Massive-Multiple Inputs and Multiple Outputs (M-MIMO) is considered as one of the standard techniques in improving the performance of Fifth Generation (5G) radio. 5G signal detection with low propagation delay and high throughput with minimum computational intricacy are some of the serious concerns in the deployment of 5G. The evaluation of 5G promises a high quality of service (QoS), a high data rate, low latency, and spectral efficiency, ensuring several applications that will improve the services in every sector. The existing detection techniques cannot be utilised in 5G and beyond 5G due to the high complexity issues in their implementation. In the proposed article, the Approximation Message Passing (AMP) is implemented and compared with the existing Minimum Mean Square Error (MMSE) and Message Passing Detector (MPD) algorithms. The outcomes of the work show that the performance of Bit Error Rate (BER) is improved with minimal complexity.
Non-orthogonal multiple access (NOMA) is a great contender for future cellular modulation due to its desirable properties like massive connectivity, high data rate transmission, and high spectral efficiency. However, its peak-to-average power ratio (PAPR) is significant, which becomes a significant disadvantage for the efficient operability of the NOMA waveform compared to current techniques. Several PAPR reduction algorithms like selective mapping (SLM), partial transmission sequence (PTS), and companding techniques have been proposed to lower the PAPR of multicarrier waveforms (MCWs). PTS reduces the PAPR but has high complexity. On the other hand, SLM has a less complex framework, but its PAPR performance is not as efficient as PTS. Companding methods reduce the PAPR by compressing the signals at the transmitter, which unfortunately reduces the dynamic range of the signal. In this work, we propose a hybrid algorithm (SLM + PTS) with a companding method for the first time for the NOMA waveform, which efficiently reduces the PAPR with low computational complexity. Furthermore, we compare the performances of a host of candidate algorithms like SLM, PTS, hybrid (SLM + PTS), hybrid + A law (SLM-PTS-A law), and hybrid + Mu law (SLM-PTS-Mu law). The results of the experiments show that the hybrid + Mu law did a better job than the existing PAPR reduction algorithms.
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