, Richardson, and Urbanke who proved that convolutional LDPC ensembles exhibit a “threshold saturation” phenomenon which converts the MAP threshold into the BP threshold for transmission over the BEC. Although our present result is less general (since we only consider the BEC) than the elegant code constructions based on polar codes which were recently introduced by several research groups, we see two potential advantages which we believe makes our construction worth considering. First, the proposed codes have a significantly better performance already for moderate lengths. Second, and perhaps more importantly, the proposed construction has the potential of being universal. More precisely, the phenomenon of spatial coupling has been observed empirically to hold for general binary memoryless symmetric channels as well. Hence, we conjecture that our construction is a universal rate-equivocation achieving construction when the main channel and wiretapper's channel are binary memoryless symmetric channels, and the wiretapper's channel is degraded with respect to the main channel.

We approximate the capacity of the Gaussian parallel relay network with general channel gains. Our strategy is to find capacity approximations for the corresponding network in the discrete superposition model and to use the fact that those are an approximation for the Gaussian capacity. The gap between our approximation and the Gaussian capacity is a constant depending only on the number of relays, hence it is a valuable characterization for the regime of high SNR and high rate.

In this paper we present regular bilayer LDPC convolutional codes for half-duplex relay channels. For the binary erasure relay channel, we prove that the proposed code construction achieves the capacities for the source-relay link and the source-destination link provided that the channel conditions are known when designing the code. Meanwhile, this code enables the highest transmission rate with decode-and-forward relaying. In addition, its regular degree distributions can easily be computed from the channel parameters, which significantly simplifies the code optimization. Numerical results are provided for the codes with finite node degrees over binary erasure channels. We can observe that the gaps between the decoding thresholds and the Shannon limits are impressively small.

The original compress-and-forward relaying scheme uses the technique of random binning at the relay node and successive decoding at the destination node. Recently, a scheme (termed the quantize-and-forward scheme in this paper) without binning and using joint decoding at the destination node has been proposed, which has been shown to achieve the same rate as the original compress-and-forward scheme. Since the previous work focuses on the so-called full duplex relay network, in this paper, an adaption of it for relay networks with a half-duplex relay is provided. Coding schemes and achievable rate results are presented for discrete memoryless half-duplex relay channels and half-duplex additive white Gaussian noise (AWGN) relay channels. Moreover, slow fading channels are considered, for which outage-related performance measures are evaluated. Specifically, the outage probability and the expected rate of the quantize-and-forward scheme are derived and compared with other well-known schemes. Furthermore, the diversity-multiplexing tradeoff is derived. It is shown that the quantize-and-forward scheme is a more suitable scheme than the compress-and-forward scheme over slow fading channels and it achieves the optimal diversity-multiplexing trade-off of a half-duplex relay channel.

We present a new achievable rate region for the discrete memoryless Z channel (DM-ZC) using Marton coding with rate splitting. The region is shown to include previously known achievable rate regions. Secondly we study a class of degraded Z channels, the bijective degraded Z channel (BDZC). An outer bound for the BDZC is proved, which is shown to meet the inner bound for the deterministic settings. For the Gaussian Z channel with weak crossover link, we show that if Gaussian inputs are optimal then a coding scheme based on Marton coding without rate splitting achieves to within half a bit per real dimension from the boundary of the capacity region.

In this paper we construct a family of rate-compatible LDPC convolutional codes for Type-II HARQ systems. For each code family, the codes of lower rates are constructed by successively extending the graph of the high-rate base code. Theoretically, the proposed rate-compatible family includes all rates from 0 to 1. We prove analytically that all LDPC convolutional codes in the family are capacity achieving over the binary erasure channel (BEC). Thus, if applied to an idealized HARQ system over the BEC where the channel parameter stays constant within one complete information delivery, the throughput achieves the capacity of the channel. Moreover, the code construction is realized by regular degree distributions, which greatly simplifies the optimization.

Minimum mean square error (MMSE) estimation of block sparse signals from noisy linear measurements is considered. Unlike in the standard compressive sensing setup where the non-zero entries of the signal are independently and uniformly distributed across the vector of interest, the information bearing components appear here in large mutually dependent clusters. Using the replica method from statistical physics, we derive a simple closed-form solution for the MMSE obtained by the optimum estimator. We show that the MMSE is a version of the Tse-Hanly formula with system load and MSE scaled by a parameter that depends on the sparsity pattern of the source. It turns out that this is equal to the MSE obtained by a genie-aided MMSE estimator which is informed in advance about the exact locations of the non-zero blocks. The asymptotic results obtained by the non-rigorous replica method are found to have an excellent agreement with finite sized numerical simulations.

We investigate efficient scheduling algorithms for a relay-aided broadcasting system using random network codes, where our objective is to maximize the transmission efficiency. The broadcast from a base-station (BS) is divided into an information phase and a redundancy phase, where the half-duplex relay assists in the redundancy phase. Time-division transmission is used over packet-erasure channels, where the erasure probabilities of the BS-to-relay and relay-to-user links are lower than the BS-to-user links. Following the information phase, each user provides feedback on the status of received packets to the BS and the relay, which in turn both generate redundancy packets for the redundancy phase. To improve efficiency, we formulate a scheduling problem for the transmissions of redundancy packets from the BS and the relay. We consider two scenarios; namely instantaneous feedback after each redundancy packet, and feedback after multiple redundancy packets. In the first case the schedule is determined using a greedy algorithm, while in the second case the schedule is determined using dynamic programming. To determine the performance with instantaneous feedback, we develop an analytic approach based on a Markov chain. Numerical results show that the transmission efficiency of the dynamic programming algorithm is close to the performance of the greedy algorithm, but requires significantly less feedback.

We propose a new code design for compress-and-forward relaying over bandlimited relay-to-destination channels. The main contribution of this paper is a code design based on joint (source-channel) coding and modulation that uses the correlation between the observations at the relay and the destination as protection against channel errors. This allows for relay nodes with reduced complexity, shifting most of the processing requirements to the destination node. Moreover, by using scalar quantizers with an entropy constraint our system provides remarkable performance in channel conditions where neither amplify-and-forward nor compress-and-forward efficiently exploit the presence of a relay node. Simulation results confirm the benefits of our proposed system.

In this paper, we investigate the wiretap channel in the presence of a cooperative relay node. We analyze and compare the outage performance of three cooperatives schemes: cooperative jamming (CJ), decode-and-forward (DF), and amplify-and-forward (AF) for the Rayleigh slow fading channel. In particular, we derive a closed-form expression for the outage probability for the DF and CJ strategies, which allows an optimal strategy selection in terms of outage performance. We compare the three cooperative schemes through numerical simulations.

Recently, it has been shown that the capacity of certain Gaussian networks can be approximated by the capacity of the corresponding network in the discrete superposition model (DSM). The gap between the capacities is an additive constant only depending on the number of nodes in the network. Hence, the capacity in the DSM is a good approximation in the high SNR regime. Finding this capacity involves optimizing over a finite set of coding strategies. However, the problem space grows with both the number of nodes and with SNR, rendering the optimization infeasible. In this paper we find upper and lower bounds on the capacity in the DSM. We start with the point-to-point channel, and we extend our strategy to the multiple-access channel. We show that the gap between our bounds is at most an additive constant independent of the channel gains. Hence, combining our results with the results, we find closed form bounds on the Gaussian capacity to within an additive constant.

In this article, we consider practical approaches to Costa precoding (also known as dirty paper coding). Specifically, we propose a symbol-by-symbol scheme for cancellation of interference known at the transmitter in a relay-aided downlink channel. For finite-alphabet signaling and interference, we derive the optimal (in terms of maximum mutual information) modulator under a given power constraint. A sub-optimal modulator is also proposed by formulating an optimization problem that maximizes the minimum distance of the signal constellation, and this non-convex optimization problem is approximately solved by semi-definite relaxation. For the case of binary signaling with binary interference, we obtain a closed-form solution for the sub-optimal modulator, which only suffers little performance degradation compared to the optimal modulator in the region of interest. For more general signal constellations and more general interference distributions, we propose an optimized Tomlinson-Harashima precoder (THP), which uniformly outperforms conventional THP with heuristic parameters. Bit-level simulation shows that the optimal and sub-optimal modulators can achieve significant gains over the THP benchmark as well as over non-Costa reference schemes, especially when the power of the interference is larger than the power of the noise.

We investigate cooperative network coding strategies for relay-aided two-source two-destination wireless networks with a backhaul connection between the source nodes. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding schemes, namely, finite field and linear network coding, and lattice coding. To further exploit the backhaul connection, we also propose network coding based beamforming. We measure the performance in term of achievable rates over Gaussian channels, and observe significant gains over benchmark schemes. We derive the achievable rate regions for these schemes and find the cut-set bound for our system. We also show that the cut-set bound can be achieved by network coding based beamforming when the signal-to-noise ratios lie in the sphere defined by the source-relay and relay-destination channel gains.

We study the impact of multiple access strategies on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. The networks contain multiple independent sources, multiple half-duplex decode-and-forward (DF) relays, and one common destination. Instead of separately retransmitting each source message, the relays employ a class of spectrally efficient finite field network codes to assist the sources. It is shown that fully orthogonal or fully non-orthogonal transmission among sources/relays does not necessarily provide optimized DMT performance. We propose a novel transmission protocol that divides the sources and relays into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the multiple sources and relays.

A constant gap between the deterministic model of a class of a wireless relay network and its Gaussian model counterpart is derived. The method is constructive in the sense that a transmission solution in the deterministic model that obeys certain constraints can directly be translated into a transmission in the Gaussian model. We show that the rate in the Gaussian model is at most a constant gap below the rate in the deterministic model, and we derive an upper bound on this gap.

We study the degrees of freedom (DOF) of a single- antenna M-user time-varying parallel relay network, where the communications between M pairs of unconnected sources and destinations are provided by a large number of half-duplex decode-and-forward (DF) relays. Unlike the conventional relaying strategy which demands all the relays to simultaneously assist the sources, we divide the relays into two clusters and permit them to take turns forwarding the source messages. With appropriate interference alignment design, it is proved that the M-user time-varying relay network has M DOF, provided that the number of relays is infinitely large.

We propose a new compress-and-forward implementation for the relay channel based on joint source-channel coding techniques. The relay performs scalar quantization of its observation in combination with a redundant index mapping. Our system utilizes the correlation between the quantized signal and the direct-link observation of the transmitted symbols as redundancy for error protection on the relay-to-destination link. In order to fully exploit this correlation the destination requires iterative decoding to recover the quantized observation sent by the relay. Once regenerated, this quantized signal is optimally combined with the direct-link observation to decode the message conveyed by the source. By quantizing the observed signal itself rather than a measure on the reliability of the information bits (e.g. a posteriori probabilities from a decoder), and by using digital communication methods on the relay- to-destination link our system yields superior performance to that of amplify-and-forward, decode- and-forward and previous implementations of compress-and-forward based on soft decoding.

We derive achievable rate regions for a 2-user Gaussian Z-interference channel with conferencing decoders. We identify different cases where the rate-limitedness of the conference link from the interference-free receiver to the interfered receiver affects the conferencing strategy as well as the achievable rate region. Furthermore, an outer bound to the capacity region based on cut-set and genie-aided bounds is presented.

We investigate the design and use of systematic binary deterministic rateless (BDR) codes for information transmission over block-erasure broadcast channels. BDR codes are designed to obtain a level of maximal distance separable (MDS) properties, making these codes ideal for the considered broadcast scenario. For a certain number of encoded redundancy blocks, we derive an expression for the probability that the MDS properties are maintained. Moreover, if limited feedback is available, we extend the BDR coding protocol to further improve the system performance. Numerical results show that for a finite number of source blocks and as the number of users grows the proposed systematic BDR codes performs significantly better than LT codes. The proposed schemes with feedback have better performance than traditional ARQ schemes.

In this work we provide an achievable rate region for the cellular downlink with three users where two users want to communicate with each other. Due to the side information from the prior uplink, gains are achievable by combining bidirectional and classical broadcast channel coding strategies. A coding theorem for the bidirectional broadcast channel with random state non-causally known at the encoder is generalized to continuous alphabets and applied to Gaussian channels with an average power constraint. The single-user capacities are achievable if one decoder additionally knows the channel state. Accordingly, we see that a more comprehensive view on the information flow in a multi-user network can lead to a larger achievable rate region.

In this paper, a novel cooperative diversity transmission protocol is proposed for a four-node network where a single-antenna source communicates with its intended N-antenna destination with the help of two K-antenna decode-and-forward (DF) relays. Without requiring complex coding strategies at the relays, a sufficiently strong or weak inter-relay channel, or destination-source feedback, the proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff performance the four-node network can provide.

We consider a source coding problem where the encoder can take actions that influence the availability and/or quality of the side information which is available partially and noncausally at the encoder and the decoder. We then characterize the associated achievable tradeoffs between rate, distortion, and cost. In addition, we state and discuss a capacity result for the channel coding dual problem where the formula duality of special cases is recognized.

We study the impact of efficient network coding and multiple access techniques in a two-source two-relay one-destination wireless network through the diversity-multiplexing tradeoff (DMT) analysis. We compare the standard relaying protocol without network coding and the protocols using either a binary network coding (BNC) or an appropriately designed non-binary finite-field network coding (FFNC) at the relays. It is shown that the use of the non-binary FFNC strictly outperforms the other two protocols in terms of DMT. In addition, we propose a new transmission strategy based on the non-binary FFNC design and the non-orthogonal multiple access technique to further improve the DMT performance. Our results highlight the advantages of applying appropriate network coding in multi-source multi-relay networks.

This paper considers a discrete memoryless four-node network where two nodes want to send three independent messages to the other two nodes. The two receiving nodes are allowed to cooperate by means of a unidirectional noiseless link with finite capacity. A coding scheme is proposed which combines rate splitting, block Markov multi-level superposition coding with binning and joint decoding. The general achievable rates are then specialized to degraded channel and Gaussian channel, where it is shown that the sum capacity for the Gaussian channel is achieved under certain conditions. Results in this paper recover and unify previously known results for the discrete memoryless Z channel without cooperation, and results for the Gaussian Z-interference channel with a digital relay link.

We investigate cooperative strategies for relay-aided multi-source multi-destination wireless networks with backhaul support. Each source multicasts information to all destinations using a shared relay. We study cooperative strategies based on different network coding (NC) schemes, namely, finite field NC (FNC), linear NC (LNC), and lattice coding. To further exploit the backhaul connection, we also propose NC-based beam-forming (NBF). We measure the performance in term of achievable rates over Gaussian channels and observe significant gains over a benchmark scheme. The benefit of using backhaul is also clearly demonstrated in most of scenarios.

We determine the rate region of a source coding problem with common reconstruction and action-dependent side information where an action sequence is taken by an encoder over a rate-limited link. We show that the rate region depends only on the sum-rate and the sum-rate distortion and cost function is characterized. The result serves as a fundamental limit in transmission scenarios where the encoder wants to control and monitor the quality of the decoder's reconstruction via the respective uses of action sequences and a common reconstruction constraint.

It has been shown in the literature that network coding can improve the transmission efficiency of wireless broadcasting as compared to traditional ARQ schemes. In this paper, we propose an improved network coding scheme that can asymptotically achieve the theoretical lower bound on transmission overhead for a sufficiently large number of information blocks. The proposed scheme makes use of an index allocation algorithm that distributes information blocks that have been erased during transmission into a minimum number of encoding sets, where each set represents the erased blocks to be jointly network encoded and retransmitted. Numerical results show that the proposed scheme enables higher transmission efficiencies than traditional ARQ, and previously proposed networks coding schemes for wireless broadcasting.

In this paper we propose a new adaptive coding scheme for distributed channel coding for the three-node relay channel. In order to make it feasible for application in wireless sensor networks, the distributed code is built from standard components like Turbo and convolutional codes, and adaptation at the relay is obtained by puncturing the input and output of the employed channel code. The proposed code structure includes distributed Turbo codes and distributed serially concatenated codes as special cases. As the results of our optimization show, significant improvements in terms of rate and coverage are obtained. Compared to theoretical limits a decent performance is achieved considering that the focus is on feasibility.

A novel cooperative diversity transmission protocol using two K-antenna decode-and-forward relays to take turns assisting in the communication between a single-antenna source and its intended N-antenna destination is studied. A (1+2⌈logK⌉)-bit destination-relay feedback signal is exploited to perform relay/antenna selection. Two different simple forwarding strategies are applied at the relays: one relay uses a superposition-coding strategy while the other relay uses a repetition-coding strategy. When the source's frame length is sufficiently large, the optimal diversity- multiplexing tradeoff of the four-node network can be achieved asymptotically if K ≥ 3.

The three-node relay channel with a Gaussian source is studied for transmission subject to a low-delay constraint. A design algorithm for joint source¿channel mappings is proposed and numerically evaluated. The designed system is compared with reference systems, based on modular source and channel coding, and the distortion-rate function for the Gaussian source using known achievable rates for the relay channel. There is a significant gain, in terms of decreased power, in using the (locally) optimized systems compared with the reference systems. The structure of the resulting source mapping and the relay mapping is visualized and discussed in order to gain understanding of fundamental properties of optimized systems. Interestingly, the design algorithm generally produces relay mappings with a structure that resembles Wyner-Ziv compression.

We look at the three-node relay channel and the transmission of an information symbol from the source node to the destination node. We let the relay be a memoryless function and formulate necessary conditions for the optimality of the relay mapping and the detector. Based on these, we propose a design algorithm to find relay mappings such that the symbol error rate at the destination is minimized. The optimized relay mappings are illustrated for different scenarios and the dependency between the relay mapping and the link qualities is discussed in detail. Furthermore, the performance is compared with existing schemes, such as decode-and-forward, amplify-and-forward, and estimate-and-forward. It is shown that there is a significant gain in terms of decreased symbol error rate if the optimized relay mappings are used.

We consider the three-node Gaussian relay channel where the relay observes a noisy version of the interference present in the source--destination link. We investigate three fundamental relaying approaches: linear relaying, memoryless nonlinear relaying, and compress-and-forward (CF). For interference-limited cases, we illustrate that optimized memoryless nonlinear relaying almost achieves the capacity.

We propose a new scheme for cooperative wireless networking based on linear network codes. The network consists of multiple (M ≥ 2) users having independent information to be transmitted to a common basestation (BS), assuming block-fading channels with independent fading for different codewords. The users collaborate in relaying messages. Because of potential transmission errors in links, resulting in erasures, the network topology is dynamic. To efficiently exploit the diversity available by cooperation and time-varying fading, we propose the use of diversity network codes (DNCs) over finite fields. These codes are designed such that the BS is able to rebuild the user information from a minimum possible set of coded blocks conveyed through the dynamic network. We show the existence of deterministic DNCs. We also show that the resulting diversity order using the proposed DNCs is 2 M - 1, which is higher than schemes without network coding or with binary network coding. Numerical results from simulations also show substantial improvement by the proposed DNCs over the benchmark schemes. We also propose simplified versions of the DNCs, which have much lower design complexity and still achieve the diversity order 2 M - 1.

We consider the multiple-access relay channel with two source nodes, one relay node and one destination node. For practical simplicity, we consider orthogonal transmission of the source messages and half-duplex relaying. We also assume that the relay is memoryless and is implemented based on a two-to-one deterministic mapping. Our focus is on proposing and investigating such mappings. Essentially, the proposed relaying functions combine the two incoming analog signals and forward them to the destination, thus we term them as analog network coding mappings. Both linear and non-linear relaying are investigated for the multiple-access relay channel and the special case of the multiple-access two-hop channel. In particular, we suggest to use mappings based on the Archimedean spiral for analog non-linear combining. In addition, we propose to couple spiral mappings with sawtooth-like mappings to exploit the potential side information provided by the direct links of the multiple-access relay channel. In the case of symmetric topology, our proposed scheme can be seen as an extension to the amplify-and-forward scheme, where the asymmetric input/output dimensionality in the relay node is handled before amplifying. We investigate the resulting achievable rate regions and sum rates, and demonstrate significant gains over conventional relaying schemes.

The Gaussian, three-node relay channel with orthogonal receive components (i.e., the transmitted signals from the source and the relay do not interfere with each other) is investigated. For such channels, linear relaying is a suboptimal strategy in general. This is because a linear scheme merely repeats the received noisy signal and does not utilize the available degrees of freedom efficiently. At this background, nonlinear, symbol-wise (as opposed to block-wise) relaying strategies are developed to compensate for the shortcomings of the linear strategy. Optimal strategies are presented for two special cases of the general scenario, and it is shown that memoryless relaying can achieve the capacity. Furthermore, for the general Gaussian relay channel, a parametricpiecewise linear(PL) mapping is proposed and analyzed. The achievable rates obtained by the PL mapping are computed numerically and optimized for a certain number of design parameters. It is concluded that optimized PL relaying always outperforms conventional instantaneous linear relaying (amplify-and-forward). It is also illustrated that the proposed PL relaying scheme can improve on sophisticated block Markov encoding (i.e., decode-and-forward) when the source-relay link is ill-conditioned (relative to other links). Furthermore, PL relaying can work at rates close to those achieved by side-information encoding (i.e., compress-and-forward), but at a much lower complexity.

The optimality of nondynamic DF relaying over a general three-node Nakagami fading channel in terms of the diversity-multiplexing tradeoff (DMT) is established. In particular, when the fading of the relay-source link is less severe than that of the relay-destination link, both orthogonal and nonorthogonal DF protocols are shown to be DMT-optimal over certain ranges of the multiplexing gains. It is also shown that when the multiplexing gains are sufficiently high, the DMT of the DF protocols is completely independent of the statistical properties of the relay-destination link.

A novel cooperative diversity transmission protocol is proposed for a four-node network, in which a single-antenna source communicates with an N-antenna destination with the help of two K-antenna half-duplex decode-and-forward (DF) relays. The proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff (DMT) the system can provide. The resulting DMT performance does not require complex coding strategies at the relays, extremely strong or weak inter-relay channel conditions, or feedback from the destination to the source, which highlights the advantages of the proposed protocol over state of the art techniques.

The problem of designing simple and energy-efficient nonlinear distributed source-channel codes is considered. By demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. Based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. It is shown that the code has a substantial gain compared to a linear source-channel code.

Optimal rate allocation in a networked control system with limited communication resources is instrumental to achieve satisfactory overall performance. In this paper, a practical rate allocation technique for state estimation in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall distortion is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved by using Lagrange duality. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

Several previous works have confirmed that a joint design that combines channel estimation, channel coding and space-time transmission can improve the system performance over that of a separate design. These conclusions are however in general based on unstructured solutions obtained using computer search. The coding gain of these joint designs is therefore limited by both the computer-searchable ¿short¿ code length and the compromise between ¿suboptimal¿ performance and ¿high¿ complexity of their optimal decoding. At this background, we propose a systematic space-time code construction for joint channel estimation and error correction for a two-transmit-antenna and half-rate system. Also proposed is itsmaximum-likelihooddecoder that follows a priority-first search principle. Our systematic code construction, together with a fairly low-complexity optimal decoder, then allows one to work with longer codes with no sacrifice in performance. For codes of short block length, our simulations illustrate that the codes we propose have comparable performance to the best computer-searched codes. For codes of long block lengths that are almost beyond the searchable range of existing computer systems, our codes are still better than some reference designs based on separate channel estimation and error correction components.

We study a three-node Gaussian relay channel with interference which is non-causally known at the source. It is assumed that the interference affects only the relay-destination link. This model is motivated by a downlink scenario, where the source (base station), communicates with two destinations. We present several transmission strategies for this class of relay channels. Our proposed relaying schemes can be divided into two main categories: instantaneous relaying and causal relaying. In the former, the relay functionality is restricted to a memoryless, symbol-by-symbol mapping (linear as well as non-linear). While in the latter, the relay has an infinite memory and utilizes past received blocks to cooperate in the present block. For causal relaying, we investigate decode-and-forward (DF), compress-and-forward (CF), and combined DF and CF. To utilize the knowledge of the interference at the source, superposition coding at the source and Costa encoding at the relay are employed. One interesting observation is that instantaneous relaying can achieve higher rates than those achieved with causal relaying.

In this work, coding for a discrete memoryless broadcast channel with random states and two receivers is studied. Each receiver knows one of the two information sources at the sender and wants to know the other one. Since it is assumed that the sender knows the channel state sequence non-causally, an achievable rate region using Gel'fand-Pinsker-coding is derived. Further, a simple outer bound to the capacity region as well as convexity and cardinality properties regarding the input probability distributions are discussed. The problem is motivated by the application of bidirectional communication between two terminals in a cellular system.

We study the problem of optimal design of relay mappings for the Gaussian relay channel in order to maximize the reliable transmission rate. We consider both Gaussian and modulation constrained signaling at the source. To optimize the relay mapping, we use an iterative integral equation as a necessary condition for optimality. The optimized relay mappings demonstrate significant rate improvement over conventional linear relaying (amplify-and-forward). The optimized mappings allow an efficient utilization of the side information received via the source-destination link at the destination. Hence, the proposed mappings can be considered as an analog, memoryless approach to implementing compress-and-forward relaying with Wyner-Ziv compression in the relay.

We investigate the design of network codes for multiple-user multiple-relay (MUMR) wireless networks. In the networks, multiple (M ges 2) users have independent information to be transmitted to a common base station (BS), with the help of N (N ges 2) relays. The networks consist of independent quasistatic fading channels. We investigate such networks in terms of outage probabilities (to measure asymptotic performance), and propose network codes with linearly independent global encoding kernels for all possible source-relay channel situations (outage or not) to achieve asymptotic optimality. We compare the performance of proposed finite-field network coding (FFNC) and superposition coding (real-domain network coding) in the relays. The results show that proposed FFNC has better performance than superposition coding in high rate regions.

We consider the multiple-access relay channel with two source nodes, one relay node and one destination node. For practical simplicity, we consider orthogonal transmission of the source messages and half-duplex relaying. Further, we assume that the relay is memoryless and is implemented based on a two-to-one deterministic mapping. Essentially the proposed mappings combine the two incoming analog signals and forward them to the destination, thus we term them analog network coding mappings. We investigate both linear and non-linear mappings. In particular, we propose to use mappings based on the Archimedean spiral for analog non-linear combining. In addition, we also propose to couple spiral mappings with sawtooth-like mappings to exploit the potential side information provided by the direct links. We investigate the resulting achievable rate regions and sum rates, and demonstrate significant gains.

We consider a Gaussian relay channel, with the source and relay operating in different frequency bands. Hence, the received signals at the destination are orthogonal. We also assume that the source reuses the frequency band in which the relay is operating, to communicate with another destination. This results in a scenario that can be modeled in such a way that the relay-destination link suffers from an additive interference which is non-causally known at the source. We present different achievable rates for this model, focusing on instantaneous relaying, i.e., the relay output depends solely on the current received signal at the relay. The main conclusion of our work is that to achieve high rates, one should resort to joint design of precoding with non-linear relaying.

We propose a new method of applying network coding for cooperative wireless networks. The network consists of multiple (M ges 2) users having independent information to be transmitted to a common base station (BS). These users form partners and relay information for each other. The transmission blocks are subject to block-fading with independent fading coefficients for each block. Designed non-binary network codes over finite fields are used on top of channel codes. Assuming perfect error detection, erroneous blocks out from channel decoders are discarded (erasure). Thus, relaying nodes may not have information messages of some partners (erasure in inter-user channels), and the BS may not decode some blocks correctly either. The network topology from the point of view of network coding is dynamic. To improve performance, we propose dynamic-network codes (deterministic codes for dynamic networks) for the cooperative networks. The codes are designed such that the BS can rebuild user information from a minimum possible set of coding blocks. In this sense, dynamic-network codes achieve the min-cut for cooperative networks with a dynamic topology. For block fading channels, the proposed scheme obtains high asymptotic performance. For two-user networks, we calculate the resulting outage probabilities. We also present simulations with specific channel codes. Numerical results show substantial improvement over previous schemes. Then, we generalize the results to multiple-user (M > 2) networks. We investigate the existence of deterministic dynamic-network codes for multiple-user networks, and show that the diversity order of the proposed scheme can achieve 2M - 1.

We consider the multiple-access two-hop channel where two source nodes transmit to a destination node via a relay node. The relaying function is memoryless, in contrast to the conventional schemes based on coding with long codewords. That is, we model the operation of the relay as a two-to-one deterministic mapping, which combines the two received analog signals from the sources. This procedure resembles the concept of network coding where information combining is applied in the intermediate nodes. However, as our mapping directly combines the received analog signals without decoding, we coin the term (memoryless) analog network coding mapping. In this paper, both linear and non-linear mappings are studied. In particular, the Archimedean spiral is used for the non-linear 2:1 mapping, inspired by similar work in the context of joint source-channel coding. We discuss both the achievable rate regions and sum rates and demonstrate significant gains of applying the proposed analog mappings in the relay.

This paper investigates the efficiency of cooperative transmission when it is applied in wireless multihop networks. We consider regular linear networks and derive the achievable rate-delay tradeoff when selective relaying through a single relay node is used in each hop. We show that relaying achieves significant gain particularly in the high throughput - high delay regime.

To achieve satisfactory overall performance, optimal rate allocation in a networked control system with highly limited communication resources is instrumental. In this paper, a rate allocation technique for state feedback control in linear dynamic systems over a noisy channel is proposed. The method consists of two steps: (i) the overall cost is expressed as a function of rates at all time instants by means of high-rate quantization theory, and (ii) a constrained optimization problem to minimize the overall distortion is solved. It is shown that a non-uniform quantization is in general the best strategy for state feedback control over noisy channels. Monte Carlo simulations illustrate the proposed scheme, which is shown to have good performance when compared to arbitrarily selected rate allocations.

The problem of designing simple and energy-efficient sensor nodes in a wireless sensor network is considered from a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers for orthogonal channels is proposed and evaluated. In particular the cases of the binary symmetric channel as well as the additive white Gaussian noise channel are studied. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non- ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.

The problem of designing simple and energy efficient nonlinear distributed source-channel codes is considered. By demonstrating similarities between this problem and the problem of bandwidth expansion, a structure for source-channel codes is presented and analyzed. Based on this analysis an understanding about desirable properties for such a system is gained and used to produce an explicit source-channel code which is then analyzed and simulated. One of the main advantages of the proposed scheme is that it is implementable for many sources, contrary to most existing nonlinear distributed source-channel coding systems.

In many communication applications one is interested in transmitting a time-discrete analog-valued (i.e. continuous alphabet) source over a time-discrete analog channel. We study this problem in the case of bandwidth expansion, in the sense that one source sample, X, is transmitted over N-orthogonal channels. An analog source-channel code based on orthogonal polynomials is proposed and analyzed. The code can be generated using a Gram-Schmidt procedure, to fit virtually any source distribution.

The compress-and-forward (CF) strategy achieves the optimal diversity-multiplexing tradeoff (DMT) of a three-node half-duplex relay network in slow fading, under the assumption that the relay has perfect knowledge of all three channel coefficients and that the relay makes use of Wyner-Ziv (WZ) source coding with side information. This paper studies the achievable DMT of the same network when the relay is constrained to make use of standard (non-WZ) source coding. Under a short-term power constraint at the relay, using source coding without side information results in a significant loss in terms of the DMT. For multiplexing gainsrles2/3, this loss can be fully compensated for by using power control at the relay. On the contrary, forrisin (2/3,1), the loss with respect to WZ coding remains significant.

Hybrid digital-analog coding schemes have been proposed in source-channel coding to increase the robustness toward channel mismatch, in the absence of transmitter channel state information (CSIT). Recognizing that the same kind of robustness is needed at the relay in a three-node relay network, we propose several novel relaying protocols based on hybrid digital-analog transmission. We compare the performance of the new schemes with traditional digital-only (decode-and-forward or compress-and-forward) or analog-only (amplify-and-forward) relaying, as well as to performance bounds corresponding to genie-aided compress-and-forward relaying. Our new protocols achieve significant gains in terms of achievable expected rates, and they are able to close in on the performance bounds. In particular, we conclude that the best overall performance is obtained by an adaptive combination of decode-and-forward and hybrid digital-analog relaying.

We look at the three-node relay channel and the transmission of an information symbol from the source node to the destination node. We let the relay be a memoryless function and formulate necessary conditions for the optimality of the relay mapping and the detector. Based on these, we propose a design algorithm to find relay mappings such that the symbol error rate at the destination is minimized. The optimized relay mappings are illustrated for different scenarios and the dependency between the relay mapping and the link qualities is discussed in detail. Furthermore, the performance is compared to the existing schemes detect-and-forward, amplify-and-forward, and estimate-and-forward. It is shown that there is a significant gain in terms of decreased symbol error rate if the optimized relay mapping is used.

The three-node relay channel with a Gaussian source is studied for transmission subject to a low-delay constraint. A joint source-channel coding design algorithm is proposed and numerically evaluated. The designed system is compared to a reference system, based on modular source and channel coding, and the distortion-rate function for the Gaussian source, using known achievable rates on the relay channel. The structure of the source encoder and the relay mapping is visualized and discussed in order to gain understanding of how the system works. The relay mapping gets a structure that resembles a Wyner-Ziv code.

Hybrid digital-analog schemes for bandwidth compression/expansion have been proposed in source-channel coding to increase the robustness in terms of end-to-end distortion, and to combat threshold and leveling-off effects in the absence of transmitter channel state information. In the scenario that the dimension of the incoming codeword is compressed by the relay, we address a similar problem in a three-node half-duplex orthogonal relay network over slow fading channels, and propose two hybrid digital-analog relaying protocols that show significant improvements over digital-only (compress-and-forward and decode-and-forward) and analog-only (amplify-and- forward) schemes, in terms of the maximum achieved expected rate from source to destination.

The problem of transmitting a Gaussian source over a half-duplex fading relay channel with limited channel state feedback is studied. It is shown that under a short-term power constraint, combining a simple feedback scheme with separate source and channel coding outperforms the best known no-feedback strategies even with only a few bits of feedback information. Partial power control is shown to be instrumental in achieving a very fast decaying average distortion, especially in the regime of high bandwidth ratios. Performance limitation due to the lack of full channel state information at the destination is also investigated, where the degradation in terms of the distortion exponent is shown to be significant. However, even in such restrictive scenarios, using partial feedback still yields distortion exponents superior to any no-feedback schemes.

We illustrate how channel optimized vector quantization (COVQ) can be used for channels with both bit-errors and bit-erasures. First, a memoryless channel model is presented, and the performance of COVQ's trained for this channel is evaluated for an i.i.d. Gaussian source. Then, the new method is applied in implementing an error-robust sub-band image coder, and we present image results that illustrate the resulting performance. Our experiments show that the new approach is able to outperform a traditional scheme based on separate source and channel coding.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. Our scheme is based on joint optimization of a modulator and demodulator, subject to a constraint on the average transmit power. The demodulator picks the information symbol (as a function of the received symbol) that minimizes the average error probability. We emphasize that our focus is on transmission in a single (complex) dimension, and hence the proposed technique is a "modulation" rather than a "coding" scheme. We illustrate that the new scheme outperforms Tomlinson-Harashima precoding, which is a classical but suboptimal solution to the one-dimensional known-interference precoding problem. In our simulations, the new approach is able to perform close to the no-interference bound.

The problem of resource allocation to maximize the outage exponent over a fading relay channel using the decode-and-forward protocol with quantized channel state feedback (CSF) is studied. Three different scenarios are considered: relay-to-source, destination-to-relay, and destination-to-source-and-relay CSF. In the relay-to-source CSF scenario, it is found that using merely one bit of CSF to control the source transmit power is sufficient to achieve the multiantenna upper bound in a range of multiplexing gains. In the destination-to-relay CSF scenario, the systems slightly outperform dynamic decode-and-forward (DDF) at high multiplexing gains, even with only one bit of feedback. Finally, in the destination-to-source-and-relay CSF scenario, if the source-relay channel gain is unknown to the feedback quantizer at the destination, the diversity gain only grows linearly in the number of feedback levels, in sharp contrast to an exponential growth for multiantenna channels. In this last scenario, a simple scheme is shown to perform close to the corresponding upper bound.

A simple structure to exploit both long-term and partial short-term channel state information at the transmitter (CSIT) over a family of correlated multiple-antenna channels is proposed. Partial short-term CSIT in the form of a weighting matrix is combined with a unitary transformation based on the long-term channel statistics. The heavily quantized feedback link is directly optimized to maximize the expected achievable rate under different power constraints, using vector quantization and convex optimization techniques on a sample channel distribution. Robustness against errors in the feedback link is also pursued with tools in channel optimized vector quantization. Simulations indicate the benefits of the proposed scheme.

The objective of this work is to assess some fundamental limits for opportunistic spectrum reuse via cognitive radio in a frequency-planned environment. We present a first-order analysis of the signal-to-noise-and-interference situation in a wireless cellular network, and analyze the impact of cognitive users starting to transmit. Two main conclusions emerge from our study. First, obtaining any substantial benefits from opportunistic spatial spectrum reuse in a frequency-planned network without causing substantial interference is going to be very challenging. Second, the cognitive users need to be more sensitive, by orders of magnitude, than the receivers in the primary system, especially if there is significant shadow fading. This latter problem can be alleviated by having cognitive users cooperate, but only if they are separated far apart so that they experience independent shadowing.

The objective of this work is to assess some fundamental limits of operation for cognitive radios in a frequency- planned environment. We present a first-order analysis of the carrier-to-noise-and-interference situation in a cellular wireless network, and analyze the impact of cognitive users starting to transmit. The main conclusion is that introducing cognitive transmitters in a frequency-planned cellular network without causing substantial interference is very challenging.

Sensor nodes in wireless sensor networks should preferable be both cheap and energy efficient. To cope with these requirements an algorithm for designing distributed scalar quantizers optimized for noisy channels is proposed and evaluated. Applying the algorithm results in locally optimal systems. It is demonstrated that the correlation between the sources can be used to reduce the quantization distortion when the channel is close to error-free. If, on the other hand, there are a lot of disturbances on the channel the correlation can be used to introduce protection against channel errors.

Low-rate feedback design for multiple-input multiple-output broadcast channels is studied under a vector quantization framework. Iterative algorithms are proposed to design the partial feedback link, the scheduler, and the linear precoding codebook. It is demonstrated that the gain due to multi-user diversity can be significant even with heavily quantized channel state information at the transmitter. Our results highlight the potential of multi-user diversity, even with simple schemes and extremely-low-rate feedback.

We present a symbol-by-symbol approach to the problem of canceling known interference at the transmitter in a communication system. In the envisioned system, the modulator maps an information symbol (taken from a finite alphabet) and an interference symbol (from the complex field) onto a transmitted constellation point. The demodulator picks the information symbol (as a function of the received symbol) which minimizes the average error probability. We find the optimal modulator-demodulator pair, in the minimum-probability-of-symbol-error sense, via an iterative optimization procedure, for fixed average transmit power. We illustrate that the new scheme can perform close to the no-interference bound, and in particular that it outperforms Tomlinson-Harashima precoding, which is a classical but suboptimal solution to the problem under study.

We study a closed-loop multivariable control system with sensor feedback transmitted over a discrete noisy channel. For this problem, we propose a joint design of the state measurement quantization, protection against channel errors, and control. The proposed algorithm leads to a practically feasible design of time-varying non-uniform encoding and control. Numerical results demonstrate the performance obtained by employing the proposed iterative optimization algorithm.

The problem of source-channel coding over a multiple-antenna channel with quantized channel state information at the transmitter (CSIT) is considered. Upperbounds on the distortion exponents achieved with partial CSIT are developed. The achievable distortion exponent of some hybrid schemes with heavily quantized feedback under both short- and long-term power constraints is also derived. The results demonstrate that excellent performance can be achieved by combining simple schemes with a very coarse feedback link.

The problem of designing simple and energy efficient sensor nodes for a wireless sensor network is considered in a joint source-channel coding perspective. An algorithm for designing distributed scalar quantizers optimized for orthogonal additive white Gaussian noise channels is proposed and evaluated. It is demonstrated that correlation between sources can be useful in order to reduce quantization distortion as well as protecting data when being transmitted over non-ideal channels. It is also demonstrated that the obtained system is robust against channel SNR mismatch.

We study a multiple-layer variable-rate system employing quantized feedback to maximize the expected rate over a single-input single-output slowly fading Gaussian channel. The transmitter uses partial channel-state information, which is obtained via an optimized resolution-constrained feedback link, to adapt the power and to assign code layer rates, subject to different power constraints. To systematically design the system parameters, we develop a simple iterative algorithm that successfully exploits results in the study of parallel broadcast channels. We present the necessary and sufficient conditions for single-layer coding to be optimal, irrespective of the number of code layers that the system can afford. Unlike in the ergodic case, even coarsely quantized feedback is shown to improve the expected rate considerably. Our results also indicate that with as little as one bit of feedback information, the role of multilayer coding reduces significantly

Data transmission through frequency-selective block fading channels is considered in the case where neither the transmitter nor the receiver has any knowledge of the channel coefficients. Standard code design approaches for this scenario take channel uncertainty at the receiver into account by splitting the available channel coherence time into a part dedicated to training symbols utilized for channel estimation and a second part using an error-control coding scheme that is designed without channel uncertainty in mind. In contrast, in this correspondence joint codes are designed that are optimized for communication over the unknown channel and operate over the full coherence time. Using an approximation of the union bound on codeword error probability as design criterion, codes based on general complex-valued symbols are obtained with a gradient search optimization technique. Numerical examples for both single antenna as well as multiple-antenna systems illustrate that significant improvement over training-based schemes can be obtained

The design of signal constellations for a communication system using multiple transmitter antennas over a Rayleigh-fading channel is considered under the assumption that no channel state information (CSI) is available at the transmitter and the receiver has acquired a CSI estimate with known error covariance. This setup encompasses the well-studied scenarios of perfect and no channel knowledge at the receiver and allows a smooth transition between these two cases. The data detection performance as a function of the CSI error covariance is analyzed and used to investigate the design of training blocks if such blocks are transmitted to provide CSI to the receiver. Moreover, two approaches to design constellations adapted to the error covariance of the receiver CSI are presented. Whereas the first approach is based on a generic gradient search method, the second approach uses an appropriate combination of constellations designed for perfect and no CSI at the receiver. Simulations confirm the benefits of the presented designs.

The diversity-multiplexing (D-M) tradeoff in a multi antenna channel with optimized resolution-constrained channel state feedback is characterized. The concept ofminimum guaranteed multiplexing gainin the forward link is introduced and shown to significantly influence the optimal D-M tradeoff. It is demonstrated that power control based on the feedback is instrumental in achieving the D-M tradeoff, and that rate adaptation is important in obtaining a high diversity gain even at high rates. A criterion to determine finite-length codes to be tradeoff optimal is presented, leading to a useful geometric characterization of the class ofextended approximately universal codes. With codes from this class, the optimal D-M tradeoff is achievable by the combination of a feedback-dependent power controller and a single code-book for single-rate or two codebooks for adaptive-rate transmission. Finally, lower bounds to the optimal D-M tradeoffs based on Gaussian coding arguments are also studied. In contrast to the no-feedback case, these random coding bounds are only asymptotically tight, but can quickly approach the optimal tradeoff even with moderate codeword lengths.

Summary form only given. This paper proposes a technique that would address the problem of designing multiple description source codes for J channels. Instead of implementing optimal combining we propose to simply average the decoded output of the individual channels and then adjust the length of the resulting vector based on a theoretical analysis valid for permutation codes. The choice of using permutation codes comes from the fact that their low complexity makes high dimensional vector quantization possible, i.e. large TV's, and our simulations have indicated that the random generation of rotation matrices works well when the dimension is high. For low dimensions, different outcomes of the generated rotation matrices seem to yield quite different performance, meaning that the random design may not be as appropriate for this case. Hence, any vector quantization scheme able to perform quantization in high dimensions could potentially replace the permutation coding in the proposed scheme. We also extend the method to use a fraction rho of the rate R to quantize the quantization error of the decoded data, when all descriptors are received, rather then using the whole rate to quantize the individual descriptors. This improves the performance when receiving all the descriptors at the cost of a decreased performance when some of the descriptors are lost. Varying rho therefore produces different operation points in the tradeoff between side and central distortion. The main advantages of the proposed method are its relatively low complexity and its ability to easily implement any number of descriptions

We design an optimum modulator for the Costa (dirty-paper) precoding problem under the constraint of a binary signaling alphabet, and assuming the interference symbols belong to a binary constellation. We evaluate the performance of our technique in terms of the mutual information between the channel input and output, and compare it to that of Tomlinson-Harashima precoding (THP) with optimized parameters. We show that our optimal modulator is always better than THP. In many relevant scenarios, the performance difference is significant

A simple structure to exploit both long-term and partial short-term channel state information at the transmitter (CSIT) over a family of correlated multiple-antenna channels is proposed. Partial short-term CSIT in the form of a weighting matrix is obtained via a resolution-constrained feedback link, combined with a unitary transformation based on the long-term channel statistics. The feedback link is optimized to maximize the expected achievable rate under different power constraints, using vector quantization techniques. Simulation indicate the benefits of the proposed scheme in all scenarios considered.

Transmit power control to minimize the outage probability over a slowly fading multiple-antenna channel utilizing partial channel state information at the transmitter is studied. The optimal power quantizer is shown to have a "circular" structure. The design problem is then explicitly formulated and numerically solved based on a Gaussian approximation. Asymptotic behavior of the outage probability achieved with a finite-size power codebook is investigated. It is shown that the diversity gain of such a system is a K-order polynomial of the product of the number of transmit and receive antennas, where K is the size of the power codebook. The interesting concept of power-control diversity is discussed.

Encoder-decoder design is considered for a closed-loop scalar control system with feedback transmitted over a binary symmetric channel. We propose an iterative procedure which can jointly optimize adaptive encoder-decoder pairs for a certainty equivalence controller. The goal is to minimize a design criterion, in particular, the linear quadratic (LQ) cost function over a finite horizon. The algorithm leads to a practically feasible design of time-varying non-uniform encoding and decoding. Numerical results demonstrate the promising performance obtained by employing the proposed iterative optimization algorithm

The diversity-multiplexing (D-M) tradeoff over a multiantenna channel with resolution-constrained feedback is characterized. The concept of minimum guaranteed multiplexing gain in the forward link is introduced and shown to significantly influence the optimal D-M tradeoff. It is demonstrated that rate adaptation is important in obtaining a high diversity gain even at high rates. The class of extended approximately universal codes is shown to be tradeoff optimal. With codes from this class, the optimal D-M tradeoff is achievable by the combination of a feedback-dependent power controller and only two codebooks. Two novel lower bounds to the optimal D-M tradeoff based on Gaussian coding arguments are presented. These bounds are only asymptotically tight, but can quickly approach the optimal tradeoff even with moderate codeword lengths

We introduce a new method for multiple description quantization (MDQ), based on sorting a frame of samples and transmitting, as side-information/redundancy, an index that describes the resulting permutation. The sorting-based approach has a similar performance to multiple description scalar quantization and a flexible structure, providing straightforward implementation of multidimensional MDQ

An approach to hybrid digital-analog (HDA) source-channel coding for the communication of analog sources over memoryless Gaussian channels is introduced. The HDA system, which exploits the advantages of both digital and analog systems, generalizes a scheme previously presented by the authors, and can operate for any bandwidth ratio (bandwidth compression and expansion). It is based on vector quantization and features turbo coding in its digital component and linear/nonlinear processing in its analog part. Simulations illustrate that, under both bandwidth compression and expansion modes of operation, the HDA system provides a robust and graceful performance with good reproduction fidelity for a wide range of channel conditions

We consider the design of space-time constellations when the channel state information that is available at the receiver is an estimate of the channel coefficients with known error covariance. This setup encompasses the well-studied scenarios of perfect and no channel knowledge and allows a smooth transition between these two cases. We perform an asymptotic pairwise error probability analysis and derive a criterion to design constellations matched to the level of channel knowledge available at the receiver. Moreover, we use the criterion to assess the power tradeoff between data transmission and channel coefficient acquisition for any given specific set of constellations. Simulation results illustrate the benefit of the proposed criterion

Designing a communication system using joint source-channel coding in general makes it possible to achieve a better performance than when the source and channel codes are designed separately, especially under strict delay-constraints. The majority of work done in joint source-channel coding uses a discrete channel model, corresponding to an analog channel in conjunction with a hard decision modulation scheme. The performance of such a system can however be improved by using soft decision modulation. The main cost is a higher decoding complexity. An alternative is to quantize the soft information and store the pre-calculated soft decision values in a lookup table. In this paper we propose new methods for quantizing soft channel information, to be used in conjunction with soft-decision source decoding. We achieve a performance close to that of a system using unquantized soft information

An inner space-time block code (STBC) is concatenated with a powerful outer code such as the turbo codes to achieve the low probability of error in a system with multiple antennas at the receiver and the transmitter. We study the overall performance of the system by computing the random coding exponent of the channel by the outer code.

We present a novel structure to efficiently exploit channel state information at the transmitter (CSIT) of a multiple-input multiple-output system. At the heart of our transmission scheme lies a mixing matrix that constructively combines the coded symbols before a CSIT-dependent weighting matrix is applied. A method to design the mixing matrix is presented. When combined with turbo-coded modulation, our system performs very close to the capacity limits. With only a few bits per channel use to feedback CSIT, we can achieve a substantial portion of the possible gain with perfect CSIT.

The objective with multiple description coding (MDC) is to code one source of data into multiple bitstreams. The coding is done in such a way that multiple levels of quality is achieved. This means that even if one or a few of the bitstreams are lost, the received bits should make it possible to get an approximated version of the original data. One way to do this is to use pairwise correlating transforms which introduce correlation between the bitstreams. This correlation can be used in order to get an estimate of a lost stream. In this paper a new approach for MDC using pairwise correlating transforms is presented. In this approach, contrary to previous work, quantization of the source data is performed after the data has been transformed. This makes it possible to improve the shape of the quantization cells and to tailor these to the employed transform. We demonstrate that this offers a substantial performance gain compared with previous approaches to MDC using pairwise correlating transforms.

So far, the assumption of no channel knowledge at the transmitter has generally been inherent in the design of space-time codes. This paper, on the other hand, assumes that quantized channel information obtained from a feedback link is available at the transmitter and investigates how such channel information can be incorporated into the design of unstructured space-time block codes. Efficient codes are found by means of a gradient search over a continuous alphabet. Simulation results for an uncorrelated Rayleigh fading scenario using two and four transmit antennas and one receive antenna show the benefits of the code designs.

This work considers how the presence of quantized channel information obtained from a feedback link may be utilized for determining a transmit weighting matrix that improves the performance of a predetermined orthogonal space-time block (OSTB) code. To reduce the effects of feedback delay, quantization errors and feedback channel bit errors, methods based on vector quantization for noisy channels are used in the design of the feedback link. The resulting transmission scheme and feedback link take the imperfect nature of the channel information into account while combining the benefits of conventional beamforming with those provided by OSTB coding.

A closed-loop transmit diversity system is evaluated, taking several major feedback non-idealities into account both separately and in combination, contrasting most previous work in the field. The focus of the study is on the trade-off between quantization errors and feedback periodicity (i.e., a trade-off data). Different number of transmit antennas, transmission rates and receiver velocities are investigated. In addition, we also study the impact of feedback delay. Some main results are as follows: for each receiver velocity and feedback channel rate, there exists an optimal choice of quantization resolution, and hence an optimal choice of feedback period. Furthermore, there is an optimum choice of the number of transmit antennas to employ for a given degree of Doppler and a given feedback rate. Finally, the bit error performance for a fixed feedback rate and a given receiver velocity is practically independent of the transmission rate.

We consider the design of space-time constellations for communication over frequency-selective fading channels where neither the transmitter nor the receiver has any channel state information. The design is based on the asymptotic union bound on error probability as design criterion and the optimization is carried out using a gradient search algorithm. Full multi-antenna multipath diversity gains are demonstrated to be achieved by the designed codes. Compared to other constellations proposed by Hochwald et al., power savings of up to 2 dB are shown to be possible.

We investigate the capacity of a system with multiple transmit and receive antennas, assuming that the transmitter and receiver both have access to (possibly defective) channel-state information. Two different special cases of a general system are studied in detail. Our main results are capacity expressions for these cases and a general conclusion that the encoder can be split into separate "space-time coding" and "direction weighting" or "beamforming," without capacity loss. We also present numerical results illustrating the dependence of capacity on the parameters of a quantization scheme providing channel-state information to the transmitter from the receiver. These results have high practical value since the assumptions behind them are closely related to the ones of the closed-loop mode in the UMTS/WCDMA standard.

The design of space-time codes has so far mostly focused on one-tap channel models while in this paper code design for multi-tap (frequency-selective) channels is discussed when joint channel estimation and data detection is used at the receiver. A transmission model using multiple transmitter and receiver antennas for multi-path propagation through unknown channels is presented and shown to be closely linked to a single-antenna model which has been investigated earlier. Using a randomized search strategy, codes of arbitrary rates for combined channel estimation and error protection over these channels can be designed. Simulations demonstrate the performance of codes designed with the proposed scheme. An example code outperforms an approach based on an extension of the Alamouti scheme.

Multiple transmit and receive antennas can be used in wireless systems to achieve high data rate communication. Efficient space-time codes have been developed that utilize a large portion of the available capacity. These codes are designed under the assumption that the transmitter has no knowledge about the channel. In this work, on the other hand, we consider the case when the transmitter has partial, but not perfect, knowledge about the channel and how to improve a predetermined code so that this fact is taken into account. A performance criterion is derived for a frequency-nonselective fading channel and then utilized to optimize a linear transformation of the predetermined code. The resulting optimization problem turns out to be convex and can thus be efficiently solved using standard methods. In addition, a particularly efficient solution method is developed for the special case of independently fading channel coefficients. The proposed transmission scheme combines the benefits of conventional beamforming with those given by orthogonal space-time block coding. Simulation results for a narrow-band system with multiple transmit antennas and one or more receive antennas demonstrate significant gains over conventional methods in a scenario with nonperfect channel knowledge.

We consider the problem of data transmission over a linear-filter channel with an unknown filter response. Traditionally, the various operations (equalization, detection, decoding) carried out by the receiver of a system that operates over an unknown channel assume access to high-quality estimates of the channel parameters. Such estimates are typically computed by a separate device for channel identification. In contrast, this paper focuses on combined channel estimation, equalization, and decoding. We also study the problem of code design, assuming such combined decoding. More precisely, we investigate nonlinear block coding and present a design criterion and a design algorithm, under the assumptions that a statistical description of the channel is available, and that the structure of the decoder is known to the transmitter. Numerical simulations demonstrate that the new scheme is capable of outperforming four different benchmark schemes.

A joint source-channel hybrid digital-analog (HDA) vector quantization (VQ) system is presented. The main advantage of the new VQ-based HDA system is that it achieves excellent rate-distortion-capacity performance at the design signal-to-noise ratio (SNR) while maintaining a "graceful improvement" characteristic at higher SNRs. It is demonstrated that, within the HDA framework, the parameters of the system can be optimized using an iterative procedure similar to that of channel-optimized vector quantizer design. Comparisons are made with three purely digital systems and one purely analog system. It is found that, at high SNRs, the VQ-based HDA system is superior to the other investigated systems. At low SNRs, the performance of the new scheme can be improved using the optimization procedure and using soft decoding in the digital part of the system. These results demonstrate that the introduced scheme provides an attractive method for terrestrial broadcasting applications.

Previously, space-time codes have been developed that achieve a large portion of the capacity available in communication systems equipped with multiple transmit and receive antennas. These codes are designed under the assumption that the transmitter has no knowledge about the channel. In this work, on the other hand, we consider how the presence of vector quantized channel information obtained from a feedback link may be utilized for improving the performance of a space-time code. The transmission scheme we propose takes the non-perfect nature of the channel information into account while combining the benefits of conventional beamforming with those given by orthogonal space-time block codes. Simulation results demonstrate significant gains over conventional methods and also robustness to feedback channel errors.

We consider joint source-channel and multiuser decoding for frequency selective Rayleigh fading code-division multiple-access channels. The block source-channel encoder is defined by a vector quantizer. We investigate optimal (minimum mean-square error) decoding and "user-separated" decoding of lower complexity. The studied decoders are soft in the sense that they utilize all soft information available at the receiver. Simulations indicate significant performance gains of the introduced decoders compared with a tandem approach that uses maximum-likelihood multiuser detection plus table-lookup decoding.

We introduce a new technique for source coding over noisy channels with intersymbol interference. The focus is on the decoding problem, and we present decoder structures that allow the decoding to be based on soft estimates of the transmitted bits. The new bit-estimate based decoders are optimal for, so called, linear mapping codebooks, and they provide structured low complexity approximations to optimal decoding for general codebooks. We also investigate encoder optimization, and combined source-channel coding design. Numerical simulations demonstrate that the bit-estimate based decoders are able to outperform a two-stage approach that uses Viterbi detection plus table look-up decoding.

We provide a general treatment of optimal soft decoding for vector quantization over noisy channels with finite memory. The main result is a recursive implementation of optimal decoding. We also consider an approach to suboptimal decoding, of lower complexity, being based on a generalization of the Viterbi algorithm. Finally, we treat the problem of combined encoder-decoder design. Simulations compare the new decoders to a decision-based approach that uses Viterbi detection plus table lookup decoding. Optimal soft decoding significantly outperforms the benchmark decoder. The introduced suboptimal decoder is able to perform close to the optimal and to outperform the benchmark scheme at a comparable complexity.

This correspondence introduces an approach to the design of channel robust vector quantizers. Design criteria are derived based on a new expression for the channel distortion as a function of the code vectors of the quantizer. The introduced framework for design and analysis holds for arbitrary discrete memoryless channels. Simulations demonstrate good performance. The correspondence also presents new results regarding the channel distortion as a function of the index assignment given to the code vectors of a vector quantizer.

We present an estimator-based, or soft, vector quantizer decoder for communication over a noisy channel. The decoder is optimal according to the mean-square error criterion, and Hadamard-based in the sense that a Hadamard transform representation of the vector quantizer is utilized in the implementation of the decoder. An efficient algorithm for optimal decoding is derived. We furthermore investigate suboptimal versions of the decoder, providing good performance at lower complexity. The issue of joint encoder-decoder design is considered both for optimal and suboptimal decoding. Results regarding the channel distortion and the structure of a channel robust code are also provided. Through numerical simulations, soft decoding is demonstrated to outperform hard decoding in several aspects.

This paper presents an approach to speech vector quantization of sources exhibiting intervector dependency. We present the optimal decoder based on a collection of received indices. We also present the optimal encoder for such decoding. The optimal decoder can be implemented as a table look-up decoder, however the size of the decoder codebook grows very fast with the size of the collection of utilized indices. This leads us to introduce a method for storing an approximation to the set of optimal decoder vectors, based on linear mapping of a block code vector quantization. In this approach a heavily reduced set of parameters is employed to represent the codebook. Furthermore, we illustrate that the proposed scheme has an interpretation as nonlinear predictive quantization. Numerical results indicate high gain over memoryless coding and memory quantization based on linear predictive coding. The results also show that the sub-optimal approach performs close to the optimal.

An approach to optimal soft decoding for vector quantization (VQ) over a code-division multiple-access (CDMA) channel is presented. The decoder of the system is soft in the sense that the unquantized outputs of the matched filters are utilized directly for decoding (no decisions are taken), and optimal according to the minimum mean-squared error (MMSE) criterion. The derived decoder utilizes a priori source information and knowledge of the channel characteristics to combat channel noise and multiuser interference in an optimal fashion. Hadamard transform representations for the user VQs are employed in the derivation and for the implementation of the decoder. The advantages of this approach are emphasized. Suboptimal versions of the optimal decoder are also considered. Simulations show the soft decoders to outperform decoding based on maximum-likelihood (ML) multiuser detection. Furthermore, the suboptimal versions are demonstrated to perform close to the optimal, at a significantly lower complexity in the number of users. The introduced decoders are, moreover, shown to exhibit near-far resistance. Simulations also demonstrate that combined source-channel encoding, with joint source-channel and multiuser decoding, can significantly outperform a tandem source-channel coding scheme employing multiuser detection plus table lookup source decoding.

A Hadamard-based framework for soft decoding in vector quantization over a Rayleigh fading channel is presented. We also provide an efficient algorithm for decoding calculations. The system has relatively low complexity, and gives low transmission rate since no redundant channel coding is used. Our image coding simulations indicate that the soft decoder outperforms its hard decoding counterpart. The relative gain is larger for bad channels. Simulations also indicate that encoder training for hard decoding suffices to get good results with the soft decoder.

A soft decision decoder is presented. The soft decision decoder is optimal in the mean square sense, if the encoder entropy is full. A source vector estimate is obtained as a linear mapping of a soft Hadamard column. The soft Hadamard column is formed as a generally nonlinear mapping of soft information bits. It is shown that the best index assignment, on the encoder, is obtained in the special case of a linear mapping from the soft information bits. Simulations indicate that the jointly trained system performs better than channel optimized VQ with hard decisions. The interesting case, for applications, of using an ordinary VQ codebook as encoder, together with our soft decision decoder, is also investigated. In our examples this approach gives comparable performance to channel optimized VQ with hard decisions.>