This paper considers a reconfigurable intelligent surface (RIS) aided orthogonal frequency division multiplexing (OFDM) relaying system, and investigates the joint design of RIS passive beamforming and subcarrier matching under two cases, where Case-I ignores the source-RIS-destination signal, while Case-II explores this signal for rate enhancement. We formulate a mixed-integer nonlinear programming (MINIP) problem to maximize the sum achievable rate of all subcarriers by jointly optimizing the passive beamforming and subcarrier matching. To solve this problem, we first develop a branch-and-bound (BnB)-based alternating optimization algorithm for attaining a near-optimal solution. Then, a low-complexity difference-of-convex penalty-based algorithm and learning-to-optimize approach are also proposed. Finally, simulation results demonstrate that the RIS-assisted OFDM relaying system achieves a substantial achievable rate gain as compared to that without RIS since RIS recasts the subcarrier matching and balances the signal-to-noise ratio (SNR) among different subcarrier pairs.
翻译:本文审议了可重新整合的智能表面(RIS) 辅助正方位频率分解(OFDM) 转接系统,并调查了在两种情况下,即Case-I忽略了源-RIS-目的地信号,而Cas-II则探索了这个信号以提升率。我们制定了混合整数的非线性编程(MINIP)问题,以便通过联合优化被动波形和子载体匹配,使所有子载体的可实现总速率最大化。为了解决这一问题,我们首先开发了基于分带和分带的(BnB)交替优化算法,以达成接近最佳的解决方案。然后,还提出了低兼容性-convex惩罚法差异和学习-Oppimiz 方法。最后,模拟结果表明,由RIS辅助的DM中继系统实现了与自IRIS重新定位后没有在子载体与子载体对准和平衡信号-磁力率比对等的大幅可实现的速率增速增速率收益。