Deep learning based beamforming for MISO systems with dirty‐paper coding. Issue 2 (16th January 2023) - Lou, Xingliang; Xia, Wenchao; Wen, Wanli; Zhao, Haitao; Li, Xiaohui; Wang, Bin
Abstract: Beamforming technique can effectively improve the spectrum utilization in the multi‐antenna systems, while the dirty‐paper coding (DPC) technique can reduce the inter‐user interference. In this letter, it is aimed to maximize the weighted sum‐rate under the total power constraint in the multiple‐input‐single‐output (MISO) system with the DPC technique. However, the existing methods of beamforming optimization mainly rely on customized iterative algorithms, which have high computational complexity. To address this issue, the beamforming neural network (BFNNet) is devised by utilizing the deep learning technique and the uplink‐downlink duality and exploring the optimal solution structure, which includes the deep neural network module and the signal processing module. Simulation results show that the BFNNet can achieve near‐optimal solutions and significantly reduce computational complexity. Abstract : We consider a MISO system with the DPC technique and formulate a sum‐rate maximization problem under a total power constraint. By utilizing the DL technique and the uplink‐downlink duality, we devise a beamforming neural network (BFNNet). And simulation results show that a well‐trained BFNNet can find near‐optimal solutions with a significantly lower computational complexity.