In modern wireless communication scenarios, non-orthogonal multiple access (NOMA) provides high throughput and spectral efficiency for fifth generation (5G) and beyond 5G systems. Traditional NOMA detectors are based on successive interference cancellation (SIC) techniques at both uplink and downlink NOMA transmissions. However, due to imperfect SIC, these detectors are not suitable for defense applications. In this paper, we investigate the 5G multiple-input multiple-output NOMA deep learning technique for defense applications and proposed a learning approach that investigates the communication system’s channel state information automatically and identifies the initial transmission sequences. With the use of the proposed deep neural network, the optimal solution is provided, and performance is much better than the traditional SIC-based NOMA detectors. Through simulations, the analytical outcomes are verified.
The energy harvesting (EH) technique has ignited a rising research interest due to its ability to enhance the battery power of user equipment by harvesting energy by using a simple radio frequency circuit. In this paper, the simultaneous wireless information and power transfer non-orthogonal multiple access (NOMA) technique is investigated over Rayleigh fading channel conditions. EH NOMA improves the power efficiency, and it results in better throughput as compared to conventional NOMA techniques. In this paper, we investigate the achievable data rate and the outage probability performance of the near user, also called the relay user (RU), and the far user (FU). It can clearly be shown that the RU data rate is saturated and at 1 bits/s/Hz will become constant, while the FU data rate increases with an increase in transmitted power. It is seen by analysis that the RU data rate would saturate due to EH. It is further shown that with a transmitted power of 18 dBm, the net output of the FUs is readily shown to be based around a value of 2.80 bits/s/Hz. In comparison, it is shown that the FU’s instantaneous information rate dropped below the target data rate.
In 5G and beyond 5G wireless communication networks, the NOMA scheme is widely considered a major non-orthogonal access technique for improving system capacity and data rates. The main challenges in current NOMA systems are limited channel feedback and the difficulty of integrating it with advanced adaptive coding and modulation algorithms. This study analyses S-LSTM-based DL NOMA receivers in i.i.d. Nakagami-m fading channel circumstances as opposed to previously presented solutions. The LSTM has the advantage of responding dynamically to changing channel conditions. When compared to a typical NOMA system, a typical NOMA system has a 12% lower outage probability, a 39% increase in net throughput, and a maximum SER reduction of 48%. Complex modulated M-ary PSK and M-ary QAM data symbols are employed in D/L NOMA transmission. Classic receivers such as LS and MMSE are outperformed by the S-LSTM-based DL-NOMA receiver. The CP and non-linear clipping noise simulation curves compare the performance of the MMSE and LS receivers with that of the DL NOMA receiver in real-time propagation circumstances. The DL-based detector outperforms the MMSE for SNRs greater than 15 dB because the S-LSTM method is more robust than the clipping noise.
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