Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs’ inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels’ mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cramér-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

This paper considers the problem of lossy neural image compression (NIC). Current state-of-the-art (SOTA) methods adopt uniform posterior to approximate quantization noise, and single-sample pathwise estimator to approximate the gradient of evidence lower bound (ELBO). In this paper, we propose to train NIC with multiple-sample importance weighted autoencoder (IWAE) target, which is tighter than ELBO and converges to log likelihood as sample size increases. First, we identify that the uniform posterior of NIC has special properties, which affect the variance and bias of pathwise and score function estimators of the IWAE target. Moreover, we provide insights on a commonly adopted trick in NIC from gradient variance perspective. Based on those analysis, we further propose multiple-sample NIC (MS-NIC), an enhanced IWAE target for NIC. Experimental results demonstrate that it improves SOTA NIC methods. Our MS-NIC is plug-and-play, and can be easily extended to neural video compression.

Large-scale antenna arrays employed by the base station (BS) constitute an essential next-generation communications technique. However, due to the constraints of size, cost, and power consumption, it is usually considered unrealistic to use a large-scale antenna array at the user side. Inspired by the emerging technique of reconfigurable intelligent surfaces (RIS), we firstly propose the concept of user-specific RIS (US-RIS) for facilitating the employment of a large-scale antenna array at the user side in a cost- and energy-efficient way. In contrast to the existing employments of RIS, which belong to the family of base-station-specific RISs (BSS-RISs), the US-RIS concept by definition facilitates the employment of RIS at the user side for the first time. This is achieved by conceiving a multi-layer structure to realize a compact form-factor. Furthermore, our theoretical results demonstrate that, in contrast to the existing single-layer structure, where only the phase of the signal reflected from RIS can be adjusted, the amplitude of the signal penetrating multi-layer US-RIS can also be partially controlled, which brings about a new degree of freedom (DoF) for beamformer design that can be beneficially exploited for performance enhancement. In addition, based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem of US-RIS-aided communications. Due to the non-convexity of the problem introduced by this multi-layer structure, we propose a multi-layer transmit beamformer design relying on an iterative algorithm for finding the optimal solution by alternately updating each variable. Finally, our simulation results verify the superiority of the proposed multi-layer US-RIS as a compact realization of a large-scale antenna array at the user side for uplink transmission.

Massive multiple-input multiple-output (MIMO) with a large-scale antenna array at the base station (BS) side is one of the most essential techniques for 5G wireless communications. However, due to the forbidden hardware cost and mismatched size, it is physically limited to deploy massive MIMO at the user side. To break this limitation, inspired by the promising technique called reconfigurable intelligent surface (RIS), we firstly propose the concept of user-side RIS (US-RIS) which is a cost- and energy-efficient realization for large-scale array at the user side. Different from the existing RISs that work as base-station-side RISs (BSS-RISs), US-RIS is the first usage of RIS at the user side. Then, we propose a novel architecture of user with the aid of US-RIS with a multi-layer structure for compact implementation. Based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem in the US-RIS-aided communications. To tackle the challenge of solving this non-convex problem, we propose a multi-layer precoding design that can obtain the optimal parameters at transceivers and US-RIS by iterative optimization. Finally, simulation results are shown to verify the practicability and superiorities of the proposed multi-layer US-RIS as a realization of the large-scale array at the user side.

With the ability to overcome the inter-cell interferences, cell-free network is a promising network paradigm to achieve high spectrum efficiency for future 6G communications. However, the increasing number of distributed base stations in cell-free network introduces very high power consumption. To address this issue, inspired by the reconfigurable intelligent surface (RIS) technique with low power consumption, the concept of RIS-aided cell-free network has been recently proposed to improve the spectrum efficiency at a low cost of power consumption. In this paper, we take a further step to investigate the energy-efficiency fairness (EEF) of RIS-aided cell-free network. Specifically, we formulate the precoding design problem to maximize the energy efficiency of the worst user in a wideband RIS-aided cell-free network. To solve this problem, we then propose an iterative precoding algorithm by using Lagrangian transform and fractional programming (FP) to tackle the highly decoupled optimization objective. Through alternatingly solving subproblems of subcarrier assignment, power allocation, combining, and precoding, the objective will finally converge. Simulation results demonstrate the effectiveness of the proposed precoding algorithm, and the energy efficiency of users in cell-free network can be efficiently increased by the aid of RISs.