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.

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

In this 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.

This paper investigates radar-assisted user acquisition for downlink multi-user multiple-input multiple-output (MIMO) transmission using Orthogonal Frequency Division Multiplexing (OFDM) signals. Specifically, we formulate a concise mathematical model for the user acquisition problem, where each user is characterized by its delay and beamspace response. Therefore, we propose a two-stage method for user acquisition, where the Multiple Signal Classification (MUSIC) algorithm is adopted for delay estimation, and then a least absolute shrinkage and selection operator (LASSO) is applied for estimating the user response in the beamspace. Furthermore, we also provide a comprehensive performance analysis of the considered problem based on the pair-wise error probability (PEP). Particularly, we show that the rank and the geometric mean of non-zero eigenvalues of the squared beamspace difference matrix determines the user acquisition performance. More importantly, we reveal that simultaneously probing multiple beams outperforms concentrating power on a specific beam direction in each time slot under the power constraint, when only limited OFDM symbols are transmitted. Our numerical results confirm our conclusions and also demonstrate a promising acquisition performance of the proposed two-stage method.

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

We 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.

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

We 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.

In spatial channel models used in multi-antenna wireless communications, the propagation from a single-antenna transmitter (e.g. a user) to an M-antenna receiver (e.g. a Base Station) occurs through scattering clusters located in the far field of the receiving array. The angular power spread function (APSF) of the corresponding M-dim channel vector describes the angular density of the received signal power at the array. In many applications, such as channel sounding and Uplink Downlink covariance transformation in FDD systems, estimating the APSF is required either implicitly or explicitly. However, the existing literature on the subject has mainly focused on channel covariance estimation from a set of noisy pilot observations. It is also assumed that the APSF consists only of discrete components corresponding to Line-of-Sight (LoS) paths and specular scattering. It turns out that while covariance estimation is a well-posed problem, APSF estimation is a much harder task and is in general ill-posed. The reason is that the propagation environment can also include diffuse scattering elements, resulting in continuous APSF components. Therefore, the APSF is a function belonging to the infinite-dimensional space of nonnegative measures over the angle domain. In this paper, we show that under a geometrically-consistent, group-sparse structure on the APSF, which is prevalent in massive MIMO channels, one is able to estimate the APSF properly. We propose an algorithm based on deep neural networks (DNNs) that learns this structure and yields precise APSF estimates, even when the number of available pilot observations is relatively small. We empirically show that our proposed method outperforms the state-of-the-art method in various performance metrics.