SPECTRALLY-EFFICIENT HYBRID OPTICAL WIRELESS ACCESS NETWORK WITH TRANSFER LEARNING APPROACH IN THE SCENARIO OF DIFFERENT FADING ENVIRONMENT

Abstract

The next-generation technology had the vision to support services and cross-domain sharing in the upcoming 5G beyond technology-enabled networks. The rising requirement of increased bandwidth and resources are challenging the delivery of high-quality services while challenging the concept of green energy. This paper proposes a spectrally efficient and power-aware resource allocation scheme in Hybrid Optical Wireless Networks to wisely manage resource utilization in a spectrally efficient manner with minimal power consumption. The spectral efficiency of the power-aware scheme is evaluated in terms of Bit Error Rate (BER) and performance in terms of power consumption, throughput, latency, and balanced resource distribution in terms of Jain’s Fairness Index. The proposed algorithm architecture uses enhanced machine learning to check how the network performs and trains the system using Artificial Neural Network with the credentials inspired by Levenberg’s propagation model and further evaluated in Rician and Rayleigh fading channels. The simulation analysis demonstrated that with the optimal resource allocation the average throughput had reached 89% with reduced power consumption and network latency. The comparative analysis in terms of BER shows that the proposed work achieved minimal BER when evaluated using Rician and Rayleigh fading.

Keywords:

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: Hybrid Optical Wireless Access Networks (HOWANs), Spectral Efficiency, Resource Allocation, Power Consumption, Throughput, Fading.

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References

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