Lens-Type Redirective Intelligent Surfaces for Multi-User MIMO Communication

This paper explores the idea of using redirective reconfigurable intelligent surfaces (RedRIS) to overcome many of the challenges associated with the conventional reflective RIS. We develop a framework for jointly optimizing the switching matrix of the lens-type RedRIS ports along with the active precoding matrix at the base station (BS) and the receive scaling factor. A joint non-convex optimization problem is formulated under the minimum mean-square error (MMSE) criterion with the aim to maximize the spectral efficiency of each user. In the single-cell scenario, the optimum active precoding matrix at the multi-antenna BS and the receive scaling factor are found in closed-form by applying Lagrange optimization, while the optimal switching matrix of the lens-type RedRIS is obtained by means of a newly developed alternating optimization algorithm. We then extend the framework to the multi-cell scenario with single-antenna base stations that are aided by the same lens-type RedRIS. We further present two methods for reducing the number of effective connections of the RedRIS ports that result in appreciable overhead savings while enhancing the robustness of the system. The proposed RedRIS-based schemes are gauged against conventional reflective RIS-aided systems under both perfect and imperfect channel state information (CSI). The simulation results show the superiority of the proposed schemes in terms of overall throughput while incurring much less control overhead.

Next-slot OFDM-CSI Prediction: Multi-head Self-attention or State Space Model?

The ongoing fifth-generation (5G) standardization is exploring the use of deep learning (DL) methods to enhance the new radio (NR) interface. Both in academia and industry, researchers are investigating the performance and complexity of multiple DL architecture candidates for specific one-sided and two-sided use cases such as channel state estimation (CSI) feedback, CSI prediction, beam management, and positioning. In this paper, we set focus on the CSI prediction task and study the performance and generalization of the two main DL layers that are being extensively benchmarked within the DL community, namely, multi-head self-attention (MSA) and state-space model (SSM). We train and evaluate MSA and SSM layers to predict the next slot for uplink and downlink communication scenarios over urban microcell (UMi) and urban macrocell (UMa) OFDM 5G channel models. Our numerical results demonstrate that SSMs exhibit better prediction and generalization capabilities than MSAs only for SISO cases. For MIMO scenarios, however, the MSA layer outperforms the SSM one. While both layers represent potential DL architectures for future DL-enabled 5G use cases, the overall investigation of this paper favors MSAs over SSMs.

Towards 6G MIMO: Massive Spatial Multiplexing, Dense Arrays, and Interplay Between Electromagnetics and Processing

The increasing demand for wireless data transfer has been the driving force behind the widespread adoption of Massive MIMO (multiple-input multiple-output) technology in 5G. The next-generation MIMO technology is now being developed to cater to the new data traffic and performance expectations generated by new user devices and services in the next decade. The evolution towards "ultra-massive MIMO (UM-MIMO)" is not only about adding more antennas but will also uncover new propagation and hardware phenomena that can only be treated by jointly utilizing insights from the communication, electromagnetic (EM), and circuit theory areas. This article offers a comprehensive overview of the key benefits of the UM-MIMO technology and the associated challenges. It explores massive multiplexing facilitated by radiative near-field effects, characterizes the spatial degrees-of-freedom, and practical channel estimation schemes tailored for massive arrays. Moreover, we provide a tutorial on EM theory and circuit theory, and how it is used to obtain physically consistent antenna and channel models. Subsequently, the article describes different ways to implement massive and dense antenna arrays, and how to co-design antennas with signal processing. The main open research challenges are identified at the end.

Optimal Placement of Transmissive RIS in the Near Field for Capacity Maximization in THz Communications

This study centers on Line-of-Sight (LoS) MIMO communication enabled by a Transmissive Reconfigurable Intelligent Surface (RIS) operating in the Terahertz (THz) frequency bands. The study demonstrates that the introduction of RIS can render the curvature of the wavefront apparent over the transmit and receive arrays, even when they are positioned in the far field from each other. This phenomenon contributes to an enhancement in spatial multiplexing. Notably, simulation results underline that the optimal placement of the RIS in the near-field is not solely contingent on proximity to the transmitter (Tx) or receiver (Rx) but relies on the inter-antenna spacing of the Tx and Rx.

Reconfigurable Intelligent Surfaces-Enabled Intra-Cell Pilot Reuse in Massive MIMO Systems

Channel state information (CSI) estimation is a critical issue in the design of modern massive multiple-input multiple-output (mMIMO) networks. With the increasing number of users, assigning orthogonal pilots to everyone incurs a large overhead that strongly penalizes the system's spectral efficiency (SE). It becomes thus necessary to reuse pilots, giving rise to pilot contamination, a vital performance bottleneck of mMIMO networks. Reusing pilots among the users of the same cell is a desirable operation condition from the perspective of reducing training overheads; however, the intra-cell pilot contamination might worsen due to the users' proximity. Reconfigurable intelligent surfaces (RISs), capable of smartly controlling the wireless channel, can be leveraged for intra-cell pilot reuse. In this paper, our main contribution is a RIS-aided approach for intra-cell pilot reuse and the corresponding channel estimation method. Relying upon the knowledge of only statistical CSI, we optimize the RIS phase shifts based on a manifold optimization framework and the RIS positioning based on a deterministic approach. The extensive numerical results highlight the remarkable performance improvements the proposed scheme achieves (for both uplink and downlink transmissions) compared to other alternatives.

Channel Estimation in RIS-Enabled mmWave Wireless Systems: A Variational Inference Approach

We propose a variational inference (VI)-based channel state information (CSI) estimation approach in a fully-passive reconfigurable intelligent surface (RIS)-aided mmWave single-user single-input multiple-output (SIMO) communication system. Specifically, we first propose a VI-based joint channel estimation method to estimate the user-equipment (UE) to RIS (UE-RIS) and RIS to base station (RIS-BS) channels using uplink training signals in a passive RIS setup. However, updating the phase-shifts based on the instantaneous CSI (I-CSI) leads to a high signaling overhead especially due to the short coherence block of the UE-RIS channel. Therefore, to reduce the signaling complexity, we propose a VI-based method to estimate the RIS-BS channel along with the covariance matrix of the UE-RIS channel that remains quasi-static for a longer period than the instantaneous UE-RIS channel. In the VI framework, we approximate the posterior of the channel gains/covariance matrix with convenient distributions given the received uplink training signals. Then, the learned distributions, which are close to the true posterior distributions in terms of Kullback-Leibler divergence, are leveraged to obtain the maximum a posteriori (MAP) estimation of the considered CSI. The simulation results demonstrate that MAP channel estimation using approximated posteriors yields a capacity that is close to the one achieved with true posteriors, thus demonstrating the effectiveness of the proposed methods. Furthermore, our results show that estimating the channel covariance matrix improves the spectral efficiency by reducing the pilot signaling required to obtain the phase-shifts for the RIS elements in a channel-varying environment.

Channel Estimation for Multicarrier Systems with Tightly-Coupled Broadband Arrays

This paper develops a linear minimum mean-square error (LMMSE) channel estimator for single and multicarrier systems that takes advantage of the mutual coupling in antenna arrays. We model the mutual coupling through multiport networks and express the single-user multiple-input multiple-output (MIMO) communication channel in terms of the impedance and scattering parameters of the antenna arrays. We put forward a novel scattering description of the communication channel which requires only the scattering parameters of the arrays as well as the terminated far-field embedded antenna patterns. In multi-antenna single-carrier systems under frequency-flat channels, we show that neglecting the mutual coupling effects leads to inaccurate characterization of the channel and noise correlations. We also extend the analysis to frequency-selective multicarrier channels wherein we further demonstrate that the coupling between the antenna elements within each array increases the number of resolvable channel taps. Standard LMMSE estimators based on existing inaccurate channel models become sub-optimal when applied to the new physically consistent model. We hence develop a new LMMSE estimator that calibrates the coupling and optimally estimates the MIMO channel. It is shown that appropriately accounting for mutual coupling through the developed physically consistent model leads to remarkable performance improvements both in terms of channel estimation accuracy and achievable rate. We demonstrate those gains in a rich-scattering environment using a connected array of slot antennas both at the transmitter and receiver sides.

Domain Generalization in Machine Learning Models for Wireless Communications: Concepts, State-of-the-Art, and Open Issues

Data-driven machine learning (ML) is promoted as one potential technology to be used in next-generations wireless systems. This led to a large body of research work that applies ML techniques to solve problems in different layers of the wireless transmission link. However, most of these applications rely on supervised learning which assumes that the source (training) and target (test) data are independent and identically distributed (i.i.d). This assumption is often violated in the real world due to domain or distribution shifts between the source and the target data. Thus, it is important to ensure that these algorithms generalize to out-of-distribution (OOD) data. In this context, domain generalization (DG) tackles the OOD-related issues by learning models on different and distinct source domains/datasets with generalization capabilities to unseen new domains without additional finetuning. Motivated by the importance of DG requirements for wireless applications, we present a comprehensive overview of the recent developments in DG and the different sources of domain shift. We also summarize the existing DG methods and review their applications in selected wireless communication problems, and conclude with insights and open questions.

Continual Learning-Based MIMO Channel Estimation: A Benchmarking Study

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication system parameters, e.g., signal-to-noise ratio (SNR) and coherence time. Therefore, the performance of these learning-based solutions will degrade when the models are tested on different settings than the ones used for training. This motivates our work in which we investigate continual supervised learning (CL) to mitigate the shortcomings of the current approaches. In particular, we design a set of channel estimation tasks wherein we vary different parameters of the channel model. We focus on Gauss-Markov Rayleigh fading channel estimation to assess the impact of non-stationarity on performance in terms of the mean square error (MSE) criterion. We study a selection of state-of-the-art CL methods and we showcase empirically the importance of catastrophic forgetting in continuously evolving channel settings. Our results demonstrate that the CL algorithms can improve the interference performance in two channel estimation tasks governed by changes in the SNR level and coherence time.

Nonlocal Reconfigurable Intelligent Surfaces for Wireless Communication: Modeling and Physical Layer Aspects

Delivering wireless ultrahigh-speed access at wider coverage is becoming considerably challenging due to the prohibitive investment costs per user and the necessary shift to range-limited millimeter-wave (mmWave) transmissions. Reconfigurable intelligent surfaces (RIS) are expected to extend the reach of mmWave and TeraHz signals more cost-effectively in situations where fiber backhaul and fronthaul are not accessible or infrastructure densification is costly. This paper investigates some challenges facing this technology, particularly in terms of scalability and the question of what type of RIS configurations would be appropriate for mmWave networks and what design strategies can be adopted to optimize the performance and minimize the signaling overhead. We conclude that RIS configurations for the wireless infrastructure likely need to be nonlocal (i.e., redirective, wavefront-selective) rather than local (i.e., reflective) to support communications and networking tasks such as integrated fronthaul and access (IFA) most efficiently.