Enhancing the features of 5G Communication using NOMA CRN ANN technologies

Abstract

There exist certain issues in the evolution of 5G communication systems, namely in terms of spectral-efficiency, latency, reliability, and security. Cognitive Radio Networks (CRNs) provide a remedy to the problem of resource scarcity in terms of spectrum sharing dynamics. While incorporating technologies such as MIMO and NOMA, it provides greater efficiency to the system but is bounded with interference levels affecting signal quality and security, specifically in MIMO-NOMA communication systems. Conventional methods of equalizers have limitations to maintain corresponding security levels among users, resulting in confidentiality concerns. This paper presents a wise approach using artificial neural network (ANN) solutions to offer improved physical-layer security to MIMO-NOMA communication systems in the context of Cognitive Radio Networks (CRNs). The proposed system treats multi-user interference and distortion related to channels, meeting power levels to provide improved performance towards increased security rate with decreased bit error rates despite the challenge in practical scenarios. An ANN-based equalizer has been implemented to achieve superior performance compared to conventional equalizer techniques (ZF, MMSE, and DFE) in terms of corresponding security levels with higher robustness towards practical scenarios with improved performance metrics with a score value at 94.95%. It clearly validates its benefits towards improving corresponding shared-spectrum 5G security and viability towards conventional technology solutions.