Efficient Spectrum Sharing in Cognitive Radio Networks With NOMA Using Computational Intelligence

Abstract

The integration of Cognitive Radio Networks (CRNs) with Non-Orthogonal Multiple Access (NOMA) offers great potential for improving spectral efficiency in 5G and Beyond-5G (B5G) networks. This study proposes an efficient spectrum-sharing technique for dual-hop CRNs using NOMA, optimized by an Improved Artificial Bee Colony (IABC) algorithm and guided by a Single Input Single Output Fuzzy Rule-Based (SISO-FRBS) System. In this setup, a distant primary transmitter communicates with the primary receiver via a secondary NOMA relay. The objective is to maximize the sum data rate of secondary users (SUs) while minimizing total transmission power. SISO-FRBS enhances IABC search process by dynamically guiding the search agents, improving both optimization quality and convergence. Simulation results show that the proposed scheme achieves the primary data rate benchmark of 5bit/s/Hz at a transmit power of 19mW, compared to 23mW with traditional ABC, achieving a 19.04% improvement in power efficiency.