ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

Ananth Kumar M.S.

doi:10.1117/12.2675073

10 April 2023 ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system

Ananth Kumar M.S.

Proceedings Volume 12616, International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022); 1261607 (2023) https://doi.org/10.1117/12.2675073
Event: International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022), 2022, Istanbul, Turkey

Abstract

Non-orthogonal multiple access (NOMA) system is one of the assuring solutions for the upcoming wireless communication systems for achieving better spectral efficiency. The NOMA based wireless communication system is mainly transformed with cognitive radio network (CRN) to improve the performance. This research work focuses on signal detection and optimal power allocation for uplink CRN-NOMA system using optimal deep learning (DL) model. This model combines the channel state estimation process and signal detection. In the proposed work, the signal detection is carried out on the basis of allocated power to the user. Initially, the power allocation process is carried out by the Bisection water filling algorithm to achieve optimal power allocation. Then, the signal detection process is enabled by the DL model ResNet-152. Then, the metaheuristic optimization using Imperialist Competitive Algorithm (ICA) is exploited to provide optimal solution for better signal detection. The noisy channel’s information like Rayleigh fading and AWGN are set for wireless transmission medium to transmit a signal. The combined model of power allocation and signal detection is carried out on MATLAB platform. The performance measures like spectral efficiency, energy efficiency, BER and average response rate of the proposed techniques are compared with the previous research works. When the value of mean harvested energy is 4.5 μJ, the energy efficiency of the proposed model, deep Q learning, conventional TDMA and conventional NOMA/ TDMA are 0.74 [bit/s/Hz], 0.71 [bit/s/Hz], 0.67 [bit/s/Hz] and 0.57 [bit/s/Hz] respectively. It has been hence proved that the performance of this methodology is accurate and effective than the existing models.

Citation Download Citation

Ananth Kumar M.S. "ICA-ResNet-based signal detection and optimal power allocation for CRN-NOMA system", Proc. SPIE 12616, International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022), 1261607 (10 April 2023); https://doi.org/10.1117/12.2675073

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KEYWORDS

Signal detection

Energy efficiency

Signal to noise ratio

Data transmission

Deep learning

Performance modeling

Receivers

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