Cross-Layer Codebook Allocation for Uplink SCMA and PDNOMA-SCMA Video Transmission Systems and a Deep Learning-Based Approach

Sparse code multiple access (SCMA) and power-domain nonorthogonal multiple access (PDNOMA) have been considered for use with fifth-generation (5G) and beyond mobile communications, in order to increase the bandwidth efficiency, reduce the latency in terms of waiting for resources, and increase the numbers of connections. Existing codebook allocation schemes for SCMA and PDNOMA-SCMA systems consider only the physical layer. We propose an iterative cross-physical-and-application-layer codebook allocation for uplink video communications. We allocate the codebook according to the channel state information of the physical layer, and codebooks are exchanged among users to minimize the sum of the video mean square error in order to increase the average peak signal-to-noise ratio (PSNR). Furthermore, we propose a deep neural network (DNN) approach to reduce the computational complexity. We propose a novel postprocessing algorithm that is applied to the DNN output to ensure the constraints of codebook allocation are satisfied. Simulation results show that the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme outperforms an existing scheme that uses only the physical layer by 1.27 dB in terms of PSNR (video quality) for 12 users, four subcarriers, and SNR = 21 dB. The proposed DNN-based approach has an execution time that is 67% lower than the proposed iterative PDNOMA-SCMA cross-layer codebook allocation scheme, at the cost of a performance degradation of 0.84 dB.