Wireless communication systems demand efficient solutions, and non-orthogonal multiple access (NOMA) has emerged as a promising approach. The present investigation compares NOMA against orthogonal multiple access (OMA) methods, specifically focusing on TDMA. Through an analysis of the performance metrics, specific optimal power coefficients were derived, ensuring superior performance of NOMA schemes. It is important to note that these optimal power coefficients are dependent on the channel coefficients, meaning they will vary as the channel conditions of the users change. Using copula functions, we modeled the dependence between the channel coefficients and successfully obtained the dependences among the fading coefficients. Utilizing specific optimal power coefficients, calculations for the average secrecy rates of NOMA were conducted, considering the modeled dependencies. The analysis of mathematical and numerical results in this study reveals a significant performance improvement in the average secrecy rates of Downlink NOMA when considering dependent channel coefficients and optimal specific coefficients, as compared to independent NOMA.
Ajami Khales, F., & Abed Hodtani, G. (2023). Enhancing Average Secrecy Rates in Downlink NOMA with Dependent Channel Coefficients and Optimal Power Coefficients. Computer and Knowledge Engineering, (), -. doi: 10.22067/cke.2023.83435.1096
MLA
Faramarz Ajami Khales; Ghosheh Abed Hodtani. "Enhancing Average Secrecy Rates in Downlink NOMA with Dependent Channel Coefficients and Optimal Power Coefficients". Computer and Knowledge Engineering, , , 2023, -. doi: 10.22067/cke.2023.83435.1096
HARVARD
Ajami Khales, F., Abed Hodtani, G. (2023). 'Enhancing Average Secrecy Rates in Downlink NOMA with Dependent Channel Coefficients and Optimal Power Coefficients', Computer and Knowledge Engineering, (), pp. -. doi: 10.22067/cke.2023.83435.1096
VANCOUVER
Ajami Khales, F., Abed Hodtani, G. Enhancing Average Secrecy Rates in Downlink NOMA with Dependent Channel Coefficients and Optimal Power Coefficients. Computer and Knowledge Engineering, 2023; (): -. doi: 10.22067/cke.2023.83435.1096
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