Multitask deep learning-based multiuser hybrid beamforming for mm-wave orthogonal frequency division multiple access systems

Abstract

Multiuser hybrid beamforming of a wideband millimeter-wave (mm-wave) system is a complex combinatorial optimization problem. It not only needs large training data, but also tends to overfit and incur long run-time when multiple serial deep learning network models are used to solve this problem directly. Preferably, multitask deep learning (MTDL) model could jointly learn multiple related tasks and share their knowledge among the tasks, and this has been demonstrated to improve performance, compared to learning the tasks individually. Therefore, this work presents a first attempt to exploit MTDL for multiuser hybrid beamforming for mm-wave massive multiple-input multiple-output orthogonal frequency division multiple access systems. The MTDL model includes a multitask network architecture, which consists of two tasks-user scheduling and multiuser analog beamforming. First, we use the effective channel with a low dimension as input data for the two parallel tasks to reduce the computational complexity of deep neural networks. In a shallow shared layer of the MTDL model, we utilize hard parameter sharing in which the knowledge of multiuser analog beamforming task is shared with the user scheduling task to mitigate multiuser interference. Second, in the training process of the MTDL model, we use the exhaustive search algorithm to generate training data to ensure optimal performance. Finally, we choose the weight coefficient of each task by traversing all weight coefficient combinations in the training phase. Simulation results prove that our proposed MTDL-based multiuser hybrid beamforming scheme could achieve better performance than traditional algorithms and multiple serial single tasks deep learning scheme.