Low-Complexity Channel Estimation and Passive Beamforming for RIS-Assisted MIMO Systems Relying on Discrete Phase Shifts

Abstract

Reconfigurable intelligent surfaces (RISs) are capable of enhancing the capacity of wireless networks at a low cost. In practical RIS-assisted communication systems, the acquisition of channel state information (CSI) and RIS reflection optimization constitute a pair of challenges. In this paper, a lowcomplexity channel estimation and passive beamforming design is proposed. First of all, we conceive a low-complexity framework for maximizing the achievable rate of RIS-assisted multipleinput multiple-output (MIMO) systems having discrete phase shifts at each RIS element. In contrast to existing solutions, the proposed arrangement partitions the channel training stage into several phases, where the RIS reflection coefficients are pre-designed and the effective superposed channel is estimated instead of separately training the source-destination and source-RIS-destination links. Based on this, the active beamformer can be designed at low complexity and the RIS reflection optimization is performed by selecting that one from the pre-designed training set which maximizes the achievable rate. Secondly, we propose novel techniques for generating the training set of RIS reflection coefficients. The theoretical performance of the proposed scheme is analyzed and compared to the optimal RIS configuration. Finally, our simulation results demonstrate that the proposed framework is more competitive than its existing counterparts when relying on imperfect CSI, especially for rapidly timevarying channels having short channel coherence time.

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System

“…The is the phase shift of IRS reflecting element , and is the reflection coefficient amplitude. Several works have been proposed in the literature to perform P-BF using channel estimation, while the estimation of channel coefficients for AP-IRS-UE links in IRSs aided MIMO communication system is discussed in detail [ 26 , 40 , 41 ]. However, this is not the main scope of this work so we use the model described in [ 26 ] to perform P-BF.…”

“…In this scheme, estimated UEs angle of arrival (AoA) data, that can be generated from Wi-Fi band using multiple signal classification (MUSIC) algorithm, are used as input features for our NN. Secondly, to design the IRSs phase shift, we adapt customized channel estimation based P-BF scheme that had been proposed in [ 26 ]. Furthermore, we discuss optimal placement of multi-IRSs in a MIMO system, where our placement criterion aims to maximize the average summation of UEs SINR in the network.…”

“…In this scheme, estimated UEs angle of arrival (AoA) data, that can be generated from Wi-Fi band using multiple signal classification (MUSIC) algorithm, are used as input features for our NN. Secondly, to design the IRSs phase shift, we adapt customized channel estimation based P-BF scheme that had been proposed in [ 26 ].…”

Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems

“…Note that loc BS , loc RIS , and loc u U E consisting of their 3D coordinates, which will be transformed to the form of the inputs of the BS-RIS NN and BS-RIS NN. Finally, we generate UF corresponding composite channels H as training labels via the traditional channel estimation method [10].…”

“…For the sake of brevity, the BS-RIS channel is considered as the LoS propagation.We assume that there are two scatterers distributed between the RIS and the UE. The locations of the two scatterers are(5, 40,10) m and (5, 45, 5) m, respectively. The distance-dependent Algorithm 2 The Location-Aware DRL-based Algorithm Input: loc BS , loc RIS , loc U E .…”