Joint Metaheuristics and Deep Learning for Beamforming in 6G Advanced Antenna Systems

This letter introduces a joint metaheuristics and deep learning (DL) approach for beamforming (BF) in sixth-generation (6G) advanced antenna systems. Specifically, the bat algorithm (BA) is employed to generate a dataset for training three deep neural networks. Simulation results show that the DL-based BF approach achieves stable null placement in two interference cases while maintaining an acceptable side-lobe level and focusing on the target signal. The modified VGG network (MVGG)-based one, which features the fewest learnable parameters and floating-point operations, delivers a speedup of ×9400 times over the BA-based one and comes close to the sub-millisecond latency requirements of 6G systems.