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Cooperative communication that integrates cognitive radio and non-orthogonal multiple access (CR-NOMA) is recognized as an emerging technology for fifth-generation and beyond wireless networks. However, security is still a critical issue because of the risk that confidential information will be overheard in wireless transmission environments. Besides, CR-NOMA is prone to eavesdroppers due to the increased usage of wireless signals. It is considered one of the major issues in enhancing the information security of CR-NOMA. The physical-layer security (PLS) appears as a powerful alternative to complement, or even replace, the traditional use of encryption-based approaches. Thereby, in this work, a novel bio-inspired metaheuristic optimization-based power allocation and relay-selection scheme (BMO-PARS) is proposed to enhance the security of physical-layer by maximizing the secrecy sum-rate in a cooperative relaying CR-NOMA system. First, the optimization problem is subject to the constraints of minimum data rate at each secondary user and maximum transmission power at the secondary base station and the relay is formulated. Next, the proposed scheme employs Improved Red Panda Lyrebird Optimization Algorithm (IRPLO) in order to use encryption techniques for achieving optimal joint relay as well as power allocation (PA). The benefits of IRPLO is that it accomplishes better balance between exploration and exploitation phases. Here, the message is delivered to the secondary users (SUs) through two phases in the scheme. In the first phase, the message is transmitted from the secondary base station (BS) to the relay, whereas in the second phase, it is relayed to the SUs. To deal with secrecy rate and delay in CR-NOMA, the best joint relays are chosen depending on the objective functions for maximizing the secrecy sum rate (SSR) and throughput. The BMO-PARS model is implemented using Matlab software and the performance is evaluated by comparing with prevailing methods. As a result, the implemented outcomes prove that the BMO-PARS model has attained a better SSR and throughput than the prevailing methods.
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