We study the input-entropy-constrained Gaussian channel capacity problem in the asymptotic high signal-to-noise ratio (SNR) regime. We show that the capacity-achieving distribution as SNR goes to infinity is given by a discrete Gaussian distribution supported on a scaled integer lattice. Further, we show that the gap between the input entropy and the capacity decreases to zero exponentially in SNR, and characterize this exponent.

Low Earth orbit (LEO) satellite systems experience significant Doppler effects due to high mobility. While Doppler shifts can be largely compensated, residual frequency uncertainty induces a structured form of channel uncertainty that can limit achievable rates. We model this effect using a block-fading channel of the form $ \mathbf{H} = \mathbf{F} + s \mathbf{G} $, where $s$ is an unknown scalar random parameter. We first study this model in a general $N\times N$ MIMO setting. For this channel, we derive achievable rate lower bounds based on explicit transmission schemes and capacity upper bounds using a duality approach. We study Gaussian signaling and propose a practical superposition scheme with subspace alignment (SN) and successive interference cancellation, where a coarse-layer stream serves as an implicit pilot for decoding refined-layer data. We characterize asymptotic capacity in the near-coherent and high-SNR regimes, and show via Doppler-OFDM simulations that the proposed SN scheme achieves near-optimal rates with low complexity.

Scalar quantization and probabilistic shaping are applied to the distributed source coding of Gaussian sources, with mean-square error distortion. A coding scheme with a modulo interval, dithering, and truncated Gaussian shaping is shown to achieve the corner points of the Berger-Tung region. The theory is illustrated by designing short-block-length multilevel 5G polar codes for Wyner-Ziv (WZ) polar coded quantization (PCQ). WZ-PCQ substantially reduces the total distortion compared to separate PCQ of the source blocks.

We study communication with noiseless feedback over Gaussian channels with stationary autoregressive noise of arbitrary finite order. We introduce a class of Gaussian feedback coding schemes, called SK(2), in which the message process follows a second-order deterministic recursion, and derive a closed-form characterization of its maximal achievable rate under an average-power constraint. As a first-order benchmark, we formulate the SK(1) scheme and show that it provides the branch-complete and sign-consistent reformulation of Butman's equal-energy linear-feedback construction. The SK(2) coding scheme achieves feedback capacity for the additive white Gaussian noise channel and stationary AR(1) Gaussian channels. For certain stationary AR(2) Gaussian channels, genuinely second-order SK(2) scheme strictly outperforms SK(1); for the subclass obtained by interleaving two independent AR(1) noise processes, SK(2) also achieves feedback capacity. These results show that first-order SK/Butman coding scheme is not universally optimal beyond first-order autoregressive noise and disprove the corrected form of Butman's conjecture.

For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under mixed delay constraints over a single communication channel with delayed feedback. We propose a novel layered adaptive causal random linear network coding (LAC-RLNC) approach with forward error correction. LAC-RLNC is a variable-to-variable coding scheme, i.e., a variable amount of recovered information at the receiver over variable short block length and rate. Specifically, for data streaming with base and enhancement layers of content, we characterize a high-dimensional throughput-delay trade-off managed by the adaptive causal layering coding scheme. The base layer is designed to satisfy the strict delay constraints, as it contains the data needed to allow the streaming service. Then, the sender can manage the throughput-delay trade-off of the second layer by adjusting the retransmission rate a-priori and a-posteriori since the enhancement layer, which contains the remaining data to augment the streaming service’s quality, operates under relaxed delay constraints. We provide numerical evidence that the layered network coding strategy significantly boosts performance. Specifically, our results show that LAC-RLNC achieves up to a twofold reduction in mean delay for the base layer compared to the non-layered method, up to 3.5× compared to SR-ARQ, nearing the theoretical lower limit, while maintaining comparable enhancement layer delay and slightly improving throughput. These findings are corroborated by our analytical work, which produces bounds that closely align with simulation results and facilitates the management of the throughput-delay trade-off.

We present a family of relatively simple and unified lower bounds on the capacity of the Gaussian channel under a set of pointwise additive input constraints. Specifically, the admissible channel input vectorsx= (x1, . . . ,xn) must satisfykadditive cost constraints of the form ∑ni=1 ∅j(xi) ≤nΓj,j= 1, 2, . . . ,k, which are enforced pointwise for everyx, rather than merely in expectation. More generally, we also consider cost functions that depend on a sliding window of fixed lengthm, namely,∑ni=mϕj(xi,xi−1, . . . ,xi−m+1) ≤nΓj,j= 1, 2, . . . ,k, a formulation that naturally accommodates correlation constraints as well as a broad range of other constraints of practical relevance. We propose two classes of lower bounds, derived by two methodologies that both rely on the exact evaluation of the volume exponent associated with the set of input vectors satisfying the given constraints. This evaluation exploits extensions of the method of types to continuous alphabets, the saddle-point method of integration, and basic tools from large deviations theory. The first class of bounds is obtained via the entropy power inequality (EPI), and therefore applies exclusively to continuous-valued inputs. The second class, by contrast, is more general, and it applies to discrete input alphabets as well. It is based on a direct manipulation of mutual information, and it yields stronger and tighter bounds, though at the cost of greater technical complexity. Numerical examples illustrating both types of bounds are provided, and several extensions and refinements are also discussed.

As the dense deployment of access points (APs) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems presents significant challenges, per-AP coverage can be expanded using large-scale antenna arrays (LAAs). However, this approach incurs high implementation costs and substantial fronthaul demands due to the need for dedicated RF chains for all antennas. To address these challenges, we propose a hybrid beamforming framework that integrates wave-domain beamforming via stacked intelligent metasurfaces (SIM) with conventional digital processing. By dynamically manipulating electromagnetic waves, SIM-equipped APs enhance beamforming gains while significantly reducing RF chain requirements. We formulate a joint optimization problem for digital and wave-domain beamforming along with fronthaul compression to maximize the weighted sum-rate for both uplink and downlink transmission under finite-capacity fronthaul constraints. Given the high dimensionality and non-convexity of the problem, we develop alternating optimization-based algorithms that iteratively optimize digital and wave-domain variables. Numerical results demonstrate that the proposed hybrid schemes outperform conventional hybrid schemes, that rely on randomly set wave-domain beamformers or restrict digital beamforming to simple power control. Moreover, the proposed scheme employing sufficiently deep SIMs achieves near fully-digital performance with fewer RF chains in the high signal-to-noise ratios regime.

The min-sum approximation is widely used in the decoding of polar codes. Although it is a numerical approximation, hardly any penalties are incurred in practice. We give a theoretical justification for this. We consider the common case of a binary-input, memoryless, and symmetric channel, decoded using successive cancellation and the min-sum approximation. Under mild assumptions, we show the following. For the finite length case, we show how to exactly calculate the error probabilities of all synthetic (bit) channels in time$\mathcal{O}\left(N^{1.585}\right)$, where$N$is the codeword length. This implies a code construction algorithm with the above complexity. For the asymptotic case, we develop two rate thresholds, denoted$R_{\mathrm{L}}=R_{\mathrm{L}}(\lambda)$and$R_{\mathrm{U}}=R_{\mathrm{U}}(\lambda)$, where$\lambda(\cdot)$is the labeler of the channel outputs (essentially, a quantizer). For any$0<\beta<\frac{1}{2}$and any code rate$R<R_{\mathrm{L}}$, there exists a family of polar codes with growing lengths such that their rates are at least$R$and their error probabilities are at most$2^{-N^{\beta}}$. That is, strong polarization continues to hold under the min-sum approximation. Conversely, for code rates exceeding$R_{\mathrm{U}}$, the error probability approaches 1 as the code-length increases, irrespective of which bits are frozen. We show that$0

We study the capacity of the power-constrained additive Gaussian channel with an entropy constraint at the input. In particular, we characterize this capacity in the low signal-to-noise ratio regime at small entropy. This follows as a corollary of the following general result on a moment matching problem: We show that for any continuous random variable with finite moments, the largest number of initial moments that can be matched by a discrete random variable of sufficiently small but positive entropy is three.

Scalar lattice quantization with a modulo operator, dithering, and probabilistic shaping is applied to the Wyner-Ziv (WZ) problem with a Gaussian source and mean square error distortion. The method achieves the WZ rate-distortion pairs. The analysis is similar to that for dirty paper coding but requires additional steps to bound the distortion because the modulo shift is correlated with the source noise. The results extend to vector sources by reverse waterfilling on the spectrum of the covariance matrix of the source noise. Simulations with short polar codes illustrate the performance and compare with scalar quantizers and polar coded quantization without dithering.

This paper studies the ergodic achievable rates of a primitive Gaussian diamond channel with Rayleigh fading. The system is modeled as a two-hop relay channel where a single user communicates with a central processor (CP) through two relays. These relays are agnostic to the user's codebooks and are considered “primitive” because the fronthaul links are error-free but have limited capacity. In this setup, the channel state information (CSI) is assumed to be available only at the relays and not at the CP. Despite the simplicity of this configuration, deriving an accurate characterization of the ergodic capacity is surprisingly challenging. To address this, we first establish an analytical rate upper bound, assuming that the relays can cooperate and that the CP has access to the CSI as well. In order to obtain lower bounds, we resort to specific analytically/numerically tractable achievability strategies. When designing such strategies, we need to take into account that the CP has no CSI and that each relay has only statistical knowledge of the CSI other relay. Under these constraints, we propose two achievable schemes employing different estimation and compression methods at relays. Simulation results show that these schemes achieve performance close to the derived upper bound over a wide range of system parameters.

In this paper we consider the upper bound on the capacity of bandlimited linear, time-invariant (LTI) channels with additive white Gaussian noise (AWGN) in which the input signal is subject to a peak amplitude constraint. It has been shown that when the channel impulse response (CIR) is square-integrable, unit processes attain the largest achievable rate for this channel, [Ozarow et al., 1988], yet the characterization of such processes is associated with a high numerical complexity. In this work we use a characterization of the autocorrelation function for unit processes derived in [Quintanilla, 2008], which facilitates a simpler and structured numerical evaluation of necessary condition for the autocorrelation function of unit processes, originally characterized in [Mcmillan, 1955] and [Shepp, 1967]. We translate this characterization into spectral constraints on the input power spectral density (PSD), following the approach applied in [Shamai and Bar-David, 1989], to tighten their bound on the capacity of such channels.

Predictive models are often required to produce reliable predictions under statistical conditions that are not matched to the training data. A common type of training-testing mismatch is covariate shift, where the conditional distribution of the target variable given the input features remains fixed, while the marginal distribution of the inputs changes. Weighted conformal risk control (W-CRC) uses data collected during the training phase to convert point predictions into prediction sets with valid risk guarantees at test time despite the presence of a covariate shift. However, while W-CRC provides statistical reliability, its efficiency - measured by the size of the prediction sets - can only be assessed at test time. In this work, we relate the generalization properties of the base predictor to the efficiency of W-CRC under covariate shifts. Specifically, we derive a bound on the inefficiency of the W-CRC predictor that depends on algorithmic hyperparameters and task-specific quantities available at training time. This bound offers insights on relationships between the informativeness of the prediction sets, the extent of the covariate shift, and the size of the calibration and training sets. Experiments on fingerprinting-based localization validate the theoretical results.

In this paper, we examine the distribution and convergence properties of the estimation error $W = X - \hat X(Y)$, where $\hat X(Y)$ is the Bayesian estimator of a random variable X from a noisy observation Y = X + σZ where σ is the parameter indicating the strength of noise Z. Using the conditional expectation framework (that is, $\hat X(Y)$ is the conditional mean), we define the normalized error ${{\mathcal{E}}_\sigma } = \frac{W}{\sigma }$ and explore its properties.Specifically, in the first part of the paper, we characterize the probability density function of W and ${{\mathcal{E}}_\sigma }$. Along the way, we also find conditions for the existence of the inverse functions for the conditional expectations. In the second part, we study pointwise (i.e., almost sure) convergence of ${{\mathcal{E}}_\sigma }$ as σ → 0 under various assumptions about the noise and the underlying distributions. Our results extend some of the previous limits of ${{\mathcal{E}}_\sigma }$ as σ → 0 studied under the L2convergence, known as the MMSE dimension, to the pointwise case.

This work investigates a collaborative sensing and data collection system in which multiple uncrewed aerial vehicles (UAVs) sense an area of interest and transmit images to a cloud server (CS) for processing. To accelerate the completion of sensing missions, including data transmission, the sensing task is divided into individual private sensing tasks for each UAV and a common sensing task that is executed by all UAVs to enable cooperative transmission. Unlike existing studies, we explore the use of an advanced cell-free multiple-input-multiple-output (MIMO) network, which effectively manages inter-UAV interference. To further optimize wireless channel utilization, we propose a hybrid transmission strategy that combines time-division multiple access (TDMA), nonorthogonal multiple access (NOMA), and cooperative transmission. The problem of jointly optimizing task splitting ratios and the hybrid TDMA-NOMA-cooperative transmission strategy is formulated with the objective of minimizing mission completion time. Extensive numerical results demonstrate the effectiveness of the proposed task allocation and hybrid transmission scheme in accelerating the completion of sensing missions.

We investigate the primitive diamond relay channel model comprising Gaussian channels with identical frequency responses from the user to the relays and with lossless fronthaul links with a limited rate from the relays to the destination. The model is further extended by addressing correlated noise at the relays, which can be present in the uplink system, for example, due to interference or a jammer. We use the oblivious compress and forward (CF) scheme with distributed compression, and the decode and forward (DF) scheme. The CF system rate is calculated for the correlated noise case and a closed-form formula is derived. The effect of positive and negative correlation on the system rate is shown. It is proved that CF-DF time-sharing scheme is advantageous over a CF-DF superposition coding (SPC) scheme for the correlated noise case. We also analyze another scheme to combine CF and DF, which is based on dirty paper coding (DPC). This scheme’s analysis relies on the correlated noise CF results, providing another motivation for examining this case. It is proved that also in this setting, the CF-DF time-sharing scheme is advantageous over the CF-DF DPC scheme. The optimal time-sharing proportion between CF and DF, and each frequency’s power and rate allocations are determined for positive noise correlation.

This paper proposes coding schemes and information-theoretic converse results for the transmission of heterogeneous delay-constrained traffic over interference networks with random user activity and random data arrivals. The heterogeneous delay-constrained traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent delay constraints. Even for the delay-tolerant traffic, the total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. Each transmitter is assumed to be active with probability$\rho \in [{0,1}]$, and we study two different models for traffic arrival, each model reflecting a different application type. In Model 1, each active transmitter sends a delay-tolerant message, and with probability$\rho _{f} \in [{0,1}]$also transmits an additional delay-sensitive message; in Model 2, each active transmitter sends either a delay-sensitive message with probability$\rho _{f}$or a delay-tolerant message with probability$1- \rho _{f}$. For both models, we derive inner and outer bounds on the fundamental per-user multiplexing gain (MG) region of the symmetric Wyner network as well as inner bounds on the fundamental MG region of the hexagonal model. The per-user MG of an interference network describes the logarithmic growth of the largest average per-user rate that can be achieved over the network at high signal-to-noise ratios (SNR). Our inner and outer bounds on the per-user MG are generally close and coincide in special cases. They also show that when both transmitters and receivers can cooperate, then under Model 1, transmitting delay-sensitive messages hardly causes any penalty on the sum per-user MG, and under Model 2, operating at large delay-sensitive per-user MGs incurs no penalty on the delay-tolerant per-user MG and thus even increases the sum per-user MG. However, when only receivers can cooperate, the maximum delay-tolerant per-user MG that our bounds achieve at maximum delay-sensitive per-user MG is significantly decreased.

Hierarchical federated learning (HFL) is a key technology enabling distributed learning with reduced communication overhead. However, practical HFL systems encounter two major challenges: limited resources and data heterogeneity. In particular, limited resources can result in intolerable system latency, while heterogeneous data across clients can significantly degrade model accuracy and convergence rates. To address these issues and fully leverage the potential of HFL, we propose a novel framework called graph-based joint client and resource orchestration. This framework addresses the challenges of practical networks through joint client clustering and resource allocation. First, we propose a learning process where edge servers employ hypernetworks to achieve edge aggregation. This method can generate personalized client models and extract data distributions without directly exposing data distributions. Then, to characterize the joint effects of limited resources and data heterogeneity, we propose a graph-based modeling method and formulate a joint optimization problem that aims to balance data distributions and minimize latency. Subsequently, we propose a graph neural network-based algorithm to tackle the formulated problem with low-complexity optimization. Numerical results demonstrate significant benefits over existing algorithms in terms of convergence latency, model accuracy, scalability, and adaptability to new distributions.

This work considers a discrete-time Poisson noise channel with an input amplitude constraint A and a dark current parameter A. It is known that the capacity-achieving distribution for this channel is discrete with finitely many points. Recently, for$A$= 0, a lower bound of order ✓A and an upper bound of order A log2 (A) have been demonstrated on the cardinality of the support of the optimal input distribution. In this work, we improve these results in several ways. First, we provide upper and lower bounds that hold for non-zero dark current. Second, we produce a sharper upper bound with a far simpler technique. In particular, for$A$= 0, we sharpen the upper bound from the order of A log2 (A) to the order of A. Finally, some other additional information about the location of the support is provided.

Conformal risk control (CRC) is a recently proposed technique that applies post-hoc to a conventional point predictor to provide calibration guarantees. Generalizing conformal prediction (CP), with CRC, calibration is ensured for a set predictor that is extracted from the point predictor to control a risk function such as the probability of miscoverage or the false negative rate. The original CRC requires the available data set to be split between training and validation data sets. This can be problematic when data availability is limited, resulting in inefficient set predictors. In this paper, a novel CRC method is introduced that is based on cross-validation, rather than on validation as the original CRC. The proposed cross-validation CRC (CV-CRC) extends a version of the jackknife-minmax from CP to CRC, allowing for the control of a broader range of risk functions. CV-CRC is proved to offer theoretical guarantees on the average risk of the set predictor. Furthermore, numerical experiments show that CV-CRC can reduce the average set size with respect to CRC when the available data are limited.

Handling peak-to-average power ratio is a major challenge in the design of communications systems, as current signal designs constrain the power of the generated signal and therefore its peak amplitude is considered as an uncontrolled outcome of the power-constrained signal generation scheme. An alternative signal design approach would be to restrict the peak of the signal's amplitude. The capacity of continuous-time bandlimited linear channels with additive Gaussian noise and peak input amplitude constraint is unknown to date; however, if the channel impulse response has finite energy, then any rate achieved by peak-amplitude constrained waveforms can be achieved by binary waveforms (unit processes). This fact is the basis for the two major previous works that have derived lower bounds on the achievable rate of this channel for the ideal bandlimited case. In this work we propose a different approach for obtaining lower bounds on the capacity of this channel, particularly relevant for linear, time-invariant channels with non-ideal frequency responses. Our approach is based on modulating a subset of the Walsh basis functions and using a fundamental relationship between the peak amplitude and the power of such signals. This approach yields achievable rates for general linear channels.

It has been known for a long time that the mutual information between the input sequence and output sequence of a binary symmetric channel (BSC) is upper bounded by the mutual information between the same input sequence and the output sequence of a binary erasure channel (BEC) with the same capacity. Recently, Samorodnitsky discovered that one may also lower bound the BSC mutual information in terms of the mutual information between the same input sequence and a more capable BEC. In this paper, we strengthen Samorodnitsky's bound for the special case where the input to the channel is distributed uniformly over a linear code. Furthermore, for a general (not necessarily binary) input distribution$P_{X}$and channel$W_{Y\vert X}$, we derive a new lower bound on the mutual information$I(X;Y^{n})$for$n$transmissions of$X\sim P_{X}$through the channel$W_{Y\vert X}$.

A point-to-point integrated sensing and communication (ISAC) system is considered where a transmitter conveys a message to a receiver over a discrete memoryless channel (DMC) and simultaneously estimates the state of the channel through the backscattered signals of the emitted waveform. We derive achievability and converse bounds on the rate-distortion-error tradeoff in the finite blocklength regime, and also characterize the second-order rate-distortion-error region for the proposed setup. Numerical analysis shows that our proposed joint ISAC scheme significantly outperforms traditional time-sharing based schemes where the available resources are split between the sensing and communication tasks.

A coding scheme with scalar lattices is applied to K-receiver, Gaussian, vector broadcast channels with K independent messages, one for each receiver. The method decomposes each receiver channel into parallel scalar channels with known interference and applies dirty paper coding with a modulo interval, amplitude shift keying (ASK), and probabilistic shaping to each scalar channel. The achievable rate tuples include all points inside the capacity region by choosing truncated Gaussian shaping, large ASK alphabets, and large modulo intervals.

In this paper, we consider a remote source coding problem with binary phase shift keying (BPSK) modulation sources, where observations are corrupted by additive white Gaussian noise (AWGN). An intermediate node, such as a relay, receives these observations and performs further compression to find the optimal trade-off between complexity and relevance. This problem can be formulated as an information bottleneck (IB) problem with Bernoulli sources and Gaussian mixture observations, for which no closed-form solution is known. To address this challenge, we propose a unified achievable scheme that employs three different compression strategies for intermediate node processing, i.e., two-level quantization, multi-level deterministic quantization, and soft quantization with tanh function. Comparative analyses with existing methods, such as the Blahut-Arimoto (BA) algorithm and the Information Dropout approach, are performed through numerical evaluations. The proposed analytic scheme is observed to consistently approach the (numerically) optimal performance over a range of signal-to-noise ratios (SNRs), confirming its effectiveness in the considered setting.

We consider the continuous-time ARMA(1,1) Gaussian channel and derive its feedback capacity in closed form. More specifically, the channel is given by$\boldsymbol {y}(t) =\boldsymbol {x}(t) +\boldsymbol {z}(t)$, where the channel input$\{\boldsymbol {x}(t) \}$satisfies average power constraint P and the noise$\{\boldsymbol {z}(t)\}$is a first-order autoregressive moving average (ARMA(1,1)) Gaussian process satisfying$\boldsymbol {z}^{\prime } (t)+\kappa \boldsymbol {z}(t)=(\kappa +\lambda)\boldsymbol {w}(t)+\boldsymbol {w}^{\prime } (t)$, where$\kappa \gt 0,~\lambda \in \mathbb {R}$and$\{\boldsymbol {w}(t) \}$is a white Gaussian process with unit double-sided spectral density. We show that the feedback capacity of this channel is equal to the unique positive root of the equation$P(x+\kappa)^{2} = 2x(x+\vert \kappa +\lambda \vert)^{2}$when$-2\kappa \lt \lambda \lt 0$and is equal to$P/2$otherwise. Among many others, this result shows that, as opposed to a discrete-time additive Gaussian channel, feedback may not increase the capacity of a continuous-time additive Gaussian channel even if the noise process is colored. The formula enables us to conduct a thorough analysis of the effect of feedback on the capacity for such a channel. We characterize when the feedback capacity equals or doubles the non-feedback capacity; moreover, we disprove continuous-time analogues of the half-bit bound and Cover’s$2P$conjecture for discrete-time additive Gaussian channels.

This paper is focused on multiuser load modulation arrays (MU-LMAs) which are attractive due to their low system complexity and reduced cost for millimeter wave (mmWave) multi-input multi-output (MIMO) systems. The existing precoding algorithm for downlink MU-LMA relies on a sub-array structured (SAS) transmitter which may suffer from decreased degrees of freedom and complex system configuration. Furthermore, a conventional LMA codebook with codewords uniformly distributed on a hypersphere may not be channel-adaptive and may lead to increased signal detection complexity. In this paper, we conceive an MU-LMA system employing a full-array structured (FAS) transmitter and propose two algorithms accordingly. The proposed FAS-based system addresses the SAS structural problems and can support larger numbers of users. For LMA-imposed constant-power downlink precoding, we propose an FAS-based normalized block diagonalization (FAS-NBD) algorithm. However, the forced normalization may result in performance degradation. This degradation, together with the aforementioned codebook design problems, is difficult to solve analytically. This motivates us to propose a Deep Learning-enhanced (FAS-DL-NBD) algorithm for adaptive codebook design and codebook-independent decoding. It is shown that the proposed algorithms are robust to imperfect knowledge of channel state information and yield excellent error performance. Moreover, the FAS-DL-NBD algorithm enables signal detection with low complexity as the number of bits per codeword increases.

A point-to-point communication is considered where a roadside unite (RSU) wishes to simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services to a vehicle. The eMBB message arrives at the beginning of a block and its transmission lasts over the entire block. During each eMBB transmission block, random arrivals of URLLC messages are assumed. To improve the reliability of the URLLC transmissions, the RSU reinforces their transmissions by mitigating the interference of eMBB transmission by means of dirty paper coding (DPC). In the proposed coding scheme, the eMBB messages are decoded based on two approaches: treating interference as noise, and successive interference cancellation. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than bounds for standard time-sharing.

Artificial Intelligent (AI) tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabling to quantify their outputs uncertainty. While Bayesian learning can enhance calibration by capturing epistemic uncertainty caused by limited data availability, formal calibration guarantees only hold under strong assumptions about the ground-truth, unknown, data generation mechanism. We propose to leverage the conformal prediction framework to obtain data-driven set predictions whose calibration properties hold irrespective of the data distribution. Specifically, we investigate the design of baseband demodulators in the presence of hard-to-model nonlinearities such as hardware imperfections, and propose set-based demodulators based on conformal prediction. Numerical results confirm the theoretical validity of the proposed demodulators, and bring insights into their average prediction set size efficiency.

The paper characterizes the fundamental limits of integrated sensing and communication (ISAC) systems with a bi-static radar, where the radar receiver is located close to the transmitter and estimates or detects the state based on the transmitter’s channel inputs and the backscattered signals. Two models are considered. In the first model, the memoryless state sequence is distributed according to a fixed distribution and the goal of the radar receiver is to reconstruct this state-sequence with smallest possible distortion. In the second model, the memoryless state is distributed either according to PSor to QSand the radar’s goal is to detect this underlying distribution so that the missed-detection error probability has maximum exponential decay-rate (maximum Stein exponent). Similarly to previous results, our fundamental limits show that the tradeoff between sensing and communication solely stems from the empirical statistics of the transmitted codewords which influences both performances. The main technical contribution are two strong converse proofs that hold for all probabilities of communication error ϵ and excess-distortion probability or false-alarm probability δ summing to less than 1, ϵ+δ < 1. These proofs are based on two parallel change-of-measure arguments on the sets of typical sequences, one change-of-measure to obtain the desired bound on the communication rate, and the second to bound the sensing performance.

We study the state-dependent wiretap channel with non-causal channel state informations at the encoder in an integrated sensing and communications (ISAC) scenario. In this scenario, the transmitter communicates a message and a state sequence to a legitimate receiver while keeping the message and state-information secret from an external eavesdropper. This paper presents a new achievability result for this doubly-secret scenario, which recovers as special cases the best-known achievability results for the setups without security constraints or with only a security constraint on the message. The impact of the secrecy constraint (no secrecy-constraint, secrecy constraint only on the message, or on the message and the state) is analyzed at hand of a Gaussian-state and Gaussian-channel example.

We consider the problem of reliable communication over a discrete memoryless channel (DMC) with the help of a relay, termed the information bottleneck (IB) channel. There is no direct link between the source and the destination, and the information flows in two hops. The first hop is a noisy channel from the source to the relay. The second hop is a noiseless but limited-capacity backhaul link from the relay to the decoder. We further assume that the relay is oblivious to the transmission codebook. We examine two mismatch scenarios. In the first setting, we assume the decoder is restricted to use some fixed decoding rule, which is mismatched to the actual channel. In the second setting, we assume that the relay is restricted to use some fixed compression metric, which is again mismatched to the statistics of the relay input. We establish bounds on the random-coding capacity of both settings, some of which are shown to be ensemble tight.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and MIMO Rayleigh fading. The channel state is an independent and identically distributed (i.i.d.) process known at the relays but unknown to the destination. The relays are oblivious, i.e., they are unaware of the codebook and treat the transmitted signal as a random process with known statistics. The bottleneck constraints prevent the relays to communicate the channel state information (CSI) perfectly to the destination. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

We derive an explicit formula of the feedback capacity for a continuous-time OU-colored AWGN channel. Among many others, this result shows that at least in some cases, the continuous-time Schalkwijk-Kailath coding scheme achieves the feedback capacity for such a channel, and feedback may not increase the capacity of a continuous-time ACGN channel even if the noise process is colored.

In the quest for efficient multiple access schemes for future wireless systems, sparse code-domain non-orthogonal multiple access (NOMA) has gained considerable interest, potentially achieving significant performance enhancement in overloaded settings at feasible complexity. This paper revisits an uplink model with two classes of users distinguished by their received powers, each employing regular sparse code-domain NOMA (where a fixed and finite number of orthogonal resources is occupied by each user and vice versa). Introducing random coordinate transformations, the achievable ergodic class throughput region is analytically specified in the large system limit, and shown to strictly contain the achievable region with randomly spread dense code-domain NOMA, while closing the gap to the Cover-Wyner capacity region. Furthermore, harnessing tools from free probability theory, an exact closed form expression is derived for the total achievable sum-rate, which has been so far characterized in analogous settings by means of lower and upper bounds. The analysis significantly broadens the information theoretic perspective on code-domain NOMA applications, and establishes key tools for generalizing the results to more complex models for future systems.

Consider a channel$\mathbf {Y}= \mathbf {X}+ \mathbf {N}$where$\mathbf {X}$is an$n$-dimensional random vector, and$\mathbf {N}$is a multivariate Gaussian vector with a full-rank covariance matrix$\boldsymbol {\mathsf {K}}_{ \mathbf {N}}$. The object under consideration in this paper is the conditional mean of$\mathbf {X}$given$\mathbf {Y}={\mathbf{y}}$, that is${\mathbf{y}} \mapsto \mathbb {E} [\mathbf {X}| \mathbf {Y}={\mathbf{y}}]$. Several identities in the literature connect$\mathbb {E}[\mathbf {X}| \mathbf {Y}={\mathbf{y}}]$to other quantities such as the conditional variance, score functions, and higher-order conditional moments. The objective of this paper is to provide a unifying view of these identities. In the first part of the paper, a general derivative identity for the conditional mean estimator is derived. Specifically, for the Markov chain$\mathbf {U}\leftrightarrow \mathbf {X}\leftrightarrow \mathbf {Y}$, it is shown that the Jacobian matrix of$\mathbb {E}[\mathbf {U}| \mathbf {Y}={\mathbf{y}}]$is given by$\boldsymbol {\mathsf {K}}_{ \mathbf {N}}^{-1} \boldsymbol {\mathsf {Cov}} (\mathbf {X}, \mathbf {U}| \mathbf {Y}={\mathbf{y}})$where$\boldsymbol {\mathsf {Cov}} (\mathbf {X}, \mathbf {U}| \mathbf {Y}={\mathbf{y}})$is the conditional covariance. In the second part of the paper, via various choices of the random vector$\mathbf {U}$, the new identity is used to recover and generalize many of the known identities and derive some new identities. First, a simple proof of the Hatsel and Nolte identity for the conditional variance is shown. Second, a simple proof of the recursive identity due to Jaffer is provided. The Jaffer identity is then further explored, and several equivalent statements are derived, such as an identity for the higher-order conditional expectation (i.e.,$\mathbb {E}[\mathbf {X}^{k}| \mathbf {Y}]$) in terms of the derivatives of the conditional expectation. Third, a new fundamental connection between the conditional cumulants and the conditional expectation is demonstrated. In particular, in the univariate case, it is shown that the$k$-th derivative of the conditional expectation is proportional to the$(k+1)$-th conditional cumulant. A similar expression is derived in the multivariate case.

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates general rate splitting for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. We consider linear beamforming at the BS and joint decoding at each user in the slow fading and fast fading scenarios. In the slow fading scenario, we consider the maximization of the weighted sum average rate, which is a challenging nonconvex stochastic problem with numerous variables. To reduce computational complexity, we decouple the original nonconvex stochastic problem into multiple nonconvex deterministic problems, one for each system channel state. Then, we propose an iterative algorithm for each deterministic problem to obtain a Karush-Kuhn-Tucker (KKT) point using the concave-convex procedure (CCCP). In the fast fading scenario, we consider the maximization of the weighted sum ergodic rate. This problem is more challenging than the one for the slow fading scenario, as it is not separable. First, we propose a stochastic iterative algorithm to obtain a KKT point using stochastic successive convex approximation (SSCA) and the exact penalty method. Then, we propose two low-complexity iterative algorithms to obtain feasible points with promising performance for two cases of channel distributions using approximation and CCCP. The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes in both scenarios and reveal the design insights of general rate splitting for general multicast.

A common way of characterizing minimax estimators in point estimation is by moving the problem into the Bayesian estimation domain and finding a least favorable prior distribution. The Bayesian estimator induced by a least favorable prior, under mild conditions, is then known to be minimax. However, finding least favorable distributions can be challenging due to inherent optimization over the space of probability distributions, which is infinite-dimensional. This paper develops a dimensionality reduction method that allows us to move the optimization to a finite-dimensional setting with an explicit bound on the dimension. The benefit of this dimensionality reduction is that it permits the use of popular algorithms such as projected gradient ascent to find least favorable priors. Throughout the paper, in order to make progress on the problem, we restrict ourselves to Bayesian risks induced by a relatively large class of loss functions, namely Bregman divergences.

Wyner's soft-handoff network is considered where transmitters simultaneously send messages of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. Due to the low-latency requirements, the URLLC messages are transmitted over fewer channel uses compared to the eMBB messages. To improve the reliability of the URLLC transmissions, we propose a coding scheme with finite blocklength codewords that exploits dirty-paper coding (DPC) to precancel the interference from eMBB transmissions. Rigorous bounds are derived for the error probabilities of eMBB and URLLC transmissions achieved by our scheme. Numerical results illustrate that they are lower than for standard time-sharing.

Immersive video, such as virtual reality (VR) and multi-view videos, is growing in popularity. Its wireless streaming is an instance of general multicast, extending conventional unicast and multicast, whose effective design is still open. This paper investigates the optimization of general rate splitting with linear beamforming for general multicast. Specifically, we consider a multi-carrier single-cell wireless network where a multi-antenna base station (BS) communicates to multiple single-antenna users via general multicast. Linear beamforming is adopted at the BS, and joint decoding is adopted at each user. We consider the maximization of the weighted sum rate, which is a challenging nonconvex problem. Then, we propose an iterative algorithm for the problem to obtain a KKT point using the concave-convex procedure (CCCP). The proposed optimization framework generalizes the existing ones for rate splitting for various types of services. Finally, we numerically show substantial gains of the proposed solutions over existing schemes and reveal the design insights of general rate splitting for general multicast.

For data streaming applications, existing solutions are not yet able to close the gap between high data rates and low delay. This work considers the problem of data streaming under mixed delay constraints over a single communication channel with delayed feedback. We propose a novel layered adaptive causal random linear network coding (LAC-RLNC) approach with forward error correction. LAC-RLNC is a variable-to-variable coding scheme, i.e., variable recovered information data at the receiver over variable short block length and rate is proposed. Specifically, for data streaming with base and enhancement layers of content, we characterize a high dimensional throughput-delay trade-off managed by the adaptive causal layering coding scheme. The base layer is designed to satisfy the strict delay constraints, as it contains the data needed to allow the streaming service. Then, the sender can manage the throughput-delay trade-off of the second layer by adjusting the retransmission rate a priori and posterior as the enhancement layer, that contains the remaining data to augment the streaming service’s quality, is with the relax delay constraints. We numerically show that the layered network coding approach can dramatically increase performance. We demonstrate that LAC-RLNC compared with the non-layered approach gains a factor of three in mean and maximum delay for the base layer, close to the lower bound, and factor two for the enhancement layer.

A diamond network is considered in which the central processor is connected, via backhaul noiseless links, to multiple conferencing base stations that communicate with a single user over a multiple access channel. We propose coding techniques along with lower and upper bounds on the capacity. Our achievability scheme uses a common cloud coding strategy based on the technique proposed by Wand, Wigger, and Zaidi (2018) and extends it beyond two relays. Our upper bounds generalize the method proposed by Bidokhti and Kramer (2016) and lead to new bounds for the multiple conferencing relay setting. Specializing our upper bounds to the two relay scenario, we provide new bounds and improve the state-of-the-art.

Motivated by the emerging technology of oblivious processing in remote radio heads with universal decoders, we formulate and analyze in this paper a compound version of the information bottleneck problem. In this problem, a Markov chain X→Y→ Z is assumed, and the marginals PXand PYare set. The mutual information between X and Z is sought to be maximized over the choice of the conditional probability of Z given Y from a given class, under the worst choice of the joint probability of the pair (X,Y) from a different class. We provide values, bounds, and various characterizations for specific instances of this problem: the binary symmetric case, the scalar Gaussian case, the vector Gaussian case, the symmetric modulo-additive case, and the total variation constraints case. Finally, for the general case, we propose a Blahut-Arimoto type of alternating iterations algorithm to find a consistent solution to this problem.

A broadcast/multicast communication system is studied in which layered division multiplexing (LDM) is applied to support differential quality-of-service (QoS) levels. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled during deployment. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and is shown to be monotonically decreasing as the dataset grows larger. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; and that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves an expected rate close to that achieved when the transmitter knows the fading distribution.

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and Rayleigh fading. Due to the bottleneck constraint, it is impossible for the relays to inform the destination node of the perfect channel state information (CSI) in each realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also three achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

The information bottleneck (IB) problem of jointly stationary Gaussian sources is considered. A water-filling solution for the IB rate is given in terms of its SNR spectrum and whose rate is attained via frequency domain test-channel realization. A time-domain realization of the IB rate, based on linear prediction, is also proposed, which lends itself to an efficient implementation of the corresponding remote source-coding problem. A compound version of the problem is addressed, in which the joint distribution of the source is not precisely specified but rather in terms of a lower bound on the guaranteed mutual information. It is proved that a white SNR spectrum is optimal for this setting.

Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst channel conditions. Targeting ultra-reliable communication scenarios, this paper takes a complementary approach by introducing the conditional value-at-risk (CVaR) rate as the expected rate of a worst-case fraction of clients. To support differential quality-of-service (QoS) levels in this class of clients, layered division multiplexing (LDM) is applied, which enables decoding at different rates. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled offline. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and the sample complexity is shown to increase as the designated fraction of worst-case clients decreases. Considering this theoretical result, meta-learning is introduced as a means to reduce sample complexity by leveraging data from previous deployments. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; that, for sufficiently large datasets, the proposed mirror-descent-based layer optimization scheme achieves a CVaR rate close to that achieved when the transmitter knows the fading distribution; and that meta-learning can significantly reduce data requirements.

We investigate a special case of diamond relay comprising Gaussian channels with an identical frequency response from the user to the relays, and with lossless fronthaul links with limited rate from the relays to the destination. We use the oblivious compress and forward (CF) scheme with with distributed compression, and a decode and forward (DF) scheme, where each relay decodes the whole message and sends half of the bits to the destination. It is proved that optimal CF-DF time-sharing scheme is advantageous over the CF-DF superposition scheme. We derive an achievable rate by using time-sharing between CF and DF. The optimal time-sharing proportion between CF and DF, and the power and rate allocations are different for each frequency and are fully determined.

It has been shown that the SK scheme, which was proposed by Schalkwijk and Kailath, is a self-secure capacity-achieving (SSCA) feedback scheme for the Gaussian wiretap channel, i.e., the SK scheme not only achieves the feedback capacity of the Gaussian channel, but also is secure by itself and achieves the feedback secrecy capacity of the Gaussian wiretap channel. For the multi-user wiretap channels, very recently, it has been shown that Ozarow’s capacity-achieving feedback scheme for the two-user Gaussian multiple-access channel (GMAC) is the SSCA feedback scheme for the two-user Gaussian multiple-access wiretap channel (GMAC-WT). In this paper, first, we propose a SSCA feedback scheme for the two-user GMAC-WT with degraded message sets (GMAC-WT-DMS). Next, we extend the above scheme to the two-user GMAC-WT-DMS with noncausal channel state information at the transmitters (NCSIT), and show that the extended scheme is also a SSCA feedback scheme. Finally, we derive outer bounds on the secrecy capacity regions of the two-user GMAC-WT-DMS with or without NCSIT, and numerical results show the rate gains by the feedback.

We present a condition for 1/2 degree of freedom for each user in constant$K$-user single-antenna interference channels. This condition is sufficient for all and necessary for almost all channel matrices. Moreover, it applies to all channel topologies, i.e., to fully-connected channels as well as channels that have individual links absent, reflected by corresponding zeros in the channel matrix. Moreover, it captures the essence of interference alignment by virtue of being expressed in terms of a generic injectivity condition that guarantees separability of signal and interference. Finally, we provide codebook constructions achieving 1/2 degree of freedom for each user for all channel matrices satisfying the condition we identified.

For a continuous-time additive white Gaussian noise (AWGN) channel with possible feedback, it has been shown that as sampling gets infinitesimally fine, the mutual information of the associative discrete-time channels converges to that of the original continuous-time channel. We give in this paper more quantitative strengthenings of this result, which, among other implications, characterize how over-sampling approaches the true mutual information of a continuous-time Gaussian channel with bandwidth limit. The assumptions in our results are relatively mild. In particular, for the non-feedback case, compared to the Shannon-Nyquist sampling theorem, a widely used tool to connect continuous-time Gaussian channels to their discrete-time counterparts that requires the band-limitedness of the channel input, our results only require some integrability conditions on the power spectral density function of the input.

We consider a two-terminal variant (double-sided) of the information bottleneck problem, which is related to biclus-tering. In our setup,$x$and Y are dependent random variables and the problem is to find two independent channels$\mathrm{P}_{\cup 1\times}$and$\mathrm{p}_{\vee 1!}$(setting the Markovian structure$\cup\rightarrow\times\rightarrow \mathrm{Y}\rightarrow$V) that maximize$I(\cup;\mathrm{V})$subject to constraints on the relevant mutual information expressions:$I(\cup;\mathrm{X})$and$I(\mathrm{V};\mathrm{Y})$. For jointly Gaussian X and Y, we show that Gaussian channels are optimal in the low-SNR regime, but not for general SNR. Similarly, it is shown that for a doubly symmetric binary source, binary symmetric channels are optimal when the correlation is low, and are suboptimal for high correlation. We conjecture that Z and S channels are optimal when the correlation is 1 (i.e.,$\mathrm{X}=\mathrm{Y})$, and provide supporting numerical evidence.

Reconfigurable intelligent surfaces (RISs) are typically used in multi-user systems to mitigate interference among active transmitters. In contrast, this paper studies a setting with a conventional active encoder as well as a passive encoder that modulates the reflection pattern of the RIS. The RIS hence serves the dual purpose of improving the rate of the active encoder and of enabling communication from the second encoder. The capacity region is characterized, and information-theoretic insights regarding the trade-offs between the rates of the two encoders are derived by focusing on the high- and low-power regimes.

The classic wiretap channel (WTC) problem is concerned with a transmitter (Alice) that wants to send a message$W$to the intended receiver (Bob) while keeping it secret from a passive eavesdropper (Eve). However, under certain communication scenarios, the user may not be interested in hiding the entire message from the eavesdropper, but rather in hiding its most sensitive attributes. While classic wiretap coding is capable of hiding these salient message attributes, it may be too stringent and better communication rates may be achievable. Motivated by the above, in this paper, we introduce and study the latent variable wiretap channel (LV-WTC) problem. Under this setting, the transmitter is interested in sending the message$W$to the intended receiver while keeping a correlated latent variable$S$(which models privacy sensitive attributes) secret from the eavesdropper. We present a message splitting based achievable scheme for the LV-WTC problem, which adapts to the structure of the conditional distribution PS|Wto achieve higher rates compared to the classical WTC. Several open problems and future directions that originate from this new communication problem are also discussed.

This paper considers the information bottleneck (IB) problem of a Rayleigh fading multiple-input multiple-out (MIMO) channel. Due to the bottleneck constraint, it is impossible for the oblivious relay to inform the destination node of the perfect channel state information (CSI) in each channel realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node can get the perfect CSI at no cost and two achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

Spectrally efficient non-orthogonal multiple access (NOMA) schemes are of paramount importance for 5G and beyond wireless networks, among which code-domain NOMA is a prominent technology. Two extreme paradigms have lead to analytically tractable benchmarks for code-domain NOMA performance. The first relies on independent random dense spreading signatures (fully utilizing the available orthogonal resources) and suits settings with sporadic user activity, where signature coordination is hard to enforce. The second paradigm is regular sparse NOMA (where a small fixed number of resources is allocated to each user, and vice versa). It exhibits superior performance, while facilitating near-optimal multiuser detection using iterative message-passing algorithms. However, it also requires fully coordinated signatures and might be impractical in certain use-cases. In this paper, we investigate for the first time a mixed setting, where one class of users obeys the latter paradigm, while a second class obeys the former. The achievable ergodic class-throughput region is derived, while considering the large-system limit. Particularly, the total achievable sum-rate is characterized by means of a free additive convolution of probability measures, while relying on a recent strong representation theorem. The corresponding low-SNR characterization is further shown to admit closed-form expressions. The analysis provides insightful tools for investigating various use-cases of interest for future networks.

This paper analyses the multiplexing gain (MG) achievable over Wyner’s symmetric network with random user activity and random arrival of mixed-delay traffic. The mixed-delay traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from transmitter and receiver cooperation since the latter is subject to stringent decoding delays. The total number of cooperation rounds at transmitter and receiver sides is limited to D rounds. We derive inner and outer bounds on the MG region. In the limit as D$\rightarrow\infty$, the bounds coincide and the results show that transmitting delaysensitive messages does not cause any penalty on the sum MG. For finite D our bounds are still close and prove that the penalty caused by delay-sensitive transmissions is small.

A communication link aided by a reconfigurable intelligent surface (RIS) is studied in which the transmitter can control the state of the RIS via a finite-rate control link. Channel state information (CSI) is acquired at the receiver based on pilot-assisted channel estimation, and it may or may not be shared with the transmitter. Considering quasi-static fading channels with imperfect CSI, capacity-achieving signalling is shown to implement joint encoding of the transmitted signal and of the response of the RIS. This demonstrates the information-theoretic optimality of RIS-based modulation, or “single-RF MIMO” systems. In addition, a novel signalling strategy based on separate layered encoding that enables practical successive cancellation-type decoding at the receiver is proposed. Numerical experiments show that the conventional scheme that fixes the reflection pattern of the RIS, irrespective of the transmitted information, as to maximize the achievable rate is strictly suboptimal, and is outperformed by the proposed adaptive coding strategies at all practical signal-to-noise ratio (SNR) levels.

This paper analyzes the multiplexing gains (MG) for simultaneous transmission of delay-sensitive and delay-tolerant data over interference networks. In the considered model, only delay-tolerant data can profit from coordinated multipoint (CoMP) transmission or reception techniques, because delay-sensitive data has to be transmitted without further delay. Transmission of delay-tolerant data is also subject to a delay constraint, which is however less stringent than the one on delay-sensitive data. Different coding schemes are proposed, and the corresponding MG pairs for delay-sensitive and delay-tolerant data characterized for Wyner’s linear symmetric network and for Wyner’s two-dimensional hexagonal network with and without sectorization. Information-theoretic converses are established for these models. For Wyners linear symmetric network the bounds match whenever the cooperation rates are sufficiently large or the delay-sensitive MG is small or moderate. These results show that on Wyner’s symmetric linear network and for sufficiently large cooperation rates, the largest MG for delay-sensitive data can be achieved without penalizing the maximum sum-MG of both delay-sensitive and delay-tolerant data. Our achievable schemes show that a similar conclusion holds for Wyner’s hexagonal network only for the model with sectorization. In the model without sectorization, a penalty in sum-MG is incurred whenever one insists on a positive delay-sensitive MG.

This work considers a layered coding approach for efficient transmission of data over a wireless block fading channel without transmitter channel state information (CSI), which is connected to a limited capacity reliable link, known as the bottleneck channel. Two main approaches are considered, the first is an oblivious approach, where the sampled noisy observations are compressed and transmitted over the bottleneck channel without having any knowledge of the original information codebook. The second approach is a non-oblivious decode-forward (DF) relay where the sampled noisy data is decoded, and whatever is successfully decoded is reliably transmitted over the bottleneck channel. The bottleneck channel from relay to destination has a fixed capacity C. We examine also the case where the channel capacity can dynamically change due to variable loads on the backhaul link. The broadcast approach is analyzed for cases that only the relay knows the available capacity for next block, and for the case that neither source nor relay know the capacity per block, only its capacity distribution. Fortunately, it is possible to analytically describe in closed form expressions, the optimal continuous layering power distribution which maximizes the average achievable rate. Numerical results demonstrate the achievable broadcast rates.

Effective interference management in the multiuser interference channel strongly hinges on the channel state information's availability at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), acquiring the CSIT is prohibitive due to the extensive information exchange that it imposes. As a result, the interference management approaches that rely on the CSIT lose their effectiveness in such circumstances. This article focuses on the two-user interference channel and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are entirely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative gain between the direct and interfering channels, each receiver opportunistically decodes a subset of both transmitters' received layers. An average achievable rate region is delineated, serving as an inner bound on the Gaussian interference channel's average capacity region in the absence of CSIT. Finally, an upper bound on the gap between the achievable sum-rate and the sum-rate capacity is established.

This work considers a Poisson noise channel with an amplitude constraint. It is well-known that the capacity-achieving input distribution for this channel is discrete with finitely many points. We sharpen this result by introducing upper and lower bounds on the number of mass points. Concretely, an upper bound of order$\mathsf {A}\log ^{2}(\mathsf {A})$and a lower bound of order$\sqrt { \mathsf {A}}$are established where$\mathsf {A}$is the constraint on the input amplitude. In addition, along the way, we show several other properties of the capacity and capacity-achieving distribution. For example, it is shown that the capacity is equal to$- \log P_{Y^\star }(0)$where$P_{Y^\star }$is the optimal output distribution. Moreover, an upper bound on the values of the probability masses of the capacity-achieving distribution and a lower bound on the probability of the largest mass point are established. Furthermore, on the per-symbol basis, a nonvanishing lower bound on the probability of error for detecting the capacity-achieving distribution is established under the maximum a posteriori rule.

We consider the use of the well-known dual capacity bounding technique for deriving upper bounds on the capacity of indecomposable finite-state channels (FSCs) with finite input and output alphabets. In this technique, capacity upper bounds are obtained by choosing suitable test distributions on the sequence of channel outputs. We propose test distributions that arise from certain graphical structures called Q-graphs. As we show in this paper, the advantage of this choice of test distribution is that, for the important sub-classes of unifilar and input-driven FSCs, the resulting upper bounds can be formulated as a dynamic programming (DP) problem, which makes the bounds tractable. We illustrate this for several examples of FSCs, where we are able to solve the associated DP problems explicitly to obtain capacity upper bounds that either match or beat the best previously reported bounds. For instance, for the classical trapdoor channel, we improve the best known upper bound of 0.661 (due to Lutz (2014)) to 0.584, shrinking the gap to the best known lower bound of 0.572, all bounds being in units of bits per channel use.

In this paper, we consider a general K-user Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC). We assume that the channel state is deterministic and known to all the nodes. While the private-message capacity region is well known to be achievable with dirty paper coding (DPC), we are interested in the simpler linearly precoded transmission schemes. In particular, we focus on linear precoding schemes combined with rate-splitting (RS). First, we derive an achievable rate region with minimum mean square error (MMSE) precoding at the transmitter and joint decoding of the sub-messages at the receivers. Then, we study the achievable sum rate of this scheme and obtain two findings: 1) an analytically tractable upper bound on the sum rate that is shown numerically to be a close approximation, and 2) how to reduce the number of active streams - crucial to the overall complexity - while preserving the sum rate to within a constant loss. The latter results in two practical algorithms: a stream elimination algorithm and a stream ordering algorithm. Finally, we investigate the constant-gap optimality of linearly precoded RS with respect to the capacity. Our result reveals that, while the achievable rate of linear precoding alone can be arbitrarily far from the capacity, the introduction of RS can help achieve the capacity region to within a constant gap in the two-user case. Nevertheless, we prove that the RS scheme's constant-gap optimality does not extend to the three-user case. Specifically, we show, through a pathological example, that the gap between the sum rate and the sum capacity can be unbounded.

In many information-theoretic channel coding problems, adding an input cost constraint to the operational setup amounts to restricting the optimization domain in the capacity formula. This paper shows that, in contrast to common belief, such a simple modification does not hold for the cost-constrained (CC) wiretap channel (WTC). The secrecy-capacity of the discrete memoryless (DM) WTC without cost constraints is described by a single auxiliary random variable. For the CC DM-WTC, however, we show that two auxiliaries are necessary to achieve capacity. Specifically, we first derive the secrecy-capacity formula, proving the direct part via superposition coding. Then, we provide an example of a CC DM-WTC whose secrecy-capacity cannot be achieved using a single auxiliary. This establishes the fundamental role of superposition coding over CC WTCs.

This work studies the uplink of a multi-tenant cloud radio access network (C-RAN) system with spectrum pooling. In the system, each operator has a cloud processor (CP) connected to a set of proprietary radio units (RUs) through finite-capacity fronthaul links. The uplink spectrum is divided into private and shared subbands, and all the user equipments (UEs) of the participating operators can simultaneously transmit signals on the shared subband. To mitigate inter-operator interference on the shared subband, the CPs of the participating operators can exchange compressed uplink baseband signals on finite-capacity backhaul links. This work tackles the problem of jointly optimizing bandwidth allocation, transmit power control and fronthaul compression strategies. In the optimization, we impose that the inter-operator privacy loss be limited by a given threshold value. An iterative algorithm is proposed to find a suboptimal solution based on the matrix fractional programming approach. Numerical results validate the advantages of the proposed optimized spectrum pooling scheme.

This paper provides a general derivative identity for the conditional mean estimator of an arbitrary vector signal in Gaussian noise with an arbitrary covariance matrix. This new identity is used to recover and generalize many known identities in the literature and derive some new identities. For example, a new identity is discovered, which shows that an arbitrary higher-order conditional moment is completely determined by the first conditional moment.Several applications of the identities are shown. For instance, by using one of the identities, a simple proof of the uniqueness of the conditional mean estimator as a function of the distribution of the signal is shown. Moreover, one of the identities is used to extend the notion of empirical Bayes to higher-order conditional moments. Specifically, based on a random sample of noisy observations, a consistent estimator for a conditional expectation of any order is derived.

A communication link aided by a Reconfigurable Intelligent Surface (RIS) is studied, in which the transmitter can control the state of the RIS via a finite-rate control link. Prior work mostly assumed a fixed RIS configuration irrespective of the transmitted information. In contrast, this work derives information-theoretic limits, and demonstrates that the capacity is achieved by a scheme that jointly encodes information in the transmitted signal as well as in the RIS configuration. In addition, a novel signaling strategy based on layered encoding is proposed that enables practical successive cancellation-type decoding at the receiver. Numerical experiments demonstrate that the standard max-SNR scheme that fixes the configuration of the RIS as to maximize the Signal-to-Noise Ratio (SNR) at the receiver is strictly suboptimal, and is outperformed by the proposed strategies at all practical SNR levels.

Non-orthogonal multiple access (NOMA) is a promising technology in the design of efficient state-of-the-art communication, particularly 5G and beyond cellular systems. Understanding its fundamental information-theoretic limits is hence of paramount interest. This paper focuses on regular sparse NOMA (where only a fixed and finite number of orthogonal resources is allocated to any designated user, and vice versa), and extends a previous analysis by the authors to a setting where the system comprises two classes of users with different power constraints. Explicit rigorous closed-form analytical inner and outer bounds on the achievable rate (total class throughput) region in the large-system limit are derived. The inner bound is based on the conditional vector entropy power inequality (EPI), while the outer bound relies on a recent strengthened version of the EPI by Courtade. The closed-form bounds provide valuable insights into the potential performance gains of regular sparse NOMA in practically oriented settings, comprising, e.g., a combination of low-complexity devices and broadband users with higher transmit power capabilities, or combinations of cell-edge users with users located close to the cell center. Conditions are identified where superior performance over dense code-domain NOMA is guaranteed, and a relatively small gap to the ultimate performance limits is attainable. The bounds may also serve as a useful tool for future analyses involving interference networks, as, e.g., Wyner-type cellular models.

Effective interference management in the multiuser interference channel strongly hinges on the availability of the channel state information at the transmitters (CSIT). In a broad range of emerging large-scale and distributed networks (e.g., the Internet of Things), however, acquiring the CSIT is prohibitive, due to the extensive information exchange that it imposes. In such circumstances, as a result, the interference management approaches that rely on the CSIT lose their effectiveness. This paper focuses on the two-user interference channel, and proposes a broadcast approach to interference management. Its hallmark is that the transmitters, unlike the receivers, are completely oblivious to instantaneous channel states. Each transmitter splits its message into multiple superimposed encoded information layers, where each layer is adapted to a given possible state for the combined states of all channels. Depending on the relative strengths of the direct and interfering channels, each receiver opportunistically decodes a subset of the received layers from both transmitters. An average achievable rate region is delineated serving as an inner bound on the average capacity region of the Gaussian interference channel in the absence of CSIT. Finally, it characterizes the gap between the achievable average sum-rate and the sum-rate capacity with the full CSIT in the asymptote of high signal-to-noise ratio. Numerical evaluations show that the cost of lacking CSIT is often insignificant.

The Schalkwijk-Kailath (SK) feedback scheme is a capacity-achieving coding scheme for the point-to-point white Gaussian channel with feedback. Recently, it has been shown that the SK scheme, which is not designed with consideration of secrecy, already achieves perfect weak secrecy by itself, i.e., the secrecy capacity of the Gaussian wiretap channel with feedback equals the capacity of the same model without secrecy constraint. In this paper, we propose a capacity-achieving SK type feedback scheme for the two-user Gaussian multiple-access channel with degraded message sets (GMAC-DMS). Similarly to the inherent secrecy nature of the classical SK scheme, we show that the proposed scheme is also secure by itself, which indicates that the feedback secrecy capacity of the two-user Gaussian multiple-access wiretap channel with degraded message sets (GMAC-WT-DMS) equals the capacity of the same model without secrecy constraint.

Consider the conditional mean estimator of the random variable X from the noisy observation Y = X + N where N is zero mean Gaussian with variance σ2(i.e., E[X|Y]). This work characterizes the probability distribution of E[X|Y]. As part of the proof, several new identities and results are shown. For example, it is shown that the k-th derivative of the conditional expectation is proportional to the (k + 1)-th conditional cumulant. It is also shown that the compositional inverse of the conditional expectation is well-defined and is characterized in terms of a power series by using Lagrange inversion theorem.

This paper analyses the multiplexing gain (MG) achievable over a general interference network with random user activity and random arrival of mixed-delay traffic. The mixed-delay traffic is composed of delay-tolerant traffic and delay-sensitive traffic where only the former can benefit from receiver cooperation since the latter is subject to stringent decoding delays. Two setups are considered. In the first setup, each active transmitter always has delay-tolerant data to send and delay-sensitive data arrival is random. In the second setup, both delay-tolerant and delay-sensitive data arrivals are random, and only one of them is present at any given transmitter. The MG regions of both setups are completely characterized for Wyner’s soft-handoff network. For Wyner’s symmetric linear and hexagonal networks inner bounds on the MG region are presented.

Rate splitting (RS) is a potentially powerful and flexible technique for multi-antenna downlink transmission. In this paper, we address several technical challenges towards its practical implementation for beyond 5G systems. To this end, we focus on a single-cell system with a multi-antenna base station (BS) and K single-antenna receivers. We consider RS in its most general form with 2K-1 streams, and joint decoding to fully exploit the potential of RS. First, we investigate the achievable rates under joint decoding and formulate the precoder design problems to maximize a general utility function, or to minimize the transmit power under pre-defined rate targets. Building upon the concave-convex procedure (CCCP), we propose precoder design algorithms for an arbitrary number of users. Our proposed algorithms approximate the intractable non-convex problems with a number of successively refined convex problems, and provably converge to stationary points of the original problems. Then, to reduce the decoding complexity, we consider the optimization of the precoder and the decoding order under successive decoding. Further, we propose a stream selection algorithm to reduce the number of precoded signals. With a reduced number of streams and successive decoding at the receivers, our proposed algorithm can even be implemented when the number of users is relatively large, whereas the complexity was previously considered as prohibitively high in the same setting. Finally, we propose a simple adaptation of our algorithms to account for the imperfection of the channel state information at the transmitter. Numerical results demonstrate that the general RS scheme provides a substantial performance gain as compared to state-of-the-art linear precoding schemes, especially with a moderately large number of users.

A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering contents to mobile users. Using an information-theoretic approach, this work tackles the problem of quantifying the potential latency reduction that can be obtained by enabling Device-to-Device (D2D) communication over out-of-band broadcast links. Following prior work, the Normalized Delivery Time (NDT) - a metric that captures the high signal-to-noise ratio worst-case latency - is adopted as the performance criterion of interest. Joint edge caching, downlink transmission, and D2D communication policies based on compress-and-forward are proposed that are shown to be information-theoretically optimal to within a constant multiplicative factor of two for all values of the problem parameters, and to achieve the minimum NDT for a number of special cases. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.

We study the trade-off between secret message (SM) and secret key (SK) rates, simultaneously achievable over a state-dependent (SD) wiretap channel (WTC) with non-causal channel state information (CSI) at the encoder. This model subsumes other instances of CSI availability as special cases, and calls for efficient utilization of the state sequence for both reliability and security purposes. An inner bound on the semantic-security (SS) SM-SK capacity region is derived based on a superposition coding scheme inspired by a past work of the authors. The region is shown to attain capacity for a certain class of SD-WTCs. SS is established by virtue of two versions of the strong soft-covering lemma. The derived region yields an improvement upon the previously best known SM-SK trade-off result reported by Prabhakaran et al., and, to the best of our knowledge, upon all other existing lower bounds for either SM or SK for this setup, even if the semantic security requirement is relaxed to weak secrecy. It is demonstrated that our region can be strictly larger than those reported in the preceding works.

This work investigates the general two-user compound Broadcast Channel (BC) in which an encoder wishes to transmit two private messages W1 and W2 to two receivers while being oblivious to the actual channel realizations controlling the communication. The focus is on the characterization of the largest achievable rate region by resorting to more involved encoding and decoding techniques than the usual coding schemes of the standard BC. Involved decoding schemes are first explored, and an achievable rate region is derived based on the principle of Interference Decoding (ID), in which each receiver decodes its intended message and chooses to (non-uniquely) decode, or not, the interfering non-itended message. This decoding scheme is shown to be capacity achieving for a class of non-trivial compound BEC/BSC broadcast channels while the worst-case of Marton's inner bound-based on No Interference Decoding (NID)-fails to achieve the capacity region. Involved encoding schemes are later investigated, and an achievable rate region is derived based on Multiple Description (MD) coding wherin the encoder transmits a common description as well as multiple dedicated private descriptions to the many possible channel realizations of the users. It turns out that MD coding yields larger inner bounds than the single description scheme-Common Description (CD) coding-for a class of compound Multiple Input Single Output Broadcast Channels (MISO BC).

We investigate the state-dependent Gaussian wiretap channel with noncausal channel state information at the transmitter (GWTC-N-CSIT), and explore whether three strategies (i.e., taking action on the state, legitimate receiver's channel output feedback, and combining the former two strategies together) help to enhance the secrecy capacity of the GWTC-N-CSIT. To be specific, we first determine the secrecy capacity of the GWTC-N-CSIT with noiseless feedback. Next, we derive lower and upper bounds on the secrecy capacity of the GWTC-N-CSIT with action-dependent state. Finally, we derive lower and upper bounds on the secrecy capacity of the GWTC-N-CSIT with both action-dependent state and noiseless feedback, and show that these bounds meet for a special case. Numerical results of this paper indicate that all three strategies enhance the secrecy capacity of the GWTC-N-CSIT. The study of this paper offers new options for enhancing the secrecy rates of the state-dependent wiretap channel models.

This paper studies an n -dimensional additive Gaussian noise channel with a peak-power-constrained input. It is well known that, in this case, when n=1 the capacity-achieving input distribution is discrete with finitely many mass points, and when n > 1 the capacity-achieving input distribution is supported on finitely many concentric shells. However, due to the previous proof technique, not even a bound on the exact number of mass points/shells was available. This paper provides an alternative proof of the finiteness of the number mass points/shells of the capacity-achieving input distribution while producing the first firm bounds on the number of mass points and shells, paving an alternative way for approaching many such problems. The first main result of this paper is an order tight implicit bound which shows that the number of mass points in the capacity-achieving input distribution is within a factor of two from the number of zeros of the downward shifted capacity-achieving output probability density function. Next, this implicit bound is utilized to provide a first firm upper on the support size of optimal input distribution, an O(A2) upper bound where A denotes the constraint on the input amplitude. The second main result of this paper generalizes the first one to the case when n > 1, showing that, for each and every dimension n ≥ 1, the number of shells that the optimal input distribution contains is O(A2). Finally, the third main result of this paper reconsiders the case n = 1 with an additional average power constraint, demonstrating a similar O(A2) bound.

The dirty paper wiretap channel, also referred to as the Gaussian wiretap channel with noncausal state at the transmitter, is revisited. First, we determine the secrecy capacity of the dirty paper wiretap channel with noiseless feedback, where the feedback channel is from the legitimate receiver to the transmitter. Next, we obtain lower and upper bounds on the secrecy capacity of the action-dependent dirty paper wiretap channel with noiseless feedback, and show that these bounds meet for a special case. Unlike the fact that action on the state helps to enhance the capacity of the dirty paper channel with feedback, numerical results of this paper indicate that it may not help to enhance the secrecy capacity of the dirty paper wiretap channel with feedback.

The problem of channel coding over the Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC) with additive independent Gaussian states is considered. The states are known in a noncausal manner to the encoder, and it wishes to minimize the amount of information that the receivers can learn from the channel outputs about the state sequence. The state leakage rate is measured as a normalized blockwise mutual information between the state sequence and the channel outputs' sequences. We employ a new version of a state-dependent extremal inequality and show that Gaussian input maximizes the state-dependent version of Marton's outer bound. Further, we show that our inner bound coincides with the outer bound. Our result generalizes previously studied scalar Gaussian BC with state and MIMO BC without the state.

In this paper, we study the capacity of unifilar finite-state channels. We derive upper bounds that are based on the dual capacity bounding technique using test distributions with memory on directed Q-graphs. The bounds hold for any choice of graph-based test distribution and result in a multi-letter expression. The computability of the upper bound is shown via a novel dynamic programming formulation that can be efficiently evaluated. We further show that the bounds can be simplified to simple single-letter expressions by solving the corresponding Bellman equation explicitly. In particular, for the Ising and Trapdoor channels, we provide simple analytic upper bounds which outperform all previous bounds from the literature.

A Fog-Radio Access Network (F-RAN) with arbitrary number of edge nodes and users is studied in which the users are able to cooperate by communicating over out-of-band broadcast Device-to-Device (D2D) links. Placement and delivery strategies are proposed with the aim of minimizing the Normalized Delivery Time (NDT) - a metric that captures the high signal-to-noise ratio worst-case latency for delivering any subset of requested contents to the users. The proposed strategies, based on compress-and-forward, are shown to be optimal to within a constant multiplicative factor of two for all values of the problem parameters. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.

A renowned information-theoretic formula by Shannon expresses the mutual information rate of a white Gaussian channel with a stationary Gaussian input as an integral of a simple function of the power spectral density of the channel input. We give in this paper a rigorous yet elementary proof of this classical formula. As opposed to all the conventional approaches, which either rely on heavy mathematical machineries or have to resort to some "external" results, our proof, which hinges on a recently proven sampling theorem, is elementary and self- contained, only using some well-known facts from basic calculus and matrix theory.

The multiple access channel (MAC) with degraded message sets and feedback is considered. We show that feedback does not increase the capacity region of this setting, and present a capacity-achieving coding scheme. The coding scheme is inspired by the posterior matching principle for the memoryless channel, but for two transmitters. It is shown that the recursive design of the transmitters and decoder is also maintained in this multiuser setting, leading to a constructive and simple coding scheme. It is interesting to note that the weak transmitter performs its encoding with respect to the decoder's belief as expected, but the strong encoder performs its encoding with respect to the weak encoder's belief and not the decoder's belief. To the best of our knowledge, this is the first matching scheme for a multi-user setting with finite alphabets and feedback.

This paper studies an n-dimensional additive Gaussian noise channel with a peak-power-constrained input. It is well known that, in this case, the capacity-achieving input distribution is supported on finitely many concentric shells. However, due to the previous proof technique, neither the exact number of shells of the optimal input distribution nor a bound on it was available. This paper provides an alternative proof of the finiteness of the number shells of the capacity-achieving input distribution and produces the first firm upper bound on the number of shells, paving an alternative way for approaching many such problems. In particular, for every dimension n, it is shown that the number of shells is given by O(A2) where A is the constraint on the input amplitude. Moreover, this paper also provides bounds on the number of points for the case of n = 1 with an additional power constraint.

A cloud radio access network (C-RAN) is considered where the first hop from the user equipments (UEs) to the basestations (BSs) is modeled by the fading Wyner soft-handoff model. The focus is on mixed-delay constraints where a set of messages (so called “slow” messages) are jointly decoded in the cloud unit (CU), whereas the remaining messages (called “fast” messages) have to be decoded immediately at the BSs. This paper presents inner and outer bounds on the capacity region for such a setup. Moreover, the multiplexing gain region is characterized exactly. The presented results show that for small fronthaul capacity it is beneficial to send both “fast” and “slow” messages. However, when the rate of “fast” messages is already large, then increasing it further, deteriorates the sum-rate of the system. In this regime, the stringent decoding delay on the “fast” messages penalizes the overall performance. Our results indicate that this penalty is larger at moderate SNR than at high SNR and it is also larger for random time-varying fading coefficients than for static ones.

Consider a primitive diamond relay channel, where a source X wants to send information to a destination with the help of two relays Y1and Y2, and the two relays can communicate to the destination via error-free digital links of capacities C1and C2respectively, while Y1and Y2are conditionally independent given X. In this paper, we develop new upper bounds on the capacity of such primitive diamond relay channels that are tighter than the cut-set bound. Our results include both the Gaussian and the discrete memoryless case and build on the information inequalities recently developed in [6]-[8] that characterize the tension between information measures in a certain Markov chain.

Hybrid beamforming is known to be a cost-effective and wide-spread solution for a system with large-scale antenna arrays. This paper studies the optimization of the analog and digital components of the hybrid beamforming solution for remote radio heads (RRHs) in a downlink cloud radio access network architecture. Digital processing is carried out at a baseband processing unit (BBU) in the “cloud,” and the precoded baseband signals are quantized prior to transmission to the RRHs via finite-capacity fronthaul links. In this system, we consider two different channel state information (CSI) scenarios: 1) ideal CSI at the BBU and 2) imperfect effective CSI. The optimization of digital beamforming and fronthaul quantization strategies at the BBU as well as analog radio-frequency (RF) beamforming at the RRHs is a coupled problem since the effect of the quantization noise at the receiver depends on the precoding matrices. The resulting joint optimization problem is examined with the goal of maximizing the weighted downlink sum-rate and the network energy efficiency. Fronthaul capacity and per-RRH power constraints are enforced along with constant modulus constraint on the RF beamforming matrices. For the case of perfect CSI, a block coordinate descent scheme is proposed based on the weighted minimum-mean-square-error approach by relaxing the constant modulus constraint of the analog beamformer. Also, we present the impact of imperfect CSI on the weighted sum-rate and network energy efficiency performance, and the algorithm is extended by applying the sample average approximation. The numerical results confirm the effectiveness of the proposed scheme and show that the proposed algorithm is robust to estimation errors.

A two-user multiple access channel is considered, in which the channels undergo slow block fading and the state of each channel is known only to its corresponding transmitter. This paper proposes a novel broadcast strategy for multiple access communication in this channel. In the broadcast approach, in principle, a transmitter with CSI uncertainty sends multiple independent superimposed information layers where the rate of each layer is adapted to a specific channel realization. In the existing broadcast approaches to multiuser communication, the transmitters often directly adopt a single-user strategy and each transmitter adapts its transmission to one unknown channel. The novel aspect of the proposed strategy is that it adapts the designed codebooks to the state of the entire network. This is motivated by the fact that the contribution of each user to the network-wide measures (e.g., capacity region) depends not only on the user’s direct channel to the receiver, but also on the qualities of other channels. Average achievable rate region and outer bounds on the capacity region are characterized. Furthermore, the expected capacity region is investigated, where most part of the capacity region boundary is characterized. Finally, an asymptotic capacity region is also characterized.

It is a well-known fact that feedback does not increase the capacity of point-to-point memoryless channels, however, its effect in secure communications is not fully understood yet. In this paper, an achievable scheme for the wiretap channel with generalized feedback is presented. This scheme, which uses the feedback signal to generate a shared secret key between the legitimate users, encrypts the message to be sent at the bit level. New capacity results for a class of channels are provided, as well as some new insights into the secret key agreement problem. Moreover, this scheme recovers previously reported rate regions from the literature, and thus it can be seen as a generalization that unifies several results in the field.

This paper studies the capacity of an n-dimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius R centered at the origin. It is known that in this setting, the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions, and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint R, such that the input distribution supported on a single sphere is optimal. The maximum R̅n, such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that ̅Rn scales as √n and the exact limit of R̅n/√n is found.

Shannon's mutual information of a random multiple antenna and multipath time varying channel is studied in the general case where the process constructed from the channel coefficients is an ergodic and stationary process which is assumed to be available at the receiver. From this viewpoint, the channel can also be represented by an ergodic self-adjoint block-Jacobi operator, which is close in many aspects to a block version of a random Schrödinger operator. The mutual information is then related to the so-called density of states of this operator. In this paper, it is shown that under the weakest assumptions on the channel, the mutual information can be expressed in terms of a matrix-valued stochastic process coupled with the channel process. This allows numerical approximations of the mutual information in this general setting. Moreover, assuming further that the channel coefficient process is a Markov process, a representation for the mutual information offset in the large signal to noise ratio regime is obtained in terms of another related Markov process. This generalizes previous results from Levy et al.. It is also illustrated how the mutual information expressions that are closely related to those predicted by the random matrix theory can be recovered in the large dimensional regime.

In this paper, the finite-order autoregressive moving average (ARMA) Gaussian wiretap channel with noiseless causal feedback is considered, in which an eavesdropper receives noisy observations of signals in both forward and feedback channels. It is shown that the generalized Schalkwijk-Kailath scheme, a capacity-achieving coding scheme for the Gaussian feedback channel, achieves the same maximum rate for the same channel even with the presence of an eavesdropper. Therefore, the secrecy capacity is equal to the feedback capacity without the presence of an eavesdropper for the Gaussian feedback channel. Furthermore, the results are extended to the additive white Gaussian noise (AWGN) channel with quantized feedback. It is shown that the proposed coding scheme achieves a positive secrecy rate. Our result implies that as the amplitude of the quantization noise decreases to zero, the secrecy rate converges to the capacity of the AWGN channel.

This paper investigates the Gaussian state-dependent interference channel (IC) and Z-IC, in which two receivers are corrupted respectively by two different but correlated states that are noncausally known to two transmitters but are unknown to the receivers. Three interference regimes are studied, and the capacity region boundary or the sum capacity boundary is characterized either fully or partially under various channel parameters. In particular, the impact of the correlation between states on cancellation of state and interference as well as achievability of capacity is explored with numerical illustrations. For the very strong interference regime, the capacity region is achieved by the scheme where the two transmitters implement a cooperative dirty paper coding. For the strong but not very strong interference regime, the sum-rate capacity is characterized by rate splitting, layered dirty paper coding, and successive cancellation. For the weak interference regime, the sum-rate capacity is achieved via dirty paper coding individually at two receivers as well as treating interference as noise. This paper also provides the achievable region for the state-dependent IC with each state known at its corresponding transmitter.

In this paper, we study outer bounds on the capacity region of the downlink multicell processing model with finite backhaul capacity for the simple case of two base stations and two mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity and the second hop from the base stations to the mobile users via a Gaussian interference channel. The outer bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our outer bound improves upon the existing outer bounds greatly in the medium backhaul capacity range, and as a result, the gap between the outer bounds and the rate of the time-sharing of the known achievable schemes is significantly reduced.

The state-dependent (SD) wiretap channel (WTC) with non-causal channel state information (CSI) available at the encoder is considered. An inner bound on the trade-off region between admissible secret key (SK) and secret message (SM) rates is provided. The result is derived under the stringent semantic-security metric. Our inner bound recovers the best-known achievability results for either SK generation, SM transmission, or simultaneous execution of both. Since some of these past benchmarks were derived under weaker security metrics, our results imply that an upgrade to semantic-security is possible without inflicting any rate loss. It is shown that for certain instances of the considered SD-WTC, the derived region is strictly larger than the previously best-known SK-SM trade-off region reported by Prabhakaran et al., and that a recently reported SK rate for this setup cannot be achieved.

Given a large wireless network consisting of randomly deployed nodes, where each of the nodes wants to transmit to a random destination node within the network at some equal rate, how fast can the sum rate grow as the number of nodes scales up at fixed density? This question is important because it captures the bottleneck of message exchanging among randomly deployed Internet-of-Things (IoT) devices, and it models nicely the wireless backhaul communication among access points or within airborne communication systems. Previous work has shown that, given an extended network with fixed density, multihop routing based approach provides sum rate that scales at most as the square root of network size, where as hierarchical cooperation protocols have the potential to support linear scaling. With limited power, the SNR decreases at least inverse proportional to the network size, and therefore the benefit of hierarchical cooperation will be curbed by the combined effects of path loss and local communication cost. We show in this paper how the path loss and local cooperation cost reshape the capacity scaling law results.

This paper studies optimization of the minimum mean square error (MMSE) in order to characterize the structure of the least favorable prior distributions. In the first part, the paper characterizes the local behavior of the MMSE in terms of the input distribution and finds the directional derivative of the MMSE at the distribution$P_{\mathbf{X}}$in the direction of the distribution$Q_{\mathbf{X}}$. In the second part of the paper, the directional derivative together with the theory of convex optimization is used to characterize the structure of least favorable distributions. In particular, under mild regularity conditions, it is shown that the support of the least favorable distributions must necessarily be very small and is contained in a nowhere dense set of Lebesgue measure zero. The results of this paper produce both sufficient and necessary conditions for optimality, do not rely on Gaussian statistics assumptions, and are not sensitive to the dimensionality of random vectors. The results are evaluated for the univariate and multivariate random Gaussian cases, and the Poisson case. Finally, as one of the applications, it is shown how the results can be used to characterize the capacity of Gaussian MIMO channels with an amplitude constraint.

Stotz et al., 2016, reported a sufficient (injectivity) condition for each user in a$K$-user single-antenna constant interference channel to achieve 1/2 degree of freedom. The present paper proves that this condition is necessary as well and hence provides an equivalence characterization of interference channel matrices allowing full degrees of freedom.

Device-to-Device (D2D) communication can support the operation of cellular systems by reducing the traffic in the network infrastructure. In this paper, the benefits of D2D communication are investigated in the context of a Fog-Radio Access Network (F-RAN) that leverages edge caching and fron-thaul connectivity for the purpose of content delivery. Assuming offline caching, out-of-band D2D communication, and an F-RAN with two edge nodes and two user equipments, an information-theoretically optimal caching and delivery strategy is presented that minimizes the delivery time in the high signal- to- noise ratio regime. The delivery time accounts for the latency caused by fronthaul, downlink, and D2D transmissions. The proposed optimal strategy is based on a novel scheme for an X -channel with receiver cooperation that leverages tools from real interference alignment. Insights are provided on the regimes in which D2D communication is beneficial.

In this paper, the finite-order autoregressive moving average (ARMA) Gaussian wiretap channel with noiseless causal feedback is considered, in which an eavesdropper receives noisy observations of the signals in both forward and feedback channels. It is shown that the generalized Schalkwijk-Kailath scheme, a capacity-achieving coding scheme for the feedback Gaussian channel, achieves the same maximum rate for the same channel with the presence of an eavesdropper. Therefore, the secrecy capacity is equal to the feedback capacity without the presence of an eavesdropper for the feedback channel. Furthermore, the results are extended to the additive white Gaussian noise (AWGN) channel with quantized feedback. It is shown that the proposed coding scheme achieves a positive secrecy rate. As the amplitude of the quantization noise decreases to zero, the secrecy rate converges to the capacity of the AWGN channel.

Wyner's soft-handoff network with mixed delay constraints is considered when neighbouring receivers can cooperate over rate-limited links. Each source message is a combination of independent “fast” and “slow” bits, where the former are subject to a stringent decoding delay. Inner and outer bounds on the capacity region are derived, and the multiplexing gain region is characterized when only transmitters or only receivers cooperate.

Understanding fundamental limits of the various technologies suggested for future 5G and beyond cellular systems is crucial for developing efficient state-of-the-art designs. A leading technology of major interest is non-orthogonal multiple-access (NOMA). In this paper, we derive an explicit rigorous closed-form analytical expression for the optimum spectral efficiency in the large-system limit of regular sparse NOMA, where only a fixed and finite number of orthogonal resources are allocated to any designated user, and vice versa. The basic Verdú-Shamai formula for (dense) randomly-spread code-division multiple-access (RS-CDMA) turns out to coincide with the limit of the derived expression, when the number of orthogonal resources per user grows large. Furthermore, regular sparse NOMA is rigorously shown to be spectrally more efficient than RS-CDMA across the entire system load range. It may therefore serve as an efficient means for reducing the throughput gap to orthogonal transmission in the underloaded regime, and to the ultimate Cover-Wyner bound in overloaded systems. The results analytically reinforce preliminary conclusions in [1], which mostly relied on heuristics and numerical observations. The spectral efficiency is also derived in closed form for the sub-optimal linear minimum-mean-square-error (LMMSE) receiver, which again extends the corresponding Verdti-Shamai LMMSE formula to regular sparse NOMA.

In this paper, an analysis of an input distribution that achieves the secrecy capacity of a general degraded additive noise wiretap channel is presented. In particular, using convex optimization methods, an input distribution that achieves the secrecy capacity is characterized by conditions expressed in terms of integral equations. The new conditions are used to study the structure of the optimal input distribution for three different additive noise cases: vector Gaussian; scalar Cauchy; and scalar exponential.

This paper studies the capacity of an n-dimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius R centered at the origin. It is known that in this setting the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint R such that the input distribution supported on a single sphere is optimal. The maximum $\overline{R}_{n}$, such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that $\overline{R}_{n}$ scales as $\sqrt{n}$ and the exact limit of $\overline{R}_{n}\overline{\sqrt{n}}$ is found.

Coding schemes are proposed for Wyner's soft-handoff model and for the sectorized hexagonal model when some of the messages are delay-sensitive and cannot profit from transmitter or receiver cooperation. For the soft-handoff network we also provide a converse. It matches the multiplexing-gain achieved by our scheme when the multiplexing gain of the delay-sensitive messages is low or moderate or when the cooperation links have high capacities. In these cases, the sum-multiplexing gain is the same as if only delay-tolerant messages (which can profit from cooperation) were sent. A similar conclusion holds for the sectorized hexagonal model, when the capacities of the cooperation links are large.

We consider systems in which the transmitter conveys messages to the receiver through a capacity-limited relay station. The channel between the transmitter and the relay station is assumed to be a frequency-selective additive Gaussian noise channel. It is assumed that the transmitter can shape the spectrum and adapt the coding technique so as to optimize performance. The relay operation is oblivious (nomadic transmitters), that is, the specific codebooks used are unknown. We find the reliable information rate that can be achieved with Gaussian signaling in this setting, and to that end, employ Gaussian bottleneck results combined with Shannon's incremental frequency approach. We also prove that, unlike classical water pouring, the allocated spectrum (power and bit rate) of the optimal solution could frequently be discontinuous. These results can be applied also to a MIMO transmission scheme. We also investigate the case of an entropy-limited relay. We show that the optimal relay function is always deterministic, present lower and upper bounds on the optimal performance (in terms of mutual information), and derive an analytical approximation.

This paper considers a Gaussian channel with one transmitter and two receivers. The goal is to maximize the communication rate at the intended/primary receiver subject to a disturbance constraint at the unintended/secondary receiver. The disturbance is measured in terms of the minimum mean square error (MMSE) of the interference that the transmission to the primary receiver inflicts on the secondary receiver. This paper presents a new upper bound for the problem of maximizing the mutual information subject to an MMSE constraint. The new bound holds for vector inputs of any length and recovers a previously known limiting (when the length of the vector input tends to infinity) expression from the work of Bustin et al. The key technical novelty is a new upper bound on the MMSE. This bound allows one to bound the MMSE for all signal-to-noise ratio (SNR) values below a certain SNR at which the MMSE is known (which corresponds to the disturbance constraint). The bound also complements the “single-crossing point property” of the MMSE that upper bounds the MMSE for all SNR values above a certain value at which the MMSE value is known. The MMSE upper bound provides a refined characterization of the phase-transition phenomenon, which manifests, in the limit as the length of the vector input goes to infinity, as a discontinuity of the MMSE for the problem at hand. For vector inputs of size n = 1, a matching lower bound, to within an additive gap of order O(log log(1/MMSE)) (where MMSE is the disturbance constraint), is shown by means of the mixed inputs technique recently introduced by Dytso et al.

Consider a Gaussian multiple-input multiple-output (MIMO) multiple-access channel (MAC) with channel matrix H and a Gaussian MIMO broadcast channel (BC) with channel matrix HT. For the MIMO MAC, the integer-forcing architecture consists of first decoding integer-linear combinations of the transmitted codewords, which are then solved for the original messages. For the MIMO BC, the integer-forcing architecture consists of pre-inverting the integer-linear combinations at the transmitter, so that each receiver can obtain its desired codeword by decoding an integer-linear combination. In both the cases, integer-forcing offers higher achievable rates than zero-forcing while maintaining a similar implementation complexity. This paper establishes an uplink-downlink duality relationship for integer-forcing, i.e., any sum rate that is achievable via integer-forcing on the MIMO MAC can be achieved via integer-forcing on the MIMO BC with the same sum power and vice versa. Using this duality relationship, it is shown that integer-forcing can operate within a constant gap of the MIMO BC sum capacity. Finally, the paper proposes a duality-based iterative algorithm for the non-convex problem of selecting optimal beamforming and equalization vectors, and establishes that it converges to a local optimum.

This paper studies a two-user state-dependent Gaussian multiple-access channel (MAC) with state noncausally known at one encoder. Two scenarios are considered: 1) each user wishes to communicate an independent message to the common receiver; and 2) the two encoders send a common message to the receiver and the non-cognitive encoder (i.e., the encoder that does not know the state) sends an independent individual message (this model is also known as the MAC with degraded message sets). For both scenarios, new outer bounds on the capacity region are derived, which improve uniformly over the best known outer bounds. In the first scenario, the two corner points of the capacity region as well as the sum rate capacity are established, and it is shown that a single-letter solution is adequate to achieve both the corner points and the sum rate capacity. Furthermore, the full capacity region is characterized in situations in which the sum rate capacity is equal to the capacity of the helper problem. The proof exploits the optimal-transportation idea of Polyanskiy and Wu (which was used previously to establish an outer bound on the capacity region of the interference channel) and the worst case Gaussian noise result for the case in which the input and the noise are dependent.

The K-receiver degraded broadcast channel with secrecy outside a bounded range is studied, in which a transmitter sends K messages to K receivers, and the channel quality gradually degrades from receiver K to receiver 1. Each receiver k is required to decode message W1, ..., Wk, for 1 ≤ k ≤ K, and to be kept ignorant of Wk+2, .. ., WK, fork = 1, ..., K -2. Thus, each message Wkis kept secure from receivers with at least two-level worse channel quality, i.e., receivers 1, ..., k-2. The secrecy capacity region is fully characterized. The achievable scheme designates one superposition layer to each message with binning employed for each layer. Joint embedded coding and binning are employed to protect all upper-layer messages from lower-layer receivers. Furthermore, the scheme allows adjacent layers to share rates so that part of the rate of each message can be shared with its immediate upper-layer message to enlarge the rate region. More importantly, an induction approach is developed to perform Fourier-Motzkin elimination of 2Kvariables from the order of K2bounds to obtain a close-form achievable rate region. An outer bound is developed that matches the achievable rate region, whose proof involves recursive construction of the rate bounds and exploits the intuition gained from the achievable scheme.

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via network functions virtualization, enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This paper focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely, the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel with fixed scheduling and cell association in which the RCC has global but delayed channel state information due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.

The Writing onto Fast Fading Dirt (WFFD) channel is investigated to study the effect of partial channel knowledge on the performance of interference pre-cancellation. The WFFD channel is the Gel'fand-Pinsker channel in which the channel output is the sum of the channel input, white Gaussian noise, and a fading-times-state term. The fading-times-state term is obtained as the product of the channel state sequence, known only at the transmitter, and a fast fading process, known only at the receiver. We consider the case of Gaussian-distributed channel states and derive an approximate characterization of capacity for different classes of fading distributions, both continuous and discrete. In particular, we prove that if the fading distribution concentrates in a sufficiently small interval, then capacity is approximately equal to the AWGN capacity times the probability of such interval. We also show that there exists a class of fading distributions for which having the transmitter treat the fading-times-state term as additional noise closely approaches capacity.

In this work, novel upper and lower bounds on the capacity of channels with arbitrary constraints on the support of the channel input symbols are derived. As an immediate practical application, the case of multiple-input multiple- output channels with amplitude constraints is considered. The bounds are shown to be within a constant gap if the channel matrix is invertible and are tight in the high amplitude regime for arbitrary channel matrices. Moreover, in the high amplitude regime, it is shown that the capacity scales linearly with the minimum of the numbers of transmit and receive antennas, similarly to the case of average power-constrained inputs.

This paper studies a two-user state-dependent Gaussian multiple-access channel with state noncausally known at one encoder. Two new outer bounds on the capacity region are derived, which improve uniformly over the best known (genie-aided) outer bound. The two corner points of the capacity region as well as the sum rate capacity are established, and it is shown that a single-letter solution is adequate to achieve both the corner points and the sum rate capacity. Furthermore, the full capacity region is characterized in situations in which the sum rate capacity is equal to the capacity of the helper problem. The proof exploits the optimal-transportation idea of Polyanskiy and Wu (which was used previously to establish an outer bound on the capacity region of the interference channel) and the worst-case Gaussian noise result for the case in which the input and the noise are dependent.

We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying à-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and “noisy network coding” are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish the inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression and decoding and the role of time sharing.

The downlink of symmetric Cloud Radio Access Networks (C-RANs) with multiple relays and a single receiver is studied. Lower and upper bounds are derived on the capacity. The lower bound is achieved by Marton's coding which facilitates dependence among the multiple-access channel inputs. The upper bound uses Ozarow's technique to augment the system with an auxiliary random variable. The bounds are studied over scalar Gaussian C-RANs and are shown to meet and characterize the capacity for interesting regimes of operation.

This paper considers a problem of communication over an additive noise channel where the noise is distributed according to a Generalized Gaussian (GG) distribution. In the first part of the paper, a number of properties of the family of GG distributions are derived which are of independent interest. For example, considerable attention is given to the properties of the characteristic function of the GG distribution. In the second part of the paper, the capacity of an additive noise channel with GG noise is considered under p-th absolute moment constraints. It is shown that, even though Shannon's upper bound is achievable in some instances, in general such achievability is not possible. Moreover, it is shown that discrete inputs can achieve capacity within a constant gap or full degree of freedom for any p-th absolute moment constraint. Following the seminal work of Smith, the paper also gives a condition under which discrete inputs are exactly optimal.

In this paper, a generalized Ozarow-Wyner capacity bound is presented that holds for arbitrary noise channels. The bound is then used to approximate the capacity of a large class of additive noise channels that are subject to a p-th moment input constraint, where p is some positive real number, as well as to the Cauchy noise channel with a logarithmic moment constraint. For both channel models the gap to the capacity is precisely specified.

The capacity of two semi-deterministic channels with the presence of non-causal channel state information (CSI) is characterized. The first channel is a state-dependent semi-deterministic relay channel. The CSI is available only at the transmitter and receiver, but not at the relay. The second channel is a state-dependent multiple access channel (MAC) with partial cribbing and CSI only at one transmitter and the receiver. In the semi-deterministic relay channel without states, the capacity can be achieved using partial-decode-forward scheme. The transmission is split to blocks; in each block, the relay decodes a part of the message and cooperation is established using those bits. When the channel depends on a state, the decoding procedure at the relay reduces the transmission rate. Recently, a cooperative bin forward scheme has been proposed which establishes cooperation without requiring the relay to decode a part of the message. In this scheme, the relay maps its received sequence, which is a deterministic function of the transmitted sequence, into bins. The transmitter coordinates its transmission with the bin index that is chosen by the relay. This scheme achieves the capacity when the CSI is available causally. In this work, we present a variation of the cooperative-bin-forward scheme that achieves capacity for non-causal CSI. The bin index corresponding to the deterministic output of the relay is selected by the transmitter in such a way that the relay's transmission is coordinated with the states. This coding scheme also applies for the MAC with partial cribbing and non-causal CSI at one transmitter and receiver. The capacity is achieved by the new variation of cooperative bin-forward. On top of that, we show an example in which the capacity with non-causal CSI is strictly greater than with causal CSI.

A closed-form analytical expression is derived for the limiting empirical squared singular value density of a spreading (signature) matrix corresponding to sparse low-density code-domain (LDCD) non-orthogonal multiple-access (NOMA) with regular random user-resource allocation. The derivation relies on associating the spreading matrix with the adjacency matrix of a large semiregular bipartite graph. For a simple repetition-based sparse spreading scheme, the result directly follows from a rigorous analysis of spectral measures of infinite graphs. Turning to random (sparse) binary spreading, we harness the cavity method from statistical physics, and show that the limiting spectral density coincides in both cases. Next, we use this density to compute the normalized input-output mutual information of the underlying vector channel in the large-system limit. The latter may be interpreted as the achievable total throughput per dimension with optimum processing in a corresponding multiple-access channel setting or, alternatively, in a fully-symmetric broadcast channel setting with full decoding capabilities at each receiver. Surprisingly, the total throughput of regular LDCD-NOMA is found to be not only superior to that achieved with irregular user-resource allocation, but also to the total throughput of dense randomly-spread NOMA, for which optimum processing is computationally intractable. In contrast, the superior performance of regular LDCD-NOMA can be potentially achieved with a feasible message-passing algorithm. This observation may advocate employing regular, rather than irregular, LDCD-NOMA in 5G cellular physical layer design.

This paper investigates the Gaussian state-dependent Z-interference channel (Z-IC), in which two receivers are corrupted respectively by two correlated states that are noncausally known to transmitters and unknown to receivers. Three interference regimes are studied, and the capacity region or sum capacity boundary is characterized either fully or partially under various channel parameters. The impact of correlation between the states on state and interference cancellation as well as the achievability of the capacity is demonstrated via numerical analysis.

In this paper, we study upper bounds on the sum capacity of the downlink multicell processing model with finite backhaul capacity for the simple case of 2 base stations and 2 mobile users. It is modeled as a two-user multiple access diamond channel. It consists of a first hop from the central processor to the base stations via orthogonal links of finite capacity, and the second hop from the base stations to the mobile users via a Gaussian interference channel. The upper bound is derived using the converse tools of the multiple access diamond channel and that of the Gaussian MIMO broadcast channel. Through numerical results, it is shown that our upper bound improves upon the existing upper bound greatly in the medium backhaul capacity range, and as a result, the gap between the upper bounds and the sum rate of the time-sharing of the known achievable schemes is significantly reduced.

This study addresses the achievable sum-rate for the uplink of a cloud radio access network (C-RAN) operating in a linear Wyner-type topology, i.e., with partial channel connectivity. In the system, the radio units (RUs) communicate with a central, or cloud, unit (CU) by means of digital finite-capacity fronthaul links. The messages sent by the user equipments (UEs) are jointly decoded by the CU based on the compressed baseband signals received on the fronthaul links. Unlike prior works, each RU is assumed to be also connected to its neighboring RUs via finite-capacity fronthaul links. Under the standard assumption that the RUs do not perform channel decoding (i.e., oblivious RUs), each RU performs in-network processing of the uplink received signal and of the compressed baseband signal received from the adjacent RU, with the CU carrying out channel decoding. A closed-form expression of the achievable sum-rate is derived assuming point-to-point compression, and then analytical expressions are provided for more advanced fronthaul compression schemes that leverage side information. Numerical examples provide insights into the advantages of inter-RU communications and into the performance gap to existing sum-rate upper bounds.

In this paper, the approximate capacity of the 3-receiver “writing on random dirty paper” (WRDP) channel is derived. In the M-receiver WRDP channel, the channel output is obtained as the sum of the channel input, white Gaussian noise and a channel state sequence randomly selected among a set of M independent Gaussian sequences. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which one is selected to produce the channel output. In the following, we derive upper and lower bounds to the capacity of the 3-receiver WRDP channel which are to within a distance of at most 3 bits-per-channel-use (bpcu) for all channel parameters. In the achievability proof, the channel input is composed of the superposition of three codewords: the receiver opportunistically decodes a different set of codewords, depending on the variance of the channel state appearing in the channel output. Time-sharing among multiple transmission phases is employed to guarantee that transmitted message can be decoded regardless of the state realization. In the converse proof, we derive a novel outer bound which matches the pre-log coefficient arising in the achievability proof due to time-sharing. Although developed for the case of three possible state realizations, our results can be extended the general WRDP.

Fronthaul limitations in terms of capacity and latency motivate the growing interest in the wireless industry for the study of alternative functional splits between cloud and edge nodes in Cloud Radio Access Network (C-RAN). This work contributes to this line of work by investigating the optimal functional split of control and data plane functionalities at the edge nodes and at the Remote Cloud Center (RCC) as a function of the fronthaul latency. The model under study consists of a two-user time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while edge nodes have local but timely CSI. Adopting the adaptive sum-rate as the performance criterion, functional splits whereby the control functionality of rate selection and the decoding of the data-plane frames are carried out at either the edge nodes or at the RCC are compared, demonstrating the potential advantages of implementing control functions at the edge.

In this paper, we analyze the sum-rate capacity of two-user Poisson multiple input multiple output multiple-access channels (MACs), when both the transmitters and the receiver are equipped with multiple antennas. Although the sum-rate capacity of Poisson MISO MAC when the receiver is equipped with a single antenna has been characterized by us, the inclusion of multiple antennas at the receiver makes the problem more challenging and requires the development of new analytical tools. We first characterize the sum-rate capacity of the Poisson MAC when each transmitter has a single antenna and the receiver has multiple antennas. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity, and for certain channel parameters, it is optimal for both users to transmit. We then characterize the sum-rate capacity of the channel where both the transmitters and the receiver are equipped with multiple antennas. We show that the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter, and has thus been characterized by the former case. We show this by developing a novel channel transformation argument.

In this paper, we analyze the sum-rate capacity of two-user Poisson multiple access channels (MAC), when the receiver is equipped with single antenna. We first characterize the sum-rate capacity of the non-symmetric Poisson MAC when each transmitter has a single antenna. While the sum-rate capacity of the symmetric Poisson MAC with single antenna at each transmitter has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which both users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity. We then characterize the sum-rate capacity of the Poisson multiple-input single-output (MISO) MAC with multiple antennas at each transmitter and single antenna at the receiver. By converting a non-convex optimization problem with a large number of variables into a non-convex optimization problem with two variables, we show that the sum-rate capacity of the Poisson MISO MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter.

The capacity region of the multicast Cognitive InterFerence Channel (CIFC) is investigated. This channel consists of two independent transmitters that wish to multicast two different messages, each to a different set of users. In addition, one of the transmitters-commonly referred to as the cognitive transmitter-has prior non-causal knowledge of both messages to be transmitted. This scenario subsumes some long-standing open problems, such as the interference channel, the broadcast channel, and multicast communications. The aim of this paper is the derivation of optimal interference mitigation techniques under different interference regimes. To this end, two settings, namely the multi-primary CIFC, i.e., M = 1 and N ≥ 1, and its complementary, the multi-secondary CIFC, i.e., N = 1 and M ≥ 1, are investigated as an attempt to build a thorough understanding for the more general multicast CIFC setting. It is shown that, for some interference regimes, well-known coding techniques for the standard CIFC remain still optimal under the constraint of multicasting to multiple users. However, in other interference regimes, capacity achieving coding and decoding schemes prove to be more involved. A careful combination of these coding schemes and new outer bounding techniques allows to characterize the capacity region for several classes of discrete memoryless and Gaussian multicast CIFC under different interference regimes.

The “carbon copy onto dirty paper” (CCDP) channel is the compound “writing on dirty paper” channel in which the channel output is obtained as the sum of the channel input, white Gaussian noise and a Gaussian state sequence randomly selected among a set possible realizations. The transmitter has non-causal knowledge of the set of possible state sequences but does not know which sequence is selected to produce the channel output. We study the capacity of the CCDP channel for two scenarios: 1) the state sequences are independent and identically distributed; and 2) the state sequences are scaled versions of the same sequence. In the first scenario, we show that a combination of superposition coding, time-sharing, and Gel'fand-Pinsker binning is sufficient to approach the capacity to within 3 bits per channel use for any number of possible state realizations. In the second scenario, we derive capacity to within 4 bits per channel use for the case of two possible state sequences. This result is extended to the CCDP channel with any number of possible state sequences under certain conditions on the scaling parameters, which we denote as “strong fading” regime. We conclude by providing some remarks on the capacity of the CCDP channel in which the state sequences have any jointly Gaussian distribution.

We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean squared error distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the sources, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean squared error distortion measure. It is shown that when the channel parameters satisfy certain general conditions, the induced distortion tuples are on the boundary of the achievable distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouple the overall problem into several simpler sub-problems. Optimality results previously unknown in the literature are obtained using this novel approach. Explicit and novel outer bounds are derived for the two problems as the byproducts of our investigation.

The capacity of a point-to-point discrete-time multi-input-multiple-output (MIMO) channel with phase uncertainty (MIMO phase noise channel) is still open. As a matter of fact, even the pre-log (multiplexing gain) of the capacity in the high signal-to-noise ratio (SNR) regime is unknown in general. We make some progress in this direction for two classes of such channels. With phase noise on the individual paths of the channel (model A), we show that the multiplexing gain is 1/2, which implies that the capacity does not scale with the channel dimension at high SNR. With phase noise at both the input and output of the channel (model B), the multiplexing gain is upper-bounded by 1/2 min{nt,(nr- 2)++ 1}, and lower-bounded by 1/2 min {nt, ⌊nr+1/2⌋}, where ntand nrare the number of transmit and receive antennas, respectively. The multiplexing gain is enhanced to 1/2 min{nt, nr} without receive phase noise, and to 1/2 min {2nt-1, nr} without transmit phase noise. In all the cases of model B, the multiplexing gain scales linearly with min {nt, nr}. Our main results rely on the derivation of non-trivial upper and lower bounds on the capacity of such channels.

The problem of simultaneously generating multiple keys for a cellular source model with a helper is investigated. In the model considered, there are four terminals, X0, X1, X2, and X3, each of which observes one component of a vector source. Terminal X0wishes to generate two secret keys K1and K2, respectively, with terminals X1and X2under the help of terminal X3. All terminals are allowed to communicate over a public channel. An eavesdropper is assumed to have access to the public discussion. Both symmetric and asymmetric key generations are considered. In symmetric key generation models, model 1a (with a trusted helper) requires that the two keys are concealed from the eavesdropper, and model 1b (with an untrusted helper) further requires that the two keys are concealed from the helper in addition to the eavesdropper. The asymmetric key generation models 2a and 2b are the same as symmetric key generation models 1a and 1b, respectively, except that the key K2is further required to be concealed from terminal X1. For all models studied, the key capacity region is established by designing a unified achievable strategy to achieve the cut-set outer bounds. We also study the problem of generating more than two keys and characterize its key capacity region when all the cellular terminals are required to generate independent keys with the base station.

In the downlink of the Cloud-Radio Access Network (C-RAN) cellular architecture, complex baseband signals are transmitted from a central unit (CU) in the “cloud” over digital fronthaul links to distributed radio units (RUs). The standard design of digital fronthauling is based on quantization that operates separately over each fronthaul link. In this paper, a fronthaul quantization scheme is proposed that, unlike conventional schemes, implements a joint quantization mapping across all fronthaul links that is adapted to the current channel conditions. As compared to the current standard approach, the proposed multivariate quantization (MQ) scheme only requires additional processing at the CU, while no modification is needed at the RUs. The algorithm is extended to enable variable-length compression, and is compared via numerical results to a related approach based on the information-theoretic technique of multivariate compression.

In this paper, we characterize the sum-rate capacity of the non-symmetric Poisson multiple access channel (MAC). While the sum-rate capacity of the symmetric Poisson MAC has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-rate capacity by solving a non-convex optimization problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which all users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity.

This paper investigates the problem of secret key generation over a wiretap channel when the terminals have access to correlated sources. These sources are independent of the main channel and the users observe them before the transmission takes place. A novel achievable scheme for this model is proposed and is shown to be optimal under certain less noisy conditions. This result improves upon the existing literature where the more stringent condition of degradedness was needed.

The problem of estimating an arbitrary random vector from its observation corrupted by additive white Gaussian noise, where the cost function is taken to be the minimum mean pth error (MMPE), is considered. The classical minimum mean square error (MMSE) is a special case of the MMPE. Several bounds, properties, and applications of the MMPE are derived and discussed. The optimal MMPE estimator is found for Gaussian and binary input distributions. Properties of the MMPE as a function of the input distribution, signal-to-noiseratio (SNR) and order p are derived. The “single-crossing-point property” (SCPP) which provides an upper bound on the MMSE, and which together with the mutual information-MMSE relationship is a powerful tool in deriving converse proofs in multiuser information theory, is extended to the MMPE. Moreover, a complementary bound to the SCPP is derived. As a first application of the MMPE, a bound on the conditional differential entropy in terms of the MMPE is provided, which then yields a generalization of the Ozarow-Wyner lower bound on the mutual information achieved by a discrete input on a Gaussian noise channel. As a second application, the MMPE is shown to improve on previous characterizations of the phase transition phenomenon that manifests, in the limit as the length of the capacity achieving code goes to infinity, as a discontinuity of the MMSE as a function of SNR. As a final application, the MMPE is used to show new bounds on the second derivative of mutual information, or the first derivative of the MMSE.

Motivated by the ability of modern terminals to receive simultaneously from multiple networks (e.g., WLAN and Cellular), we extend the single shared link network with caching at the user nodes to the case of r parallel partially shared links, where users in different classes receive from the server simultaneously and in parallel through different set of links. For this setting, we give an order-optimal rate and (maximal) delay region characterization for the case of r = 2 links with two classes of users, one receiving only from link 1 and the other from both links 1 and 2. We also extend these results to r = 3 with three classes of users, receiving from link 1, from links 1 and 2, and from links 1 and 3, respectively.

This paper studies the joint design of cloud and edge processing for the downlink of a fog radio access network (F-RAN). In an F-RAN, as in cloud-RAN (C-RAN), a baseband processing unit (BBU) can perform joint baseband processing on behalf of the remote radio heads (RRHs) that are connected to the BBU by means of the fronthaul links. In addition to the minimal functionalities of conventional RRHs in C-RAN, the RRHs in an F-RAN may be equipped with local caches, in which frequently requested contents can be stored, as well as with baseband processing capabilities. They are hence referred to as enhanced RRH (eRRH). This paper focuses on the design of the delivery phase for an arbitrary pre-fetching strategy used to populate the caches of the eRRHs. Two fronthauling modes are considered, namely, a hard-transfer mode, whereby non-cached files are communicated over the fronthaul links to a subset of eRRHs, and a soft-transfer mode, whereby the fronthaul links are used to convey quantized baseband signals as in a C-RAN. Unlike the hard-transfer mode in which baseband processing is traditionally carried out only at the eRRHs, the soft-transfer mode enables both centralized precoding at the BBU and local precoding at the eRRHs based on the cached contents, by means of a novel superposition coding approach. To attain the advantages of both approaches, a hybrid design of soft- and hard-transfer modes is also proposed. The problem of maximizing the delivery rate is tackled under fronthaul capacity and per-eRRH power constraints. Numerical results are provided to compare the performance of hard- and soft-transfer fronthauling modes, as well as of the hybrid scheme, for different baseline pre-fetching strategies.

Interference pre-cancellation as in the “writing onto dirty paper” channel crucially depends on the transmitter having exact knowledge of the way in which input and channel state combine to produce the channel output. The presence of even a small amount of uncertainty in such knowledge, gravely hampers the ability of the encoder to pre-code its transmissions against the channel state. This is particularly disappointing as it implies that interference pre-coding in practical systems is effective only when the channel estimates have very high precision, a condition which is generally unattainable in wireless environments. In this paper we show that state decoding, instead of state pre-cancellation, can be approximately optimal for a channel with discrete states when only partial channel knowledge is available. More specifically, we consider a variation of the “writing onto dirty paper” channel in which a discrete-valued state sequence is multiplied by a fast fading process and derive conditions on the fading distribution for which state decoding closely approaches capacity. This channel model is a special case of the Gelf'and-Pinsker channel and our results show an instance of this problem in which state decoding is approximately optimal.

Stotz and Bölcskei, 2015, identified an explicit condition for K/2 degrees of freedom (DoF) in constant single-antenna interference channels (ICs). This condition is expressed in terms of linear independence—over the rationals—of monomials in the off-diagonal entries of the IC matrix and is satisfied for almost all IC matrices. There is, however, a prominent class of IC matrices that admits K/2 DoF but fails to satisfy this condition. The main contribution of the present paper is a more general condition for K/2 DoF (in fact for 1/2 DoF for each user) that, inter alia, encompasses this example class. While the existing condition by Stotz and Bölcskei is of algebraic nature, the new condition is canonical in the sense of capturing the essence of interference alignment by virtue of being expressed in terms of a generic injectivity condition that guarantees separability of signal and interference.

This paper investigates the two-user state-dependent Gaussian multiple access channel (MAC) with a helper. The channel is corrupted by an additive Gaussian state sequence known to neither the transmitters nor the receiver, but to a helper noncausally, which assists state cancellation at the receiver. Inner and outer bounds on the capacity region are first derived, which improve the previous bounds given by Duan et al. Further comparison of these bounds yields either segments on the capacity region boundary or the full capacity region by considering various cases of channel parameters.

Hierarchical cooperation schemes in wireless networks rely on local cooperation among neighboring nodes to create virtual multiple-input multiple-output (MIMO) connections between clusters of nodes. It was shown that, by applying the virtual MIMO technique recursively in a hierarchical manner, the sum rate of all source-destination pairs can scale linearly with the number of nodes in the network. In this paper we focus on the impact of local cooperation and establish new capacity scaling bounds for the virtual MIMO transmission taking into account the constraints of local communication both at the transmitters and the receivers. We show that the cost of local communication, which is inevitable to establish the virtual MIMO transmission, grows exponentially with the number of layers in the cooperation hierarchy and plays a vital role in determining the overall performance of the hierarchical virtual MIMO cooperation.

This paper considers the minimum mean p-th error (MMPE) estimation problem: estimating a random vector in the presence of additive white Gaussian noise (AWGN) in order to minimize an Lpnorm of the estimation error. The MMPE generalizes the classical minimum mean square error (MMSE) estimation problem. This paper derives basic properties of the optimal MMPE estimator and MMPE functional. Optimal estimators are found for several inputs of interests, such as Gaussian and binary symbols. Under an appropriate p-th moment constraint, the Gaussian input is shown to be asymptotically the hardest to estimate for any p ≥ 1. By using a conditional version of the MMPE, the famous “MMSE single-crossing point” bound is shown to hold for the MMPE too for all p ≥ 1, up to a multiplicative constant. Finally, the paper develops connections between the conditional differential entropy and the MMPE, which leads to a tighter version of the Ozarow-Wyner lower bound on the rate achieved by discrete inputs on AWGN channels.

We present upper and lower bounds on the per-user multiplexing gain (MG) of Wyner's circular soft-handoff model and Wyner's circular full model with cognitive transmitters and receivers with cache memories. The bounds are tight for cache memories with prelog μ that exceeds 2/3D in the soft-handoff model and exceeds D in the full model, where D denotes the number of possibly demanded files. In these cases the per-user MG of the two models is 1 + μ/D, the same as for non-interfering point-to-point links with caches at the receivers. Large receiver cache-memories thus allow to completely mitigate interference in these networks.

We consider the point-to-point multi-input-multiple-output (MIMO) channel with phase uncertainty (MIMO phase noise channel) at high SNR. With phase noise on the individual paths of the channel (model A), we show that the multiplexing gain is 1/2, which implies that the capacity does not scale with the channel dimension at high SNR. With phase noise at both the input and output of the channel (model B), the multiplexing gain is upper-bounded by 1/2 min{nt, (nr- 2)++ 1}, and lower-bounded by 1/2 min{nt, ⌊nr+1/2⌋}, where ntand nrare the number of transmit and receive antennas, respectively. The multiplexing gain is enhanced to 1/2 min{nt, nr} without receive phase noise, and to 1/2 min{2nt- 1, nr} without transmit phase noise. Our main results rely on the derivation of non-trivial upper and lower bounds on the capacity of such channels.

A K-receiver degraded broadcast channel with secrecy outside a bounded range is studied, in which a transmitter sends K messages respectively to K receivers, and the channel quality gradually degrades from receiver K to receiver 1. Each receiver k is required to decode messages W1, …, Wk, for 1 ≤ k ≤ K. Furthermore, each message Wkshould be kept secure from receivers with two-level worse channel quality, i.e., receivers 1, …, k − 2. The secrecy capacity region is fully characterized. The achievable scheme designates one superposition layer to each message with random binning employed for each layer for protecting all upper-layer messages from lower-layer receivers. Furthermore, the scheme allows adjacent layers to share rates so that part of the rate of each message can potentially be shared with its immediate upper-layer message to enlarge the rate region. More importantly, an induction approach is developed to perform Fourier-Motzkin elimination over 2K variables among Θ(K2) bounds to obtain a close-form achievable rate region. A converse proof is developed that matches the achievable rate region, which involves recursive construction of the rate bounds.

This letter studies the robust design of downlink precoding for cloud radio access network (C-RAN) in the presence of asynchronism among remote radio heads (RRHs). Specifically, a C-RAN downlink system is considered in which nonideal fronthaul links connecting two RRHs to a baseband unit (BBU) may cause a time offset, as well as a phase offset, between the transmissions of the two RRHs. The offsets are a priori not known to the BBU. With the aim of counteracting the unknown time offset, a robust precoding scheme is considered that is based on the idea of correlating the signal transmitted by one RRH with a number of delayed versions of the signal transmitted by the other RRH. For this transmission strategy, the problem of maximizing the worst-case minimum rate is tackled while satisfying per-RRH transmit power constraints. Numerical results are reported that verify the advantages of the proposed robust scheme as compared to the conventional nonrobust design criteria as well as noncooperative transmission.

This paper considers the signal-to-noise ratio (SNR)-evolution, meaning the behavior as a function of the SNR, of the minimum mean-square error (MMSE) function of code sequences in several multi-user settings in the additive white Gaussian noise regime. The settings investigated in this context include the Gaussian wiretap channel, the Gaussian broadcast channel (BC), and the Gaussian BC with confidential messages (BCC). This paper shows that the specific properties of the SNR-evolution of the MMSE and conditional MMSE functions are necessary and sufficient conditions for capacity or equivocation achieving code sequences. In some cases, the complete SNR-evolution of a family of code sequences can be determined, providing significant insight into the disturbance (in terms of MMSE) such codes have on unintended receivers at other SNRs. Moreover, the effects of an additional MMSE constraint on the capacity region and on the SNR-evolution of code sequences are considered in the BC and BCC settings. Such an analysis emphasizes the tradeoff between rates and limited disturbance on unintended receivers.

The state-dependent Gaussian interference channel (IC) and Z-IC are investigated, in which two receivers are corrupted by the same but differently scaled states. The state sequence is noncausally known at both transmitters, but not known at either receiver. Three interference regimes are studied, i.e., the very strong, strong, and weak regimes. In the very strong regime, the capacity region is characterized under certain channel parameters by designing a cooperative dirty paper coding between the two transmitters to fully cancel the state. In the strong regime, points on the capacity region boundary are characterized under certain channel parameters by designing an achievable scheme based on rate splitting, layered dirty paper coding, and successive state cancellation. In the weak regime, the sum capacity is obtained by independent dirty paper coding at two transmitters. For all the above regimes, the capacity achieves that of the IC/Z-IC without state. Comparison between the state-dependent regular IC and the Z-IC suggests that even with one interference-free link, the Z-IC does not necessarily perform better, because dirty paper coded interference in the regular IC facilitates to cancel the state through the cooperative dirty paper coding between the transmitters.

The state-dependent point-to-point Gaussian channel with a helper is first studied, in which a transmitter communicates with a receiver via a state-corrupted channel. The state is not known to the transmitter nor to the receiver, but known to a helper noncausally, which then wishes to assist the receiver to cancel the state. Differently from the previous work that characterized the capacity only in the infinite state power regime, this paper explores the general case with arbitrary state power. A lower bound on the capacity is derived based on an achievable scheme that integrates direct state subtraction and single-bin dirty paper coding. By analyzing this lower bound and further comparing it with the existing upper bounds, the capacity of the channel is characterized for a wide range of channel parameters. Such an idea of characterizing the capacity is further extended to study the two-user state-dependent multiple access channel with a helper. By comparing the derived inner and outer bounds, the channel parameters are partitioned into appropriate cases, and for each case, either segments on the capacity region boundary or the full capacity region are characterized.

By means of the emerging technique of dynamic Time Division Duplex (TDD), the switching point between uplink and downlink transmissions can be optimized across a multi-cell system in order to reduce the impact of inter-cell interference. It has been recently recognized that optimizing also the order in which uplink and downlink transmissions, or more generally the two directions of a two-way link, are scheduled can lead to significant benefits in terms of interference reduction. In this work, the optimization of bi-directional scheduling is investigated in conjunction with the design of linear precoding and equalization for a general multi-link MIMO two-way system. A simple algorithm is proposed that performs the joint optimization of the ordering of the transmissions in the two directions of the two-way links and of the linear transceivers, with the aim of minimizing the interference leakage power. Numerical results demonstrate the effectiveness of the proposed strategy.

It is well-known that feedback does not increase the capacity of point-to-point memoryless channels, however, its effect in secure communications is not fully understood yet. In this work, an achievable scheme for the wiretap channel with generalized feedback -based on joint source-channel coding- is presented. This scheme recovers previous results, thus it can be seen as a generalization and unification of several results in the field. Additionally, the Gaussian wiretap channel with noisy feedback is analyzed, and the scheme achieves positive secrecy rates even in unfavorable situations where the eavesdropper experiences a much better channel than the legitimate user.

This work investigates the Multicast Cognitive Interference Channel (CIFC) where many secondary users are interested in the same cognitive message. The focus is to study the role that Multiple Description (MD) coding can play under simultaneous transmissions. Though for the very weak, very strong, and mixed very weak/strong interference regimes, resorting to a Common Description (CD) alone is capacity achieving, in the weak interference regime it becomes crucial to resort to a more evolved coding scheme relying on multiple descriptions that could each accommodate differently the interference experienced at the secondary users. A Gaussian example illustrates this claim and various capacity results are likewise reported.

This work examines the properties of code sequences for the scalar Gaussian broadcast channel (BC). Specifically, the behavior in terms of the mutual information and minimum mean-square error (MMSE) functions for all signal-to-noise ratios (SNRs) is explored. It is shown that “good”, capacity achieving, code sequences must follow the behavior of a capacity achieving superposition code sequence, even if they use a different encoding-decoding scheme (such as “Dirty Paper Coding”). Necessary and sufficient conditions for reliable decoding in general and specifically for “good” code sequences for the scalar Gaussian BC, in terms of the MMSE and conditional MMSE functions, are derived. Finally, “bad” code sequences, that do not obtain the capacity of the scalar Gaussian BC, are examined. These codes are defined by an additional MMSE constraint at some other SNR. This constraint limits the amount of disturbance these codes may have on some unintended receiver at that SNR. The capacity region, given this constraint, is fully depicted.

A type of state-dependent Gaussian Z-interference channels is studied, in which transmitters 1 and 2 wish to send two messages to receivers 1 and 2, and only receiver 1 is interfered by transmitter 2's signal. Both receivers are corrupted by the same but differently scaled state sequence. The state information is assumed to be known noncausally at both transmitters. The channel is partitioned into very strong, strong, and weak interference regimes based on the strength of the interference. Respectively for the very strong and strong regimes, the capacity region and points on the capacity region boundary are characterized under certain channel parameters by designing joint dirty paper coding between two transmitters to cancel the state at both receivers. For the weak interference regime, the sum capacity is characterized by independent dirty paper coding at two transmitters. Comparison between the state-dependent regular and Z-interference channels indicates that although with one interference-free link, Z-interference channel does not necessarily perform better, because the dirty paper coded interference can be useful to help to fully cancel the state via joint dirty paper coding between the transmitters.

In this paper we study the cooperative multiple access channel (MAC) with oblivious encoders and characterize its capacity region. Cooperation means that one encoder sends a message to the other encoder via a rate-limited link prior to transmission, while partial cribbing means that one encoder obtains a deterministic function of the other encoder's output. Partial cribbing can be done strictly-causally, causally, and non-causally. Prior work in this field dealt with the case where the two encoders are aware of each other's chosen codebook. In this paper we consider the case of oblivious encoding, where each user in a network is unaware of the codebook chosen by other users. Since an oblivious encoder cannot decode the message sent from another encoder, it cannot implement the Decode-and-Forward coding scheme, as is usually done in cases of partial cribbing with codebook-aware encoders. To overcome this, we introduce the method of Bin-and-Forward, which does not require decoding. Since the possible cribbed signals are not known a priori, in our new coding scheme binning is done on all typical possible cribbed sequences. Instead of sending a decoded message, the oblivious encoder sends the bin in which the cribbed sequence lies. We compare the gain achieved using cooperation and partial cribbing with and without codebook knowledge. Evidently, in the cases of causal and non-causal perfect cribbing, the capacity region is the same for oblivious and codebook-aware encoding. As an example, we consider the Gaussian MAC with cooperation and quantized cribbing. For this model, we give an achievability scheme that shows how knowing the codebooks affects the capacity region for cooperation alone, for partial cribbing alone, and for combined cooperation and partial cribbing.

Costa's “writing on dirty paper” result establishes that full state pre-cancellation can be attained in the Gel'fand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the assumptions that full channel knowledge is available at both the transmitter and the receiver. In this work we consider the scenario in which the state is multiplied by an ergodic fading process which is not known at the encoder. We study both the case in which the receiver has knowledge of the fading and the case in which it does not: for both models we derive inner and outer bounds to capacity and determine the distance between the two bounds when possible. For the channel without fading knowledge at either the transmitter or the receiver, the gap between inner and outer bounds is finite for a class of fading distributions which includes a number of canonical fading models. In the capacity approaching strategy for this class, the transmitter performs Costa's pre-coding against the mean value of the fading times the state while the receiver treats the remaining signal as noise. For the case in which only the receiver has knowledge of the fading, we determine a finite gap between inner and outer bounds for two classes of discrete fading distribution. The first class of distributions is the one in which there exists a probability mass larger than one half while the second class is the one in which the fading is uniformly distributed over values that are exponentially spaced apart. Unfortunately, the capacity in the case of a continuous fading distribution remains very hard to characterize.

We investigate whether uncoded schemes are optimal for Gaussian sources on multiuser Gaussian channels. Particularly, we consider two problems: the first is to send correlated Gaussian sources on a Gaussian broadcast channel where each receiver is interested in reconstructing only one source component (or one specific linear function of the sources) under the mean squared error distortion measure; the second is to send correlated Gaussian sources on a Gaussian multiple-access channel, where each transmitter observes a noisy combination of the source, and the receiver wishes to reconstruct the individual source components (or individual linear functions) under the mean squared error distortion measure. It is shown that when the channel parameters match certain general conditions, the induced distortion tuples are on the boundary of the achievable distortion region, and thus optimal. Instead of following the conventional approach of attempting to characterize the achievable distortion region, we ask the question whether and how a match can be effectively determined. This decision problem formulation helps to circumvent the difficult optimization problem often embedded in region characterization problems, and it also leads us to focus on the critical conditions in the outer bounds that make the inequalities become equalities, which effectively decouples the overall problem into several simpler sub-problems.

The problem of simultaneously generating two keys for a cellular model is investigated, in which each of four terminals, X0, X1, X2, and X3observes one component of correlated sources. The terminal X0 wishes to generate secret keys K1and K2respectively, with terminals X1and X2under the help of terminal X3. They are allowed to communicate over a public channel. Both K1and K2are required to be concealed from an eavesdropper that has access to the public discussion. The key capacity region is established by designing a unified achievable strategy to achieve the cut-set outer bounds, which greatly simplifies the proof.

A four-receiver degraded broadcast channel with secrecy outside a bounded range is studied, over which a transmitter sends four messages to four receivers. In the model considered, the channel quality gradually degrades from receiver 4 to receiver 1, and receiver k is required to decode the first k messages for k = 1, …, 4. Furthermore, message 3 is required to be secured from receiver 1, and message 4 is required to be secured from receivers 1 and 2. The secrecy capacity region is established. The achievable scheme includes not only superposition, binning and embedded coding used in previous studies, but also rate splitting and sharing particularly designed for this model, which is shown to be critical to further enlarge the achievable region and enable the development of the converse proof.

The capacity of the Multicast Cognitive Interference Channel (CIFC), where many primary users are interested in the same message, is investigated. In the light of the existing results on the CIFC, capacity is characterized for the very weak and very strong interference regimes as well as the mixed weak/strong interference scenario through novel inner and outer bounds. Moreover, the capacity regions of the corresponding Gaussian settings are likewise reported.

This work examines the properties of “good” codes for the scalar Gaussian wiretap channel that achieve the maximum level of equivocation. Specifically, the minimum mean-square error (MMSE) behavior of these codes is explored as a function of the signal-to-noise ratio (SNR). It is first shown that reliable decoding of the codeword at the legitimate receiver and at the eavesdropper, conditioned on the transmitted message, is a necessary and sufficient condition for an optimally secure code sequence. Moreover, it is observed that a stochastic encoder is required for any code sequence with rate below the channel point-to-point capacity. Then, for code sequences attaining the maximum level of equivocation, it is shown that their codebook sequences must resemble “good” point-to-point, capacity achieving, code sequences. Finally, it is shown that the mapping over such “good” codebook sequences that produces a maximum equivocation code must saturate the eavesdropper. These results support several “rules of thumb” in the design of capacity achieving codes for the Gaussian wiretap.

In this paper, we study how the number of conferencing rounds effects the capacity of large interference networks. We take Wyner's asymmetric linear (soft-handoff) model, include conferencing links between closely located transmitters and receivers, and we consider the per-user asymptotic multiplexing gain. Our results show, for example, that when the capacities of the conferencing links scale at most (1/4) log P with the power P and when one can choose which transmitters and receivers cooperate, then there is no loss in terms of asymptotic multiplexing gain in having only one round of conferencing. In contrast, when the capacities of the conferencing links grow faster than (1/4) log P, then the asymptotic multiplexing gain with one round of conferencing is strictly smaller than that achieved with multiple rounds.

A three-receiver degraded broadcast channel with secrecy outside of a bounded range is studied, in which the channel quality gradually degrades from receiver 3 to receiver 1. The transmitter has three messages intended for the receivers with receiver 3 decoding all messages, receiver 2 decoding the first two messages, and receiver 1 decoding only the first message. Furthermore, the third message should be kept secure from receiver 1. The discrete memoryless channel is studied and the secrecy capacity region is characterized. The achievable scheme is based on superposition coding and random binning, in which one superposition layer and random binning together provide secrecy. The converse proof is derived based on the insight obtained from the achievable scheme so that manipulations of terms yield tight rate bounds.

Recent information-theoretic results on a class of broadcast channels with layered decoding and/or layered secrecy are reviewed. In this class of models, a transmitter sends multiple messages to a set of legitimate receivers in the presence of a set of eavesdroppers, whose channels can be ordered based on the quality of received signals. Receivers with better channel quality are required to decode more messages, and eavesdroppers with worse channel quality are required to be kept ignorant of more messages. The design of achievable schemes and the characterization of the corresponding secrecy capacity regions are presented. Comparison of the designs for different models is discussed. Applications of these information-theoretic models to the study of secure communication over fading wiretap channels and secret sharing are also presented to illustrate potential applications of these models.

Precoding schemes in the framework of vector perturbation (VP) for the multiple-input multiple-output (MIMO) Gaussian broadcast channel (GBC) are investigated. The VP scheme, originally a “one-shot” technique, is generalized to encompass processing over multiple time instances. Using lattice-based extended alphabets (“perturbations”), and considering the infinite time-span extension limit, a lower bound on the achievable sum-rate using the generalized VP scheme is analytically obtained. The lower bound is shown to asymptotically achieve the optimum sum-rate in the high signal-to-noise ratio (SNR) regime (both in terms of degrees-of-freedom and power offset), for any number of users and transmit antennas. For the two-user cases, it is shown that the lower bound coincides with the sum-capacity for low SNR. The above lower bound is constructively obtained by means of an efficient practically oriented suboptimal transmit energy minimization algorithm, which exhibits a polynomial complexity in the number of users. The proposed precoding scheme demonstrates that the “shaping gain” is achievable for VP schemes, when employing “good” multidimensional lattices. It is also shown that the suboptimum algorithm has its merits, even when processing over multiple time instances is not employed. For the $2\times 2$ MIMO GBC, the VP scheme is generalized further, and an inner bound for the entire achievable rate region is obtained, by which an interesting correspondence is identified with the ultimate capacity region, as obtained by “dirty paper coding”.

We consider the discrete-time intersymbol interference (ISI) channel model, with additive Gaussian noise and fixed independent identically distributed inputs. In this setting, we investigate the expression put forth by Shamai and Laroia as a conjectured lower bound for the input-output mutual information after application of a minimum mean-square error decision-feedback equalizer receiver. A low-signal to noise ratio (SNR) expansion is used to prove that the conjectured bound does not hold under general conditions, and to characterize inputs for which it is particularly ill-suited. One such input is used to construct a counterexample, indicating that the Shamai-Laroia expression does not always bound even the achievable rate of the channel, thus excluding a natural relaxation of the original conjectured bound. However, this relaxed bound is then shown to hold for any finite entropy input and ISI channel, when the SNR is sufficiently high. We derive two conditions under which the relaxed bound holds, involving compound channel capacity and quasiconvexity arguments. Finally, new simple bounds for the achievable rate are proven, and compared with other known bounds. Information-estimation relations and estimation-theoretic bounds play a key role in establishing our results.

We compare the maximum achievable rates in single-carrier (SC) and orthogonal frequency-division multiplexing (OFDM) modulation schemes, under the practical assumptions of independent identically distributed finite alphabet inputs and linear intersymbol interference with additive Gaussian noise. We show that the Shamai-Laroia approximation serves as a bridge between the two rates: while it is well known that this approximation is often a lower bound on the SC achievable rate, it is revealed to also essentially upper bound the OFDM achievable rate. We apply information-estimation relations in order to rigorously establish this result for both general input distributions and to sharpen it for commonly used pulse-amplitude modulation (PAM) and quadratic-amplitude modulation constellations. To this end, novel bounds on minimum mean-square error estimation of PAM inputs to a scalar Gaussian channel are derived, which may be of general interest. Our results show that, under reasonable assumptions, optimal SC schemes may offer spectral efficiency significantly superior to that of OFDM, motivating further research of such systems.

The growing interest in action-dependent channels motivates us to extend the study of action-dependent settings, which until now focused on point-to-point models, to multiple-access channels (MACs). In this paper, we consider a two-user, state-dependent MAC, in which one of the encoders, called the informed (cognitive) encoder, is allowed to take an action that affects the formation of the channel states. Two independent messages are to be sent through the channel: (1) a common message known to both encoders and (2) a private message known only to the informed encoder. In addition, the informed encoder has access to the sequence of channel states in a noncausal manner. Our framework generalizes the previously evaluated settings of state-dependent point-to-point channels with actions and MACs with common messages. We derive a single letter characterization of the capacity region for this setting. Using this general result, we obtain and compute the capacity region for the Gaussian action-dependent MAC. The special methods used in solving the Gaussian case are then applied to obtain the capacity of the Gaussian action-dependent point-to-point channel, a problem left open and solved now. Finally, we establish some dualities between action-dependent channel coding and source coding problems. In particular, we obtain a duality equivalence between the considered MAC setting and the rate distortion model known as successive refinement with actions. This is done by developing a set of simple duality principles that enables us to successfully evaluate the outcome of one problem given the outcome of the other.

State-dependent parallel networks with a common state-cognitive helper is studied, in which K transmitters wish to send K messages to their corresponding receivers over K state-corrupted parallel channels, and a helper who knows the state information noncausally wishes to assist these receivers to cancel state interference. Furthermore, the helper also has its own message to be sent simultaneously to its corresponding receiver. Since the state information is known only to the helper, but not to other transmitters, transmitter-side state cognition and receiver-side state interference are mismatched. Our focus is on the high state power regime, i.e., the state power goes to infinity. Three (sub)models are studied. Model I serves as a basic model, which consists of only one transmitter-receiver (with state corruption) pair in addition to a helper that assists the receiver to cancel state in addition to transmitting its own message. Model II consists of two transmitter-receiver pairs in addition to a helper, and only one receiver is interfered by a state sequence. Model III generalizes model I to include multiple transmitter- receiver pairs with each receiver corrupted by independent state. For all models, the inner and outer bounds on the capacity region are derived, and comparison of the two bounds yields characterization of either full or partial boundary of the capacity region under various channel parameters.

We take an information theoretic perspective on a classical sparse-sampling noisy linear model and present an analytical expression for the mutual information, which plays a central role in a variety of communications/signal processing problems. Such an expression was addressed previously by bounds, by simulations, and by the (nonrigorous) replica method. The expression of the mutual information is based on techniques used, addressing the minimum mean square error analysis. Using these expressions, we study specifically a variety of sparse linear communication models, which include coding in various settings, accounting also for multiple access channels, broadcast channels, and different wiretap problems. For those, we provide single-letter expressions and derive achievable rates, capturing the communications/signal processing features of these contemporary models.

The Poisson interference channel, which models optical communication systems with multiple transceivers in the short-noise-limited regime, is investigated. Conditions for the strong interference regime are characterized and the corresponding capacity region is derived, which is the same as that of the compound Poisson multiple-access channel with each receiver decoding both messages. For the cases when the strong interference conditions are not satisfied, inner and outer bounds on the capacity region are derived. The inner bound is derived via approximating the Poisson interference channel by a binary interference channel and then evaluating the corresponding Han-Kobayashi region. The outer bounds are obtained via various techniques, including noise reduction, genie-aided scheme, and channel transformation. The Poisson Z-interference channel is then studied. The sum rate capacity is obtained when the cross link coefficient is either sufficiently small or sufficiently large.

We consider the problem of channel state amplification in a Gaussian channel with additive Gaussian channel states, where the encoder observes noncausally a noisy version of these states. A complete characterization is provided for the minimum reconstruction distortion under a transmitter power constraint, and it is shown that a simple analog scheme with power control is optimal. More precisely, if the power available to the encoder is below certain threshold, the analog scheme using full power is optimal, however, when the power available to the encoder is above that threshold, analog transmission using only a fixed amount of the available power is optimal. Furthermore, the problem of simultaneous message transmission and Gaussian state amplification with noisy observations is studied, for which an inner bound and two nontrivial outer bounds to the optimal tradeoff between the transmission rate and the state reconstruction distortion are provided. The coding scheme underlying the inner bound combines analog signaling and Gelfand-Pinsker coding, where the latter deviates from the operating point of Costa's dirty paper coding. The first outer bound is obtained by extending the channel decomposition technique, while the second outer bound requires a strategic analysis of the covariance matrix of the relevant random variables.

The degrees of freedom (DoFs) of the K -user Gaussian interference channel determine the asymptotic growth of the maximal sum rate as a function of the signal-to-noise ratio. Subject to a very general sufficient condition on the cross-channel gains, we give a formula for the DoFs of the scalar interference channel as a function of the deterministic channel matrix, which involves maximization of a sum of information dimensions over K scalar input distributions. Known special cases are recovered, and even generalized in certain cases with unified proofs.

A novel information theoretic approach is proposed to solve the secret sharing problem, in which a dealer distributes one or multiple secrets among a set of participants in such a manner that for each secret only qualified sets of users can recover this secret by pooling their shares together while nonqualified sets of users obtain no information about the secret even if they pool their shares together. While existing secret sharing systems (implicitly) assume that communications between the dealer and participants are noiseless, this paper takes a more practical assumption that the dealer delivers shares to the participants via a noisy broadcast channel. Thus, in contrast to the existing solutions that are mainly based on number theoretic tools, an information theoretic approach is proposed, which exploits the channel randomness during delivery of shares as additional resources to achieve secret sharing requirements. In this way, secret sharing problems can be reformulated as equivalent secure communication problems via wiretap channel models, and can hence be solved by employing the powerful information theoretic security techniques. This approach is first developed for the classic secret sharing problem, in which only one secret is to be shared. This classic problem is shown to be equivalent to a communication problem over a compound wiretap channel. Thus, the lower and upper bounds on the secrecy capacity of the compound channel provide the corresponding bounds on the secret sharing rate, and the secrecy scheme designed for the compound channel provides the secret sharing schemes. The power of the approach is further demonstrated by a more general layered multisecret sharing problem, which is shown to be equivalent to the degraded broadcast multiple-input multiple-output (MIMO) channel with layered decoding and secrecy constraints. The secrecy capacity region for the degraded MIMO broadcast channel is characterized, which provides the secret sharing capacity region. Furthermore, the secure encoding scheme that achieves the secrecy capacity region provides an information theoretic scheme for sharing the secrets.

The 3-user multiple-input single-output (MISO) broadcast channel (BC) with hybrid channel state information at the transmitter (CSIT) is considered. In this framework, there is perfect and instantaneous CSIT from a subset of users and delayed CSIT from the remaining users. We present new results on the sum degrees of freedom (DoF) of the 3-user MISO BC with hybrid CSIT. In particular, for the case of 2 transmit antennas, we show that with perfect CSIT from one user and delayed CSIT from the remaining two users, the optimal sum DoF is 5/3. For the case of 3 transmit antennas and the same hybrid CSIT setting, it is shown that a higher sum DoF of 9/5 is achievable and this result improves upon the best known bound. Furthermore, with 3 transmit antennas, and the hybrid CSIT setting in which there is perfect CSIT from two users and delayed CSIT from the third one, a novel scheme is presented which achieves 9/4 sum DoF. Our results also reveal new insights on how to utilize hybrid channel knowledge for multi-user scenarios.

It is shown that, for a subset of input distributions, the effect of maximum rate transmission on an additional transmitted message, over the additive Gaussian noise channel, is, effectively, that of an additional additive Gaussian noise. Meaning, that the behavior of the mutual information and minimum mean-square error are as if additional additive Gaussian noise were transmitted. Such an observation provides corner points of the two-user Gaussian interference channel, for this subset.

The state-dependent parallel Gaussian channel with a common helper is investigated, in which transmitters 1 and 2 transmit two messages respectively to receivers 1 and 2 over the parallel channel. Furthermore, both parallel subchannels can be corrupted by independent state sequences, respectively, which are unknown to both transmitters and receivers. There is a common helper that knows the states noncausally and assists communication between transmitters and receivers. Our focus is on the high state power regime, i.e., the state power goes to infinity. Two Gaussian models are studied with model I having only receiver 1 interfered by the state and with model II having both receivers interfered by independent states. Each model has its unique challenge to address. For both models, inner and outer bounds on the capacity region are derived, and comparison of the two bounds leads to capacity results under certain channel parameters.

Consider a MIMO uplink channel with channel matrix H and a MIMO downlink channel with channel matrix H⊤. It is well-known that any rate tuple that is achievable on the uplink is also achievable on the downlink under the same total power constraint, i.e., there is an uplink-downlink duality relationship. In this paper, we consider the integer-forcing strategy, in which users steer the channel towards an integer-valued effective channel matrix so that the receiver(s) can decode integer-linear combinations of the transmitted codewords. Recent efforts have demonstrated the benefits of this strategy for uplink, downlink, and interference alignment scenarios. Here, we establish that uplink-downlink duality holds for integer-forcing. Specifically, in the uplink, L transmitters communicate over channel matrix H to an L-antenna receiver with target integer matrix A. In the downlink, an L-antenna transmitter communicates over channel matrix H⊤to L single-antenna receivers with target integer matrix A⊤. We show that any computation rate tuple that is achievable in the uplink is achievable for the same total power in the downlink and vice versa.

In this paper we study the multiple access channel (MAC) with combined cooperation and partial cribbing and characterize its capacity region. Cooperation means that the two encoders send a message to one another via a rate-limited link prior to transmission, while partial cribbing means that each of the two encoders obtains a deterministic function of the other encoder's output with or without delay. Prior work in this field dealt separately with cooperation and partial cribbing. However, by combining these two methods we can achieve significantly higher rates. Remarkably, the capacity region does not require an additional auxiliary random variable (RV) since the purpose of both cooperation and partial cribbing is to generate a common message between the encoders. In the proof we combine methods of block Markov coding, backward decoding, double rate-splitting, and joint typicality decoding. Furthermore, we present the Gaussian MAC with combined one-sided cooperation and quantized cribbing. For this model, we give an achievability scheme that shows how many cooperation or quantization bits are required in order to achieve a Gaussian MAC with full cooperation/cribbing capacity region. After establishing our main results, we show that in a state-dependent MAC with cooperation, where the state is known at a partially cribbing encoder and at the decoder, only one auxiliary RV is needed to incorporate both the cribbing and the cooperation. However, there are cases where more than one auxiliary RV is needed, e.g., when the cooperation and cribbing are not used for the same purposes. We present a MAC with an action-dependent state where the action is based on the cooperation but not on the cribbing. Therefore, in this case more than one auxiliary RV is needed. We deduce a general rule for this result.

In the downlink of cloud radio access networks, a central encoder is connected to multiple multi-antenna base stations (BSs) via finite-capacity backhaul links. At the central encoder, precoding is followed by compression in order to produce the rate-limited bit streams delivered to each BS over the corresponding backhaul link. In current state-of-the-art schemes, the signals intended for different BSs are compressed independently. In contrast, this work proposes to leverage joint compression, also referred to as multivariate compression, of the signals for different BSs in order to better control the effect of the additive quantization noises at the mobile stations (MSs). The problem of maximizing the weighted sum-rate over precoding and compression strategies is formulated subject to power and backhaul capacity constraints. An iterative algorithm is proposed that achieves a stationary point of the problem. From numerical results, it is confirmed that the proposed joint precoding and compression strategy outperforms conventional approaches based on independent compression across the BSs.

This work investigates efficient backhaul compression strategies for the uplink of cloud radio access networks with a general multihop backhaul topology. In these systems, each radio unit (RU) communicates with the managing control unit (CU) through a set of intermediate RUs. A baseline multiplex-and-forward (MF) scheme is first studied in which each RU forwards the bit streams received from the connected RUs without any processing. It is observed that this strategy may cause significant performance degradation in the presence of a dense deployment of RUs. To obviate this problem, a scheme is proposed in which each RU decompresses the received bit streams and performs linear in-network processing of the decompressed signals. For both the MF and the decompress-process-and-recompress (DPR) backhaul schemes, the optimal design is addressed with the aim of maximizing the sum-rate under the backhaul capacity constraints. Based on the analysis, numerical results are provided to compare the performance of the MF and DPR schemes, highlighting the potential advantage of in-network processing.

The impact of phase fading and side information on the classic Costa's dirty paper coding channel is studied. A variation of this model is considered in which the channel state is affected by a phase fading sequence which is known at the receiver but not at the transmitter. Although the capacity of this channel has been established, it is expressed as the solution of the maximization which cannot be easily determined. To circumvent such difficulty, we derive alternative inner and outer bounds to capacity and determine a regime in which the two expressions are to within a finite distance. We consider two distributions of the phase fading process: circular binomial and circular uniform. For circular binomial fading we show that binning with Gaussian signaling approaches capacity, as in the channel without phase fading. When fading is circular uniform, instead, binning with Gaussian signaling is no longer effective and novel interference avoidance strategies are developed for this case.

The multiple-input single-output (MISO) wiretap channel with K legitimate single-antenna receivers, one single-antenna eavesdropper in presence of delayed channel state information at the transmitter (CSIT) is considered. The transmitter is equipped with (K+1) antennas and has independent messages intended for each one of the K legitimate receivers. While for the case of a single receiver wiretap channel, i.e., K = 1, the optimal secure degrees of freedom (SDoF) with delayed CSIT was recently characterized in [1] and shown to be 2/3, the extension to the multi-receiver case (K > 1) is far from straightforward. We present new results and insights for the simplest non-trivial extension of this problem, i.e., for the case of K = 2 receiver MISO wiretap channel with delayed CSIT. The contribution of this paper is two fold: a) an asymptotic achievable scheme is presented for the case of two legitimate receivers which achieves a sum-SDoF of 36/37 and b) a novel converse proof is presented which shows that the sum-SDoF is upper bounded by 16/15.

We consider a MIMO (linear Gaussian) channel where the inputs are turned on and off at random, and the outputs are sampled at random with probability p. In particular, for a given probability of “on” input q (input sparsity), we consider a scenario where the transmitter wishes to send information to a family of possible receivers characterized by different random sampling rates p ∈ [0,1]. For this setting, we focus on the broadcast approach, i.e., a coding technique where the transmitter sends information encoded into superposition layers, such that the number of decoded layers depends on the receiver sampling rate p. We obtain a method for calculating the power allocation across the layers for given statistics of the MIMO channel matrix in order to maximize the system weighted sum rate for arbitrary non-negative weighting function w(p). In particular, we provide analytical solutions both for iid and Haar distributed MIMO channel matrices. The latter case accounts also for DFT matrices (see [1]), with application to sparse spectrum signals with random sub-Nyquist sampling.

We consider a two-user multiaccess channel with degraded messages sets in which the channel state information (CSI) is revealed, strictly causally or with one-unit delay, to only the encoder that sends the common message. We study the capacity region of this model. We establish inner and outer bounds on the capacity region. We also identify some special cases in which the bounds meet, thereby characterizing the capacity region in these cases. The outer bound is non-trivial and has a relatively simple convenient expression (it incorporates only one auxiliary random variable). The coding scheme that we use for the inner bound utilizes rate-splitting to resolve a tension at the informed encoder among exploiting the knowledge of the (delayed) CSI (through a noisy network coding or quantize-map-and-forward state compression) and sending information cooperatively with the other encoder. Together with some previous results on closely related models, the results in this paper shed more light on the utility of delayed CSI for increasing the capacity region of multiaccess channels; and tie with some recent progress in this framework.

In this paper, the degraded Gaussian Multiple-Input-Multiple-Output (MIMO) broadcast channel with layered decoding and secrecy constraints is investigated. In this model, there are in total K messages and K receivers that are ordered by the channel quality. Each receiver is required to decode one more message than the receiver with one level worse channel quality. Furthermore, this message should be kept secure from the receivers with worse channel qualities. The secrecy capacity region for this model is fully characterized. The converse proof relies on a novel construction of a series of covariance matrices. An application of this model to the problem of sharing multiple secrets, which is difficult to solve using number theoretic tools, is investigated. The secret sharing capacity region is characterized by reformulating the secret sharing problem as the secure communication problem over the K-receiver degraded Gaussian MIMO broadcast channel.

This work investigates the “2 by 1” two-user Compound Broadcast Channel. We investigate an evolved encoding scheme, based on the use of Multiple Description (MD) coding, where the source transmits both common and private descriptions to the many channel instances of the same user. We derive the resulting MD inner bound and evaluate it for the compound MISO BC resorting to a Dirty Paper Code (DPC). The suggested MD-DPC “strictly” outperforms Common Description (CD)-DPC and hence, palliates better the effect of channel uncertainty.

The state-dependent broadcast channel with a helper is investigated, in which a transmitter wishes to send messages to two receivers via a broadcast channel. The channel is corrupted by an independent and identically distributed (i.i.d.) state sequence which is known to neither the transmitter nor the receivers. A helper that knows the state sequence noncausally assists the broadcast transmission to cancel state interference. Two scenarios are studied. In scenario 1, the transmitter sends one message to both receivers, and in scenario II, the transmitter sends two private messages respectively to two receivers. Our focus is on the Gaussian channel with additive state. Inner and outer bounds are derived for both scenarios. By comparing the inner and outer bounds, capacity/capacity region are characterized under various ranges of channel parameters. Practical impact of the model and results are discussed.

The classic “writing on dirty paper” capacity result establishes that full state pre-cancellation can be attained in Gelfand-Pinsker problem with additive state and additive white Gaussian noise. This result holds under the assumption that both the transmitter and the receiver have perfect knowledge of the channel. We are interested in characterizing capacity under the more realistic assumption that only partial channel knowledge is available at the transmitter. To this end we study the “dirty paper channel with slow fading dirt”, a variation of the dirty paper channel in which the state sequence is multiplied by a slow fading value known only at the receiver. For this model we establish two approximate characterizations of capacity, one for the case in which fading takes only two values and one for the case in which fading takes M possible values but these values are greatly spaced apart. For both results, a naive strategy in which the encoder pre-codes against different fading realizations in different time slots is sufficient to approach capacity.

We provide a rigorous mathematical analysis of two communication strategies: soft decode-and-forward (soft-DF) for relay channels and soft partial interference-cancelation (soft-IC) for interference channels. Both strategies involve soft estimation, which assists the decoding process. We consider LDPC codes, not because of their practical benefits, but because of their analytic tractability, which enables an asymptotic analysis similar to random coding methods of information theory. Unlike some works on the closely-related demodulate-and-forward, we assume non-memoryless, code-structure-aware estimation. With soft-DF, we develop simultaneous density evolution to bound the decoding error probability at the destination. This result applies to erasure relay channels. In one variant of soft-DF, the relay applies Wyner-Ziv coding to enhance its communication with the destination, borrowing from compress-and-forward. To analyze soft-IC, we adapt existing techniques for iterative multiuser detection, and focus on binary-input additive white Gaussian noise interference channels. We prove that optimal point-to-point codes are unsuitable for soft-IC, as well as for all strategies that apply partial decoding to improve upon single-user detection and multiuser detection, including Han-Kobayashi.

We study an interference network where equally numbered transmitters and receivers lie on two parallel lines, with each transmitter opposite its intended receiver. We consider two short-range interference models: the asymmetric network, where the signal sent by each transmitter is interfered only by the signal sent by its left neighbor (if present), and a symmetric network, where it is interfered by both its left and its right neighbors. Each transmitter is cognizant of its own message, the messages of the tℓtransmitters to its left, and the messages of the trtransmitters to its right. Each receiver decodes its message based on the signals received at its own antenna, at the rrreceive antennas to its left, and at the rrreceive antennas to its right. For such networks, we provide upper and lower bounds on the multiplexing gain, i.e., on the high signal-to-noise ratio asymptotic logarithmic growth of the sum-rate capacity. In some cases, our bounds coincide, e.g., for the asymmetric network. Our results exhibit an equivalence between the transmitter sideinformation parameters tℓ, tr and the receiver side-information parameters rℓ, rrin the sense that increasing/decreasing tℓor trby a positive integer δ has the same effect on the multiplexing gain as increasing/decreasing rℓor rrby δ. Moreover-even in asymmetric networks-there is an equivalence between the left side-information parameters (tℓ, rℓ) and the right sideinformation parameters (tr, rr).

A (layered) broadcast approach is studied for the fading wiretap channel without the channel state information (CSI) at the transmitter. Two broadcast schemes, based on superposition coding and embedded coding, respectively, are developed to encode information into a number of layers and use stochastic encoding to keep the corresponding information secret from an eavesdropper. The layers that can be successfully and securely transmitted are determined by the channel states to the legitimate receiver and the eavesdropper. The advantage of these broadcast approaches is that the transmitter does not need to know the CSI to the legitimate receiver and the eavesdropper, but the scheme still adapts to the channel states of the legitimate receiver and the eavesdropper. Three scenarios of block fading wiretap channels with stringent delay constraints are studied, in which either the legitimate receiver's channel, the eavesdropper's channel, or both channels are fading. For each scenario, the secrecy rate that can be achieved via the broadcast approach developed in this paper is derived, and the optimal power allocation over the layers (or the conditions on the optimal power allocation) is also characterized. A notion of probabilistic secrecy, which characterizes the probability that a certain secrecy rate of decoded messages is achieved during one block, is also introduced and studied for scenarios when the eavesdropper's channel is fading. Numerical examples are provided to demonstrate the impact of the CSI at the transmitter and the channel fluctuations of the eavesdropper on the average secrecy rate. These examples also demonstrate the advantage of the proposed broadcast approach over the compound channel approach.

We consider the source-channel separation architecture for lossy source coding in communication networks. It is shown that the separation approach is optimal in two general scenarios and is approximately optimal in a third scenario. The two scenarios for which separation is optimal complement each other: the first is when the memoryless sources at source nodes are arbitrarily correlated, each of which is to be reconstructed at possibly multiple destinations within certain distortions, but the channels in this network are synchronized, orthogonal, and memoryless point-to-point channels; the second is when the memoryless sources are mutually independent, each of which is to be reconstructed only at one destination within a certain distortion, but the channels are general, including multi-user channels, such as multiple access, broadcast, interference, and relay channels, possibly with feedback. The third scenario, for which we demonstrate approximate optimality of source-channel separation, generalizes the second scenario by allowing each source to be reconstructed at multiple destinations with different distortions. For this case, the loss from optimality using the separation approach can be upper-bounded when a difference distortion measure is taken, and in the special case of quadratic distortion measure, this leads to universal constant bounds.

This paper investigates the problem of source-channel coding for secure transmission with arbitrarily correlated side informations at both receivers. This scenario consists of an encoder (referred to as Alice) that wishes to compress a source and send it through a noisy channel to a legitimate receiver (referred to as Bob). In this context, Alice must simultaneously satisfy the desired requirements on the distortion level at Bob and the equivocation rate at the eavesdropper (referred to as Eve). This setting can be seen as a generalization of the problems of secure source coding with (uncoded) side information at the decoders and the wiretap channel. A general outer bound on the rate-distortion-equivocation region, as well as an inner bound based on a pure digital scheme, is derived for arbitrary channels and side informations. In some special cases of interest, it is proved that this digital scheme is optimal and that separation holds. However, it is also shown through a simple counterexample with a binary source that a pure analog scheme can outperform the digital one while being optimal. According to these observations and assuming matched bandwidth, a novel hybrid digital/analog scheme that aims to gather the advantages of both digital and analog ones is then presented. In the quadratic Gaussian setup when side information is only present at the eavesdropper, this strategy is proved to be optimal. Furthermore, it outperforms both digital and analog schemes and cannot be achieved via time-sharing. Through an appropriate coding, the presence of any statistical difference among the side informations, the channel noises, and the distortion at Bob can be fully exploited in terms of secrecy.

We consider a cooperative two-user multiaccess channel in which the transmission is controlled by a random state. Both encoders transmit a common message and, one of the encoders also transmits an individual message. We study the capacity region of this communication model for different degrees of availability of the states at the encoders, causally or strictly causally. In the case in which the states are revealed causally to both encoders but not to the decoder we find an explicit characterization of the capacity region in the discrete memoryless case. In the case in which the states are revealed only strictly causally to both encoders, we establish inner and outer bounds on the capacity region. The outer bound is nontrivial, and has a relatively simple form. It has the advantage of incorporating only one auxiliary random variable. In particular, it suggests that there is none, or at best only little, to gain from having the encoder that transmits both messages also sending an individual description of the state to the receiver, in addition to the compressed version that is sent cooperatively with the other encoder. We then introduce a class of cooperative multiaccess channels with states known strictly causally at both encoders for which the inner and outer bounds agree, and so we characterize the capacity region for this class. In this class of channels, the state can be obtained as a deterministic function of the channel inputs and output. We also study the model in which the states are revealed, strictly causally, in an asymmetric manner, to only one encoder. Throughout this paper, we discuss a number of examples, and compute the capacity region of some of these examples. The results shed more light on the utility of delayed channel state information for increasing the capacity region of state-dependent cooperative multiaccess channels, and tie with recent progress in this framework.

In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the uplink, an effective solution for the communication between BSs and the corresponding CU on the backhaul links is based on compressing and forwarding the baseband received signal from each BS. In the presence of ergodic fading, communicating the channel state information (CSI) from the BSs to the CU may require a sizable part of the backhaul capacity. In a prior work, this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach, whereby the BSs compress the training signal and CSI estimation takes place at the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is investigated, whereby the BSs perform CSI estimation and forward a compressed version of the CSI to the CU. This choice is motivated by the information theoretic optimality of separate estimation and compression. Various ECF strategies are proposed that perform either separate or joint compression of estimated CSI and received signal. Moreover, the proposed strategies are combined with distributed source coding when considering multiple BSs. "Semi-coherent" strategies are also proposed that do not convey any CSI or training information on the backhaul links. Via numerical results, it is shown that a proper design of ECF strategies based on joint received signal and estimated CSI compression or of semi-coherent schemes leads to substantial performance gains compared to more conventional approaches based on non-coherent transmission or the CFE approach.

The problem of simultaneous message transmission and state amplification in a Gaussian channel with additive Gaussian state is studied when the sender has imperfect non-causal knowledge of the state sequence. Inner and outer bounds to the rate-state-distortion region are provided. The coding scheme underlying the inner bound combines analog signaling and Gelfand-Pinsker coding, where the latter deviates from the operating point of Costa's dirty paper coding.

The Gaussian interference channel with additive state at two receivers is investigated, in which the state information is noncausally known at both transmitters but not known at either receiver. For the very strong Gaussian interference channel with state, the capacity region is obtained under certain conditions on channel parameters. For the strong (but not very strong) Gaussian interference channel with state, points on the boundary of the capacity region are characterized under corresponding conditions on channel parameters. Finally, for the weak Gaussian interference channel with state, the sum capacity is obtained for certain channel parameters. All the above capacity-achieving rate points achieve the capacity for the corresponding channel without state.

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Lower bounds on average achievable throughput are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

“Layered” rate-splitting based transmission strategies are investigated for the uplink of cellular communication systems employing clustered cooperative processing. Accordingly, partial decoding of some received out-of-cluster “layers” is employed, while undecoded “layers” are treated as noise. A two-dimensional Wyner-type system model is considered, by which only adjacent cell interference is present and characterized by a single parameter α ∊ (0, 1]. Focusing on the average throughput per cell, the setting is shown to be equivalent to a certain multiple-input multiple-output (MIMO) multiple access channel (MAC). An achievable average throughput is then obtained by efficiently solving an appropriately formalized Complementary Geometric Programming (CGP) problem. Singnificant performance enhancement is demonstrated compared to non-cooperative single-cell processing, as well as to “naive” cooperation, where out-of-cluster interference is treated as noise.

The degrees of freedom (DoF) of the two-user multiple-input single-output (MISO) broadcast channel (BC) are studied under the assumption that the form, Ii, i = 1, 2, of the channel state information at the transmitter (CSIT) for each user's channel can be either perfect (P), delayed (D) or not available (N), i.e., I1, I2ϵ {P, N, D}, and therefore the overall CSIT can alternate between the 9 resulting states I1I2. The fraction of time associated with CSIT state I1I2is denoted by the parameter λI1I2and it is assumed throughout that λI1I2= λI2I1, i.e., λPN= λNP, λPD= λDP, λDN= λND. Under this assumption of symmetry, the main contribution of this paper is a complete characterization of the DoF region of the two user MISO BC with alternating CSIT. The results highlight the synergistic benefits of alternating CSIT and the tradeoffs between various forms of CSIT for any given DoF value.

We consider mean squared estimation of a continuous-time signal corrupted by additive white Gaussian noise. We investigate the trade-off between lookahead and estimation-loss under this model. We study the class of continuous-time stationary Gauss-Markov processes (Ornstein-Uhlenbeck processes) as channel inputs, and explicitly characterize the behavior of the minimum mean squared error (MMSE) with finite lookahead and signal-to-noise ratio (SNR). The MMSE with lookahead is shown to converge exponentially rapidly to the non-causal error, with the exponent being the reciprocal of the non-causal error. We extend our results to mixtures of Ornstein-Uhlenbeck processes, and use the insight gained to present lower and upper bounds on the MMSE with lookahead for a class of stationary Gaussian input processes, whose spectrum can be expressed as a mixture of Ornstein-Uhlenbeck spectra.

We consider a two-user state-dependent multiaccess channel in which the states of the channel are known, causally or only strictly causally, at both encoders, but not at the decoder. Both encoders transmit a common message and, one of the encoders also transmits an individual message. We study the capacity region of this communication model for both causal and strictly causal settings. For the model with causal states, we find an explicit characterization of the capacity region in the discrete memoryless case. For the model with strictly causal states, we establish inner and outer bounds on the capacity region. The outer bound is nontrivial, has a relatively simple form and has the advantage of incorporating only one auxiliary random variable. In particular, it suggests that there is none, or at best only little, to gain from having the encoder that transmits both messages also sending an individual description of the state to the receiver, in addition to the compressed version that is sent cooperatively with the other encoder. The results shed more light on the utility of delayed channel state information for increasing the capacity region of multiaccess channels; and tie with some recent progress in this framework.

Two-user fading multiple access channel (MAC) is investigated, which is corrupted by random fading coefficients and additive Gaussian noise. It is assumed that the channel is block fading, and each transmitter knows only its own channel state to the receiver, but does not know the other transmitter's channel state. The receiver has full knowledge of channel state information (CSI). The performance measure, the expected capacity region over channel statistics, is studied for two scenarios. For the first scenario, in which user 1 has multiple states, and user 2 has one state, most part of the boundary of the expected capacity region is characterized. Interestingly, these rate points are also on the boundary of the capacity region (i.e., the best achievable rate pairs) when the CSI is fully known at both transmitters. Furthermore the expected capacity region is fully characterized for some asymptotic regimes. For the second scenario, in which both users 1 and 2 have two states, a number of achievable regions are studied, and are demonstrated to be close to an outer bound numerically.

A state-dependent Gaussian Z-interference channel model is investigated in the regime of high state power, in which transmitters 1 and 2 communicate with receivers 1 and 2, and only receiver 2 is interfered by transmitter 1's signal and a random state sequence. The state sequence is known noncausally only to transmitter 1, not to the corresponding transmitter 2. A layered coding scheme is designed for transmitter 1 to help interference cancelation at receiver 2 (using a cognitive dirty paper coding) and to transmit its own message to receiver 1. Inner and outer bounds are derived, and are further analyzed to characterize the boundary of the capacity region either fully or partially for all Gaussian channel parameters. Our results imply that the capacity region of such a channel with mismatched transmitter-side state cognition and receiver-side state interference is strictly less than that of the corresponding channel without state, which is in contrast to Costa type of dirty channels, for which dirty paper coding achieves the capacity of the corresponding channels without state.

In this paper, we investigate a three-user cognitive communication network where a primary two-user Multiple Access Channel (MAC) suffers interference from a secondary point-to-point (P2P) channel, sharing the same medium. While the P2P channel transmitter — transmitter 3 — causes an interference at the primary MAC receiver, we assume that the primary channel transmitters — transmitters 1 and 2 — do not cause any interference at the P2P receiver. It is assumed that one of the MAC transmitters has cognitive capabilities and cribs causally from the other MAC transmitter. Furthermore, we assume that the cognitive transmitter knows the message of transmitter 3 in a noncausal manner, thus introducing the three-user Multiple Access Cognitive Z-Interference Channel (MA-CZIC). We obtain inner and outer bounds on the capacity region of the three-user MA-CZIC for both strictly and nonstrictly causal cribbing cognitive encoders.

We investigate the problem of secure transmission over a two-user multi-input multi-output (MIMO) X-channel with noiseless local feedback and delayed channel state information (CSI) available at transmitters. The transmitters are equipped with M antennas each, and the receivers are equipped with N antennas each. For this model, we characterize the optimal sum secure degrees of freedom (SDoF) region. We show that, in presence of local feedback and delayed CSI, the sum SDoF region of the MIMO X-channel is same as the SDoF region of a two-user MIMO BC with 2M antennas at the transmitter and N antennas at each receiver. This result shows that, upon availability of feedback and delayed CSI, there is no performance loss in sum SDoF due to the distributed nature of the transmitters. Next, we show that this result also holds if only global feedback is conveyed to the transmitters. We also study the case in which only local feedback is provided to the transmitters, i.e., without CSI, and derive a lower bound on the sum SDoF for this model. Furthermore, we specialize our results to the case in which there are no security constraints. In particular, similar to the setting with security constraints, we show that the optimal sum degrees of freedom (sum DoF) region of the (M, M, N, N)-MIMO X-channel is same of the DoF region of a two-user MIMO BC with 2M antennas at the transmitter and N antennas at each receiver. We illustrate our results with some numerical examples.

This work considers a setting in which an encoder wishes to communicate with a decoder through a relay that is connected to the decoder via a finite-capacity link. Motivated by communication on the uplink of a cloud radio access cellular network, it is assumed that the relay compresses and forwards the received signal; moreover, the decoder has side information about the transmitted signal that is subject to fading whose realization is unknown to encoder and relay. A robust transmission and compression strategy is proposed that aims at minimizing the transmitted power under competitive rate optimality constraints. This contrasts with more conventional worst-case or average performance criteria. The transmission strategy is based on a broadcast coding and is parameterized by the maximum tolerable delay in terms of number of fading coherence blocks. Numerical results demonstrate the role of delay and the advantages of broadcast coding over the conventional single-layer transmission.

This paper addresses robust communication on a fading relay channel in which the relay is connected to the decoder via an out-of-band digital link of limited capacity. Both the source-to-relay and the source-to-destination links are subject to fading gains, which are generally unknown to the encoder prior to transmission. To overcome this impairment, a hybrid automatic retransmission request (HARQ) protocol is combined with multi-layer broadcast transmission, thus allowing for variable-rate decoding. Moreover, motivated by cloud radio access network applications, the relay operation is limited to compress-and-forward. The aim is maximizing the throughput performance as measured by the average number of successfully received bits per channel use, under either long-term static channel (LTSC) or short-term static channel (STSC) models. In order to opportunistically leverage better channel states based on the HARQ feedback from the decoder, an adaptive compression strategy at the relay is also proposed. Numerical results confirm the effectiveness of the proposed strategies.

In this work, joint decompression and decoding is studied for the uplink of multi-antenna cloud radio access networks. In this system, a set of multi-antenna mobile stations (MSs) wish to communicate with a “cloud” decoder through a set of multi-antenna base stations (BSs), which are connected to the cloud decoder through digital backhaul links of limited capacity. The BSs compress the received signal and send it to the cloud decoder, which performs joint decoding of the signals from all MSs. While the conventional solution prescribes that the cloud decoder performs first decompression and then decoding, recent work has shown that potentially larger rates can be achieved with joint decompression and decoding (JDD) at the cloud decoder. The sum-rate maximization problem with JDD, under the assumption of Gaussian test channels, is shown here to be an instance of a class of non-convex problems known as Difference of Convex (DC) problems. Based on this observation, an iterative algorithm based on the Majorization Minimization (MM) approach is proposed that guarantees convergence to a stationary point of the sum-rate maximization problem. Numerical results demonstrate the advantage of the proposed algorithm compared to the conventional approach based on separate decompression and decoding.

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

We investigate the problem of secure transmission over a two-user multi-input multi-output (MIMO) X-channel in which channel state information is provided with one-unit delay to both transmitters (CSIT), and each receiver feeds back its channel output to a different transmitter. We refer to this model as MIMO X-channel with asymmetric output feedback and delayed CSIT. The transmitters are equipped with M antennas each, and the receivers are equipped with N antennas each. For this model, accounting for both messages at each receiver, we characterize the optimal sum secure degrees of freedom (SDoF) region. We show that, in the presence of asymmetric output feedback and delayed CSIT, the sum SDoF region of the MIMO X-channel is the same as the SDoF region of a two-user MIMO BC with 2M antennas at the transmitter, N antennas at each receiver, and delayed CSIT. This result shows that, upon availability of asymmetric output feedback and delayed CSIT, there is no performance loss in terms of sum SDoF due to the distributed nature of the transmitters. Next, we show that this result also holds if only output feedback is conveyed to the transmitters, but in a symmetric manner, i.e., each receiver feeds back its output to both transmitters and no CSIT. We also study the case in which only asymmetric output feedback is provided to the transmitters, i.e., without CSIT, and derive a lower bound on the sum SDoF for this model. Furthermore, we specialize our results to the case in which there are no security constraints. In particular, similar to the setting with security constraints, we show that the optimal sum DoF region of the (M,M,N,N)-MIMO X-channel with asymmetric output feedback and delayed CSIT is the same as the DoF region of a two-user MIMO BC with 2M antennas at the transmitter, N antennas at each receiver, and delayed CSIT. We illustrate our results with some numerical examples.

The scalar additive Gaussian noise channel has the “single crossing point” property between the minimum mean square error (MMSE) in the estimation of the input given the channel output, assuming a Gaussian input to the channel, and the MMSE assuming an arbitrary input. This paper extends the result to the parallel vector additive Gaussian channel in three phases. 1) The channel matrix is the identity matrix, and we limit the Gaussian input to a vector of Gaussian i.i.d. elements. The “single crossing point” property is with respect to the signal-to-noise ratio (as in the scalar case). 2) The channel matrix is arbitrary, and the Gaussian input is limited to an independent Gaussian input. A “single crossing point” property is derived for each diagonal element of the MMSE matrix. 3) The Gaussian input is allowed to be an arbitrary Gaussian random vector. A “single crossing point” property is derived for each eigenvalue of the difference matrix between the two MMSE matrices. These three extensions are then translated to new information theoretic properties on the mutual information, using the I-MMSE relationship, a fundamental relationship between estimation theory and information theory revealed by Guo and coworkers. The results of the last phase are also translated to a new property of Fisher information. Finally, the applicability of all three extensions on information theoretic problems is demonstrated through a proof of a special case of Shannon's vector entropy power inequality, a converse proof of the capacity region of the parallel degraded broadcast channel (BC) under an input per-antenna power constraint and under an input covariance constraint, and a converse proof of the capacity region of the compound parallel degraded BC under an input covariance constraint.

We examine codes, over the additive Gaussian noise channel, designed for reliable communication at some specific signal-to-noise ratio (SNR) and constrained by the permitted minimum mean-square error (MMSE) at lower SNRs. The maximum possible rate is below point-to-point capacity, and hence, these are nonoptimal codes (alternatively referred to as “bad” codes). We show that the maximum possible rate is the one attained by superposition codebooks. Moreover, the MMSE and mutual information behavior as a function of SNR, for any code attaining the maximum rate under the MMSE constraint, is known for all SNR. We also provide a lower bound on the MMSE for finite length codes, as a function of the error probability of the code.

We establish capacity regions for some classes of broadcast channels with binary inputs and symmetric outputs. We investigate the more capable partial order and establish that the binary erasure channel and the binary symmetric channel form the two extremes for channels having the same capacity. Further, we apply the results to identify a class of broadcast channels for which the best-known inner and outer bounds on the capacity region differ.

Channel coding over arbitrarily permuted parallel channels was first studied by Willems and coworkers. This paper introduces capacity-achieving polar coding schemes for arbitrarily permuted parallel channels where the component channels are memoryless, binary-input, and output-symmetric.

This paper presents two new results on multiple-input multiple-output (MIMO) Gaussian broadcast channels with confidential messages. First, the MIMO Gaussian wiretap channel is revisited. A matrix characterization of the capacity-equivocation region is provided, which extends the previous result on the secrecy capacity to the more general imperfect secrecy setting. Next, the MIMO Gaussian broadcast channel with two receivers and three independent messages: a common message intended for both receivers, and two confidential messages each intended for one of the receivers but needing to be kept asymptotically perfectly secret from the other, is considered. A precise characterization of the capacity region is provided, generalizing the previous results which considered only two out of three possible messages.

We study the discrete memoryless Z-interference channel where the transmitter of the pair that suffers from interference is cognitive. We first provide an outer bound on the capacity region of this channel. We then show that, when the channel of the transmitter–receiver pair that does not experience interference is deterministic and invertible, our proposed outer bound matches the best known inner bound. The obtained results imply that in the considered channel, superposition encoding at the noncognitive transmitter as well as Gel'fand–Pinsker encoding at the cognitive transmitter is needed in order to minimize the impact of interference. As a byproduct of the obtained capacity region, we obtain the capacity under the generalized Gel'fand–Pinsker setting where a transmitter–receiver pair communicates in the presence of interference noncausally known at the encoder.

The degrees of freedom (DoFs) of the two-user multiple-input single-output (MISO) broadcast channel (BC) are studied under the assumption that the form,$I_{i},\;i=1, 2$, of the channel state information at the transmitter (CSIT) for each user's channel can be either perfect$(P)$, delayed$(D)$, or not available$(N)$, i.e.,$I_{1},I_{2} \in \{P,N,D\}$, and therefore, the overall CSIT can alternate between the nine resulting states$I_{1}I_{2}$. The fraction of time associated with CSIT state$I_{1}I_{2}$is denoted by the parameter$\lambda_{I_{1}I_{2}}$and it is assumed throughout that$\lambda_{I_{1}I_{2}} = \lambda_{I_{2}I_{1}}$, i.e.,$\lambda_{PN} = \lambda_{NP}, \lambda_{PD}=\lambda_{DP}, \lambda_{DN}=\lambda_{ND}$. Under this assumption of symmetry, the main contribution of this paper is a complete characterization of the DoF region of the two-user MISO BC with alternating CSIT. Surprisingly, the DoF region is found to depend only on the marginal probabilities$(\lambda_{P}, \lambda_{D},\lambda_{N})=\left(\sum_{I_{2}}\lambda_{PI_{2}},\sum_{I_{2}}\lambda_{DI_{2}}, \sum_{I_{2}}\lambda_{NI_{2}}\right)$,$I_{2} \in \{P,D,N\}$, which represent the fraction of time that any given user (e.g., user 1) is associated with perfect, delayed, or no CSIT, respectively. As a consequence, the DoF region with all nine CSIT states,${\cal {D}}(\lambda_{I_{1}I_{2}}:I_{1},I_{2} \in \{P,D,N\})$, is the same as the DoF region with only three CSIT states${\cal {D}}(\lambda_{PP}, \lambda_{DD}, \lambda_{NN})$, under the same marginal distribution of CSIT states, i.e.,$(\lambda_{PP}, \lambda_{DD},\lambda_{NN})=(\lambda_{P},\lambda_{D},\lambda_{N})$. The sum-DoF value can be expressed as${\rm DoF}=\min \left({{4+2\lambda_{P}} \over {3}}, 1+\lambda_{P}+\lambda_{D}\right)$, from which one can uniquely identify the minimum required marginal CSIT fractions to achieve any target DoF value as$(\lambda_{P},\lambda_{D})_{\min}=\left({{3} \over {2}} {\rm DoF}-2,1- {{1} \over {2}} {\rm DoF}\right)$when${\rm DoF} \in \big [{{4} \over {3}},2\big]$and$(\lambda_{P},\lambda_{D})_{\min}=(0,({\rm DoF}-1)^{+})$when${\rm DoF} \in \big [0, {{4} \over {3}}\big)$. The results highlight the synergistic benefits of alternating CSIT and the tradeoffs between various forms of CSIT for any given DoF value. Partial results are also presented for the multiuser MISO BC with$M$transmit antennas and$K$single antenna users. For this problem, the minimum amount of perfect CSIT required per user to achieve the maximum DoFs of$\min (M,K)$is characterized. By the minimum amount of CSIT per user, we refer to the minimum fraction of time that the transmitter has access to perfect and instantaneous CSIT from a user. Through a novel converse proof and an achievable scheme, it is shown that the minimum fraction of time perfect CSIT is required per user in order to achieve the DoF of$\min (M,K)$is given by$\min (M,K)/K$.

The two-user multiple-input multiple-output (MIMO) interference channel (IC) with arbitrary numbers of antennas at each terminal is considered and the degrees of freedom (DoF) region is characterized in the presence of noiseless channel output feedback from each receiver to its respective transmitter and availability of delayed channel state information at the transmitters (CSIT). It is shown that having output feedback and delayed CSIT can strictly enlarge the DoF region of the MIMO IC when compared to the case in which only delayed CSIT is present. The proposed coding schemes that achieve the corresponding DoF region with feedback and delayed CSIT utilize both resources, i.e., feedback and delayed CSIT in a nontrivial manner. It is also shown that the DoF region with local feedback and delayed CSIT is equal to the DoF region with global feedback and delayed CSIT, i.e., local feedback and delayed CSIT is equivalent to global feedback and delayed CSIT from the perspective of the DoF region. The converse is proved for a stronger setting in which the channels to the two receivers need not be statistically equivalent.

Consider a Bernoulli-Gaussian complexn-vector whose components areVi=XiBi, withXi~C N(0,Px) and binaryBimutually independent and iid acrossi. This randomq-sparse vector is multiplied by a square random matrixU, and a randomly chosen subset, of average sizen p,p∈ [0,1], of the resulting vector components is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models whereUis typically a matrix with iid components, to allowUsatisfying a certain freeness condition. This class of matrices encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information-theoretic bounds, to study the input-output mutual information and the support recovery error rate in the limit ofn→ ∞. We also extend the scope of the large deviation approach of Rangan and characterize the performance of a class of estimators encompassing thresholded linear MMSE andl1relaxation.

The degrees of freedom (DoF) of the two-user Gaussian multiple-input and multiple-output (MIMO) broadcast channel with confidential messages is studied under the assumption that delayed channel state information (CSI) is available at the transmitter. We characterize the optimal secrecy DoF (SDoF) region and show that it can be achieved by a simple artificial noise alignment scheme. The proposed scheme sends the confidential messages superposed with the artificial noise over several time slots. Exploiting delayed CSI, the transmitter aligns the transmit signal in such a way that the useful message can be extracted at the intended receiver but is completely drowned by the artificial noise at the unintended receiver. The proposed scheme can be regarded as a nontrivial extension of Maddah-Ali Tse scheme and enables us to quantify the resource overhead, or equivalently the DoF loss, to be paid for the secure communications.

For delay-limited communication over block-fading channels, the difference between the ergodic capacity and the maximum achievable expected rate for coding over a finite number of coherent blocks represents a fundamental measure of the penalty incurred by the delay constraint. This paper introduces a notion of worst-case expected-capacity loss. Focusing on the slow-fading scenario (one-block delay), the worst-case additive and multiplicative expected-capacity losses are precisely characterized for the point-to-point fading channel. Extension to the problem of writing on fading paper is also considered, where both the ergodic capacity and the additive expected-capacity loss over one-block delay are characterized to within one bit per channel use.

We consider a two-user state-dependent multiaccess channel in which the states of the channel are known noncausally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states noncausally transmits an individual message. We find explicit characterizations of the capacity region of this communication model in both discrete memoryless and memoryless Gaussian cases. In particular, the capacity region analysis demonstrates the utility of the knowledge of the states only strictly causally at the encoder that sends only the common message in general. More specifically, in the discrete memoryless setting, we show that such a knowledge is beneficial and increases the capacity region in general. In the Gaussian setting, we show that such a knowledge does not help, and the capacity is same as if the states were completely unknown at the encoder that sends only the common message. Furthermore, we also study the special case in which the two encoders transmit only the common message and show that the knowledge of the states only strictly causally at the encoder that sends only the common message is not beneficial in this case, in both discrete memoryless and memoryless Gaussian settings. The analysis also reveals optimal ways of exploiting the knowledge of the state only strictly causally at the encoder that sends only the common message when such a knowledge is beneficial. The encoders collaborate to convey to the decoder a lossy version of the state, in addition to transmitting the information messages through a generalized Gel'fand–Pinsker binning. Particularly important in this problem are the questions of 1) optimal ways of performing the state compression and 2) whether or not the compression indices should be decoded uniquely. By developing two optimal coding schemes that perform this state compression differently, we show that when used as parts of appropriately tuned encoding and decoding processes, both compression à-la noisy network coding by Limor the quantize-map-and-forward by Avestimeher, i.e., with no binning, and compression using Wyner–Ziv binning are optimal. The scheme that uses Wyner–Ziv binning shares elements with Cover and El Gamal original compress-and-forward, but differs from it mainly in that backward decoding is employed instead of forward decoding and the compression indices are not decoded uniquely. Finally, by exploring the properties of our outer bound, we show that, although not required in general, the compression indices can in fact be decoded uniquely essentially without altering the capacity region, but at the expense of larger alphabets sizes for the auxiliary random variables.

We consider a three-terminal state-dependent relay channel with the channel state available noncausally at only the source. Such a model may be of interest for node cooperation in the framework of cognition, i.e., collaborative signal transmission involving cognitive and noncognitive radios. We study the capacity of this communication model. One principal problem is caused by the relay's not knowing the channel state. For the discrete memoryless (DM) model, we establish two lower bounds and an upper bound on channel capacity. The first lower bound is obtained by a coding scheme in which the source describes the state of the channel to the relay and destination, which then exploit the gained description for a better communication of the source's information message. The coding scheme for the second lower bound remedies the relay's not knowing the states of the channel by first computing, at the source, the appropriate input that the relay would send had the relay known the states of the channel, and then transmitting this appropriate input to the relay. The relay simply guesses the sent input and sends it in the next block. The upper bound accounts for not knowing the state at the relay and destination. For the general Gaussian model, we derive lower bounds on the channel capacity by exploiting ideas in the spirit of those we use for the DM model; and we show that these bounds are optimal for small and large noise at the relay irrespective to the strength of the interference. Furthermore, we also consider a relay model with orthogonal channels from the source to the relay and from the source and relay to the destination in which the source input component that is heard by the relay does not depend on the channel states. We establish a better upper bound for both DM and Gaussian cases and we also characterize the capacity in a number of special cases.

Two mobile users communicate with a central decoder via two base stations. Communication between the mobile users and the base stations takes place over a Gaussian interference channel with constant channel gains or quasi-static fading. Instead, the base stations are connected to the central decoder through orthogonal finite-capacity links, whose connectivity is subject to random fluctuations. There is only receive-side channel state information, and hence the mobile users are unaware of the channel state and of the backhaul connectivity state, while the base stations know the fading coefficients but are uncertain about the backhaul links' state. The base stations are oblivious to the mobile users' codebooks and employ compress-and-forward to relay information to the central decoder. Upper and lower bounds are derived on average achievable throughput with respect to the prior distribution of the fading coefficients and of the backhaul links' states. The lower bounds are obtained by proposing strategies that combine the broadcast coding approach and layered distributed compression techniques. The upper bound is obtained by assuming that all the nodes know the channel state. Numerical results confirm the advantages of the proposed approach with respect to conventional non-robust strategies in both scenarios with and without fading.

The degrees of freedom (DoF) region of the two-user MIMO interference channel (IC) is completely characterized in the presence of noiseless channel output feedback from each receiver to its respective transmitter and with the assumption of delayed channel state information (CSI) at the transmitters. It is shown that having output feedback and delayed CSI at the transmitters can strictly enlarge the DoF region when compared to the case in which only delayed CSI is available at the transmitters. Furthermore, cases are identified in which output feedback and delayed CSI alone are sufficient to achieve the DoF region achievable with perfect, instantaneous CSI.

We examine codes, over the additive Gaussian noise channel, designed for reliable communication at some specific signal-to-noise ratio (snr) and constrained by the permitted MMSE at K lower snrs. Extending the result of the single MMSE constrained code, we show that K-layers superposition codes attain the constrained capacity. Moreover, we prove that given a reliable code attaining the multi-MMSE constrained capacity, its MMSE and mutual information, as functions of snr, are completely defined. Thus, no other multi-MMSE constrained capacity achieving code attains better MMSE performance at unconstrained snrs.

This work considers the problem of transmitting two streams with different delay constraints over a single input single output slowly flat fading channel, where only the receiver has perfect channel state information (CSI). One stream has stringent delay constraints, while the other stream has a relaxed delay constraint. This is modeled by a block fading channel for the first stream, where a broadcast approach is employed. The second stream is encoded over a longer block and views an ergodic fading channel. Several encoding and decoding strategies are considered. In all cases the delay constrained stream is decoded in presence of the other stream, which can be decoded only after its long codeword is fully received. The non-delay constrained part is decoded after canceling out the first stream from the received signal. We derive here a closed form solution for the optimal layering power distribution, which maximizes the expected sum-rate, where the layering is employed on the delay constrained stream.

We consider a two-user multiple-access channel, where one of the encoders is allowed to take an action that effects the formation of the channel states. The states are know non-causally to the encoder. Two independent messages are sent: a common message and a private message transmitted by the informed encoder. We find precise characterizations of the capacity region. Our framework takes account of previously evaluated settings regarding point-to-point channels with actions and multiple-access channels with common messages. We obtain the capacity of the Gaussian action-dependent point-to-point channel.

Broadcasting over the fading wiretap channel is investigated for the situation without the channel state information (CSI) at the transmitter and subject to a delay constraint. A new broadcast approach is developed, which integrates secure superposition coding studied in the authors' previous work and embedded coding in a hybrid fashion. This scheme outperforms the previous approaches for the cases when the eavesdropper's channel is fading. The secrecy rate achievable via the new broadcast approach is derived, and the structure of the optimal power allocation function across the secure coding layers is characterized via techniques for solving the problem of constrained calculus of variations. A notion of probabilistic secrecy is introduced and studied, which characterizes the probability that a certain secrecy rate is achieved for any given fading block. Numerical examples are provided to demonstrate the impact of CSI at the transmitter if not available and the channel fluctuation of the eavesdropper on the average secrecy rate.

The sum degrees of freedom (DoF) of the two-user MIMO X-channel is characterized in the presence of output feedback and delayed channel state information (CSI). The number of antennas at each transmitters is assumed to be M and the number of antennas at each of the receivers is assumed to be N. It is shown that the sum DoF of the two-user MIMO X-channel is the same as the sum DoF of a two-user MIMO broadcast channel with 2M transmit antennas, and N antennas at each receiver. Hence, for this symmetric antenna configuration, there is no performance loss in the sum degrees of freedom due to the distributed nature of the transmitters. This result highlights the usefulness of feedback and delayed CSI for the MIMO X-channel. The K-user X-channel with a single antenna at each transmitter and each receiver is also studied. In this network, each transmitter has a message intended for each receiver. For this network, it is shown that the sum DoF with partial output feedback alone is at least 2K/(K + 1). This lower bound is strictly better than the best lower bound known for the case of delayed CSI assumption for all values of K.

This paper investigates the problem of source-channel coding for secure transmission of a Gaussian source over a Gaussian wiretap channel in presence of arbitrarily correlated side informations at both receivers. By means of an appropriate coding scheme (either hybrid digital/analog or pure digital), the optimal rate-distortion-equivocation region is characterized for most of the scenarios. These results provide the best achievable tradeoff between the requirement on the distortion level at the legitimate receiver and the equivocation rate at the eavesdropper.

For delay-limited communication over block-fading channels, the difference between the ergodic capacity and the maximum expected rate for coding over a finite number of coherent blocks represents the penalty incurred by the delay constraint. This paper introduces a notion of worst-case expected-rate loss. Focusing on the slow-fading scenario (one block delay), the worst-case expected-rate loss is precisely characterized for the point-to-point fading channel and is characterized to within one bit for the fading-paper channel.

We consider a two-user state-dependent multiaccess channel in which the states of the channel are known non-causally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states non-causally transmits an individual message. We find explicit characterizations of the capacity region of this communication model. The analysis also reveals optimal ways of exploiting the knowledge of the state only strictly causally at the encoder that sends only the common message when such a knowledge is beneficial. The encoders collaborate to convey to the decoder a lossy version of the state, in addition to transmitting the information messages through a generalized Gel'fand-Pinsker binning. Particularly important in this problem are the questions of 1) optimal ways of performing the state compression and 2) whether or not the compression indices should be decoded uniquely. We show that both compression à-la noisy network coding, i.e., with no binning, and compression using Wyner-Ziv binning are optimal. The scheme that uses Wyner-Ziv binning shares elements with Cover and El Gamal original compress-and-forward, but differs from it mainly in that backward decoding is employed instead of forward decoding and the compression indices are not decoded uniquely. Finally, by exploring the properties of our outer bound, we show that, although not required in general, the compression indices can in fact be decoded uniquely essentially without altering the capacity region, but at the expense of larger alphabets sizes for the auxiliary random variables.

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.

This work studies distributed compression for the uplink of a cloud radio access network, where multiple multi-antenna base stations (BSs) communicate with a central unit, also referred to as cloud decoder, via capacity-constrained back-haul links. Distributed source coding strategies are potentially beneficial since the signals received at different BSs are correlated. However, they require each BS to have information about the joint statistics of the received signals across the BSs, and are generally sensitive to uncertainties regarding such information. Motivated by this observation, a robust compression method is proposed to cope with uncertainties on the correlation of the received signals. The problem is formulated using a deterministic worst-case approach, and an algorithm is proposed that achieves a stationary point for the problem. From numerical results, it is observed that the proposed robust compression scheme compensates for a large fraction of the performance loss induced by the imperfect statistical information.

In a dynamic wireless network where a source terminal communicates with a destination, in presence of other users in the network, it is worthwhile to consider oblivious relaying strategies of a relay in close proximity to the source transmitter. The source-relay channel is assumed to be a fixed gain additive white Gaussian noise (AWGN) channel due to source-relay colocation. The source-destination and the relay-destination transmissions are subject to block flat fading dynamics. A perfect channel state information (CSI) at the respective receivers only is assumed. With the expected throughput as a performance measure, we incorporate a two-layer broadcast approach into a cooperative strategy based on the decode-and-forward scheme - Sequential Decode-and Forward (SDF). The broadcast approach splits the transmitted rate into superimposed layers, corresponding to a "bad" and a "good" channel states, allowing better adaptation to the actual channel conditions. The achievable rate expressions for the SDF strategy are derived under the broadcast approach for multiple settings including single user, multiple-input single-output (MISO) and the general relay setting using successive decoding technique, both numerically and analytically. Continuous broadcasting lower bounds are derived for the MISO and for the oblivious cooperation scenarios.

Recently, there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. In this paper, we take a deterministic view and revisit the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as “secret writing on dirty paper”) to within half a bit.

We study the effect of the absence of channel knowledge at the transmitters for multiple-input-multiple-output (MIMO) networks. Specifically, we assume perfect channel state information at the receivers, no channel state information at the transmitter(s), and independent identically distributed (i.i.d.) Rayleigh fading across antennas, users and time slots. We provide the characterization of the degrees of freedom (DoF) region for a 2-user MIMO broadcast channel. We then provide a DoF region outer bound for a 2-user MIMO interference channel. This bound is shown to be tight for all possible combinations of the number of antennas at each node except for one case. To analyze the unsolved case, we point out the potential of interference alignment in the 2-user MIMO interference channel with no channel state information at the transmitters. As a byproduct, we explore a special class of MIMO broadcast channels where the capacity region is established by using the outer bound developed in the DoF analysis.

An encoder, subject to a rate constraint, wishes to describe a Gaussian source under squared-error distortion. The decoder, besides receiving the encoder's description, also observes side information consisting of uncompressed source symbol subject to slow fading and noise. The decoder knows the fading realization but the encoder knows only its distribution. The rate-distortion function that simultaneously satisfies the distortion constraints for all fading states was derived by Heegard and Berger. A layered encoding strategy is considered in which each codeword layer targets a given fading state. When the side-information channel has two discrete fading states, the expected distortion is minimized by optimally allocating the encoding rate between the two codeword layers. For multiple fading states, the minimum expected distortion is formulated as the solution of a convex optimization problem with linearly many variables and constraints. Through a limiting process on the primal and dual solutions, it is shown that single-layer rate allocation is optimal when the fading probability density function is continuous and quasiconcave (e.g., Rayleigh, Rician, Nakagami, and log-normal). In particular, under Rayleigh fading, the optimal single codeword layer targets the least favorable state as if the side information was absent.

We consider the problem of multiuser detection for randomly spread direct-sequence (DS) code-division multiple access (CDMA) over flat fading channels. The analysis focuses on the case of many users, and large spreading sequences such that their ratio, which is the system load, is kept fixed. Spectral efficiency of practical linear detectors such as match-filter and decorrelator employing successive interference cancellation (SIC) at the receiver is derived. This is used to extend the notion of strongest users detectors for SIC receivers. The strongest users detectors system design relies on an outage approach where each user transmits in a single layer (fixed rate), and only users experiencing good channel conditions may be reliably decoded, while the other users are in outage, i.e., not decoded. In this scheme, iterative SIC decoding is studied, and it is shown that for equal power users, the optimal rate allocation, for maximizing the expected spectral efficiency, is equal rates for all users. This outage approach analysis is extended for multilayer coding broadcast approach per user. The expected sum-rate, under iterative decoding with linear multiuser detectors, is optimized, and the optimal layering power distribution is obtained. For small system loads, the achievable spectral efficiency with the continuous broadcast approach and a linear matched filter detector exhibits significant gains over the single layer coding approach.

A cellular multiple-input multiple-output (MIMO) downlink system is studied in which each base station (BS) transmits to some of the users, so that each user receives its intended signal from a subset of the BSs. This scenario is referred to as network MIMO with partial cooperation, since only a subset of the BSs are able to coordinate their transmission towards any user. The focus of this paper is on the optimization of linear beamforming strategies at the BSs and at the users for network MIMO with partial cooperation. Individual power constraints at the BSs are enforced. It is first shown that the system is equivalent to a MIMO interference channel with generalized linear constraints (MIMO-IFC-GC). The problem of minimizing the weighted sum mean square error (WSMSE) of the data estimates is non-convex, and suboptimal solutions with reasonable complexity need to be devised. Novel designs that aim at minimizing the WSMSE are then proposed. Extensive numerical simulations are provided to compare the performance of the considered schemes for realistic cellular systems.

We study a special class of the cognitive radio channel in which the receiver of the cognitive pair does not suffer interference from the primary user. Previously developed general encoding schemes for this channel are complex as they attempt to cope with arbitrary channel conditions, which leads to rate regions that are difficult to evaluate. The focus of our work is to derive simple rate regions that are easily computable, thereby providing more insights into achievable rates and good coding strategies under different channel conditions. We first present several explicit achievable regions for the general discrete memoryless case. We also present an improved outer bound on the capacity region for the case of high interference. We then extend these regions to Gaussian channels. With a simple outer bound we establish a new capacity region in the high-interference regime. Lastly, we provide numerical comparisons between the derived achievable rate regions and the outer bounds.

It is well-known that optimal digital coding schemes based on the source-channel separation principle suffer from the ``threshold effect" that they are sensitive to the channel condition. If the channel condition is worse than what the code is designed for, the code will fail to decode completely; on the other hand, if the channel condition is in fact better, a digital code can not provide any performance improvement. There exist more robust joint source-channel coding schemes in the literature which can achieve optimal performance for a given channel condition, and at the same time, either being useful when the channel condition is worse, or offering performance improvement when the channel condition is better, but not both. In this paper, we propose several schemes which use hybrid analog and digital signaling to provide benefits in both cases {\em simultaneously}, while still keeping it optimal for a given target channel condition. We further show that compared to the best known optimal schemes designed for the target channel condition, which takes into account only the worse channel condition, or only the better channel condition, these new set of schemes can in fact achieve the same performances for the better and the worse channel conditions simultaneously.

Precoding schemes in the framework of vector perturbation (VP) for the multiple-input multiple-output (MIMO) Gaussian broadcast channel (GBC) are investigated. The VP scheme, originally a “one-shot” technique, is generalized to encompass processing over multiple time instances. Using lattice-based extended alphabets (“perturbations”), and considering the infinite time-span extension limit, a lower bound on the achievable sum-rate using the generalized VP scheme is analytically obtained, by which it is shown to achieve the optimum sum-rate in the high signal-to-noise ratio (SNR) regime (both in terms of degrees-of-freedom and power offset). The above lower bound is constructively obtained by means of an efficient practically oriented suboptimum transmit energy minimization algorithm, which has merits of its own, and it demonstrates the significant performance enhancement that can be obtained by preprocessing over multiple symbol instances, potentially eliminating the gap to the ultimate performance at high SNRs. For the 2×2 MIMO GBC, the VP scheme is generalized further, and an inner bound for the entire achievable rate region is obtained, by which an interesting correspondence is identified to the ultimate capacity region, as obtained by “dirty paper” coding (DPC).

Recently there has been a lot of success in using the deterministic approach to provide approximate characterization of Gaussian network capacity. In this paper, we take a deterministic view and revisit the problem of wiretap channel with side information. A precise characterization of the secrecy capacity is obtained for a linear deterministic model, which naturally suggests a coding scheme which we show to achieve the secrecy capacity of the degraded Gaussian model (dubbed as “secret writing on dirty paper”) to within (1/2) log 3 bits.

We consider the secret sharing problem, in which a dealer distributes a secret among a set of participants in such a manner that only qualified sets of users can recover the secret by pooling their shares together while non-qualified sets of users will obtain no information about the secret even if they pool their shares together. In contrast to the existing solutions that are mainly based on number theoretic tools, we propose a physical layer approach that exploits the presence of random noise inherent to wireless channels for secret sharing. Two different scenarios are considered. In the first scenario, the classic secret sharing problem with a single secret message is considered, in which qualified sets are specified by a general access structure. A secret sharing scheme is proposed by constructing a secure coding scheme for an equivalent compound wiretap channel. Based on this approach, both lower and upper bounds on the secret sharing capacity are obtained. For some special cases, the secret sharing capacity is fully characterized. In the second scenario, a generalization of the classic secret sharing problem is proposed, in which multiple secret messages are required to be recovered at different qualified sets. A secret sharing scheme is provided by constructing an equivalent broadcast channel with compound eavesdroppers and constructing a secure coding scheme for the equivalent channel.

In this paper, we explore the possibility of achieving interference alignment with delayed CSIT when the transmitters are distributed. Our main contribution is an interference alignment scheme, called retrospective interference alignment, that is specialized to settings with distributed transmitters. With this scheme we show that the interference channel with 3 users with only delayed channel state information at the transmitters can achieve 9/8 DoF, while the the 2 user X channel is able to achieve 8/7 DoF. We also consider another setting where delayed channel output feedback is available to transmitters. In this setting the 3 user interference channel and the 2 user X channel are shown to achieve 6/5 and 4/3 DoF, respectively.

We investigate the capacity of a multiple access channel with cooperating encoders where partial state information is known to each encoder in a non-causal way and full state information is known to the decoder. The cooperation between the encoders has a two-fold purpose: to generate empirical state coordination between the encoders, and to share information about the private messages that each encoder has. For two-way cooperation, this two-fold purpose is achieved by double-binning, where the first layer of binning is used to generate the state coordination similarly to the two-way source coding, and the second layer of binning is used to transmit information about the private messages. The complete result provides the framework and perspective for addressing a complex level of cooperation that mixes states and messages in an optimal way. We present few examples and compare the optimal coding scheme that combines the message and the state to naive cooperation schemes that are based on separate message and state coding.

We study Wyner's asymmetric interference network (soft-handoff model) when each transmitter has local, rate-limited side-information about the messages of the J transmitters to its left and the J transmitters to its right. We distinguish two scenarios. In Scenario A the neighbors of Transmitter k can have different, individual, side-information about Message Mk. In Scenario B they all have the same side-information about Mk. For both scenarios we derive the asymptotic multiplexing gain per-user, that is, the limiting ratio of the multiplexing gain divided by the number of users K when K → ∞.

Consider a Bernoulli-Gaussian complex n-vector whose components are XiBi, with Bi~Bernoulli-q and Xi~ CN(0; σ2), iid across i and mutually independent. This random q-sparse vector is multiplied by a random matrix U, and a randomly chosen subset of the components of average size np, p ∈ [0; 1], of the resulting vector is then observed in additive Gaussian noise. We extend the scope of conventional noisy compressive sampling models where U is typically the identity or a matrix with iid components, to allow U that satisfies a certain freeness condition, which encompasses Haar matrices and other unitarily invariant matrices. We use the replica method and the decoupling principle of Guo and Verdú, as well as a number of information theoretic bounds, to study the input-output mutual information and the support recovery error rate as n → ∞.

The problem of secure lossy source-channel wiretapping with arbitrarily correlated side informations at both receivers is investigated. This scenario consists of an encoder (referred to as Alice) that wishes to compress a source and send it through a noisy channel to a legitimate receiver (referred to as Bob). In this context, Alice must simultaneously satisfy the desired requirements on the distortion level at Bob, and the equivocation rate at the eavesdropper (referred to as Eve). This setting can be seen as a generalization of the conventional problems of secure source coding with side information at the decoders, and the wiretap channel. Inner and outer bounds on the rate-distortion-equivocation region for the case of arbitrary channels and side informations are derived. In some special cases of interest, it is shown that separation holds. By means of an appropriate coding, the presence of any statistical difference among the side informations, the channel noises, and the distortion at Bob can be fully exploited in terms of secrecy.

We explore the interplay between interference, cooperation and connectivity in heterogeneous wireless interference networks. Specifically, we consider a 4-user locally-connected interference network with pairwise clustered decoding and show that its degrees of freedom (DoF) are bounded above by 12/5. Interestingly, when compared to the corresponding fully connected setting which is known to have 8/3 DoF, the locally connected network is only missing interference-carrying links, but still has lower DoF, i.e., eliminating these interference-carrying links reduces the DoF. The 12/5 DoF outer bound is obtained through a novel approach that translates insights from interference alignment over linear vector spaces into corresponding sub-modularity relationships between entropy functions.

We give a general formula for the degrees of freedom of the K-user real additive-noise interference channel involving maximization of information dimension. Previous results are recovered, and even generalized in certain cases with simplified proofs. Connections to fractal geometry are drawn.

The secrecy degrees of freedom (SDoF) of the Gaussian multiple-input and single-output (MISO) wiretap channel is studied under the assumption that delayed channel state information (CSI) is available at the transmitter and each receiver knows its own instantaneous channel. Such scenario is of practical interest since the legitimate receiver may send its channel states to the transmitter which is overheard by the eavesdropper. We first show that a strictly positive SDoF can be guaranteed whenever the transmitter has delayed CSI (either on the legitimate channel or/and the eavesdropper channel). In particular, in the case with delayed CSI on both channels, it is shown that the optimal SDoF is 2=3. We then generalize the result to the two-user Gaussian MISO broadcast channel with confidential messages and characterize the SDoF region when the transmitter has delayed CSI of both receivers. Interestingly, the artificial noise schemes are shown to provide the optimal SDoF region by masking the confidential message to the unintended receiver while aligning the interference at each receiver.

We consider a two-user state-dependent multiaccess channel in which the states of the channel are known non-causally to one of the encoders and only strictly causally to the other encoder. Both encoders transmit a common message and, in addition, the encoder that knows the states non-causally transmits an individual message. We study the capacity region of this communication model. In the discrete memoryless case, we establish inner and outer bounds on the capacity region. Although the encoder that sends both messages knows the states fully, we show that the strictly causal knowledge of these states at the other encoder can be beneficial for this encoder, and in general enlarges the capacity region. Furthermore, we find an explicit characterization of the capacity in the case in which the two encoders transmit only the common message. In the Gaussian case, we characterize the capacity region for the model with individual message as well. Our converse proof in this case shows that, for this model, strictly causal knowledge of the state at one of the encoders does not increase capacity if the other is informed non-causally, a result which sheds more light on the utility of conveying a compressed version of the state to the decoder in recent results by Lapidoth and Steinberg on a multiacess model with only strictly causal state at both encoders and independent messages.

Recent results on source-channel coding for secure transmission show that separation holds in several cases under some less-noisy conditions. However, it has also been proved through a simple counterexample that pure analog schemes can be optimal and hence outperform digital ones. According to these observations and assuming matched-bandwidth, we present a novel hybrid digital/analog scheme that aims to gather the advantages of both digital and analog ones. In the quadratic Gaussian setup when side information is only present at the eavesdropper, this strategy is proved to be optimal. Furthermore, it outperforms both digital and analog schemes and cannot be achieved via time-sharing. An application example to binary symmetric sources with side information is also investigated.

We consider a practical scenario of the Gaussian multiantenna wiretap channel where a transmitter with no channel state information wishes to send a confidential message to its legitimate receiver in the presence of an eavesdropper. It has been known that the secrecy capacity of such a channel does not scale with signal-to-noise ratio under general conditions. Taking into account the different temporal fading structures at the legitimate receiver and the eavesdropper, we characterize lower and upper bounds on the secrecy degrees of freedom (s.d.o.f.) of the channel at hand. Our results show that a positive s.d.o.f. can be ensured whenever two receivers experience the asynchronous variation. Remarkably, simple linear precoding schemes provide the optimal s.d.o.f. in most cases of interest by aligning either the confidential signal at the eavesdropper or the artificial noise at the legitimate receiver.

Consider the minimum mean-square error (MMSE) of estimating an arbitrary random variable from its observation contaminated by Gaussian noise. The MMSE can be regarded as a function of the signal-to-noise ratio (SNR) as well as a functional of the input distribution (of the random variable to be estimated). It is shown that the MMSE is concave in the input distribution at any given SNR. For a given input distribution, the MMSE is found to be infinitely differentiable at all positive SNR, and in fact a real analytic function in SNR under mild conditions. The key to these regularity results is that the posterior distribution conditioned on the observation through Gaussian channels always decays at least as quickly as some Gaussian density. Furthermore, simple expressions for the first three derivatives of the MMSE with respect to the SNR are obtained. It is also shown that, as functions of the SNR, the curves for the MMSE of a Gaussian input and that of a non-Gaussian input cross at most once over all SNRs. These properties lead to simple proofs of the facts that Gaussian inputs achieve both the secrecy capacity of scalar Gaussian wiretap channels and the capacity of scalar Gaussian broadcast channels, as well as a simple proof of the entropy power inequality in the special case where one of the variables is Gaussian.

We investigate the capacity of a multiple access channel with cooperating encoders where partial state information is known to each encoder and full state information is known to the decoder. The cooperation between the encoders has a two-fold purpose: to generate empirical state coordination between the encoders, and to share information about the private messages that each encoder has. For two-way cooperation, this two-fold purpose is achieved by double-binning, where the first layer of binning is used to generate the state coordination similarly to the two-way source coding, and the second layer of binning is used to transmit information about the private messages. The complete result provides the framework and perspective for addressing a complex level of cooperation that mixes states and messages in an optimal way.

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

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

We consider the joint source-channel coding problem of sending a Gaussian source on a K-user Gaussian broadcast channel with bandwidth mismatch. A new outer bound to the achievable distortion region is derived using the technique of introducing more than one additional auxiliary random variable, which was previously used to derive sum-rate lower bound for the symmetric Gaussian multiple description problem. By combining this outer bound with the achievability result based on source-channel separation, we provide approximate characterizations of the achievable distortion region within constant multiplicative factors. Furthermore, we show that the results can be extended to general broadcast channels, and the performance of the source-channel separation based approach is also within the same constant multiplicative factors of the optimum.

We provide a complete characterization of the achievable distortion region for the problem of sending a bivariate Gaussian source over bandwidth-matched Gaussian broadcast channels, where each receiver is interested in only one component of the source. This setting naturally generalizes the simple single Gaussian source bandwidth-matched broadcast problem for which the uncoded scheme is known to be optimal. We show that a hybrid scheme can achieve the optimum for the bivariate case, but neither an uncoded scheme alone nor a separation-based scheme alone is sufficient. We further show that in this joint source channel coding setting, the Gaussian scenario is the worst scenario among the sources and channel noises with the same covariances.

This paper extends the “single crossing point” property of the scalar MMSE function, derived by Guo, Shamai and Verdú (first presented in ISIT 2008), to the parallel degraded MIMO scenario. It is shown that the matrix Q(t), which is the difference between the MMSE assuming a Gaussian input and the MMSE assuming an arbitrary input, has, at most, a single crossing point for each of its eigenvalues. Together with the I-MMSE relationship, a fundamental connection between Information Theory and Estimation Theory, this new property is employed to derive results in Information Theory. As a simple application of this property we provide an alternative converse proof for the broadcast channel (BC) capacity region under covariance constraint in this specific setting.

We study the capacity regions of broadcast channels with binary inputs and symmetric outputs. We study the partial order induced by the more capable ordering of broadcast channels for channels belonging to this class. In particular this leads to some surprising connections regarding various notions of dominance of receivers. This study also helps us isolate some classes of symmetric channels where the best known inner and outer bounds differ.

We consider the multi-antenna wiretap channel in which the transmitter wishes to send a confidential message to its receiver while keeping it secret to the eavesdropper. It has been known that the secrecy capacity of such a channel does not increase with signal-to-noise ratio when the transmitter has no channel state information (CSI) under mild conditions. Motivated by Jafar's robust interference alignment technique, we study the so-called staggered multi-antenna block-fading wiretap channel where the legitimate receiver and the eavesdropper have different temporal correlation structures. Assuming no CSI at transmitter, we characterize lower and upper bounds on the secrecy degrees of freedom (s.d.o.f.) of the channel at hand. Our results show that a positive s.d.o.f. can be ensured whenever two receivers experience different fading variation. Remarkably, very simple linear precoding schemes provide the optimal s.d.o.f. in some cases of interest.

The Poisson interference channel is studied, which models optical communication systems with multiple transceivers. Conditions for the strong interference is characterized and the corresponding capacity region is given, which is the same as that of the compound Poisson multiple access channel with each receiver decoding both messages. For the cases when the strong interference conditions are not satisfied, inner and outer bounds on the capacity region are derived. Finally, numerical results are provided to illustrate the derived regions.

A precise matrix characterization of the capacity-equivocation region of the multiple-input multiple-output (MIMO) Gaussian wiretap channel is established. This characterization is obtained via a connection to the problem of simultaneously communicating a private and a confidential messages over a MIMO Gaussian broadcast channel, for which the secrecy capacity region can be established using previous results on the secrecy capacity of the MIMO Gaussian wiretap channel.

This paper considers the problem of secret communication over a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel. The transmitter has two independent, confidential messages and a common message. Each of the confidential messages is intended for one of the receivers but needs to be kept perfectly secret from the other, and the common message is intended for both receivers. It is shown that a natural scheme that combines secret dirty-paper coding with Gaussian superposition coding achieves the secrecy capacity region. To prove this result, a channel-enhancement approach and an extremal entropy inequality of Weingarten et al. are used.

This paper investigates the capacity of compound state-dependent channels with non-causal state information available at only the transmitter. A new lower bound on the capacity of this class of channels is derived. This bound is shown to be tight for the special case of compound channels with stochastic degraded components, yielding the full characterization of the capacity. Specific results are derived for the compound Gaussian Dirty-Paper (GDP) channel. This model consists of an additive white Gaussian noise (AWGN) channel corrupted by an additive Gaussian interfering signal, known at the transmitter only, where the input and the state signals are affected by fading coefficients whose realizations are unknown at the transmitter. Our bounds are shown to be tight for specific cases. Applications of these results arise in a variety of wireless scenarios as multicast channels, cognitive radio and problems with interference cancellation.

We consider the optimality of source-channel separation in networks, and show that such a separation approach is optimal or approximately optimal for a large class of scenarios. More precisely, for lossy coding of memoryless sources in a network, when the sources are mutually independent, and each source is needed only at one destination (or at multiple destinations at the same distortion level), the separation approach is optimal; for the same setting but each source is needed at multiple destinations under a restricted class of distortion measures, the separation approach is approximately optimal, in the sense that the loss from optimum can be upper-bounded. The communication channels in the network are general, including various multiuser channels with finite memory and feedback, the sources and channels can have different bandwidths, and the sources can be present at multiple nodes.

We provide a complete characterization of the achievable distortion region for the problem of sending a bivariate Gaussian source over a bandwidth-matched Gaussian broadcast channel, where each receiver is interested in only one component of the source. This setting naturally generalizes the simple single Gaussian source bandwidth-matched broadcast problem for which the uncoded scheme is known to be optimal. We show that a hybrid scheme can achieve the optimum for the bivariate case, but neither an uncoded scheme alone nor a separation-based scheme alone is sufficient.

We consider a three-terminal state-dependent relay channel with the channel state available non-causally at only the source. Such a model may be of interest for node cooperation in the framework of cognition, i.e., collaborative signal transmission involving cognitive and non-cognitive radios. We study the capacity of this communication model. One principal problem in this setup is caused by the relay's not knowing the channel state. In the discrete memoryless (DM) case, we establish lower bounds on channel capacity. For the Gaussian case, we derive lower and upper bounds on the channel capacity. The upper bound is strictly better than the cut-set upper bound. We show that one of the developed lower bounds comes close to the upper bound, asymptotically, for certain ranges of rates.

A polar coding scheme is suggested for the binary-input memoryless symmetric and degraded wire-tap channel. The provided scheme achieves the entire rate-equivocation region for the considered model.

A capacity-achieving polar coding scheme is introduced for reliable communications over a set of parallel communication channels. They are assumed to be arbitrarily-permuted memoryless binary-input and output-symmetric (MBIOS) channels, and they form a set of (stochastically) degraded channels. The general case where the parallel channels are not necessarily degraded is addressed in the full paper version [3], though the suggested scheme is not capacity-achieving in the general case.

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.

The one tutorial paper and eight contributed papers in this special issue focus on cooperative communications in MIMO cellular networks.

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

This work studies the throughput scaling laws of ad hoc wireless networks in the limit of a large number of nodes. A random connections model is assumed in which the channel connections between the nodes are drawn independently from a common distribution. Transmitting nodes are subject to an on-off strategy, and receiving nodes employ conventional single-user decoding. The following results are proven: 1) for a class of connection models with finite mean and variance, the throughput scaling is upper-bounded by O(n1/3) for single-hop schemes, and O(n1/2) for two-hop (and multihop) schemes; 2) the Θ(n1/2) throughput scaling is achievable for a specific connection model by a two-hop opportunistic relaying scheme, which employs full, but only local channel state information (CSI) at the receivers, and partial CSI at the transmitters; 3) by relaxing the constraints of finite mean and variance of the connection model, linear throughput scaling Θ(n) is achievable with Pareto-type fading models.

We derive bounds on the noncoherent capacity of wide-sense stationary uncorrelated scattering (WSSUS) channels that are selective both in time and frequency, and are underspread, i.e., the product of the channel's delay spread and Doppler spread is small. The underspread assumption is satisfied by virtually all wireless communication channels. For input signals that are peak constrained in time and frequency, we obtain upper and lower bounds on capacity that are explicit in the channel's scattering function, are accurate for a large range of bandwidth, and allow to coarsely identify the capacity-optimal bandwidth as a function of the peak power and the channel's scattering function. We also obtain a closed-form expression for the first-order Taylor series expansion of capacity in the infinite-bandwidth limit, and show that our bounds are tight in the wideband regime. For input signals that are peak constrained in time only (and, hence, allowed to be peaky in frequency), we provide upper and lower bounds on the infinite-bandwidth capacity. Our lower bound is closely related to a result by Viterbi (1967). We find cases where the bounds coincide and, hence, the infinite-bandwidth capacity is characterized exactly. The analysis in this paper is based on a discrete-time discrete-frequency approximation of WSSUS time- and frequency-selective channels. This discretization takes the underspread property of the channel explicitly into account.

The problem of simultaneous multicasting of multiple messages with the help of a relay terminal is considered. In particular, a model is studied in which a relay station simultaneously assists two transmitters in multicasting their independent messages to two receivers. The relay may also have an independent message of its own to multicast. As a first step to address this general model, referred to as the compound multiple access channel with a relay (cMACr), the capacity region of the multiple access channel with a “cognitive” relay is characterized, including the cases of partial and rate-limited cognition. Then, achievable rate regions for the cMACr model are presented based on decode-and-forward (DF) and compress-and-forward (CF) relaying strategies. Moreover, an outer bound is derived for the special case, called the cMACr without cross-reception, in which each transmitter has a direct link to one of the receivers while the connection to the other receiver is enabled only through the relay terminal. The capacity region is characterized for a binary modulo additive cMACr without cross-reception, showing the optimality of binary linear block codes, and thus highlighting the benefits of physical layer network coding and structured codes. Results are extended to the Gaussian channel model as well, providing achievable rate regions for DF and CF, as well as for a structured code design based on lattice codes. It is shown that the performance with lattice codes approaches the upper bound for increasing power, surpassing the rates achieved by the considered random coding-based techniques.

A message independence property and some new performance upper bounds are derived in this work for erasure, list, and decision-feedback schemes with linear block codes transmitted over memoryless symmetric channels. Similar to the classical work of Forney, this work is focused on the derivation of some Gallager-type bounds on the achievable tradeoffs for these coding schemes, where the main novelty is the suitability of the bounds for both random and structured linear block codes (or code ensembles). The bounds are applicable to finite-length codes and to the asymptotic case of infinite block length, and they are applied to low-density parity-check code ensembles.

We address an application of multiuser information theory to the study of the uplink of a communication system with randomly activated users. We first assume that all the messages are jointly decoded by a multicell processor. Then, we relax this hypothesis and study the network under the assumption that the decoding of the message of a given user is only based on the messages received within a given distance of this user. Our results show that for a general class of models, under adequate hypothesis, local decoding performs almost as well as global decoding and that the difference converges to 0 exponentially fast as the size of the decoding window goes to infinity.

In this paper, we apply the theory of random Schrödinger operators to the analysis of multiusers communication channels similar to the Wyner model, which are characterized by short-range intercell interference. WithHthe channel transfer matrix,HHfis a narrow band matrix, a fact that does not permit the use of classical random matrices theory. On the other hand,HHfis in many aspects similar to a random Schrödinger operator. We relate the per-cell sum-rate capacity of the channel to the integrated density of states of a random Schrödinger operator; the latter is then related to the top Lyapunov exponent of a random sequence of matrices via a version of the Thouless formula. We also derive several bounds on the limiting per-cell sum-rate capacity, some based on the theory of random Schrödinger operators, and some derived from information theoretical considerations. Finally, we get explicit results in the high-signal-to-noise ratio (SNR) regime for some particular cases.

This paper considers an entropy-power inequality (EPI) of Costa and presents a natural vector generalization with a real positive semidefinite matrix parameter. The new inequality is proved using a perturbation approach via a fundamental relationship between the derivative of mutual information and the minimum mean-square error (MMSE) estimate in linear vector Gaussian channels. As an application, a new extremal entropy inequality is derived from the generalized Costa EPI and then used to establish the secrecy capacity regions of the degraded vector Gaussian broadcast channel with layered confidential messages.

This paper considers the problem of secret communication over a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel. The transmitter has two independent messages, each of which is intended for one of the receivers but needs to be kept asymptotically perfectly secret from the other. It is shown that, surprisingly, under a matrix power constraint, both messages can be simultaneously transmitted at their respective maximal secrecy rates. To prove this result, the MIMO Gaussian wiretap channel is revisited and a new characterization of its secrecy capacity is provided via a new coding scheme that uses artificial noise (an additive prefix channel) and random binning.

We consider the problem of signal estimation (denoising) from a statistical mechanical perspective, using a relationship between the minimum mean square error (MMSE), of estimating a signal, and the mutual information between this signal and its noisy version. The paper consists of essentially two parts. In the first, we derive several statistical-mechanical relationships between a few important quantities in this problem area, such as the MMSE, the differential entropy, the Fisher information, the free energy, and a generalized notion of temperature. We also draw analogies and differences between certain relations pertaining to the estimation problem and the parallel relations in thermodynamics and statistical physics. In the second part of the paper, we provide several application examples, where we demonstrate how certain analysis tools that are customary in statistical physics, prove useful in the analysis of the MMSE. In most of these examples, the corresponding statistical-mechanical systems turn out to consist of strong interactions that cause phase transitions, which in turn are reflected as irregularities and discontinuities (similar to threshold effects) in the behavior of the MMSE.

A single-server queue concatenated with a multilayer channel encoder is considered. The main focus of this work is on minimization of the expected delay of a packet from entering the queue until completion of successful service. Tight bounds are derived for the expected delay for different numbers of coded layers. Numerical optimization is applied to find the optimal resource allocation minimizing the average delay. Similar bounds are also derived for the case of continuous layering. It is demonstrated that code layering may give pronounced performance gains in terms of delay, which are more impressive than those associated with throughput. This makes layering more attractive when communicating under stringent delay constraints.

We consider the problem of transmission of a common message from a single source to two cooperating destination users, exhibiting different signal-to-noise ratios (SNR). The cooperation is performed over a noiseless link with a total capacity limitCcoop, which the users may utilize for conversation. In order to maximize the achievable throughput, it is suggested to employ a multilayer broadcast approach combined with multisession cooperation, such that every session another layer can be decoded. Since the cooperation link is limited, an efficient cooperation scheme is required. The relaying user performs compression which utilizes the side information of the decoder in the Wyner-Ziv (WZ) spirit. In multisession cooperation the compression is in the form of successive-refinement WZ coding. After the first user decodes a layer, the minimal required information is sent back to the second user to allow both of them to decode the message. The minimal required information is achievable with random coding using a binning strategy. While it is expected that multisession cooperation will be more efficient than a single-session cooperation we show that the maximal throughput is achieved with a single session cooperation. This is shown for the nonfading channel and for the ergodic fading case. Nonetheless, for the block fading channel it is already known that multilayering and multisession outperforms single session cooperation.

This paper finds the capacity of single-user discrete-time channels subject to both frequency-selective and time-selective fading, where the channel output is observed in additive Gaussian noise. A coherent model is assumed where the fading coefficients are known at the receiver. Capacity depends on the first-order distributions of the fading processes in frequency and in time, which are assumed to be independent of each other, and a simple formula is given when one of the processes is independent identically distributed (i.i.d.) and the other one is sufficiently mixing. When the frequency-selective fading coefficients are known also to the transmitter, we show that the optimum normalized power spectral density is the waterfilling power allocation for a reduced signal-to-noise ratio (SNR), where the gap to the actual SNR depends on the fading distributions. Asymptotic expressions for high/low SNR and easily computable bounds on capacity are also provided.

This paper introduces the notions of variable-to-fixed and fixed-to-variable channel capacity, without feedback. For channels that satisfy the strong converse, these notions coincide with the conventional Shannon capacity. For channels that do not behave ergodically, the conventional fixed-rate Shannon capacity only depends on least-favorable channel conditions, while the variable-rate capacity notions are able to capture the whole range of channel states and their likelihood, even in the absence of any side information about channel state at the transmitter. Particular emphasis is placed on memoryless channels that are governed by finitely valued states. We show that (single-user) variable-to-fixed channel capacity is intimately connected to the capacity region of broadcast channels with degraded message sets, and we give an expression for the fixed-to-variable capacity.

In this paper, the performance of cellular networks with joint multicell processing and dedicated relay terminals is investigated. It is assumed that each relay terminal is capable of full-duplex operation and receives the transmission of relay terminals in adjacent cells. Focusing on intra-cell time division multiple access and non-fading channels, a simplified relay-aided uplink cellular model is considered. Addressing the achievable per-cell sum-rate, two non-regenerative relaying schemes are considered. Interpreting the received signal at the base stations as the outcome of a two-dimensional linear time invariant system, the multicell processing rate of an amplify-and-forward scheme is derived and shown to decrease with the inter-relay interference level. A novel form of distributed compress-and-forward scheme with decoder side information is then proposed. The corresponding multicell processing rate, which is given as a solution of a simple fixed-point equation, reveals that the compress-and-forward scheme is able to completely eliminate the inter-relay interference, and it approaches a "cut-set-like" upper bound for strong relay terminal transmission power. The benefits of base-station cooperation via multicell processing over the conventional single site processing approach is also demonstrated for both protocols.

This paper studies the throughput scaling of wireless networks over channels with random connections, in which the channel connections are independent and identically distributed (i.i.d.) according to a common distribution. The channel distribution is quite general, with the only limitations being that the mean and variance are finite. Previous works have shown that, when channel state information (CSI) of the entire network is known a priori to all the nodes, wireless networks are degrees-of- freedom limited rather than interference limited. In this work, we show that this is not the case with a less demanding CSI assumption. Specifically, we quantify the throughput scaling for different communication protocols under the assumption of perfect receiver CSI and partial transmitter CSI (via feedback). It is shown that the throughput of single-hop and two-hop schemes are upper-bounded by respectively, O(n1/3) and O(n1/2), where n is the total number of source-to-destination pairs. In addition, multihop schemes cannot do better than the two-hop relaying scheme. Furthermore, the achievability of the Theta(n1/2) scaling for the two-hop scheme is demonstrated by a constructive example.

Simultaneous multicasting of messages with the help of a relay is studied. A two-source two-destination network is considered, in which each destination can receive directly only the signal from one of the sources, so that the reception of the message from the other source (and multicasting) is enabled by the presence of the relay. An outer bound is derived, which is shown to be achievable in the case of finite-field modulo-additive channels by using linear codes, highlighting the benefits of structured codes in exploiting the underlying physical-layer structure of the network. Results are extended to the Gaussian channel model as well, providing achievable rate regions based on nested lattice codes. It is shown that for a wide range of power constraints, the performance with lattice codes approaches the upper bound and surpasses the rates achieved by the standard random coding schemes.

We study the compound multi-input multi-output (MIMO) broadcast channel with confidential messages (BCC), where one transmitter sends a common message to two receivers and two confidential messages respectively to each receiver. The channel state may take one of a finite set of states, and the transmitter knows the state set but does not know the realization of the state. We study achievable rates with perfect secrecy in the high SNR regime by characterizing an achievable secrecy degree of freedom (s.d.o.f.) region for two models, the Gaussian MIMO-BCC and the ergodic fading multi-input single-output (MISO)-BCC without a common message. We show that by exploiting an additional temporal dimension due to state variation in the ergodic fading model, the achievable s.d.o.f. region can be significantly improved compared to the Gaussian model with a constant state, although at the price of a larger delay.

We study the uplink of a linear cellular model featuring short range inter-cell interference. Specifically, we consider a K-transmitter/K-receiver interference network where the signal transmitted by a given transmitter is interfered by the signal sent by the transmitter to its left. We assume that each transmitter has side-information consisting of the messages of the Jlusers to its left and the Jrusers to its right, and that each receiver can decode its message using the signals received at its own antenna, at the ilantennas to its left, and at the irantennas to its right. For this setting, we characterize the multiplexing gain, i.e., the asymptotic logarithmic growth of the sum-rate capacity at high SNR, and point out interesting duality aspects. We also present results on the multiplexing gain of a symmetric version of this network where the signal sent by a given transmitter is interfered by the signals sent by the transmitter to its left and the transmitter to its right.

This paper provides a closed-form expression for the secrecy capacity of the multiple-input multiple-output (MIMO) Gaussian wiretap channel, under a power-covariance constraint. Furthermore, the paper specifies the input covariance matrix required in order to attain the capacity. The proof uses the fundamental relationship between information theory and estimation theory in the Gaussian channel, relating the derivative of the mutual information to the minimum mean-square error (MMSE). The proof provides the missing intuition regarding the existence and construction of an enhanced degraded channel that does not increase the secrecy capacity. The concept of enhancement has been used in a previous proof of the problem. Furthermore, the proof presents methods that can be used in proving other MIMO problems, using this fundamental relationship.

This paper considers an entropy-power inequality (EPI) of Costa and presents a natural vector generalization with a real positive semidefinite matrix parameter. This new inequality is proved using a perturbation approach via a fundamental relationship between the derivative of mutual information and the minimum mean-square error (MMSE) estimate in linear vector Gaussian channels. As an application, a new extremal entropy inequality is derived from the generalized Costa EPI and then used to establish the secrecy capacity regions of the degraded vector Gaussian broadcast channel with layered confidential messages.

This paper considers the problem of secret communication over a two-receiver multiple-input multiple-output (MIMO) Gaussian broadcast channel. The transmitter has two independent messages, each of which is intended for one of the receivers but needs to be kept asymptotically perfectly secret from the other. It is shown that, surprisingly, under a matrix power constraint both messages can be simultaneously transmitted at their respective maximal secrecy rates. To prove this result, the MIMO Gaussian wiretap channel is revisited and a new characterization of its secrecy capacity is provided via a new coding scheme that uses artificial noise (a prefix channel) and random binning.

We study the discrete memoryless Z-interference channel (ZIC) where the transmitter of the pair that suffers from interference is cognitive. We first provide upper and lower bounds on the capacity of this channel. We then show that, when the channel of the transmitter-receiver pair that does not face interference is noiseless, the two bounds coincide and therefore define the capacity region. The obtained results imply that, unlike in the Gaussian cognitive ZIC, in the considered channel superposition encoding at the non-cognitive transmitter as well as Gel'fand-Pinsker encoding at the cognitive transmitter are needed in order to minimize the impact of interference. As a byproduct of the obtained capacity region, we obtain the capacity result for a generalized Gel'fand-Pinsker problem.

In this paper, we demonstrate the advantage of the inherent algebraic structure of lattice codes, for the uplink channel of a cellular deployment. The out-of-cell interference is assumed to be symmetric, as in Wyner's model. We employ a new relaying technique, compute-and-forward, which allows cell-sites to decode equations of the transmitted bits by exploiting the channel interference. However, the standard compute-and-forward technique is penalized whenever the channel coefficients are non-integer. We develop a superposition strategy to mitigate this penalty. By using part of the power towards a private message, we can effectively modify the channel seen by compute-and-forward. We demonstrate that, in certain regimes, this mixed strategy significantly outperforms decode-and-forward, compress-and-forward, and ordinary compute-and-forward.

The problem of sending information over compound state-dependent channels with non-causal state information available at only the transmitter is investigated. We prove a coding theorem and its strong converse establishing the capacity of this scenario for the case of discrete memoryless channels. Specific results are derived for additive white Gaussian noise channels corrupted by an additive Gaussian interference which is available at the transmitter only. We focus on the case where such interference may be absent on the channel, but the transmitter is unaware of this. Applications of the compound channels with non-causal state information arise in the context of multicast and cognitive radio channels, broadcast channels with imperfect channel knowledge and robust dirty-paper coding.

We consider the joint source-channel coding problem of sending a Gaussian source over a K-user Gaussian broadcast channel with bandwidth mismatch. A new outer bound to the achievable distortion region is derived using the technique of introducing more than one additional auxiliary random variable, which was previously used to derive sum-rate lower bound for the Gaussian multiple description problem. By combining this outer bound with the source-channel-separation-based achievable region, we provide approximate characterizations of the achievable distortion region within constant multiplicative factors.

The problem of multicasting multiple messages with the help of a relay, which may also have an independent message of its own to multicast, is considered. As a first step to address this general model, referred to as the compound multiple access channel with a relay (cMACr), the capacity region of the multiple access channel with a ldquocognitiverdquo relay is characterized, including the cases of partial and rate-limited cognition. Achievable rate regions for the cMACr model are then presented based on decode-and-forward (DF) and compress-and-forward (CF) relaying strategies. Moreover, an outer bound is derived for the special case in which each transmitter has a direct link to one of the receivers while the connection to the other receiver is enabled only through the relay terminal. Numerical results for the Gaussian channel are also provided.

The secrecy capacity of the multiantenna wiretap channel was recently characterized independently by Khisti and Wornell and Oggier and Hassibi using a Sato-like argument and matrix analysis tools. This paper presents an alternative characterization of the secrecy capacity of the multiantenna wiretap channel using a channel-enhancement argument. Compared with the approach of Khisti-Wornell and Oggier-Hassibi, this characterization is by nature information- rather than matrix-theoretic. Moreover, it extends the previous results from the average total power constraint to the more general matrix power constraint.

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

Abstract This paper reviews the information theoretic characterization of security in broadcast channels, in which a transmitter has both public and confidential messages intended for multiple receivers. All messages must be successfully received by their intended receivers, and the confidential messages must be kept as secret as possible from non‐intended recipients. Various scenarios are considered in the context of two‐user broadcast channels, for which known results on the secrecy capacity region are reviewed and corresponding coding schemes for achieving rates in these regions are described. Copyright © 2009 John Wiley & Sons, Ltd.

An interference alignment example is constructed for the deterministic channel model of theK-user interference channel. The deterministic channel example is then translated into the Gaussian setting, creating the first known example of a fully connected GaussianK-user interference network with single antenna nodes, real, nonzero and constant channel coefficients, and no propagation delays where the degrees of freedom outerbound is achieved. An analogy is drawn between the propagation delay based interference alignment examples and the deterministic channel model which also allows similar constructions for the two-userXchannel as well.

The performance of nonbinary linear block codes is studied in this paper via the derivation of new upper bounds on the block error probability under maximum-likelihood (ML) decoding. The transmission of these codes is assumed to take place over a memoryless and symmetric channel. The new bounds, which are based on the Gallager bounds and their variations, are applied to the Gallager ensembles of nonbinary and regular low-density parity-check (LDPC) codes. These upper bounds are also compared with sphere-packing lower bounds. This study indicates that the new upper bounds are useful for the performance evaluation of coded communication systems which incorporate nonbinary coding techniques.

A wireless network is considered where a source is communicating with a remote destination, and where a relay terminal is occasionally present in close proximity to the source but without the source's knowledge. The channel source-relay is assumed fixed due to the short distance, while the channels source-destination and relay-destination are affected by block flat Rayleigh fading, where channel state information is known only to respective receivers.Obliviouscooperative protocols are addressed which improve performance when the relay is present and do not degrade it when the relay is absent-and all this while the source is uninformed of the actual topology. Using the expected throughput as a performance measure, several such protocols based on decode-and-forward and quantize-and-forward are proposed for this network. It turns out that block Markov decode-and-forward (BMDF) with appropriate decoding is in fact an oblivious cooperative protocol. The optimal correlation between the transmissions of the source and the relay in decode-and-forward and the corresponding optimum performance are characterized. Finally, quantize-and-forward is considered for rates which preclude the usage of decode-and-forward, and several schemes are proposed for incorporating partial side information in the relay's quantization.

We study the uplink of Wyner-type cellular models featuring short range inter-cell interference. We assume that the decoding of the message sent by a given transmitter is done locally, that is using only the signals received at the antennas in its vicinity. We derive upper and lower bounds on the achievable rate for a two-dimensional model and for a one-dimensional model with an attenuation parameter. The analysis of our cellular models will rely on rate splitting and genie-aided bounds, originally employed in the framework of the interference channel.

In this paper we study the spectrum of certain large random HermitianJacobimatrices. These matrices are known to describe certain communication setups. In particular, we are interested in an uplink cellular channel which models mobile users experiencing a soft-handoff situation under joint multicell decoding. Considering rather general fading statistics we provide a closed-form expression for the per-cell sum-rate of this channel in high signal-to-noise ratio (SNR), when an intra-cell time-division multiple-access (TDMA) protocol is employed. Since the matrices of interest aretridiagonal, their eigenvectors can be considered as sequences with second-order linear recurrence. Therefore, the problem is reduced to the study of the exponential growth of products of two-by-two matrices. For the case whereKusers are simultaneously active in each cell, we obtain a series of lower and upper bound on the high-SNR power offset of the per-cell sum-rate, which are considerably tighter than previously known bounds.

Like the conventional two-user interference channel, the cognitive interference channel consists of two transmitters whose signals interfere at two receivers. It is assumed that there is a common message (message 1) known to both transmitters, and an additional independent message (message 2) known only to the cognitive transmitter (transmitter 2). The cognitive receiver (receiver 2) needs to decode messages 1 and 2, while the non cognitive receiver (receiver 1) should decode only message 1. Furthermore, message 2 is assumed to be a confidential message which needs to be kept as secret as possible from receiver 1, which is viewed as an eavesdropper with regard to message 2. The level of secrecy is measured by the equivocation rate. In this paper, a single-letter expression for the capacity-equivocation region of the discrete memoryless cognitive interference channel is obtained. The capacity-equivocation region for the Gaussian cognitive interference channel is also obtained explicitly. Moreover, particularizing the capacity-equivocation region to the case without a secrecy constraint, the capacity region for the two-user cognitive interference channel is obtained, by providing a converse theorem.

The secrecy capacity of the multiple-antenna wiretap channel under the average total power constraint was recently characterized, independently, by Khisti and Wornell and Oggier and Hassibi using a Sato-like argument and matrix analysis tools. This paper presents an alternative characterization of the secrecy capacity of the multiple-antenna wiretap channel under a more general matrix constraint on the channel input using a channel-enhancement argument. This characterization is by nature information-theoretic and is directly built on the intuition regarding to the optimal transmission strategy in this communication scenario.

In this work, new achievable rates are derived for the uplink channel of a cellular network with joint multicell processing (MCP), where unlike previous results, the ideal backhaul network has finite capacity per cell. Namely, the cell sites are linked to the central joint processor via lossless links with finite capacity. The new rates are based on compress-and-forward schemes combined with local decoding. Further, the cellular network is abstracted by symmetric models, which render analytical treatment plausible. For this family of idealistic models, achievable rates are presented for both Gaussian and fading channels. The rates are given in closed form for the classical Wyner model and the soft-handover model. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, even for modest backhaul capacities, supporting the potential gain offered by the joint MCP approach. Particular attention is also given to the low-signal-to-noise ratio (SNR) characterization of these rates through which the effect of the limited backhaul network is explicitly revealed. In addition, the rate at which the backhaul capacity should scale in order to maintain the original high-SNR characterization of an unlimited backhaul capacity system is found.

A multiple-input multiple-output (MIMO) system with a distributed receiver is considered. The system consists of a nomadic transmitter with several antennas, whose signal is received by multiple agents, exhibiting independent channel gains and an additive circular-symmetric Gaussian noise. In the nomadic regime, we assume that the agents do not have any decoding ability. These agents process their channel observations and forward them to the final destination through unidirectional lossless links with a fixed capacity. We propose new achievable rates based on elementary compression and on Wyner–Ziv (WZ)or chief executive officer (CEO) processing, for both fast-fading and block-fading channels, as well as for general discrete channels. The simpler two agents scheme is solved, up to an implicit equation with a single variable. Limiting the nomadic transmitter to circular-symmetric Gaussian signaling, new upper bounds are derived, based on the vector version of the entropy power inequality. Several asymptotic settings are analyzed. In addition, the upper bounds are analytically shown to be tight for several examples, while numerical calculations reveal a rather small gap in a finite$2\,\times\,2$setting. The advantage of the WZ approach over elementary compression is shown, where only the former can achieve the optimal diversity–multiplexing tradeoff (DMT).

A two-user discrete memoryless compound multiple-access channel (MAC) with a common message and conferencing decoders is considered. The capacity region is characterized in the special cases of physically degraded channels and unidirectional cooperation, and achievable rate regions are provided for the general case. The results are then extended to the corresponding Gaussian model. In the Gaussian setup, the provided achievable rates are shown to lie within some constant number of bits from the boundary of the capacity region in several special cases. An alternative model, in which the encoders are connected by conferencing links rather than having a common message, is studied as well, and the capacity region for this model is also determined for the cases of physically degraded channels and unidirectional cooperation. Numerical results are also provided to obtain insights about the potential gains of conferencing at the decoders and encoders.

Cooperative decoding at the base stations (or access points) of an infrastructure wireless network is currently well recognized as a promising approach for intercell interference mitigation, thus enabling high frequency reuse. Deployment of cooperative multicell decoding depends critically on the tolopology and quality of the available backhaul links connecting the base stations. This work studies a scenario where base stations are connected only if in adjacent cells, and via finite-capacity links. Relying on a linear Wyner-type cellular model with no fading, achievable rates are derived for the two scenarios where base stations are endowed only with the codebooks of local (in-cell) mobile stations, or also with the codebooks used in adjacent cells. Moreover, both uni- and bidirectional backhaul links are considered. The analysis sheds light on the impact of codebook information, decoding delay, and network planning (frequency reuse) on the performance of multicell decoding as enabled by local and finite-capacity backhaul links. Analysis in the high-signal-to-noise ratio (SNR) regime and numerical results validate the main conclusions.

A single source communicates with a single destination via a remote wireless multiple-antenna (multiple-input multiple-output (MIMO)) transceiver. The source has access to each of the transmit antennas through a finite-capacity link, and likewise the destination is connected to the receiving antennas via capacity-constrained channels (e.g., as for wired or time-division multiple access (TDMA) channels). Targeting a nomadic communication scenario, in which the remote MIMO transceiver is designed to serve different standards or services, it is assumed that transmitters and receivers are oblivious to the encoding function shared by source and destination. Assuming a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound (that holds also for codebook-dependent operation). Closed-form expressions are derived for large numbers of antennas (and in some cases large signal-to-noise ratios (SNRs)), and asymptotics of the achievable rates are studied with respect to either link capacities or SNR. Overall, the analysis points to effective transmission/reception strategies for the distributed MIMO channel at hand, which are optimal under specified conditions. In particular, it is concluded that in certain asymptotic and nonasymptotic regimes there is no loss of optimality in designing the system for nomadic applications (i.e., assuming oblivious transmitters and receivers). Numerical results validate the analysis.

In this work, a multicell cooperative zero-forcing beamforming (ZFBF) scheme combined with a simple user selection procedure is considered for the Wyner cellular downlink channel. The approach is to transmit to the user with the ldquobestrdquo local channel in each cell. The performance of this suboptimal scheme is investigated in terms of the conventional sum-rate scaling law and the sum-rate offset for an increasing number of users per cell. We term this characterization of the sum-rate for large number of users ashigh-loadregimecharacterization, and point out the similarity of this approach to the standard affine approximation used in the high-signal-to-noise ratio (SNR) regime. It is shown that, under an overall power constraint, the suboptimal cooperative multicell ZFBF scheme achieves the same sum-rate growth rate and slightly degraded offset law, when compared to an optimal scheme deploying joint multicell dirty-paper coding (DPC), asymptotically with the number of users per cell. Moreover, the overall power constraint is shown to ensure in probability, equal per-cell power constraints when the number of users per-cell increases.

The capacity region of a compound multiple-antenna broadcast channel is characterized when the users exhibit a certain degradedness order. The channel under consideration has two users, each user has a finite set of possible realizations. The transmitter transmits two messages, one for each user, in such a manner that regardless of the actual realizations, both users will be able to decode their messages correctly. An alternative view of this channel is that of a broadcast channel with two common messages, each common message is intended to a different set of users. The degradedness order between the two sets of realizations/users is defined through an additional, fictitious, user whose channel is degraded with respect to all realizations/users from one set while all realizations/users from the other set are degraded with respect to him.

This work studies the properties of the minimum mean-square error (MMSE) of estimating an arbitrary random variable contaminated by Gaussian noise based on the observation. The MMSE can be regarded as a function of the signal-to-noise ratio (SNR), as well as a functional or transform of the input distribution. This paper shows that the MMSE is analytic in SNR for every random variable. Simple expressions for the derivatives of the MMSE as a function of the SNR are obtained. Since the input-output mutual information can be written as the integral of the MMSE as a function of SNR, the results also lead to higher derivatives of the mutual information. The MMSE and mutual informationpsilas convexity in the SNR and concavity in the input distribution are established. It is shown that there can be only one SNR for which the MMSE of a Gaussian random variable and that of a non-Gaussian random variable coincide. Application of the properties of the MMSE to the scalar Gaussian broadcast channel problem is presented.

In this paper we study the spectrum of certain large random Hermitian Jacobi matrices. These matrices are known to describe certain communication setups. In particular we are interested in an uplink cellular channel which models mobile users experiencing a soft-handoff situation under joint multicell decoding. Considering rather general fading statistics we provide a closed form expression for the per-cell sum-rate of this channel in high-SNR, when an intra-cell TDMA protocol is employed. Since the matrices of interest are tridiagonal, their eigenvectors can be considered as sequences with second order linear recurrence. Therefore, the problem is reduced to the study of the exponential growth of products of two by two matrices. For the case where K users are simultaneously active in each cell, we obtain a series of lower and upper bounds on the high-SNR power offset of the per-cell sum-rate, which are considerably tighter than previously known bounds.

A scenario in which a single source communicates with a single destination via a distributed MIMO transceiver is considered. The source operates each of the transmit antennas via finite-capacity links, and likewise the destination is connected to the receiving antennas through capacity-constrained channels. Targeting a nomadic communication scenario, in which the distributed MIMO transceiver is designed to serve different standards or services, transmitters and receivers are assumed to be oblivious to the encoding functions shared by source and destination. Adopting a Gaussian symmetric interference network as the channel model (as for regularly placed transmitters and receivers), achievable rates are investigated and compared with an upper bound. It is concluded that in certain asymptotic and non-asymptotic regimes obliviousness of transmitters and receivers does not cause any loss of optimality.

Cognitive state-dependent interference channels are analyzed. We focus on the two-user case with two message sources. One of the transmitters, referred to as the cognitive informed user, knows both messages and also the states of the channel in a non-causal manner. The other transmitter knows only one of the messages and does not know the channel states. Each of the two decoders is supposed to decode only its intended message. Inner and outer bounds on the capacity region of this channel are provided for the general finite input alphabet case. The asymmetric state-dependent Gaussian weak interference channel with non-causal state information is then considered, and a closed form formula for the capacity region is established in the regime of weak interference.

In this paper the advantages provided by multi-cell processing of signals transmitted by mobile terminals (MTs) which are received via dedicated relay terminals (RTs) are studied. It is assumed that each RT is capable of full-duplex operation and receives the transmission of adjacent relay terminals. Focusing on intra-cell TDMA and non-fading channels, a simplified relay-aided uplink cellular model based on a model introduced by Wyner is considered. Assuming a nomadic application in which the RTs are oblivious to the MTs’ codebooks, a form of distributed compress-and-forward (CF) scheme with decoder side information is employed. The per-cell sum-rate of the CF scheme is derived and is given as a solution of a simple fixed point equation. This achievable rate reveals that the CF scheme is able to completely eliminate the inter-relay interference, and it approaches a “cut-set-like” upper bound for strong RTs transmission power. The CF rate is also shown to surpass the rate of an amplify-and-forward scheme via numerical calculations for a wide range of the system parameters.

This paper studies a two source, two destination Gaussian interference channel in the presence of a cognitive relay. The cognitive relay has access to the messages transmitted by both the sources and assists them in communicating the messages successfully to their respective destinations. An achievable rate region for the system is derived by combining the Han-Kobayashi coding scheme for the general interference channel with dirty paper coding. The paper also derives outer bounds on the capacity region and obtains the degrees of freedom of the system.

We study the problem of a single source transmitting, over a non-fading Gaussian channel, to two cooperating users exhibiting different signal-to-noise ratios (SNR). Both users are required to decode the source message. This model is similar to the broadcast channel with a common message, however the users cannot reliably decode the message independently, and require some form of conversation in order to decode. The cooperation is performed over a noiseless link with a total capacity limit Ccoop, which the users may utilize for conversation. In order to maximize the achievable throughput, it is suggested to employ the multi-layer broadcast approach combined with multi-session cooperation, such that every session another layer can be decoded. Since the cooperation link is limited, an efficient cooperation scheme is required. The relaying user performs compression accounting for the side information of the decoder in the Wyner-Ziv (WZ) spirit. In multi-session cooperation the compression is in the form of successive-refinement WZ coding. After the first user decodes a layer, the minimal required information is sent back to the second user to allow both of them to decode the message. The minimal required information is achievable with random coding using a binning strategy. While it is expected that multi-session cooperation will potentially be more efficient than a single-session cooperation we show that the maximal throughput is achieved in a single session.

We show that when transmitting a Gaussian source over an average-power-constrained Gaussian channel with mismatched bandwidth, there exists an uncountable set of hybrid digital analog schemes which are optimal under the minimum mean squared error criterion. This generalizes the recent result by Bross et al. that for the bandwidth matched case, there exists an uncountable set of optimal schemes, with the uncoded transmission and the separation approach being the two extremes. The proposed schemes for bandwidth expansion and compression both require proper combination of various components such as power splitting, bandwidth splitting, rate splitting, Wyner-Ziv coding and dirty-paper coding. This set of schemes includes all the existing known optimal schemes as special cases. We show that this set of schemes can be applied to the broadcast scenario with three receivers, when the receiver with median channel SNR achieves optimal distortion, and it offers distortion tradeoff between of the good receiver and bad receiver. Interestingly, though continuous, this tradeoff curve is in fact concave, implying that its performance is worse than a direct time-sharing approach in this three user scenario. We further show even in a more general broadcast setting with a continuum of receivers the time sharing scheme is better than any given scheme in this set; somewhat surprisingly, there exists a unique time-sharing ratio for this to hold.

This paper finds the capacity of a linear time-invariant system with a given transfer function, observed in additive Gaussian noise through a memoryless fading channel. A coherent model is assumed where the fading coefficients are known at the receiver (but not the transmitter). We show that the optimum normalized power spectral density is the waterfilling solution for reduced signal-to-noise ratio, where the gap to the actual signal-to-noise ratio depends on both the fading distribution and the channel transfer function.

An interference alignment example is constructed for the deterministic channel model of the K user interference channel. The deterministic channel example is then translated into the Gaussian setting, creating the first known example of a fully connected Gaussian K user interference network with single antenna nodes, real, non-zero and contant channel coefficients, and no propagation delays where the degrees of freedom outerbound is achieved. An analogy is drawn between the propagation delay based interference alignment examples and the deterministic channel model which also allows similar constructions for the 2 user X channel as well.

The performance analysis of non-binary linear block codes is studied under ML decoding where it is assumed that the transmission takes place over memoryless symmetric channels. Gallager-type bounds are derived, and the proposed bounds are exemplified for expurgated regular ensembles of non-binary low-density parity-check (LDPC) codes. These bounds are also compared with classical and recent improved sphere-packing bounds, indicating that these bounding techniques are informative for the performance evaluation of coded communication systems which incorporate non-binary coding techniques.

Outer bounds on the capacity region of broadcast channels are reviewed and a new outer bound is presented.

The compound wire-tap channel is studied, which is based on Wynerpsilas wire-tap model with both the channel from the source to the destination and the channel from the source to the wire-tapper taking a number of states. No matter which states occur for the two channels, the source wishes to guarantee that the destination decodes its message successfully and that the wire-tapper does not obtain the source message. The semideterministic compound wire-tap channel is first studied, in which the channel from the source to the destination is deterministic and has only one state. The secrecy capacity is obtained. An example parallel Gaussian compound wire-tap channel is then studied, in which both channels have two states. Three schemes are studied, and it is shown that introducing randomness either into the source message or into the encoder achieves the maximal secrecy degree of freedom. Both channels studied in this paper demonstrate that creating an auxiliary input, and hence adding a prefix channel from this auxiliary input to the actual channel input, improves the secrecy rate.

Recent information theoretic results on cooperation in cellular systems are reviewed, addressing both multicell processing (cooperation among base stations) and relaying (cooperation at the user level). Two central issues are addressed, namely, first multicell processing is studied with either limited-capacity backhaul links to a central processor or only local (and finite-capacity) cooperation among neighboring cells. The role of codebook information, decoding delay and network planning (frequency reuse) are specifically highlighted along with the impact of different transmission/ reception strategies. Next, multicell processing is considered in the presence of cooperation at the user level, focusing on both out-of-band relaying via conferencing users and in-band relaying by means of dedicated relays. Non-fading and fading uplink and downlink channels adhering to simple Wyner-type, cellular system models are targeted.

The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broadband (eMBB) devices, by only allocating resources when necessary. The main challenge is posed by the uncertainty in the process of URLLC packet generation, which mandates the use of predictors for URLLC traffic in the coming frames. In practice, such prediction may overestimate or underestimate the amount of URLLC data to be generated, yielding either an excessive or an insufficient amount of resources to be pre-emptively allocated for URLLC packets. In this paper, we introduce a novel scheduler for URLLC packets that provides formal guarantees on reliability and latencyirrespective of the quality of the URLLC traffic predictor. The proposed method leverages recent advances inonline conformal prediction (CP), and follows the principle of dynamically adjusting the amount of allocated resources so as to meet reliability and latency requirements set by the designer.

Following the discovery of a fundamental connection between information measures and estimation measures in Gaussian channels, this paper explores the counterpart of those results in Poisson channels. In the continuous-time setting, the received signal is a doubly stochastic Poisson point process whose rate is equal to the input signal plus a dark current. It is found that, regardless of the statistics of the input, the derivative of the input-output mutual information with respect to the intensity of the additive dark current can be expressed as the expected difference between the logarithm of the input and the logarithm of its noncausal conditional mean estimate. The same holds for the derivative with respect to input scaling, but with the logarithmic function replaced by x log x. Similar relationships hold for discrete-time versions of the channel where the outputs are Poisson random variables conditioned on the input symbols.

We provide achievability as well as converse results for the degrees of freedom region of a multiple-input multiple-output (MIMO)$X$channel, i.e., a system with two transmitters, two receivers, each equipped with multiple antennas, where independent messages need to be conveyed over fixed channels from each transmitter to each receiver. The inner and outer bounds on the degrees of freedom region are tight whenever integer degrees of freedom are optimal for each message. With$M=1$antennas at each node, we find that the total (sum rate) degrees of freedom are bounded above and below as$1 \leq \eta _{X}^{\star} \leq {{ 4}\over { 3}}$. If$M>1$and channel matrices are nondegenerate then the precise degrees of freedom$\eta _{X}^{\star} = {{ 4}\over { 3}}M$. Thus, the MIMO$X$channel has noninteger degrees of freedom when$M$is not a multiple of$3$. Simple zero forcing without dirty paper encoding or successive decoding, suffices to achieve the${{ 4}\over { 3}}M$degrees of freedom. If the channels vary with time/frequency then the$X$channel with single antennas$(M=1)$at all nodes has exactly${4\over 3}$degrees of freedom. The key idea for the achievability of the degrees of freedom isinterference alignment—i.e., signal spaces are aligned at receivers where they constitute interference while they are separable at receivers where they are desired. We also explore the increase in degrees of freedom when some of the messages are made available to a transmitter or receiver in the manner of cognitive radio.

The fading broadcast channel with confidential messages (BCC) is investigated, where a source node has common information for two receivers (receivers 1 and 2), and has confidential information intended only for receiver 1. The confidential information needs to be kept as secret as possible from receiver 2. The broadcast channel from the source node to receivers 1 and 2 is corrupted by multiplicative fading gain coefficients in addition to additive Gaussian noise terms. The channel state information (CSI) is assumed to be known at both the transmitter and the receivers. The parallel BCC with independent subchannels is first studied, which serves as an information-theoretic model for the fading BCC. The secrecy capacity region of the parallel BCC is established, which gives the secrecy capacity region of the parallel BCC with degraded subchannels. The secrecy capacity region is then established for the parallel Gaussian BCC, and the optimal source power allocations that achieve the boundary of the secrecy capacity region are derived. In particular, the secrecy capacity region is established for the basic Gaussian BCC. The secrecy capacity results are then applied to study the fading BCC. The ergodic performance is first studied. The ergodic secrecy capacity region and the optimal power allocations that achieve the boundary of this region are derived. The outage performance is then studied, where a long-term power constraint is assumed. The power allocation is derived that minimizes the outage probability where either the target rate of the common message or the target rate of the confidential message is not achieved. The power allocation is also derived that minimizes the outage probability where the target rate of the confidential message is not achieved subject to the constraint that the target rate of the common message must be achieved for all channel states.

The problem of a nomadic terminal sending information to a remote destination via agents with lossless connections to the destination is investigated. Such a setting suits, e.g., access points of a wireless network where each access point is connected by a wire to a wireline-based network. The Gaussian codebook capacity for the case where the agents do not have any decoding ability is characterized for the Gaussian channel. This restriction is demonstrated to be severe, and allowing the nomadic transmitter to use other signaling improves the rate. For both general and degraded discrete memoryless channels, lower and upper bounds on the capacity are derived. An achievable rate with unrestricted agents, which are capable of decoding, is also given and then used to characterize the capacity for the deterministic channel.

Discrete-input two-dimensional (2D) Gaussian channels with memory represent an important class of systems, which appears extensively in communications and storage. In spite of their widespread use, the workings of 2D channels are still very much unknown. In this work, we try to explore their properties from the perspective of estimation theory and information theory. At the heart of our approach is a mapping of a 2D channel to an undirected graphical model, and inferring itsaposterioriprobabilities (APPs) using generalized belief propagation (GBP). The derived probabilities are shown to be practically accurate, thus enabling optimal maximumaposteriori(MAP) estimation of the transmitted symbols. Also, the Shannon-theoretic information rates are deduced either via the vector-wise Shannon-McMillan-Breiman (SMB) theorem, or via the recently derived symbol-wise Guo-Shamai-Verdu (GSV) theorem. Our approach is also described from the perspective of statistical mechanics, as the graphical model and inference algorithm have their analogues in physics. Our experimental study, based on common channel settings taken from cellular networks and magnetic recording devices, demonstrates that under nontrivial memory conditions, the performance of this fully tractable GBP estimator is almost identical to the performance of the optimal MAP estimator. It also enables a practically accurate simulation-based estimate of the information rate. Rationalization of this excellent performance of GBP in the 2-D Gaussian channel setting is addressed.

We generalize the Gel'fand–Pinsker model to encompass the setup of a memoryless multiple-access channel (MAC). According to this setup, only one of the encoders knows the state of the channel (noncausally), which is also unknown to the receiver. Two independent messages are transmitted: a common message and a message transmitted by the informed encoder. We find explicit characterizations of the capacity region with both noncausal and causal state information. Further, we study the noise-free binary case, and we also apply the general formula to the Gaussian case with noncausal channel state information, under an individual power constraint as well as a sum power constraint. In this case, the capacity region is achievable by a generalized writing-on-dirty-paper scheme.

We consider the problem of transmitting a Gaussian source on a slowly fading Gaussian channel, subject to the mean-squared error distortion measure. The channel state information is known only at the receiver but not at the transmitter. The source is assumed to be encoded in a successive refinement (SR) manner, and then transmitted over the channel using the broadcast strategy. In order to minimize the expected distortion at the receiver, optimal power allocation is essential. We propose an efficient algorithm to compute the optimal solution in linear time , when the total number of possible discrete fading states. Moreover, we provide a derivation of the optimal power allocation when the fading state is a continuum, using the classical variational method. The proposed algorithm as well as the continuous solution is based on an alternative representation of the capacity region of the Gaussian broadcast channel.

rdquoWe investigate the performance of the broadcast approach for various fading distributions, which correspond to different models of partial transmit channel state information (CSI). The first model considered is the quantized limited feedback. In this model, the receiver can send as feedback only a finite number of bits describing the fading gain. We derive the optimal power allocation for the broadcast approach for the quantized feedback model. For a Rayleigh fading channel, numerical results here show that if the feedback word can be longer than one bit, the broadcasting gain becomes negligible, due to diminished channel uncertainty. The second partial transmit CSI model is a stochastic Gaussian model with mean and variance information, which is commonly used for modeling the channel estimation error. In a single-input single-output (SISO) channel, this model also corresponds to the Ricean fading distribution, for which we derive maximal achievable broadcasting rates. We further consider a multiple-input single-output (MISO) channel, and derive the optimal power allocation strategy in a broadcast approach. Numerical results here show that uniform power allocation is preferable over beamforming power allocation in the region where broadcasting gain over single level coding is non-negligible.

Conventional hybrid automatic retransmission request (HARQ) is usually used to maximize throughput. However, high throughput is achieved at the expense of high latency. We study a novel broadcasting HARQ strategy. The multi-layer broadcast approach is suitable for the case where transmitter has no channel state information (CSI), which is the case with HARQ schemes as well. The broadcast approach enables the receiver to decode rates, which are matched to every fading gain realization. That is, the higher the fading gain realization, the more layers are reliably decoded. The broadcast approach combined with HARQ enables achieving high throughput with low latency. In a broadcast HARQ scheme every code layer supports HARQ independently. Thus HARQ is applied in every transmission block to undecoded layers only, which highly increases the broadcast approach efficiency. In this paper, both broadcast chase combining (BCC) HARQ and broadcast incremental redundancy (BIR) HARQ are studied in the limit of infinitely many layers, and for finite level coding. Interestingly, with continuous broadcasting the BCC-HARQ is found to closely approximate the BIR-HARQ, while using a sub-optimal broadcasting power distribution.

We provide achievability as well as converse results for the degrees of freedom region of a MIMO X channel, i.e., a system with two transmitters, two receivers, each equipped with multiple antennas, where independent messages need to be conveyed over fixed channels from each transmitter to each receiver. The inner and outerbounds on the degrees of freedom region are tight whenever integer degrees of freedom are optimal for each message. If all nodes have equal number of antennas M > 1 and channel matrices are non-degenerate then the degrees of freedom etaX* = 4/3 M. If the channels vary with time/frequency then the X channel with single antennas (M = 1) at all nodes has 4/3 degrees of freedom. Thus, the MIMO X channel has non-integer degrees of freedom when M is not a multiple of 3. Simple zero forcing without dirty paper encoding or successive decoding, suffices to achieve the 4/3 M degrees of freedom in all cases. The key idea for the achievability of the degrees of freedom is interference alignment - i.e., signal spaces are aligned at receivers where they constitute interference while they are separable at receivers where they are desired. With equal number of antennas at all nodes, we also explore the increase in degrees of freedom when some of the messages are made available to a transmitter or receiver in the manner of cognitive radio.

We characterize the capacity of the general class of noncoherent underspread wide-sense stationary uncorrelated scattering (WSSUS) time-frequency-selective Rayleigh fading channels, under peak constraints in time and frequency and in time only. Capacity upper and lower bounds are found which are explicit in the channel's scattering function and allow to identify the capacity-maximizing bandwidth for a given scattering function and a given peak-to-average power ratio.

We study a dirty paper channel model where the input is constrained to belong to a PAM constellation. In particular, we provide lower bounds on the capacity as well as explicit coding schemes for the binary-input dirty- paper channel. We examine the case of causal as well as non-causal side information.

For an interference network where receiver k receives the sum of the signal transmitted by Transmitter k and a scaled version of the signal transmitted by Transmitter k - 1 corrupted by Gaussian noise we compute the pre-log of the sum-rate capacity for the case where each transmitter has side- information consisting of the messages to be sent by its J predecessors.

The fading broadcast channel with confidential messages (BCC) is investigated, where a source node has common information for two receivers (receivers 1 and 2), and has confidential information intended only for receiver 1. The confidential information needs to be kept as secret as possible from receiver 2. The broadcast channel from the source node to receivers 1 and 2 is corrupted by multiplicative fading gain coefficients in addition to additive Gaussian noise terms. The channel state information (CSI) is assumed to be known at both the transmitter and the receivers. The secrecy capacity region is first established for the parallel Gaussian BCC, and the optimal source power allocations that achieve the boundary of the secrecy capacity region are derived. In particular, the secrecy capacity region is established for the Gaussian case of the Csiszar-Korner BCC model. The secrecy capacity results are then applied to give the ergodic secrecy capacity region for the fading BCC.

An achievable region, outer bounds and a capacity result are established for two-sender two-receiver interference channels with one cognitive transmitter. Specifically, we assume that one transmitter knows either the full or, more realistically, the partial message of the other transmitter due to its cognitive capabilities. The achievable region is obtained by a rate-splitting strategy, which generalizes prior strategies under both weak and strong interference conditions. The outer bounds are based on an extension of the Nair-El Gamal outer bound for the broadcast channel capacity. When only the partial message is known to the cognitive user, the capacity region in strong interference is established. In this regime, the interference is such that both receivers can decode both messages with no rate penalty.

In this contribution we present new achievable rates, for the non-fading uplink channel of a cellular network, with joint cell-site processing, where unlike previous results, the error-free backhaul network has finite capacity per-cell. Namely, the cell-sites are linked to the central joint processor via lossless links with finite capacity. The cellular network is modeled by the circular Wyner model, which yields closed form expressions for the achievable rates. For this idealistic model, we present achievable rates for cell-sites that use compress-and forward scheme, combined with local decoding, and inter-cell time-sharing. These rates are then demonstrated to be rather close to the optimal unlimited backhaul joint processing rates, already for modest backhaul capacities, supporting the potential gain offered by the joint cell-site processing approach.

In this paper we investigate the achievable rate of a system that includes a nomadic transmitter with a Gaussian codebook and several antennas, which is received by multiple agents, each with a single antenna, suffering independent channel gains and additive Gaussian noise. In the nomadic regime, we assume that the agents do not have any decoding ability. These agents process their channel observations and forward it to the final destination through lossless links with fixed capacities. This paper extends a previous work [1] by providing new upper bounds on the achievable rates, and demonstrates optimality of the suggested coding scheme in several asymptotic situations. For a finite 2 × 2 setting and Rayleigh fading, the upper-bounds for both fast and block fading are demonstrated.

We generalize the Gel'fand-Pinsker model to encompass the setup of a memoryless multiple-access channel. According to this setup, only one of the encoders knows the state of the channel (non-causally), which is also unknown to the receiver. Two independent messages are transmitted: a common message and a message transmitted by the informed encoder. We find explicit characterizations of the capacity region with both non-causal and causal state information. Further, we apply the general formula to the Gaussian case with non-causal channel state information, under an individual power constraint as well as a sum power constraint. In this case, the capacity region is achievable by a generalized writing-on-dirty-paper scheme.

In this paper the benefits provided by multi-cell processing of signals transmitted by mobile terminals which are received via dedicated relay terminals (RTs) are assessed. Unlike previous works, each RT is assumed here to be capable of full-duplex operation and receives the transmission of adjacent relay terminals. Focusing on intra-cell TDMA and non-fading channels, a simplified uplink cellular model introduced by Wyner is considered. This framework facilitates analytical derivation of the per-cell sum-rate of multi-cell and conventional single-cell receivers. In particular, the analysis is based on the observation that the signal received at the base stations can be interpreted as the outcome of a two-dimensional linear time invariant system. Numerical results are provided as well in order to provide further insight into the performance benefits of multi-cell processing with relaying.

This paper finds the capacity of linear time-invariant systems observed in additive Gaussian noise through a memoryless erasure channel. This problem requires obtaining the asymptotic spectral distribution of a submatrix of a nonnegative definite Toeplitz matrix obtained by retaining each column/row independently and with identical probability. We show that the optimum normalized power spectral density is the water filling solution for reduced signal-to-noise ratio, where the gap to the actual signal-to-noise ratio depends on both the erasure probability and the channel transfer function. We find asymptotic expressions for the capacity in the sporadic erasure and sporadic non-erasure regimes as well as the low and high signal-to-noise regimes.

The capacity region of a compound multi-antenna broadcast channel is characterized when the users exhibit a certain degradedness order. For this purpose, we bring to bear a new extremal inequality for information theory and utilize a channel enhancement technique.

We consider the problem of transmitting a Gaussian source on a slowly fading Gaussian channel, subject to the mean squared error distortion measure. The channel state information is known only at the receiver but not the transmitter. The source is assumed to be encoded in a successive refinement manner, and then transmitted over the channel using the broadcast strategy. In order to minimize the expected distortion at the receiver, optimal power allocation is essential. We propose an efficient algorithm to compute the optimal solution in linear time O(M). Moreover, we provide a derivation of the optimal power allocation when the fading state is a continuum, using the classical variational method. The proposed algorithm as well as the continuous solution is based on an alternative representation of the capacity region of the Gaussian broadcast channel.

We consider the problem of rate-R, channel coding with causal/noncausal side information at the transmitter, under an additional requirement of minimizing the amount of information that can be learned from the channel output about the state sequence, which is defined in terms of the mutual information between the state sequence and the channel output sequence. A single-letter characterization is provided for the achievable region of pairs {(R, E)}. Explicit results for the Gaussian case (Costa's dirty-paper channel) are derived in full detail.

Fountain codes are currently employed for reliable and efficient transmission of information via erasure channels with unknown erasure rates. This correspondence introduces the notion of fountain capacity for arbitrary channels. In contrast to the conventional definition of rate, in the fountain setup the definition of rate penalizes the reception of symbols by the receiver rather than their transmission. Fountain capacity measures the maximum rate compatible with reliable reception regardless of the erasure pattern. We show that fountain capacity and Shannon capacity are equal for stationary memoryless channels. In contrast, Shannon capacity may exceed fountain capacity if the channel has memory or is not stationary.

Achip-interleavedrandomly spread direct-sequence code-division multiple-access (DS-CDMA) scheme is considered, employed in two variants of Wyner's infinite linear cell-array model with flat fading. Focusing on the asymptotic setup in which both the number of users per cell and the processing gain go to infinity, while their ratio (the “cell load”) goes to some finite constant, the spectral efficiencies of the optimum and linear minimum mean-squared error (MMSE)joint multicell receiversare investigated. A dramatic performance enhancement as compared tosingle-cell-site processing is demonstrated. The asymptotic behavior of the two receivers in extreme signal-to-noise ratio (SNR) regimes and in a high cell-load regime are analyzed as well. The impact of chip interleaving versus symbol interleaving is also investigated. Chip-level interleaving is found beneficial in several cases of interests, and is conjectured to be beneficial in general.

The sum-rate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flat-fading channels. The focus is on a simple Wyner-like multicell model, where the system cells are arranged on a circle, and the cell sites are located at the boundaries of the cells. For the uplink channel, analytical expressions of the sum-rate capacities are derived for intra-cell time-division multiple-access (TDMA) scheduling, and a “wideband” (WB) scheme (where all users are active simultaneously utilizing all bandwidths for coding). Assuming individual equal per-cell power constraints, and using the Lagrangian uplink–downlink duality principle, an analytical expression for the sum-rate capacity of the downlink channel is derived for nonfading channels, and shown to coincide with the corresponding uplink result. Introducing flat-fading, lower and upper bounds on the average per-cell ergodic sum-rate capacity are derived. The bounds exhibit an$O(\log _{e} K)$multiuser diversity factor for a number of users per cell$K\gg 1$, in addition to the array diversity gain. Joint multicell processing is shown to eliminate out-of-cell interference, which is traditionally considered to be a limiting factor in high-rate reliable communications.

This work considers the problem of communication between a remote single transmitter and a destined user, with helping colocated users, over an independent block Rayleigh-fading channel. The colocation nature of the users allows cooperation, which increases the overall achievable rate, from transmitter to destination. The transmitter is ignorant of the fading coefficients, while receivers have access to perfect channel state information (CSI). We propose, for this setting, a multilayer broadcast transmission approach. The broadcast approach enables enhanced cooperation between the colocated users. That is due to the nature of broadcasting, where the better the channel quality, the more layers that can reliably be decoded. The cooperation between the users is performed over additive white Gaussian noise (AWGN) channels, with a relaying power constraint, and unlimited bandwidth. Three commonly used cooperation techniques are studied: amplify-and-forward (AF), compress–and-forward (CF), and decode–and-forward (DF). These techniques are extended by using the broadcast approach for the case of relaxed decoding delay constraint. For this case, a separate processing of the layers, which includes multisession cooperation is shown to be beneficial. Further, closed-form expressions for infinitely many AF sessions and recursive expressions for the more complex CF are given. Numerical results for the various cooperation strategies demonstrate how the multisession cooperation outperforms conventional relaying techniques.

This paper tightens previous information combining bounds on the performance of iterative decoding of binary low-density parity-check (LDPC) codes over binary-input symmetric-output channels by tracking the probability of erroneous bit in conjunction with mutual information. Evaluation of the new bounds as well as of other known bounds on different LDPC ensembles demonstrates sensitivity of the finite dimensional iterative bounds to lambda2, the fraction of edges connected to degree 2 variable nodes

The performance of maximum-likelihood (ML) decoded binary linear block codes is addressed via the derivation of tightened upper bounds on their decoding error probability. The upper bounds on the block and bit error probabilities are valid for any memoryless, binary-input and output-symmetric communication channel, and their effectiveness is exemplified for various ensembles of turbo-like codes over the additive white Gaussian noise (AWGN) channel. An expurgation of the distance spectrum of binary linear block codes further tightens the resulting upper bounds.

We investigate the outage probability of a 2 times 1 multiple-input single-output (MISO) communication link subjected to quasi-static flat Rayleigh fading, under total and individual (per antenna) average power constraints and under a maximum correlation constraint. For a system with normalized fading and noise powers and a total power constraint P, it is shown that up to a rate of approximately log (1 + 1.2564 ldr P) it is best to divide the power equally between the two antennas, while for higher rates it is preferable to use all of the power in a single antenna. When individual power constraints are imposed on each antenna, and in addition, the correlation between the transmitted signals from each antenna can not be made larger than rhomax, it is shown that for rates smaller than log(1 + P) the optimal correlation is zero, while for rates higher than log(1 + gammaP), where gamma is a constant in [1,3/2], the best correlation is rhomax. For the remaining rates, the optimal correlation is either 0 or rhomax- Finally, we consider the outage performance of a two-user multiple access channel with a common message. The effect of the rate of the common message on the optimal correlation and on the optimum outage performance is examined.

This paper introduces extensions for the broadcast approach for a multi-input multi-output (MIMO) block fading channel, with receiver only channel state information (CSI). Previous works have not been able to fully characterize the fundamental MIMO broadcasting upper bound. As it seems that analytical solution for this problem is quite difficult to achieve, we consider here sub-optimal schemes, for which achievable rates may be computed. In particular, finite level coding over a MIMO channel instead of continuous layering is analyzed, the expressions derived for the decoding probability regions allow numerical computation of finite level coding upper bounds. Noticing that the gains of two level coding over a MIMO channel are rather small, we consider sub-optimal techniques, which are more straightforward to implement. Among these techniques is the multiple-access channel (MAC) approach with single level coded streams, which is similar in concept to V-BLAST. Closed form expressions for probabilities of decoding regions here are derived, allowing numerical evaluation. We further consider multi-access permutation codes (MAPC). A Hadamard transform is compared with a suggested diagonal permutation code, which are shown to have similar performance, while diagonal permutation has lower implementation complexity. For all approaches, we derive information theoretic upper bounds of achievable rates.

In this paper, we consider the optimization of the compound capacity in a rank one Ricean multiple input multiple output channel using partial channel state information at the transmitter side. We model the channel as a deterministic matrix within a known ellipsoid, and address the compound capacity defined as the maximum worst case mutual information in the set. We find that the optimal transmit strategy is always beamforming, and can be found using a simple one dimensional search. Similar results are derived for the worst case sum-rate of a multiple access channel with individual power constraints and a total power constraint. In this multiuser setting we assume equal array response at the receiver for all users. These results motivate the growing use of systems using simple beamforming transmit strategies

We characterize the infinite bandwidth capacity behavior of the general class of underspread wide-sense stationary uncorrelated scattering (WSSUS) time-frequency selective Rayleigh fading channels. In particular, we propose a signaling scheme, termed time-frequency pulse position modulation (TF-PPM), which is shown to achieve AWGN channel capacity in the infinite bandwidth limit. As a trivial consequence of this result, the infinite bandwidth capacity of WSSUS underspread fading channels, irrespectively of the scattering function, equals the AWGN channel's infinite bandwidth capacity. The wideband slope achieved by TF-PPM is found to be zero, irrespectively of the channel's scattering function, even in the presence of perfect receive channel state information. Our proof techniques use the fact that underspread fading channels have a highly structured set of eigenfunctions and a property of orthogonal signaling schemes first presented in Butman and Klass, Jet Propulsion Lab., Tech. Rep., 1973.

The differential entropy of a random variable (or vector) can be expressed as the integral over signal-to-noise ratio (SNR) of the minimum mean-square error (MMSE) of estimating the variable (or vector) when observed in additive Gaussian noise. This representation sidesteps Fisher's information to provide simple and insightful proofs for Shannon's entropy power inequality (EPI) and two of its variations: Costa's strengthened EPI in the case in which one of the variables is Gaussian, and a generalized EPI for linear transformations of a random vector due to Zamir and Feder.

Cooperative strategies for a wireless network where a source is oblivious to the existence of another close by terminal, who can potentially serve as a relay for the source's transmission, are considered. Robust protocols which improve upon the performance when the relay is present, but do not degrade performance in the relay's absence with respect to the case where this absence is known to the source a-priori are identified. Finally, analysis is carried out to gain insight on how the performance of these cooperative protocols depends on the system parameters

We consider the problem of rate-R channel coding with causal/non-causal side information at the transmitter, under an additional requirement of minimizing the amount of information that can be learned from the channel output about the state sequence, which is defined in terms of the equivocation E (i.e., the mutual information between the state sequence and the channel output sequence). A single-letter characterization is provided for the achievable region of pairs {(R, E)}. Explicit results for the Gaussian case (Costa's dirty-paper channel) are derived in full detail.

In this paper we investigate the achievable rate of a system that includes a nomadic transmitter with several antennas, which is received by multiple agents, each with a single antenna, suffering independent channel coefficients and additive Gaussian noises. Since the transmitter is nomadic, the agents do not have any decoding ability. These agents process their channel observations and forward it to the final destination through lossless links with a fixed given capacity. Assuming Gaussian signalling, we get lower and upper bounds on the achievable rates, and demonstrate the achievability of the full multiplexing gain. We also extend the model to address multi-user systems. The asymptotic setting with numbers of agents and transmitter's antennas taken to infinity is examined, and the incompetence of the simple compression when compared to a Wyner-Ziv scheme is demonstrated. For finite setting, an upper-bound is derived, which turns out to be quite tight when compared to the Wyner-Ziv achievable rate, even for a rather small 4 × 4 system.

Fountain codes have been successfully employed for reliable and efficient transmission of information via erasure channels with unknown erasure rates. This paper introduces the notion of fountain capacity for arbitrary channels, and shows that it is equal to the conventional Shannon capacity for stationary memoryless channels. In contrast, when the channel is not stationary or has memory, Shannon capacity and fountain capacity need not be equal

The performance of maximum-likelihood (ML) decoded binary linear block codes is addressed via the derivation of tightened upper bounds on their decoding error probability. The upper bounds on the block and bit error probabilities are valid for any memoryless, binary-input and output-symmetric communication channel. The effectiveness of these bounds is exemplified for ensembles of turbo-like codes

In this paper we discuss a two user Multi-Antenna Gaussian broadcast channel with common messages and explore the achievable region suggested by Jindal and Goldsmith. We present simple outer-bounds which are shown to be tight at certain regions. We investigate the aligned channel with common messages and show that beyond a certain threshold on the common rate, the achievable region coincides with the capacity region and we also give a full characterization of the capacity region of the degraded message sets case.

We compare the rates of one-shot and iterative conferencing in a cooperative Gaussian relay channel. The relay and receiver cooperate via a conference, as introduced by Willems, in which they exchange a series of communications over orthogonal links. Under one-shot conferencing, decode-and-forward (DF) is capacity-achieving when the relay has a strong channel. On the other hand, Wyner-Ziv compress-and-forward (CF) approaches the cut-set bound when the conference link capacity is large. To contrast with one-shot conferencing, we consider a two-round iterative conference scheme; it comprises CF in the first round, and DF in the second. When the relay has a weak channel, the iterative scheme is disadvantageous. However, when the relay channel is strong, iterative cooperation, with optimal allocation of conferencing resources, outperforms one-shot cooperation provided that the conference link capacity is large. When precise allocation of conferencing resources is not possible, we consider iterative cooperation with symmetric conference links, and show that the iterative scheme still surpasses one-shot cooperation, albeit under more restricted conditions.

We present a setting for decoding of low-density parity-check (LDPC) codes jointly with channel estimation, which is suitable for transmission over memoryless compound channels. We show that the performance of the combined scheme can be rigorously evaluated by means of density evolution, and focus on density evolution as a tool for designing a channel estimator that matches not only to the channel, but also to the LDPC ensemble, as well. The utility of the concept is exemplified for a compound binary symmetric channel and an unknown phase additive white Gaussian channel.

We present simple, practical codes designed for the binary and Gaussian dirty-paper channels. We show that the dirty-paper decoding problem can be transformed into an equivalent multiple-access decoding problem, for which we apply superposition coding. Our concept is a generalization of the nested lattices approach of Zamir, Shamai, and Erez. In a theoretical setting, our constructions are capable of achieving capacity using random component codes and maximum-likelihood decoding. We also present practical implementations of the constructions, and simulation results for both dirty-paper channels. Our results for the Gaussian dirty-paper channel are on par with the best known results for nested lattices. We discuss the binary dirty- tape channel, for which we present a simple, effective coding technique. Finally, we propose a framework for extending our approach to general Gel'fand-Pinsker channels.

Contemporary wireless systems combine aspects of network theory such as scheduling, throughput, and delay as well as information theory aspects such as capacity, coding, and power control. Design of such systems requires joint optimization of both network and physical layers. In this paper, we analyze a single-user communication system composed of a transmitter preceded by a queue used for retransmissions, Gaussian block-fading channel, and a receiver. The system average delay is optimized by using combined power/rate control under average power constraints. Dynamic programming is used for calculating the optimized policies using numerical analysis as well as analytic analysis for asymptotically large buffer size. Asymptotic results are obtained for all combinations of fixed or variable power and rate controls. The most important result extends the "water-filling" result for systems with average delay constraint

We consider a wireless network where a remote source sends information to one of two colocated users, and where the second user can serve as a relay. The source's transmission is subjected to quasi-static flat Rayleigh fading, while the transmission of the relay experiences a fixed amplitude gain with a uniform random phase, capturing its close proximity to the destination. All communications share the same time/bandwith resources, and perfect channel state information is known only to the receivers. We propose relaying protocols which are based on Wyner-Ziv quantization at the relay, and demonstrate their high efficiency (in terms of expected throughput) with respect to previously reported relaying schemes based on amplify-and-forward and decode-and-forward. A salient feature of these protocols is that the relay need not know the actual fading gain experienced by the destination in order to perform the quantization. We also consider a hybrid amplify-quantize-decode-and-forward scheme which exhibits superior performance.

A two-hop relay fading channel is considered, where only decoders possess perfect channel state information (CSI). Various relaying protocols and broadcasting strategies are studied. The main focus of this work is on simple relay transmission scheduling schemes. For decode-and-forward (DF) relaying, the simple relay cannot buffer multiple packets, nor can it reschedule retransmissions. This gives rise to consideration of other relaying techniques, such as amplify-and-forward (AF), where a maximal broadcasting achievable rate is analytically derived. A quantize-and-forward (QF) relay, coupled with a single-level code at the source, uses codebooks matched to the received signal power and performs optimal quantization. This is simplified by a hybrid amplify-QF (AQF) relay, which performs scaling, and single codebook quantization on the input. It is shown that the latter is optimal by means of throughput on the relay-destination link, while maintaining a lower coding complexity than the QF setting. A further extension of the AQF allows the relay to perform successive refinement, coupled with a matched multilevel code. Numerical results show that for high signal-to-noise ratios (SNRs), the broadcast approach over AF relay may achieve higher throughput gains than other relaying protocols that are numerically tractable

The Gaussian multiple-input multiple-output (MIMO) broadcast channel (BC) is considered. The dirty-paper coding (DPC) rate region is shown to coincide with the capacity region. To that end, a new notion of an enhanced broadcast channel is introduced and is used jointly with the entropy power inequality, to show that a superposition of Gaussian codes is optimal for the degraded vector broadcast channel and that DPC is optimal for the nondegraded case. Furthermore, the capacity region is characterized under a wide range of input constraints, accounting, as special cases, for the total power and the per-antenna power constraints

This paper proposes a practical scheme to transmit reliable information through a noisy symmetric DMC using limited noiseless feedback. The ratio of feedback rate to feedforward rate is a design parameter that can be selected from zero to 1 - C, where C is the capacity of the channel. The proposed scheme uses a concatenation of low-density parity-check codes, belief propagation, and a noisy version of the closed-loop iterative doping algorithm, previously proposed by the authors for data compression using linear codes. Our scheme takes advantage of the availability of a modicum of feedback to achieve very small block error rates