Year: 2025
Type: conference-paper
Abstract: In high-mobility environments, reconfigurable intelligent surface (RIS)-assisted orthogonal time-frequency space (OTFS) systems can significantly improve system performance in terms of bit-error rate (BER), signal-to-noise ratio (SNR), and capacity. These gains depend on the optimal configuration of the RIS reflection coefficients, which must balance the tradeoff between reliability and spectral efficiency. In this work, we formulate a bi-objective optimization problem that simultaneously maximizes capacity and minimizes BER, and we solve it using the non-dominated sorting genetic algorithm (NSGA-II). Simulation results show that the proposed method efficiently captures the trade-off between BER and capacity. For instance, at SNR$=5$dB and$K=8$RIS elements, the Pareto front demonstrates that improving the achievable rate inevitably increases BER, while minimizing BER reduces capacity, allowing flexible adaptation depending on the target application (e.g., mission-critical reliability vs. high data rate). Compared with state-of-the-art methods such as the strongest delay-Doppler channel response and Frobenius norm-based optimization, our approach achieves superior BER and capacity performance. Furthermore, we propose a lowcomplexity equalizer based on QR decomposition with Givens rotations. Complexity analysis shows that, unlike conventional equalizers, the proposed scheme maintains complexity independent of the number of RIS elements$K$and achieves much better performance when$K>K_{l i m}$. Overall, the proposed framework provides an effective solution for enabling reliable and efficient OTFS-RIS systems in 6G high-mobility scenarios.
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7140217210