Metamaterial antennas are appealing for next-generation wireless networks due to their simplified hardware and much-reduced size, power, and cost. This paper investigates the holographic multiple-input multiple-output (HMIMO)-aided multi-cell systems with practical per-radio frequency (RF) chain power constraints. With multiple antennas at both base stations (BSs) and users, we design the baseband digital precoder and the tuning response of HMIMO metamaterial elements to maximize the weighted sum user rate. Specifically, under the framework of block coordinate descent (BCD) and weighted minimum mean square error (WMMSE) techniques, we derive the low-complexity closed-form solution for baseband precoder without requiring bisection search and matrix inversion. Then, for the design of HMIMO metamaterial elements under binary tuning constraints, we first propose a low-complexity suboptimal algorithm with closed-form solutions by exploiting the hidden convexity (HC) in the quadratic problem and then further propose an accelerated sphere decoding (SD)-based algorithm which yields global optimal solution in the iteration. For HMIMO metamaterial element design under the Lorentzian-constrained phase model, we propose a maximization-minorization (MM) algorithm with closed-form solutions at each iteration step. Furthermore, in a simplified multiple-input single-output (MISO) scenario, we derive the scaling law of downlink single-to-noise (SNR) for HMIMO with binary and Lorentzian tuning constraints and theoretically compare it with conventional fully digital/hybrid arrays. Simulation results demonstrate the effectiveness of our algorithms compared to benchmarks and the benefits of HMIMO compared to conventional arrays.

This paper investigates the network sensing problem in a distributed multiple-input multiple-output (MIMO) radar system. We first formulate the received signal model in distributed MIMO systems as a function of the target's location. Based on the problem formulation, we derive the Cramér-Rao lower bound (CRLB) of the location estimation error for a single target, whose dependence on the layout of the transmitters (TXs) and receivers (RXs) is revealed. Using the tools from stochastic geometry, we then model the locations of TXs and RXs as homogeneous Poisson Point Process (PPP) and investigate the network-level sensing performance. Particularly, we derive the scaling law for the average estimation error, revealing the impact of various system parameters such as the number of antennas, SNR, TX/RX densities, and path loss exponent. More importantly, we unveil that the estimation error scales with the SNR and the number of antennas to the power of -1, and with the TX/RX densities to the power of$-\gamma / 2$, where$\gamma$is the path loss exponent. Our numerical results confirm the accuracy of our theoretical derivations and the correctness of conclusions.

In this work, we study the transmit sequence design for integrated sensing and communications (ISAC) using random single-carrier communication signals. Particularly, we focus on the sensing-centric ISAC, where a family of communication codewords is optimized to yield a good sensing performance. To this end, we formulate the problem of finding the optimal communication codewords by minimizing the integrated sidelobe of the ambiguity function under the transmit power constraint. Specifically, two optimization methods are developed to solve such a problem, whose suitability with different communication shaping pulses is also highlighted. We unveil that the considered problem has non-unique optimum that can be exploited to obtain a family of communication codewords with optimized sensing performance. Furthermore, the communication performance of the derived codewords is evaluated based on both the Euclidean distance and the pairwise error probability (PEP) over multipath fading channels. Our numerical results confirm the superiority of the optimized codewords and the effectiveness of the proposed optimization methods.

In this work, we consider the target detection problem in a multistatic integrated sensing and communication (ISAC) scenario characterized by the cell-free MIMO communication network deployment, where multiple radio units (RUs) in the network cooperate with each other for the sensing task. By exploiting the angle resolution from multiple arrays deployed in the network and the delay resolution from the communication signals, i.e., orthogonal frequency division multiplexing (OFDM) signals, we formulate a cooperative sensing problem with coherent data fusion of multiple RUs' observations and propose a sparse Bayesian learning (SBL)-based method, where the global coordinates of target locations are directly detected. Intensive numerical results indicate promising target detection performance of the proposed SBL-based method. Additionally, a theoretical analysis of the considered cooperative multistatic sensing task is provided using the pairwise error probability (PEP) analysis, which can be used to provide design insights, e.g., illumination and beam patterns, for the considered problem.

The acquisition of Downlink (DL) channel state information at the transmitter (CSIT) is known to be a challenging task in multiuser massive MIMO systems when uplink/downlink channel reciprocity does not hold (e.g., in frequency division duplexing systems). From a coding viewpoint, the DL channel state acquired at the users via DL training can be seen as an information source that must be conveyed to the base station via the UL communication channels. The transmission of a source through a channel can be accomplished either by separate or joint source-channel coding (SSCC or JSCC). In this work, using classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation meansquare-error (MSE) of both JSCC and SSCC-based feedback schemes, which however requires encoding of large blocks of successive channel states and thus cannot be used in practice since it would incur in an extremely large feedback delay. We then focus on the relevant case of minimal (one slot) feedback delay and propose a practical JSCC-based feedback scheme that fully exploits the channel second-order statistics to optimize the dimension projection in the eigenspace. We analyze the large SNR behavior of the proposed JSCC-based scheme in terms of the quality scaling exponent (QSE). Given the second-order statistics of channel estimation of any feedback scheme, we further derive the closed-form of the lower bound to the ergodic sum-rate for DL data transmission under maximum ratio transmission and zero-forcing precoding. Via extensive numerical results, we show that our proposed JSCC-based scheme outperforms known JSCC, SSCC baseline and deep learning-based schemes and is able to approach the performance of the optimal DR scheme in the range of practical SNR.

In this work, we study and compare two types of CSI feedback schemes in multi-user massive MIMO systems, respectively based on joint and separate source-channel coding (JSCC and SSCC). Using the classical remote distortion-rate (DR) theory, we first provide a theoretical lower bound on the channel estimation mean-square-error (MSE) of any feedback scheme. The DR bound is achieved by using vector quantization applied to long sequences of channel state estimates and requires capacity-achieving channel coding in the uplink, resulting in a large delay in the CSI feedback loop that makes the scheme impractical. Thus we propose a practical JSCC-based feedback scheme that sends the CSI with minimal delay. Unlike previous works that simply apply linear mapping and equal power allocation to generate the feedback signal, our method applies the dimension projection in the eigenspace and optimizes power allocation by fully exploiting the channel second-order statistics. The extensive numerical results show that our proposed JSCC-based scheme not only outperforms the previous JSCC scheme with linear processing but also produces lower channel estimate MSE compared to a standard SSCC-based scheme at practical SNR.

As the array dimension of massive MIMO systems increases to unprecedented levels, two problems occur. First, the spatial stationarity assumption along the antenna elements is no longer valid. Second, the large array size results in an unacceptably high power consumption if high-resolution analog-to-digital converters are used. To address these two challenges, we consider a Bussgang linear minimum mean square error (BLMMSE)-based channel estimator for large scale massive MIMO systems with one-bit quantizers and a spatially non-stationary channel. Whereas other works usually assume that the channel covariance is known at the base station, we consider a plug-in BLMMSE estimator that uses an estimate of the channel covariance and rigorously analyze the distortion produced by using an estimated, rather than the true, covariance. To cope with the spatial non-stationarity, we introduce dithering into the quantized signals and provide a theoretical error analysis. In addition, we propose an angular domain fitting procedure which is based on solving an instance of non-negative least squares. For the multi-user data transmission phase, we further propose a BLMMSE-based receiver to handle one-bit quantized data signals. Our numerical results show that the performance of the proposed BLMMSE channel estimator is very close to the oracle-aided scheme with ideal knowledge of the channel covariance matrix. The BLMMSE receiver outperforms the conventional maximum-ratio-combining and zero-forcing receivers in terms of the resulting ergodic sum rate.

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to “effective channels”. Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.

We study the problem of providing channel state information (CSI) at the transmitter in multi-user “massive” MIMO systems operating in frequency division duplexing (FDD). The wideband MIMO channel is a vector-valued random process correlated in time, space (antennas), and frequency (subcarriers). The base station (BS) broadcasts periodically$\beta _{\mathrm{ tr}}$pilot symbols from its$M$antenna ports to$K$single-antenna users (UEs). Correspondingly, the$K$UEs send feedback messages about their channel state using$\beta _{\mathrm{ fb}}$symbols in the uplink (UL). Using results from remote rate-distortion theory, we show that, as${\sf snr}\to \infty $, the optimal feedback strategy achieves a channel state estimation mean squared error (MSE) that behaves as$\Theta {(}1)$if$\beta _{\mathrm{ tr}} < r$and as$\Theta \left ({{\sf snr}^{-\alpha }}\right)$when$\beta _{\mathrm{ tr}} \ge r$, where$\alpha = \min (\beta _{\mathrm{ fb}}/r, 1)$, where$r$is the rank of the channel covariance matrix. The MSE-optimal rate-distortion strategy implies encoding of long sequences of channel states, which would yield completely stale CSI and therefore poor multiuser precoding performance. Hence, we consider three practical “one-shot” CSI strategies with minimum one-slot delay and analyze their large-SNR channel estimation MSE behavior. These are: (1) digital feedback via entropy-coded scalar quantization (ECSQ), (2) analog feedback (AF), and (3) local channel estimation at the UEs via compressed sensing and digital feedback. These schemes have different requirements in terms of knowledge of the channel statistics at the UE and at the BS. In particular, the latter strategy requires no statistical knowledge and is closely inspired by a CSI feedback scheme currently proposed in 3GPP standardization. It is shown that ECSQ achieves optimal MSE at the price of a slight increase in feedback rate which vanishes for large SNR. AF achieves the optimal MSE decay rate of$\Theta ({\sf snr}^{-1})$whenever$\beta _{\mathrm{ tr}},\beta _{\mathrm{ fb}} \ge r$but is sub-optimal if$\beta \ge r$and$\beta _{\mathrm{ fb}} < r$. The 3GPP-inspired scheme is shown, via numerical simulations, to achieves performance similar to ECSQ and AF when the multipath channel is sufficiently sparse in the angle-delay domain, but suffers from a large performance gap if this requirement is not met.

We consider the problem of estimating the fading coefficients of a frequency-selective, spatially correlated channel via Downlink (DL) training and Uplink (UL) feedback in frequency division duplexing (FDD) massive MIMO systems. Using ratedistortion theory, we derive optimal bounds on the achievable channel state estimation error in terms of the number of training pilots in DL (βtr) and feedback dimension in UL (βfb), with random, spatially isotropic pilots. It is shown that when the number of training pilots exceeds the channel covariance rank (r), the optimal rate-distortion feedback strategy achieves an estimation error decay of ΘpSNR−αq in estimating the channel state, where α = minpβfb{r,1q is the so-called quality scaling exponent (QSE). We then discuss an "analog" feedback strategy, showing that it achieves the optimal QSE for a wide range of training and feedback dimensions with no channel covariance knowledge and simple signal processing at the user side. Our findings are supported by numerical simulations comparing these strategies in terms of channel state mean squared error and achievable ergodic sum-rate in DL with zero-forcing precoding.

This paper investigates the secrecy achievable region of multiple access wiretap (MAC-WT) channels where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver (or Bob for brevity) but only the confidential messages need to be protected from the eavesdropper (Eve). We first consider a discrete memoryless (DM) MAC-WT channel where both Bob and Eve jointly decode their interested messages. By using random coding, we find an achievable rate region, within which perfect secrecy can be realized, i.e., all users can communicate with Bob with arbitrarily small probability of error, while the confidential information leaked to Eve tends to zero. Due to the high implementation complexity of joint decoding, we also consider the DM MAC-WT channel where Bob simply decodes messages independently while Eve still applies joint decoding. We then extend the results in the DM case to a Gaussian vector (GV) MAC-WT channel. Based on the information theoretic results, we further maximize the sum secrecy rate of the GV MAC-WT system by designing precoders for all users. Since the problems are non-convex, we provide iterative algorithms to obtain suboptimal solutions. Simulation results show that compared with existing schemes, secure communication can be greatly enhanced by the proposed algorithms, and in contrast to the works which only focus on the network secrecy performance, the system spectrum efficiency can be effectively improved since open messages can be simultaneously transmitted.

In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

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.

In this paper, we study the robust secure unmanned aerial vehicle (UAV) communication system, where a UAV with full-duplex (FD) capability simultaneously receives the information signal from a ground unit (GU) and transmits jamming signal to degrade the wiretap capability of potential multiple ground eavesdroppers (Eves). With the consideration of estimation error of Eves' locations, we aim to maximize the average worst secrecy rate inside a certain flight period of the UAV by jointly optimizing the transmit power of the GU and UAV as well as the trajectory of the UAV. The resulting problem is intractable due to its non-convex nature and strongly coupled variables. Furthermore, the estimation error of Eves' locations results in an infinite number of constraints, which makes the problem even more difficult. To tackle this difficulty, we first propose an iterative algorithm based on the Schur complement lemma and successive inner approximation method to efficiently solve the problem suboptimally under the estimated Eves' locations. Then, in order to cope with the of Eves' location errors, we develop a cutting-set method, which solves the problem by alternating between optimal power-trajectory design and worst-case Eves' locations analysis. Via simulation, we show the improvement of the proposed algorithm compared to other benchmark algorithms under high FD self-interference cancellation levels.

In this paper, we study an unmanned aerial vehicle (UAV)-enabled multiuser wireless power transfer (WPT) network, where a UAV is responsible for providing wireless energy for a set of ground devices (GDs) deployed in an area. We focus on the design of UAV trajectory subject to the maximum flight speed limit, in order to maximize the minimum harvested energy among GDs over a particular charging duration. Different from prior works that considered simplified linear energy harvesting models, this paper for the first time takes into account the realistic nonlinear energy harvesting model for the UAV trajectory design. However, the formulated trajectory design problem is highly non-convex and has infinite number of variables, thus making it be challenging to be solved optimally. To tackle this difficulty, we adopt the following three-step approach to obtain an efficient solution. First, we rigorously characterize that the optimal trajectory follows a new successive-hover-and-fly (SHF) structure, where the UAV hovers at a certain set of points for efficiently transferring energy, and flies among these hovering points with the maximum speed following certain arcs (not necessarily straight lines). Next, based on this SHF structure, we transform the original problem to a new one for finding a set of turning point variables during the maximum-speed flight, at which the UAV changes the flight direction without hovering. Finally, we use the techniques of convex approximation to solve the transformed problem. According to the convexity of the nonlinear energy harvesting model, we iteratively solve a series of convex optimization problems to update the UAV trajectory towards a high-quality solution. Numerical results show the convergence of the proposed approach, and validate its performance gain over conventional designs.

Taking into consideration of vehicle mobility and fairness, in this paper we provide a cooperative offloading algorithm maximizing the energy efficiency for a vehicular edge computing network, while guaranteeing the shortest delay of the worst-case vehicle. In particular, through exploiting the geometrical feature of the unidirectional road, we formulate a mixed integer convex problem by jointly designing the offloading selection and allocating the computation resource simultaneously. The optimization is carried out by a proposed two-step optimization algorithm: We first optimize the server selection to obtain the minimized achievable delay, and subsequently optimize jointly the selection and resource allocation to maximize the energy efficiency while maintaining the optimal achievable delay. Via simulations, we show the advantage of proposed algorithms and evaluate the system performance.

Obtaining channel covariance knowledge is of great importance in various Multiple-Input Multiple-Output MIMO communication applications, including channel estimation and user grouping. Considering recently proposed massive MIMO systems, covariance estimation proves to be challenging due to the large number of antennas (M >> 1) employed in the base station. In this case, the number of pilot transmissions N becomes comparable to the number of antennas and standard estimators, such as the sample covariance, yield a poor estimate of the true covariance and are hence undesirable. In this paper, we propose a Maximum-Likelihood (ML) massive MIMO covariance estimator, based on a parametric representation of the channel angular spread function (ASF). The parametric representation emerges from super-resolving discrete ASF components plus approximating its continuous components using carefully chosen limited-support density function. We maximize the likelihood function using a Concave-Convex procedure, which is initialized via a non-negative least-squares optimization problem. Our simulation results show that the proposed method outperforms the state of the art in various estimation quality metrics.

In this work, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system with multiple ground users. We aim at solving the non-convex UAV trajectory design problem which maximizes the minimal received energy among all users by determining the UAV's flying path under given UAV speed constraints. To solve such intractable problem, we propose a genetic algorithm (GA) based successive hover-and-fly (SHF) scheme that iteratively searches the optimal hovering points and optimizes the corresponding hovering time. Moreover, we extend the study to scenarios with no-fly zones, for which an improved GA based method with a penalizing strategy is proposed accordingly. Numerical results confirm the performance advantage of the proposed GA based algorithm in comparison to the benchmark algorithms in prior works under a wide range of system parameters.

In this work we study the secrecy energy efficiency (SEE) maximization problem for a multi-carrier and multiple-input-multiple-output (MIMO) multiple eavesdroppers communication system. By utilizing the full-duplex (FD) operation, the system is simultaneously capable of bidirectional communication and jamming to the potential eavesdroppers. In particular, we opportunistically utilize different communication and jamming channel conditions at different subcarriers, raised due to multipath fading or large bandwidth, in order to improve the SEE. Due to the non-convex and non-smooth nature of the resulting optimization problem, we propose an iterative solution with a guaranteed convergence to a stationary point based on the successive inner approximation and Dinkelbach's algorithm. The numerical evaluations show a considerable improvement of the system SEE under the condition that the self-interference of the FD transceivers can be efficiently mitigated.

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.

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.