Sum Rate DL/UL Performance of Co-operative NOMA Systems Over Weibull Fading Channel

This work presents a novel system model consisting of an unmanned aerial vehicle (UAV) equipped with a half/full-duplex relay (HDR/FDR) operating as a near-user in the downlink non-orthogonal multiple access (NOMA) systems. In a disaster situation, there is no direct connectivity of the active base station (BS) to the far user due to the out-of-coverage range. Therefore, UAV communication is established to aid the transmission from the same BS to the far user via the UAV. To quantify the effect, outage probability and throughput expressions in the exact and asymptotic forms were developed over the Weibull distribution (WD) fading channel. Additionally, the separation distance of the UAV from the base station is considered to quantify the effect. In particular, this paper helps to determine the optimal location of the UAV deployment from the BS at a fixed height from the ground to either maximize the far-user throughput or attain far-user throughput over different conditions of the WD fading channel. In addition, the performance results of the UAV-HDR/FDR-NOMA system are compared with those of a conventional downlink orthogonal multiple access (OMA) system. The comparison reveals that the UAV-HDR/FDR-NOMA systems outperform corresponding OMA systems in terms of outage probability and throughput over different values of the Weibull shaping index. The analytical results are then validated through numerical simulations on MATLAB.

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“…U1 obtains both the superposed and loop interference signals at the same instant [53]. The signal received at U1 can be provided by [54]…”

Section: The System Modelmentioning

confidence: 99%

HD/FD Cooperative NOMA under UAV Deployment for a Novel Disaster-Management Model

Kumar

Kumar²

2023

Electronics

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“…In complex machine learning (ML) assignments, such as wireless communication or cooperative NOMA networks, DNNs have shown remarkable results. 18 The MIMO-NOMA DL-based detector improves the end-to-end system performance. The tactical IoT requires highly end-to-end reliable and real-time communication for massive connectivity of IoT UEs.…”

Section: Introductionmentioning

confidence: 99%

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

Shankar¹,

Sarojini²,

Mehraj

et al. 2021

Journal of Defense Modeling & Simulation

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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.

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Sum Rate DL/UL Performance of Co-operative NOMA Systems Over Weibull Fading Channel

Cited by 2 publications

References 19 publications

HD/FD Cooperative NOMA under UAV Deployment for a Novel Disaster-Management Model

HD/FD Cooperative NOMA under UAV Deployment for a Novel Disaster-Management Model

Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network

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