标题： 基于无监督学习的RIS辅助MISO-OFDMA系统的联合资源分配与波束成形设计 显示英文标题

标题： Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems

摘要： 可重构智能表面（RIS）是6G无线系统的关键使能技术。 本文研究了RIS辅助的MISO-OFDMA系统中的下行链路传输，解决了资源分配挑战。 提出了一种基于两阶段无监督学习的框架，用于联合设计RIS相移、基站波束成形和资源块（RB）分配。 该框架包括 BeamNet，它从信道状态信息（CSI）预测RIS相移，以及AllocationNet，它使用从BeamNet输出中得出的等效CSI来分配RB。 通过最大比传输和水填充实现主动波束成形。 为了在保持可微性的前提下处理离散约束，采用了量化和Gumbel-softmax技巧。 定制的损失函数和分阶段训练在QoS约束下提升了性能。 仿真结果表明，该方法在仅需其0.036%运行时间的情况下，达到了SCA基线总速率的99.93%，并且在不同信道和用户条件下仍具有鲁棒性。 显示英文摘要

摘要： Reconfigurable intelligent surfaces (RIS) are key enablers for 6G wireless systems. This paper studies downlink transmission in an RIS-assisted MISO-OFDMA system, addressing resource allocation challenges. A two-stage unsupervised learning-based framework is proposed to jointly design RIS phase shifts, BS beamforming, and resource block (RB) allocation. The framework includes BeamNet, which predicts RIS phase shifts from CSI, and AllocationNet, which allocates RBs using equivalent CSI derived from BeamNet outputs. Active beamforming is implemented via maximum ratio transmission and water-filling. To handle discrete constraints while ensuring differentiability, quantization and the Gumbel-softmax trick are adopted. A customized loss and phased training enhance performance under QoS constraints. Simulations show the method achieves 99.93% of the sum rate of the SCA baseline with only 0.036% of its runtime, and it remains robust across varying channel and user conditions.