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.
Highlights
To meet the rapidly increasing consumer demand for wireless data, especially wireless video delivery, wireless transmission technology is continuously evolving
The previous works about deep learning for radio resource allocations in [22,25,26] all learn from the suboptimal scheme, so their performance would be slightly worse than the suboptimal scheme and can’t be close to the optimal solution
The loss function modification is to skip the post-processing of the deep neural network (DNN) output during the testing stage
Summary
To meet the rapidly increasing consumer demand for wireless data, especially wireless video delivery, wireless transmission technology is continuously evolving. Proposed user grouping for multicell uplink multiuser MIMO systems to achieve higher sum rates. Non-orthogonal multiple access (NOMA) can meet the world’s demand for higher data transmission rate. Tseng et al [13] proposed an improved weak user selection scheme in the physical layer, and a power allocation/user substitution scheme in the application layer This is a cross-layer approach just as with non-NOMA systems [15,16]. The above conventional schemes are usually iterative and have high computational complexity This high complexity is called algorithm deficit and motivates the application of the deep learning [17]. The combination of NOMA and MIMO provides higher data rates to meet the increasing demand of wireless video. The addition of deep learning can significantly reduce the online computational complexity
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