Unsupervised Learning-Based Joint Resource Allocation and Beamforming Design for RIS-Assisted MISO-OFDMA Systems
This addresses resource allocation challenges in 6G wireless systems, offering a highly efficient solution with incremental improvements in speed and performance.
The paper tackles the problem of joint resource allocation and beamforming in RIS-assisted MISO-OFDMA systems for 6G wireless, proposing an unsupervised learning framework that achieves 99.93% of the sum rate of a baseline method with only 0.036% of its runtime.
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.