Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network

In this work, the deep learning (DL)-based fifth-generation (5G) non-orthogonal multiple access (NOMA) detector is investigated over the independent and identically distributed (i.i.d.) Nakagami- m fading channel conditions. The end-to-end system performance comparisons are given between the DL NOMA detector with the existing conventional successive interference cancelation (SIC)-based NOMA detector and from results, it has been proved that the DL NOMA detector performance is better than the convention SIC NOMA detector. In our analysis, the long-short term memory (LSTM) recurrent neural network (RNN) is employed, and the results are compared with the minimum mean square estimation (MMSE) and least square estimation (LS) detector’s performance considering all practical conditions such as multipath fading and nonlinear clipping distortion. It has been shown that with the increase in the relay to destination (RD) channel gain, the bit error rate (BER) improves. Also, with the increase in fading parameter m, the BER performance improves. The simulation curves demonstrate that when the clipping ratio (CR) is unity, the performance of the DL-based detector significantly improves as compared to the MMSE and LS detector for the signal-to-noise ratio (SNR) values greater than 15 dB and it proves that the DL technique is more robust to the nonlinear clipping distortion.

We investigate the beamforming for the multi antenna cooperative non-orthogonal multiple access (NOMA) system, where an access point (AP) delivers messages for multiple user terminals (UT) with successive interference cancellation (SIC) reception method. Some UTs with multiple antennas cooperate with the AP transmission to improve the diversity and the average power performance. We formally present two optimal beamforming schemes at the AP and at the cooperative UTs. One scheme has no power limitation for the cooperative UTs, while the other one does have such limitation. We guarantee that the rank one beamformer is sufficient to achieve the optimal points so that the proposed schemes have rank one semi-definite programming (SDP) structure. Simulation results show the performance gain of the multi-antenna cooperative NOMA schemes in the sense of diversity and the average power.