Improving 6G Network Spectrum Efficiency with Non-Cooperative and Cooperative Spectrum Sharing Using NOMA and Massive-MIMO
Keywords:Non-orthogonal multiple access (NOMA), Cooperative Cognitive Radio Network (CCRN), Un-Cooperative Cognitive Radio Network (Un-CCRN), massive multiple-input and multiple-output (M-MIMO), spectrum efficiency (SE)
There is an expectation that standards for sixth-generation (6G) wireless communication networks in the future would give previously unheard-of speeds for the flow of information as well as spectrum optimization. This will present new issues for 6G networks. Non-orthogonal multiple access (NOMA) is one of the most efficient ways to boost the spectrum efficiency (SE) of a 6G network. The most promising contemporary technologies, such as cognitive radio (CR) and multiple access, can be used to improve SE. When NOMA's network-oriented multi-access capabilities are combined with those of the Cognitive Radio Network (CRN), a new era of efficient communication is expected to dawn. To improve the spectral efficiency (SE) of the NOMA DL power domain (PD), this work presents two distinctive strategies that are used in conjunction with un-cooperative and cooperative CRN (Un-CCRN and CCRN) in the event that one primary user (PU) is unable to receive through the dedicated channel due to interference or noise. Users' distances, power placement coefficients, and transmit powers (TPs) vary across the proposed three network topologies, and over the proposed three network sizes of 128x128, 256x256, and 512x512 Massive Multiple Input Multiple Output (M-MIMO). Performance is analyzed while simultaneously considering channel instability and successive interference cancellation (SIC). The channels of fading are modelled after frequency-dependent Rayleigh fading. MATLAB is used to determine the proposed model's SE. With 128x128, 256x256, and 512x512 M-MIMO integrated into the DL NOMA system, the system's SE performance is improved by 73%, 82%, and 87%, respectively; with the Un-CCRN NOMA model, the improvement is 75%, 83%, and 88%; and with the CCRN-NOMA model, the improvement is 75.8%, 84%, and 88.3%. The SE is significantly improved by employing M-MIMO technology. The acquired expressions agree with the outcomes of the provided Monte Carlo simulations, providing further evidence for the validity of our investigation.
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