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Multitask deep learning-based multiuser hybrid beamforming for mm-wave orthogonal frequency division multiple access systems
Science China Information Sciences ( IF 8.8 ) Pub Date : 2020-07-15 , DOI: 10.1007/s11432-020-2937-y
Jing Jiang , Yue Li , Long Chen , Jianbo Du , Chunguo Li

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.



中文翻译:

毫米波正交频分多址系统中基于多任务深度学习的多用户混合波束成形

宽带毫米波(mm-wave)系统的多用户混合波束成形是一个复杂的组合优化问题。当使用多个串行深度学习网络模型直接解决此问题时,它不仅需要大量的训练数据,而且往往过拟合并导致较长的运行时间。优选地,多任务深度学习(MTDL)模型可以联合学习多个相关任务并在任务之间共享它们的知识,并且与单独学习任务相比,这已被证明可以提高性能。因此,这项工作提出了将MTDL用于毫米波大规模多输入多输出正交频分多址系统的多用户混合波束成形的首次尝试。MTDL模型包括多任务网络架构,它由两个任务组成-用户调度和多用户模拟波束成形。首先,我们将低维有效通道用作两个并行任务的输入数据,以降低深度神经网络的计算复杂性。在MTDL模型的浅层共享层中,我们利用硬参数共享,其中将多用户模拟波束成形任务的知识与用户调度任务共享,以减轻多用户干扰。其次,在MTDL模型的训练过程中,我们使用穷举搜索算法来生成训练数据以确保最佳性能。最后,我们通过遍历训练阶段中的所有权重系数组合来选择每个任务的权重系数。

更新日期:2020-07-18
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