Joint Beamforming Design and 3D DoA Estimation for RIS-aided Communication System

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted 3D direction-of-arrival (DoA) estimation system, in which a uniform planar array (UPA) RIS is deployed to provide virtual line-of-sight (LOS) links and reflect the uplink pilot signal to sensors. To overcome the mutually coupled problem between the beamforming design at the RIS and DoA estimation, we explore the separable sparse representation structure and propose an alternating optimization algorithm. The grid-based DoA estimation is modeled as a joint-sparse recovery problem considering the grid bias, and the Joint-2D-OMP method is used to estimate both on-grid and off-grid parts. The corresponding Cram\'er-Rao lower bound (CRLB) is derived to evaluate the estimation. Then, the beampattern at the RIS is optimized to maximize the signal-to-noise (SNR) at sensors according to the estimated angles. Numerical results show that the proposed alternating optimization algorithm can achieve lower estimation error compared to benchmarks of random beamforming design.Comment: 6 pages, 6 figure

Optimal Discrete Beamforming of RIS-Aided Wireless Communications: an Inner Product Maximization Approach

This paper addresses non-convex optimization problems in communication services using reconfigurable intelligent surfaces (RISs). Specifically, we focus on optimal beamforming in RIS-aided communications, and formulate it as a discrete inner product maximization problem. To solve this problem, we propose a highly efficient divide-and-sort (DaS) search framework that guarantees global optima with linear search complexity, both in the number of discrete levels and reflecting cells. This approach is particularly effective for large-scale problems. Our numerical studies and prototype experiments demonstrate the speed and effectiveness of the proposed DaS. We also show that for moderate resolution quantization (4-bits and above), there is no noticeable difference between continuous and discrete phase configurations

Codebook Configuration for 1-bit RIS-aided Systems Based on Implicit Neural Representations

Reconfigurable intelligent surfaces (RISs) have become one of the key technologies in 6G wireless communications. By configuring the reflection beamforming codebooks, RIS focuses signals on target receivers. In this paper, we investigate the codebook configuration for 1-bit RIS-aided systems. We propose a novel learning-based method built upon the advanced methodology of implicit neural representations. The proposed model learns a continuous and differentiable coordinate-to-codebook representation from samplings. Our method only requires the information of the user's coordinate and avoids the assumption of channel models. Moreover, we propose an encoding-decoding strategy to reduce the dimension of codebooks, and thus improve the learning efficiency of the proposed method. Experimental results on simulation and measured data demonstrated the remarkable advantages of the proposed method

Wireless Communications in Cavity: A Reconfigurable Boundary Modulation based Approach

This paper explores the potential wireless communication applications of Reconfigurable Intelligent Surfaces (RIS) in reverberant wave propagation environments. Unlike in free space, we utilize the sensitivity to boundaries of the enclosed electromagnetic (EM) field and the equivalent perturbation of RISs. For the first time, we introduce the framework of reconfigurable boundary modulation in the cavities . We have proposed a robust boundary modulation scheme that exploits the continuity of object motion and the mutation of the codebook switch, which achieves pulse position modulation (PPM) by RIS-generated equivalent pulses for wireless communication in cavities. This approach achieves around 2 Mbps bit rate in the prototype and demonstrates strong resistance to channel's frequency selectivity resulting in an extremely low bit error rate (BER)