用无监督学习联合优化智能反射面的波束赋形与资源分配
Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems
- 分两阶段设计：先预测反射面相位，再分配资源块
- 速率接近最优解，运行时间仅为传统方法的1/3000
- 适合6G无线系统中快速响应动态信道的场景
可重构智能表面（RIS）是6G无线系统的关键技术。本文研究了RIS辅助的多输入单输出正交频分多址（MISO-OFDMA）系统下行链路传输中的资源分配问题。提出一种两阶段无监督学习框架，联合设计RIS相位偏移、基站波束成形和资源块（RB）分配。框架包含BeamNet，从信道状态信息（CSI）中预测RIS相位；以及AllocationNet，利用BeamNet输出生成的等效CSI进行资源块分配。波束成形采用最大比传输和水填充算法。为处理离散约束并保持可微性，采用量化与Gumbel-softmax技巧。通过定制损失函数和分阶段训练，在满足服务质量（QoS）约束下显著提升性能。仿真表明，该方法达到原始序列凸逼近（SCA）基线99.93%的总速率，仅需其0.036%的运行时间，并在不同信道和用户条件下保持鲁棒性。
原文摘要 · Abstract (English)
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.
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