For symmetric non-orthogonal multiple access (NOMA)/multiple-input multiple-output (MIMO) systems, radio resource allocation is an important research problem. The optimal solution is of high computational complexity. Thus, one existing solution Kim et al. proposed is a suboptimal user selection and optimal power assignment for total data rate maximization. Another existing solution Tseng et al. proposed is different suboptimal user grouping and optimal power assignment for sum video distortion minimization. However, the performance of sub-optimal schemes by Kim et al. and Tseng et al. is still much lower than the optimal user grouping scheme. To approach the optimal scheme and outperform the existing sub-optimal schemes, a deep neural network (DNN) based approach, using the results from the optimal user selection (exhaustive search) as the training data, and a loss function modification specific for NOMA user selection to meet the constraint that a user cannot be in both the strong and weak set, and avoid the post processing online computational complexity, are proposed. The simulation results show that the theoretical peak signal-to-noise ratio (PSNR) of the proposed scheme is higher than the state-of-the-art suboptimal schemes Kim et al. and Tseng et al. by 0.7~2.3 dB and is only 0.4 dB less than the optimal scheme at lower online computational complexity. The online computational complexity (testing stage) of the proposed DNN user selection scheme is 60 times less than the optimal user selection scheme. The proposed DNN-based scheme outperforms the existing suboptimal solution, and slightly underperforms the optimal scheme (exhaustive search) at a much lower computation complexity.

中文翻译：

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多天线波束成形 NOMA 视频通信系统中的用户选择方法

对于对称非正交多址（NOMA）/多输入多输出（MIMO）系统，无线资源分配是一个重要的研究问题。最优解具有较高的计算复杂度。因此，一个现有的解决方案 Kim 等人。提出的是用于总数据速率最大化的次优用户选择和最优功率分配。另一个现有的解决方案 Tseng 等人。提出的是用于总和视频失真最小化的不同次优用户分组和最优功率分配。然而，Kim 等人的次优方案的性能。和曾等人。仍远低于最佳用户分组方案。为了接近最优方案并优于现有的次优方案，一种基于深度神经网络 (DNN) 的方法，以最优用户选择（穷举搜索）的结果为训练数据，针对NOMA用户选择进行损失函数修改，满足用户不能同时处于强弱集的约束，避免在线后处理计算复杂度，建议。仿真结果表明，所提出方案的理论峰值信噪比（PSNR）高于最先进的次优方案 Kim 等人。和曾等人。0.7~2.3 dB，并且仅比在线计算复杂度较低的最佳方案小 0.4 dB。所提出的 DNN 用户选择方案的在线计算复杂度（测试阶段）比最优用户选择方案低 60 倍。提出的基于 DNN 的方案优于现有的次优解决方案，