RIS Deployment Optimization with Iterative Detection and Decoding in Multiuser Multiple-Antenna Systems

This work investigates a Reconfigurable Intelligent Surface (RIS)-assisted uplink system employing iterative detection and decoding (IDD) techniques. We analyze the impact of tuning system parameter tuning for several deployment configurations, including the number of users, access point (AP) antennas, and RIS elements on the IDD performance. Analytical results for both active and passive RIS in a single-input single-output (SISO) scenario demonstrate how deployment choices affect system performance. Numerical simulations confirm the robustness of the RIS-assisted IDD system to variations in these parameters, showing performance gains in certain configurations. Moreover, the findings indicate that the insights derived from SISO analysis extend to multiuser MIMO IDD systems.

Study of Iterative Detection and Decoding for RIS-Aided Multiuser Multi-Antenna Systems

We present a novel iterative detection and decoding (IDD) scheme for Reconfigurable Intelligent Surface (RIS)-assisted multiuser multiple-antenna systems. The proposed approach introduces a joint iterative detection strategy that integrates Low-Density Parity-Check (LDPC) codes, RIS processing and iterative detection and decoding. In particular, we employ a minimum mean square error receive filter that performs truncation at the RIS and soft interference cancelation at the receiver. Simulation results evaluate the system's overall capacity and bit error rate, and demonstrate substantial improvements in bit error rate across block-fading channels.