User Selection Approach in Multiantenna Beamforming NOMA Video Communication Systems

Abstract

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.

A Survey on Machine Learning Techniques for Massive MIMO Configurations: Application Areas, Performance Limitations and Future Challenges

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PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems

“…It is worth noting that the auto-encoder is well suited to tackling the vector compression problem because of its robustness to the unstable wireless channel conditions. The deep neural network (DNN) in [ 19 ] takes the place of the conventional zero-forcing detection and offers near-optimal transmission quality with much less computational complexity than the optimal scheme. Motivated by the convolutional neural network (CNN)-based deep learning compression approaches of channel state information (CSI) [ 20 , 21 , 22 ], we propose a novel compression and quantization network architecture named PCQNet for the joint transceiver optimization.…”

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