This paper studies random-coding error exponents of randomised list decoding, in which the decoder randomly selects $L$ messages with probabilities proportional to the decoding metric of the codewords. The exponents (or bounds) are given for mismatched, and then particularised to matched and universal decoding metrics. Two regimes are studied: for fixed list size, we derive an ensemble-tight random-coding error exponent, and show that, for the matched metric, it does not improve the error exponent of ordinary decoding. For list sizes growing exponentially with the block-length, we provide a non-trivial lower bound to the error exponent that is tight at high rates under the matched metric.

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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.

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We present a unifying framework that bridges Bayesian asymptotics and information theory to analyze the asymptotic Shannon capacity of general large-scale MIMO channels including ones with nonlinearities or imperfect hardware.We derive both an analytic capacity formula and an asymptotically optimal input distribution in the large-antenna regime, each of which depends solely on the single-output channel’s Fisher information through a term we call the(tilted) Jeffreys factor. We demonstrate how our method applies broadly to scenarios with clipping, coarse quantization (including 1-bit ADCs), phase noise, fading with imperfect CSI, and even optical Poisson channels. Our asymptotic analysis motivates a practical approach to constellation design via a compander-like transformation. Furthermore, we introduce a low-complexity receiver structure that approximates the log-likelihood by quantizing the channel outputs into finitely many bins, enabling near-capacity performance with computational complexity independent of the output dimension. Numerical results confirm that the proposed method unifies and simplifies many previously intractable MIMO capacity problems and reveals how the Fisher information alone governs the channel’s asymptotic behavior.

We study universal decoding over unknown discrete additive channels. Aiming at low-complexity decoders, we study variants of noise-guessing decoders that use estimators for the probability of a noise sequence when the actual channel law is unknown. A deterministic version produces noise sequences in a fixed order, and a new randomised version draws them at random, until finding one that, subtracted from the received sequence, results in a valid codeword. In all cases, we give sufficient conditions on the family of parametric channels for the decoding strategies to be random-coding universal, and derive upper bounds for their complexity. We give examples of common families of channels in which these conditions are satisfied, and a numerical example illustrates the proposed method’s performance.

Motivated by distributed multi-armed bandit, we introduce the two-player L-armed "bandit channel" with independent Bernoulli rewards, where users choose actions (input) and observe rewards (output). We derive a bound on the error exponent as a minimum of two types of errors that we then analyze separately. We further study the mutual information and show that the support of the optimal input distribution is a subset of the three best arms. We also compute the Shannon upper bound of the capacity as a special case of a two-way channel. In the case of identically distributed arms, the upper and lower bounds are very close.

In this work, we investigate the capacity of multi-antenna fading channels with 1-bit quantized output per receive antenna. Specifically, leveraging Bayesian statistical tools, we analyze the asymptotic regime with a large number of receive antennas. In the coherent case, where the channel state information (CSI) is known at the receiver’s side, we characterize the asymptotic capacity and derive the exact scaling in the extreme regimes of signal-to-noise ratio (SNR) and the number of transmit antennas. In the non-coherent case, where the CSI is unknown but remains constant duringTsymbol periods, we first obtain the exact asymptotic capacity for$T\le 3$. Then, we propose a scheme involving uniform signaling in the covariance space and derive a non-asymptotic lower bound on the capacity for an arbitrary block sizeT. Furthermore, we propose a genie-aided upper bound where the channel is revealed to the receiver. We show that the upper and lower bounds coincide whenTis large. In the low SNR regime, we derive the asymptotic capacity up to a vanishing term, which, remarkably, matches our capacity lower bound.

We study the problem of universal decoding over memoryless channels with a decoder based on the Krichevsky-Trofimov estimator. We show that this decoder is random-coding universal for codebooks of any size, i.e., despite being ignorant of the channel in use, it has asymptotically the same random-coding error exponent as the optimal maximum-likelihood decoder for that channel. Then, we incorporate this decoding rule in schemes to decode practical linear block codes and convolutional codes when the channel is unknown to the receiver. Numerical results show that efficient performance can be achieved even for moderate blocklength or constraint length.

In this work, we investigate the capacity of a block fading one-bit multi-antenna channel without a priori channel state information. We consider the asymptotic regime with a large number of receive antennas. We show that the capacity scales as$\frac{1}{2}\begin{pmatrix}T\\ 2\end{pmatrix}\log(\alpha_{\text{snr},T}n_{\mathrm{r}})$under the peak power constraint$\mathsf{snr}$and with coherence block size$T$. In particular, we derive the exact form of$\alpha_{\text{snr},T}$for$T=2$and$T=3$. Furthermore, for an arbitrary value of$T$, we derive a lower bound of$\alpha_{\text{snr},T}$by proposing a low-complexity signaling scheme. We also obtain a closed-form expression of$\alpha_{\text{snr},T}$when$\mathsf{snr}$is small.

We address the problem of data detection for the multiple-input multiple-output (MIMO) channel employing one-bit quantizers at the receiver, taking into account different settings of channel state information (CSI) at the receiver (CSIR). In the first part and under perfect CSI conditions, we propose a two-step low-complexity data detection algorithm that reduces the maximum likelihood (ML) search space. The key idea is based on constructing a list of constellation points exploiting the Hessian matrix of the log-likelihood function. We convert the original detection problem under binary observations into the classical integer least-squares optimization enabling direct use of efficient sphere-decoding algorithms. This method is then extended to the multi-bit case along with an assessment of the computational complexity. In the second part, we focus on a real channel model and assume only the availability of statistical CSIR. We formulate the optimal detection metric under a pilot training scheme and present the main challenges in its evaluation, then employ the Laplace method to retrieve an approximation in closed form. We demonstrate through numerical experiments near-optimality of our proposed solutions in terms of vector error rates with respect to oracle lower bounds on their corresponding ML metric. Finally, we also investigate the performance in practical spatially correlated massive MIMO channels.

In this work, a low-complexity hybrid scheme is presented for a wireless network assisted by a reconfigurable intelligent surface (RIS), where channel estimation is required for only a subset of the elements. Specifically, in order to reduce the channel training overhead and boost the performance of the RIS-aided network, the RIS is partitioned in two sub-surfaces, which are sequentially activated to assist the communication. The elements of the first sub-surface align their phase shifts, based on the acquired channel state information (CSI) from a channel training period, whereas the elements of the second sub-surface randomly rotate the phase of the incident signals. The performance of the proposed scheme is investigated under the effect of imperfect CSI acquisition at the RIS. Analytical expressions for the outage probability are derived and useful insights on the optimal configuration of the RIS are provided. We show that, by optimizing the number of elements that need to be estimated, the proposed scheme provides significant performance gains and overcomes the limitations caused by the imperfect CSI acquisition.

We address in this article the uplink transmission in a multiple-input multiple-output channel employing 1-bit analog-to-digital converters at the base station, assuming no a priori channel state information. In particular, we investigate under the original "probit" statistical model, the problems of channel estimation and data detection by first formulating them as binary classification procedures based on the cross-entropy loss. Under perfect CSI, the proposed data detection scheme relaxes the exhaustive search requirement to a convex problem with a box boundary constraint that can be solved using gradient descent methods. Achievable mismatched rates of proposed metrics are evaluated with the generalized mutual information and symbol error rates are presented. Simulation results show that the proposed channel estimation scheme under the probit model does not exhibit any instability with imperfect CSI in comparison with the Bussgang linear minimum mean square error estimator.

In this paper, we investigate the performance of multiple-input multiple-output (MIMO) fading channels assisted by a reconfigurable intelligent surface (RIS), through the employment of partition-based RIS schemes. The proposed schemes are implemented without requiring any channel state information knowledge at the transmitter side; this characteristic makes them attractive for practical applications. In particular, the RIS elements are partitioned into sub-surfaces, which are periodically modified in an efficient way to assist the communication. Under this framework, we propose two low-complexity partition-based schemes, where each sub-surface is adjusted by following an amplitude-based or a phase-based approach. Specifically, the activate-reflect (AR) scheme activates each sub-surface consecutively, by changing the reflection amplitude of the corresponding elements. On the other hand, the flip-reflect (FR) scheme adjusts periodically the phase shift of the elements at each sub-surface. Through the sequential reconfiguration of each sub-surface, an equivalent parallel channel in the time domain is produced. We analyze the performance of each scheme in terms of outage probability and provide expressions for the achieved diversity-multiplexing tradeoff. Our results show that the asymptotic performance of the considered network under the partition-based schemes can be significantly enhanced in terms of diversity gain compared to the conventional case, where a single partition is considered. Moreover, the FR scheme always achieves the maximum multiplexing gain, while for the AR scheme this maximum gain can be achieved only under certain conditions with respect to the number of elements in each sub-surface.

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.

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.

We propose novel compression algorithms for time-varying channel state information (CSI) in wireless communications. The proposed scheme combines (lossy) vector quantisation and (lossless) compression. First, the new vector quantisation technique is based on a class of parametrised companders applied on each component of the normalised CSI vector. Our algorithm chooses a suitable compander in an intuitively simple way whenever empirical data are available. Then, the sequences of quantisation indices are compressed using a context-tree-based approach. Essentially, we update the estimate of the conditional distribution of the source at each instant and encode the current symbol with the estimated distribution. The algorithms have low complexity, are linear-time in both the spatial dimension and time duration, and can be implemented in an online fashion. We run simulations to demonstrate the effectiveness of the proposed algorithms in such scenarios.

We consider the joint constellation design problem for the noncoherent multiple-input multiple-output multiple-access channel (MAC). By analyzing the noncoherent maximum-likelihood detection error, we propose novel design criteria so as to minimize the error probability. As a baseline approach, we adapt several existing design criteria for the point-to-point channel to the MAC. Furthermore, we propose new design criteria. Our first proposed design metric is the dominating term in nonasymptotic lower and upper bounds on the pairwise error probability exponent. We give a geometric interpretation of the bound using Riemannian distance in the manifold of Hermitian positive definite matrices. From an analysis of this metric at high signal-to-noise ratio, we obtain further simplified metrics. For any given set of constellation sizes, the proposed metrics can be optimized over the set of constellation symbols. Motivated by the simplified metric, we propose a simple constellation construction consisting inpartitioninga single-user constellation. We also provide a generalization of our previously proposed construction based onprecodingindividual constellations of lower dimensions. For a fixed joint constellation, the design metrics can be further optimized over the per-user transmit power, especially when the users transmit at different rates. Considering unitary space-time modulation, we investigate the option of building each individual constellation as a set of truncated unitary matrices scaled by the respective transmit power. Numerical results show that our proposed metrics are meaningful, and can be used as objectives to generate constellations through numerical optimization that perform better, for the same transmission rate and power constraint, than a common pilot-based scheme and the constellations optimized with existing metrics.

Distributed computing has become one of the most important frameworks in dealing with large computation tasks. In this paper, we propose a systematic construction of coded computing schemes for MapReduce-type distributed systems. The construction builds upon placement delivery arrays (PDA), originally proposed by Yanet al.for coded caching schemes. The main contributions of our work are three-fold. First, we identify a class of PDAs, calledComp-PDAs, and show how to obtain a coded computing scheme from any Comp-PDA. We also characterize the normalized number of stored files (storage load), computed intermediate values (computation load), and communicated bits (communication load), of the obtained schemes in terms of the Comp-PDA parameters. Then, we show that the performance achieved by Comp-PDAs describing Maddah-Ali and Niesen’s coded caching schemes matches a new information-theoretic converse, thus establishing the fundamental region of all achievable performance triples. In particular, we characterizeallthe Comp-PDAs achieving the pareto-optimal storage, computation, and communication (SCC) loads of the fundamental region. Finally, we investigate the file complexity of the proposed schemes, i.e., the smallest number of files required for implementation. In particular, we describe Comp-PDAs that achieve pareto-optimal SCC triples with significantly lower file complexity than the originally proposed Comp-PDAs.

We propose a class of parametric channel models that we call generalized Gaussian model (GGM). In particular, given the input, the output is Gaussian with both mean and covariance depending on the input. More general than the conventionallinear model, the GGM can capture nonlinearities and self-interference present in more and more wireless communication systems. We focus on three key problems. First, we propose a data-driven model identification algorithm that uses training data to fit the underlying channel with a GGM. This is a generalization of the conventional channel estimation procedure. Second, for an identified GGM, we investigate the receiver design problem and propose several detection metrics. Third, we are interested in the capacity bounds of the GGM. Both the mismatched lower bound and duality upper bound are proposed. Finally, we apply the GGM to fit the multiple-input multiple-output phase-noise channel. Numerical results show the near optimality of the model identification and detection algorithms.

In a multiple-input multiple-output (MIMO) broad-cast channel (BC), the difference in spatial transmit correlation matrices of different users is called transmit correlation diversity. Recently, several works have extended this concept beyond its original scope, to include channels whose transmit correlation matrices have non-overlapping eigenspaces. In contrast to earlier analyses of overlapping eigenspaces that were mostly described in terms of degrees-of-freedom, this work presents achievable rate regions. These achievable regions are derived by rate-splitting, product superposition, or a combination thereof. Our rate expressions make explicit the contribution of the common parts and individual (non-overlapping) parts of the correlation eigenspaces toward the achievable rate region. As a by-product, a result of Hassibi and Hochwald on MIMO channel training is extended to channels with spatial correlation.

This paper investigates adaptive streaming of one or multiple tiled 360 videos from a multi-antenna base station (BS) to one or multiple single-antenna users, respectively, in a multi-carrier wireless system. We aim to maximize the video quality while keeping rebuffering time small via encoding rate adaptation at each group of pictures (GOP) and transmission adaptation at each (transmission) slot. To capture the impact of field-of-view (FoV) prediction, we consider three cases of FoV viewing probability distributions, i.e., perfect, imperfect, and unknown FoV viewing probability distributions, and use the average total utility, worst average total utility, and worst total utility as the respective performance metrics. In the single-user scenario, we optimize the encoding rates of the tiles, encoding rates of the FoVs, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. In the multi-user scenario, we adopt rate splitting with successive decoding and optimize the encoding rates of the tiles, encoding rates of the FoVs, rates of the common and private messages, and transmission beamforming vectors for all subcarriers to maximize the total utility in each case. Then, we separate the challenging optimization problem into multiple tractable problems in each scenario. In the single-user scenario, we obtain a globally optimal solution of each problem using transformation techniques and the Karush-Kuhn-Tucker (KKT) conditions. In the multi-user scenario, we obtain a KKT point of each problem using the concave-convex procedure (CCCP). Finally, numerical results demonstrate that the proposed solutions achieve notable gains in quality, quality variation, and rebuffering time over existing schemes in all three cases. To the best of our knowledge, this is the first work revealing the impact of FoV prediction on the performance of adaptive streaming of tiled 360 videos.

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.

The high-SNR capacity of the noncoherent MIMO channel has been derived for the case of independent and identically distributed (IID) Rayleigh block fading by exploiting the Gaussianity of the channel matrix. This implies the optimal degrees of freedom (DoF), i.e., the capacity pre-log factor. Nevertheless, as far as the optimal DoF is concerned, IID Rayleigh fading is apparently a sufficient but not necessary condition. In this paper, we show that the optimal DoF for the IID Rayleigh block fading channel is also the optimal DoF for a more general class of generic block fading channels, in which the random channel matrix has finite power and finite differential entropy. Our main contribution is a novel converse proof based on the duality approach.

We consider the joint constellation design problem for noncoherent multiple-input multiple-output multiple-access channels. By analyzing the noncoherent maximum-likelihood detection error, we propose novel design criteria so as to minimize the error probability. For any given set of constellation sizes, the proposed metrics can be optimized over the set of signal matrices. Based on these criteria, we propose a simple and efficient construction consisting in partitioning a single-user constellation. Numerical results show that our proposed metrics are meaningful, and can be used as objectives to generate constellations through numerical optimization that perform better, for the same transmission rate and power constraint, than a common pilot-based scheme and the constellations optimized with existing metrics.

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.

Placement delivery arrays for distributed computing (Comp-PDAs) have recently been proposed as a framework to construct universal computing schemes for MapReduce-like systems. In this work, we extend this concept to systems with straggling nodes, i.e., to systems where a subset of the nodes cannot accomplish the assigned map computations in due time. Unlike most previous works that focused on computing linear functions, our results are universal and apply for arbitrary map and reduce functions. Our contributions are as follows. Firstly, we show how to construct a universal coded computing scheme for MapReduce-like systems with straggling nodes from any given Comp-PDA. We also characterize the storage and communication loads of the resulting scheme in terms of the Comp-PDA parameters. Then, we prove an information-theoretic converse bound on the storage-communication (SC) tradeoff achieved by universal computing schemes with straggling nodes. We show that the information-theoretic bound matches the performance achieved by the coded computing schemes with straggling nodes corresponding to the Maddah-Ali and Niesen (MAN) PDAs, i.e., to the Comp-PDAs describing Maddah-Ali and Niesen's coded caching scheme. Interestingly, the MAN-PDAs are optimal for any number of straggling nodes. This implies that the map phase of optimal coded computing schemes does not need to be adapted to the number of stragglers in the system. We show that the points that lie exactly on the fundamental SC tradeoff cannot be achieved with Comp-PDAs that require smaller number of files than the MAN-PDAs. This is however possible for some of the points that lie close to the SC tradeoff. For these latter points, the decrease in the requested number of files can be exponential in the number of nodes of the system. We also model the total execution time, and numerically show that the active set size should be chosen to balance the duration of the map phase and the durations of the shuffle and reduce phases.

In this paper, we propose a practical structured constellation for non-coherent communication with a single transmit antenna over Rayleigh flat and block fading channel without instantaneous channel state information. The constellation symbols belong to the Grassmannian of lines and are defined up to a complex scaling. The constellation is generated by partitioning the Grassmannian of lines into a collection of bent hypercubes and defining a mapping onto each of these bent hypercubes such that the resulting symbols are approximately uniformly distributed on the Grassmannian. With a reasonable choice of parameters, this so-called cube-split constellation has higher packing efficiency, represented by the minimum distance, than the existing structured constellations. Furthermore, exploiting the constellation structure, we propose low-complexity greedy symbol decoder and log-likelihood ratio computation, as well as an efficient way to associate it to a multilevel code with multistage decoding. Numerical results show that the performance of the cube-split constellation is close to that of a numerically optimized constellation and better than other structured constellations. It also outperforms a coherent pilot-based scheme in terms of error probability and achievable data rate in the regime of short coherence time and large constellation size.

We consider the non-coherent single-input multiple-output (SIMO) multiple access channel with general signaling under spatially correlated Rayleigh block fading. We propose a novel soft-output multi-user detector that computes an approximate marginal posterior of each transmitted signal using only the knowledge about the channel distribution. Our detector is based on expectation propagation (EP) approximate inference and has polynomial complexity in the number of users, number of receive antennas and channel coherence time. We also propose two simplifications of this detector with reduced complexity. With Grassmannian signaling, the proposed detectors outperform a state-of-the-art non-coherent detector with projection-based interference mitigation. With pilot-assisted signaling, the EP detector outperforms, in terms of symbol error rate, some conventional coherent pilot-based detectors, including a sphere decoder and a joint channel estimation-data detection scheme. Our EP-based detectors produce accurate approximates of the true posterior leading to high achievable sum-rates. The gains of these detectors are further observed in terms of the bit error rate when using their soft outputs for a turbo channel decoder.

The optimal storage-computation tradeoff is characterized for a MapReduce-like distributed computing system with straggling nodes, where only a part of the nodes can be utilized to compute the desired output functions. The result holds for arbitrary output functions and thus generalizes previous results that restricted to linear functions. Specifically, in this work, we propose a new information-theoretical converse and a new matching coded computing scheme, that we call coded computing for straggling systems (CCS).

We consider coded caching over the fading broadcast channel, where the users, equipped with a memory of finite size, experience asymmetric fading statistics. It is known that a naive application of coded caching over the channel at hand performs poorly especially in the regime of a large number of users due to a vanishing multicast rate. We overcome this detrimental effect by a careful design of opportunistic scheduling policies such that some utility function of the long-term average rates should be maximized while balancing fairness among users. In particular, we propose a threshold-based scheduling that requires only statistical channel state information and one-bit feedback from each user. More specifically, each user indicates via feedback whenever its SNR is above a threshold determined solely by the fading statistics and the fairness requirement. Surprisingly, we prove that this simple scheme achieves the optimal utility in the regime of a large number of users.

A major practical limitation of the Maddah-Ali-Niesen coded caching techniques is their high subpacketization level. For the simple network with a single server and multiple users, Yan et al. proposed an alternative scheme with the so-called placement delivery arrays (PDA). Such a scheme requires slightly higher transmission rates but significantly reduces the subpack-etization level. In this paper, we extend the PDA framework and propose three low-subpacketization schemes for combination networks, i.e., networks with a single server, multiple relays, and multiple cache-aided users that are connected to subsets of relays. One of the schemes achieves the cutset lower bound on the link rate when the cache memories are sufficiently large. Our other two schemes apply only to resolvable combination networks. For these network and for a wide range of cache sizes, the new schemes perform closely to the coded caching schemes that directly apply Maddah-Ali-Niesen scheme while having significantly reduced subpacketization levels.

The optimal degree-of-freedom (DoF) region of the non-coherent multiple-access channels is still unknown in general. In this paper, we make some progress by deriving the entire optimal DoF region in the case of the two-user single-input multiple-output (SIMO) generic block fading channels. The achievability is based on a simple training-based scheme. The novelty of our result lies in the converse using a genie-aided bound and the duality upper bound. As a by-product, our result generalizes previous proofs for the single-user Rayleigh block fading channels.

We consider a MapReduce-like distributed computing system. We derive a lower bound on the communication cost for any given storage and computation costs. This lower bound matches the achievable bound we proposed recently. As a result, we completely characterize the optimal tradeoff between the storage, the computation, and the communication. Our result generalizes the previous one by Li et at. to also account for the number of computed intermediate values.

We consider coded caching over the fading broadcast channel, where the users, equipped with a memory of finite size, experience asymmetric fading statistics. It is known that a naive application of coded caching over the channel at hand performs poorly especially in the regime of a large number of users due to a vanishing multicast rate. We overcome this detrimental effect by a careful design of opportunistic scheduling policies such that some utility function of the long-term average rates should be maximized while balancing fairness among users. In particular, we propose a threshold-based scheduling that requires only statistical channel state information and one-bit feedback from each user. More specifically, each user indicates via feedback whenever its SNR is above a threshold determined solely by the fading statistics and the fairness requirement. Surprisingly, we prove that this simple scheme achieves the optimal utility in the regime of a large number of users. Numerical examples show that our proposed scheme performs closely to the scheduling with full channel state information, but at a significantly reduced complexity.

We consider the multiple-input multiple-output (MIMO) communication channel impaired by phase noises at both the transmitter and receiver. We focus on the maximum likelihood (ML) detection problem for uncoded single-carrier transmission. We derive an approximation of the likelihood function, based on which we propose an efficient detection algorithm. The proposed algorithm, named self-interference whitening (SIW), consists in: 1) estimating the self-interference caused by the phase noise perturbation; 2) whitening the said interference; and 3) detecting the transmitted vector. While the exact ML solution is computationally intractable, we construct a simulation-based lower bound on the error probability of ML detection. Leveraging this lower bound, we perform extensive numerical experiments demonstrating that SIW is, in most cases of interest, very close to optimal with moderate phase noise. More importantly and perhaps surprisingly, such near-ML performance can be achieved by applying only twice the nearest neighbor detection algorithm. In this sense, our results reveal a striking fact: near-ML detection of phase noise corrupted MIMO channels can be done as efficiently as for conventional MIMO channels without phase noise.

We consider the transmission of a common message from a transmitter to three receivers over a broadcast channel, referred to as a multicast channel in this case. All the receivers are allowed to cooperate with each other over full-duplex non-orthogonal cooperation links. We investigate the information-theoretic upper and lower bounds on the transmission rate. In particular, we propose a three-receiver fully interactive cooperation scheme (3FC) based on superpositions of compress-forward (CF) and decode-forward (DF) at the receivers. In the 3FC scheme, the receivers interactively perform CF simultaneously to initiate the scheme, and then DF sequentially to allow a correlation of each layer of the DF superposition in cooperation with the transmitter toward the next receiver in the chain to improve the achievable rate. The analysis leads to a closed-form expression that allows for numerical evaluation, and also gives some insight on key points to design interactive schemes. The numerical results provided in the Gaussian case show that the proposed scheme outperforms the existing schemes and show the benefits of interaction.

The state-dependent K-user memoryless broadcast channel (BC) with state feedback is investigated. We propose a novel transmission scheme and derive its corresponding achievable rate region, which, compared with some general schemes that deal with feedback, has the advantage of being relatively simple and thus is easy to evaluate. In particular, we show that the proposed scheme achieves the capacity region of the symmetric erasure BC with an arbitrary input alphabet size. For the fading Gaussian BC, numerical results show that the proposed scheme outperforms existing schemes in terms of symmetric rate. Our analysis also proves its optimality at high signal-to-noise ratio, in terms of degrees of freedom.

We consider the content delivery problem in a fading multi-input single-output channel with cache-aided users. We are interested in the scalability of the equivalent content delivery rate when the number of users, K, is large. Analytical results show that, using coded caching and wireless multicasting, without channel state information at the transmitter, linear scaling of the content delivery rate with respect to K can be achieved in some different ways. First, if the multicast transmission spans over L independent sub-channels, e.g., in quasi-static fading if L = 1, and in block fading or multi-carrier systems if L 1, linear scaling can be obtained, when the product of the number of transmit antennas and the number of sub-channels scales logarithmically with K. Second, even with a fixed number of antennas, we can achieve the linear scaling with a threshold-based user selection requiring only one-bit feedbacks from the users. When CSIT is available, we propose a mixed strategy that combines spatial multiplexing and multicasting. Numerical results show that, by optimizing the power split between spatial multiplexing and multicasting, we can achieve a significant gain of the content delivery rate with moderate cache size.

We consider content delivery over fading broadcast channels. A server wants to transmit K files to K users, each equipped with a cache of finite size. Using the coded caching scheme of Maddah-Ali and Niesen, we design an opportunistic delivery scheme where the long-term sum content delivery rate scales with the number of users in the system. The proposed delivery scheme combines superposition coding together with appropriate power allocation across sub-files intended to different subsets of users. We analyze the long- term average sum content delivery rate achieved by two special cases of our scheme: 1) a selection scheme that chooses the subset of users with the largest weighted rate, and 2) a baseline scheme that transmits to all K users using the scheme of Maddah-Ali and Niesen. We prove that coded caching with appropriate user selection is scalable since it yields a linear increase of the average sum content delivery rate.

We consider the multicast channel (MC), in which a transmitter broadcasts only a common message to two receivers. In our previous work, we have derived information-theoretic upper and lower bounds and showed that the two-round receiver cooperation (2RC) scheme, in which the receivers interactively perform compress-forward and then decode-forward, improves the MC achievable rate over different schemes from the literature, in the general case of full-duplex bi-directional non-orthogonal cooperation links. In this paper, we concentrate on the orthogonal cooperation to identify the properties of this interactive cooperation. We show that the full-duplexing between the cooperation links is not essential to take advantage of most of the gain offered by the 2RC full-duplex scheme, while the full-duplexing between the MC and the cooperation links brings most of the gain.

In this paper, we investigate the two-user erasure broadcast channel in which only one of the receivers feeds the output back to the transmitter. We propose a transmission scheme based on joint source-channel coding and show that the achievable rate region can be strictly larger than the ones previously known for this channel. In particular, the proposed scheme is partially optimal in the sense that the boundary of the achievable region overlaps in part with the boundary of the capacity region of the same channel with two-sided feedback. Thus, we identify a condition under which one-sided feedback is as good as two-sided feedback. Our result also demonstrates the limitation of bit-wise linear transmission schemes previously used for such channels.

This paper investigates the two-user Erasure Broadcast Channel (EBC), where the Channel State Information (CSI) is fully known at the destination, while the transmitter is only aware of the strictly causal CSI by state feedback and an estimate of the instantaneous CSI. We propose a novel transmission scheme that exploits both the delayed and the instantaneous CSI. Our scheme includes both the case with full CSI and the case with delayed CSI as special cases. We also derive a new outer bound region for this channel. For the symmetric EBC, we show that our scheme is capacity achieving in some nontrivial cases. Since both the inner and outer bound regions are characterized with linear constraints, numerical evaluation can be done easily.

In this paper, we study the two-user non-coherent multiple-input multiple-output broadcast channel with spatially correlated Rayleigh block fading. We propose a scheme to exploit the statistical channel state information (CSI), namely, the knowledge of the covariance matrix, and derive the corresponding achievable degrees of freedom region. The main idea of the proposed scheme is based on rate-splitting, additive superposition coding, and channel training. Our result shows that statistical CSI can play an important role in enhancing the degrees of freedom of a non-coherent broadcast channel.

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.

We study a content delivery problem in the context of a K-user erasure broadcast channel such that a content providing server wishes to deliver requested files to users, each equipped with a cache of a finite memory. Assuming that the transmitter has state feedback and user caches can be filled during off-peak hours reliably by decentralized cache placement, we characterize the achievable rate region as a function of the memory sizes and the erasure probabilities. The proposed delivery scheme, based on the broadcasting scheme proposed by Wang and Gatzianas et al., exploits the receiver side information established during the placement phase. Our results can be extended to centralized cache placement as well as multi-antenna broadcast channels with state feedback.

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.

We consider communication over heterogeneous parallel channels, where a transmitter is connected to two users via two parallel channels: a multiple-input multiple-output (MIMO) broadcast channel (BC) and a noiseless rate-limited multicast channel. We characterize the optimal degrees of freedom (DoF) region of this setting when the transmitter has delayed channel state information (CSIT) regarding the MIMO BC. Our results show that jointly coding over the two channels strictly outperforms simple channel aggregation and can even achieve the instantaneous CSIT performance with completely outdated CSIT on the MIMO BC in the sum DoF sense; this happens when the multicast rate of the second channel is larger than a certain threshold. The main idea is to send information over the MIMO BC at a rate above its capacity and then use the second channel to send additional side information to allow for reliable decoding at both receivers. We call this scheme a two-phase overload-multicast strategy. We show that such a strategy is also sum DoF optimal for the K-user MIMO BC with a parallel multicast channel when the rate of the multicast channel is high enough and can again achieve the instantaneous CSIT performance (optimal sum DoF) with completely outdated CSIT. For the regime where the capacity of the multicast channel is small, we propose another joint coding strategy, which is sum DoF optimal.

We study a content delivery problem in a K-user erasure broadcast channel such that a content providing server wishes to deliver requested files to users, each equipped with a cache of a finite size. Assuming that the transmitter has state feedback and user caches can be filled during off-peak hours reliably by the decentralized content placement, we characterize the achievable rate region as a function of the memory sizes and the erasure probabilities for some special cases. The proposed delivery scheme, based on the broadcasting scheme by Wang and Gatzianas et al., exploits the receiver side information established during the placement phase. Our results can be extended to the centralized content placement as well as multi-antenna broadcast channels with state feedback.

In this paper, we investigate the fast fading broadcast relay channel (BRC) with one source (macrocell BS), one relay (smallcell BS), and two destinations (mobile users). We assume that the instantaneous channel state information (CSI) is known only at the receivers' side, whereas the relay can obtain a noiseless CSI feedback from the destinations about the source-destination channels (with delay). An inner bound on the capacity region of this channel is derived based on a partial-decode-compress-forward (PDCF) scheme. The main idea is to let the relay decode a significant portion of the source messages and then generate and forward to the receivers some useful side information using both the decoded messages and the CSI feedback. Numerical results show that, thanks to the feedback, the proposed scheme provides a non-negligible gain over conventional decode-forward/compress-forward schemes in terms of sum-rate performance, especially in the high SNR regime. Quite remarkably, we show that overall transmit power of the macrocell network can be significantly reduced owing to the feedback from users to the smallcell BS.

We investigate the degrees of freedom (DoF) for the two-user multiple-input multiple-output interference channel (MIMO IC) with parallel multicasting channels. Specifically, in addition to the MIMO IC, each transmitter is also connected to both receivers via an out-of-band multicast channel. Our main contribution lies in the characterization of the optimal sum DoF when the channel state information (CSI) on the MIMO IC is available to the transmitters with some delay (delayed CSIT). We show that jointly coding over the parallel multicast channels can achieve higher DoF than channel aggregation does. Furthermore, as long as the rate of the multicast channels is above a certain threshold, delayed CSIT is enough to achieve the same DoF performance as with instantaneous CSIT.

In this paper, we consider the secure communication in a K-user multi-antenna broadcast channel (BC) with state feedback. We characterize the optimal secure degrees of freedom (SDoF) region of multiple-input single-output (MISO) channel. The SDoF region is achievable by a secret key based linear scheme, which generates analog secret keys by sending artificial noise and then performs space-time alignment scheme secured by these secret keys. The optimality is proved by deriving a new outer bound on the capacity region in a systematic way. Interestingly, the proposed outer bounding technique also applies to the erasure broadcast channel and provides a simpler proof as compared to the existing one. Finally, an explicit connection between the multi-antenna BC and the erasure BC is revealed. We show that, with a large number of users, secrecy cannot be guaranteed in the erasure BC, while it comes almost “for free” in the multi-antenna BC when the number of transmit antennas grows accordingly.

The capacity of a class of interference relay channels (IRCs)-the injective semideterministic IRC where the relay can only observe one of the sources-is investigated. We first derive a novel outer bound and two inner bounds which are based on a careful use of each of the available cooperative strategies together with the adequate interference decoding technique. The outer bound extends Telatar and Tse's work, whereas the inner bounds contain several known results in the literature. Our main result is the characterization of the capacity region of the Gaussian class of IRCs studied within a fixed number of bits per dimension, constant gap. The proof relies on the use of the different cooperative strategies in specific SNR regimes due to their complexity. As a matter of fact, this issue reveals the complex nature of the Gaussian IRC where the combination of a single coding scheme for the Gaussian relay and interference channel may not lead to a good coding scheme for this problem, even when the focus is only on capacity within a constant gap over all possible fading statistics.

We consider a slow fading two-hop relay channel where a source terminal communicates with a destination through a layer of relays without a direct link. First, we introduce the notion of time-varying distributed rotation and propose a linear relaying scheme called rotate-and-forward (RF). The main idea is to create a time-varying channel and to convert the spatial diversity to time diversity. It is shown that this scheme achieves the optimal diversity-multiplexing tradeoff (DMT) of the channel with full-duplex relays. While more involved non-linear relaying schemes previously proposed in the literature are optimal in the same setting, we show here that simple linear relaying can also be DMT optimal. Then, we extend the RF scheme to the relay channel with multiple hops where the DMT optimality of the two-antenna case is shown. Finally, we apply the idea of distributed rotation to the decode-and-forward relays. The same diversity order as previous schemes can be achieved with low signaling complexity.

The capacity of a class of Interference Relay Channels (IRC) is investigated. We derive a novel outer and three inner bounds which are based on a careful use of all existing cooperative strategies together with the adequate interference decoding technique. Our main result is the necessity of three different cooperative strategies to achieve a constant gap to the capacity for each SNR regime of the Gaussian IRC. Surprisingly enough, this outcome appears to be in contrast with that of the standard Gaussian RC (Relay Channel) where several cooperative strategies yield constant-gap results in all regimes.

We consider communication over heterogeneous parallel channels, where a transmitter is connected to two users via two parallel channels: (1) a MISO broadcast channel (BC), and (2) a noiseless rate-limited multicast channel. We characterize the optimal degrees of freedom (DoF) region of this setting when the transmitter has delayed channel state information (CSIT) regarding the MISO BC. Our results show that jointly coding over the two channels can strictly outperform simple channel aggregation (or channel separation) and can even achieve the same performance as with instantaneous CSIT when the CSIT on the MISO BC is completely stale; this occurs when the multicast rate of the second channel is larger than a certain threshold, in the DoF sense. The main idea to achieve full DoF with completely stale CSIT is to send information over the MISO BC at a rate above its capacity and use the second channel to send additional side information to allow for reliable decoding at both receivers.

We consider the temporally correlated multiple-input multiple-output (MIMO) broadcast channels (BC) and interference channels (IC) where the transmitter(s) has/have 1) delayed channel state information (CSI) obtained from a latency-prone feedback channel as well as 2) imperfect current CSIT, obtained, e.g., from prediction on the basis of these past channel samples based on the temporal correlation. The degrees of freedom (DoF) regions for the two-user broadcast and interference MIMO networks with general antenna configuration under such conditions are fully characterized, as a function of the prediction quality indicator. Specifically, a simple unified framework is proposed, allowing us to attain optimal DoF region for the general antenna configurations and current CSIT qualities. Such a framework builds upon block-Markov encoding with interference quantization, optimally combining the use of both outdated and instantaneous CSIT. A striking feature of our work is that, by varying the power allocation, every point in the DoF region can be achieved with one single scheme. As a result, instead of checking the achievability of every corner point of the outer bound region, as typically done in the literature, we propose a new systematic way to prove the achievability.

This work considers the multiuser multiple-input single-output (MISO) broadcast channel (BC), where a transmitter with M antennas transmits information to K single-antenna users, and where - as expected - the quality and timeliness of channel state information at the transmitter (CSIT) is imperfect. Motivated by the fundamental question of how much feedback is necessary to achieve a certain performance, this work seeks to establish bounds on the tradeoff between degrees-of-freedom (DoF) performance and CSIT feedback quality. Specifically, this work provides a novel DoF region outer bound for the general K-user M ×1 MISO BC with partial current CSIT, which naturally bridges the gap between the case of having no current CSIT (only delayed CSIT, or no CSIT) and the case with full CSIT. The work then characterizes the minimum CSIT feedback that is necessary for any point of the sum DoF, which is optimal for the case with M ≥ K, and the case with M = 2, K = 3.

The two-user Multiple-Input Multiple-Output (MIMO) broadcast channel (BC) with arbitrary antenna configuration is considered, in which the transmitter obtains (i) delayed channel state information (CSI) from a latency-prone feedback channel as well as (ii) imperfect current CSI, e.g., from prediction based on these past channel samples. The degrees of freedom (DoF) region under such a setting is fully characterized as a function of a prediction quality exponent. This work extends prior work, previously limited to MISO, to fully general antenna settings. An intriguing by-product of our results is to reveal the benefits of dealing with an asymmetric multi-user MIMO configuration (i.e., one in which terminals do not have the same number of antennas) in the case of non-perfect CSIT (e.g., caused by feedback delays or limited preciseness).

We consider the time correlated multiple-input single-output (MISO) broadcast channel where the transmitter has imperfect knowledge of the current channel state, in addition to delayed channel state information. By representing the quality of the current channel state information asP-αfor the signal-to-noise ratioPand some constant α ≥ 0, we characterize the optimal degrees of freedom region for this more general two-user MISO broadcast correlated channel. The essential ingredients of the proposed scheme lie in the quantization and multicast of the overheard interferences, while broadcasting new private messages. Our proposed scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information.

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.

We consider the time correlated MISO broadcast channel where the transmitter has partial knowledge on the current channel state, in addition to delayed channel state information (CSI). Rather than exploiting only the current CSI, as the zero-forcing precoding, or only the delayed CSI, as the Maddah-Ali-Tse (MAT) scheme, we propose a seamless strategy that takes advantage of both. The achievable degrees of freedom of the proposed scheme is characterized in terms of the quality of the current channel knowledge.

We consider the two-user MISO broadcast channel where the transmitter has imperfect knowledge on the current channel state, in addition to delayed channel state information. The degree of freedom region is completely characterized. The optimal scheme smoothly bridges between the scheme recently proposed by Maddah-Ali and Tse with no current state information and a simple zero-forcing beamforming with perfect current state information. The essential ingredients of this scheme lie in the quantization and multicasting of the overheard interferences, while broadcasting new private messages.

In this paper, we study full-duplex (FD) operation from a diversity perspective and investigate several protocols that extract diversity gains over a block fading channel. The investigated approach introduces a block-by-block transmission and requires data codewords that span several independent realizations of channel fading. This fundamental approach ensures a diversity gain at least equal to one (i.e., it does not suffer from error floors) and is employed to different relaying strategies without loop interference cancellation (LIC) and with imperfect LIC: a) Amplify-and-Forward (AF) without LIC, b) AF with imperfect LIC and c) Decode-and-Forward with imperfect LIC. The proposed protocols are analyzed from a diversity-multiplexing tradeoff (DMT) standpoint and practical universal codes that achieve the DMT of the proposed schemes are also presented. We show that AF without LIC ensures a full time diversity independently on the statistics of the loop interference, and it is introduced as a general FD-based relaying scheme. In addition, we demonstrate that imperfect LIC is not efficient for cases with strong residual loop interference and limits the diversity gain to one.

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 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.

In this paper, we derive the explicit series expansion of the eigenvalue distribution of various models, namely the case of noncentral Wishart distributions, as well as correlated zero mean Wishart distributions. The tools used extend those of the free probability framework, which have been quite successful for high dimensional statistical inference (when the size of the matrices tends to infinity), also known as free deconvolution. This contribution focuses on the finite Gaussian case and proposes algorithmic methods to compute the moments. Cases where asymptotic results fail to apply are also discussed.

It is well known that the presence of double scattering degrades the performance of a MIMO channel, in terms of both the multiplexing gain and the diversity gain. In this paper, a closed-form expression of the diversity-multiplexing tradeoff (DMT) of double scattering MIMO channels is obtained. It is shown that, for a channel withnTtransmit antennas,nRreceive antennas. andnSscatterers, the DMT only depends on the ordered version of the triple (nT,nS,nR) , for arbitrarynT,nSandnR. The condition under which the double scattering channel has the same DMT as the single scattering channel is also established.

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.

In relay networks, a conventional way to exploit spatial diversity is to introduce distributed space-time processing at the relays. In our work, we show that even simple time-varying distributed rotation can recover spatial diversity. The main idea is to convert the inherent spatial diversity to time diversity by creating an artificial fast fading channel. It turns out that the proposed framework is both tractable from the theoretical point of view and simple from the practical point of view. Furthermore, the framework is quite general and can be applied to a wide range of linear/nonlinear relaying strategies. As applications, we first propose a linear relaying scheme called rotate-and-forward for multiple-antenna two-hop layered networks. It is shown that, in some non-trivial setting, this scheme outperforms existing schemes and achieves the optimal diversity-multiplexing tradeoff. The second application is a decode-and-forward scheme based on the same idea in the single-antenna multiple-relay channel. It is shown to achieve the maximum diversity with low signaling complexity.

In this paper, we consider the layered two-hop relay channel, operating under the linear rotate-and-forward (RF) scheme, where the relays rotate the received signal before its retransmission. In this work, a limited feedback channel is considered between the relays and the destination. The destination finds the optimal rotation vector and feeds it back to the relays. We propose an iterative algorithm to find this optimal rotation vector. The proposed algorithm is shown to have optimal performance with low complexity.

This paper deals with throughput oriented adaptive modulation and coding (AMC) techniques combined with cooperative protocols where terminals are constrained by half-duplex assumption and average total network power. We propose a novel hybrid cooperation protocol that switches from cooperative to non-cooperative transmission based on the momentary direct source-destination link quality. Then, we propose anhybridcooperativeAMCmechanism, which combines AMC with hybrid cooperation. Simulation results show that our proposal improves the average system performance and reduces the average cooperation signaling cost.

We study the diversity of a Rayleigh product channel which is defined as a product of independent Rayleigh MIMO channels. More specifically, we obtain a closed-form expression of the diversity-multiplexing tradeoff (DMT) of a Rayleigh product channel with arbitrary dimensions and arbitrary number of product terms.

Summary form only given. In this paper, we consider multiantenna multihop relay channels in which the source signal arrives at the destination through N independent relaying hops in series. The main concern of this work is to design relaying strategies that efficiently utilize the relays in such a way that the diversity is maximized. First, we focus on the amplify-and-forward (AF) strategy with which the relays simply scale the received signal and retransmit it. More specifically, we characterize the diversity-multiplexing tradeoff (DMT) of the AF scheme in a general multihop channel with arbitrary number of antennas and arbitrary number of hops. The DMT is derived in closed-form expression as a function of the number of antennas at each node: as a first step, we provide some basic results on the DMT of the general Rayleigh product channels. It turns out that these results have very simple and intuitive interpretation. Then, the results are applied to the AF multihop channels which is shown to be equivalent to the Rayleigh product channel, in the DMT sense. Then, we mitigate the diversity suboptimality of the AF scheme via two kinds of partitions of the multihop channel : serial and parallel partitions. The serial partition corresponds to the intermediate decoding scheme. By supposing the relaying nodes could have full antenna cooperation, we study the fundamental problem that is when and where to decode to avoid diversity degradation. This scheme has minimum coding delay. The parallel partition corresponds to the distributed space-time processing. We establish conditions for the parallel partition to achieve the maximum diversity of the multihop channels. The parallel partition being rate-deficient in general, we propose a flip-and-forward (FF) scheme that achieves both the maximum diversity gain and multiplexing gain. The FF scheme is based on the parallel partition and works in a completely distributed manner. Coding schemes for both relaying strategies are proposed as well.

In this work, we extend the nonorthogonal amplify-and-forward (NAF) cooperative diversity scheme to the multiple-input multiple-output (MIMO) channel. A family of space-time block codes for a half-duplex MIMO NAF fading cooperative channel with N relays is constructed. The code construction is based on the nonvanishing determinant (NVD) criterion and is shown to achieve the optimal diversity-multiplexing tradeoff (DMT) of the channel. We provide a general explicit algebraic construction, followed by some examples. In particular, in the single-relay case, it is proved that the Golden code and the 4times4 Perfect code are optimal for the single-antenna and two-antenna cases, respectively. Simulation results reveal that a significant gain (up to 10 dB) can be obtained with the proposed codes, especially in the single-antenna case

In a slow-fading channel, how to find a cooperative diversity scheme that achieves the transmit diversity bound is still an open problem. In fact, all previously proposed amplify-and-forward (AF) and decode-and-forward (DF) schemes do not improve with the number of relays in terms of the diversity–multiplexing tradeoff (DMT) for multiplexing gains$r$higher than$0.5$. In this work, the class of slotted amplify-and-forward (SAF) schemes is studied. First, an upper bound on the DMT for any SAF scheme with an arbitrary number of relays$N$and number of slots$M$is established. Then, a sequential SAF scheme that can exploit the potential diversity gain in the high multiplexing gain regime is proposed. More precisely, in certain conditions, the sequential SAF scheme achieves the proposed DMT upper bound which tends to the transmit diversity bound when$M$goes to infinity. In particular, for the two-relay case, the three-slot sequential SAF scheme achieves the proposed upper bound and outperforms the two-relay nonorthorgonal amplify-and-forward (NAF) scheme of Azarianfor multiplexing gains$r\leq 2/3$. Numerical results reveal a significant gain of our scheme over the previously proposed AF schemes, especially in high spectral efficiency and large network size regime.

All previously proposed amplify-and-forward (AF) and decode-and-forward (DF) schemes do not improve with the number of relays in terms of the diversity-multiplexing tradeoff (DMT) for multiplexing gains r higher than 0.5. In this work, we study the class of slotted amplify-and-forward (SAF) schemes. We first establish an upper-bound on the DMT for any half-duplex SAF scheme with an arbitrary number of relays N and number of slots M. Then, we propose a naive SAF scheme that can exploit the potential diversity gain in the high multiplexing gain regime. More precisely, in certain conditions, the naive SAF scheme achieves the proposed DMT upper-bound which tends to the transmit diversity bound when M goes to infinity. In particular, for the two-relay case, the three-slot naive SAF scheme achieves the proposed upper-bound and outperforms the NAF scheme of Azarian et al. for multiplexing gains r les 2/3

The problem of designing space-time codes on the MIMO quasi-static channel have received considerable attention these last years. We now know how to design perfect space-time block codes, that is linear information preserving codes achieving the diversity-multiplexing gain (D-M) tradeoff (F. Oggier et al., 2004) (P. Elia et al., 2005). Recent standards using multiple antennas terminals such as IEEE 802.11n or IEEE 802.16e, for example, are based on OFDM. By using interleaving, such OFDM systems can be seen as parallel MIMO quasi-static channels. We propose, here, new perfect space-time block codes for parallel MIMO channels