Joint analysis of outage probability and energy efficiency in underlay cognitive radio-inspired NOMA systems using the AF relay in a downlink transmission

In this paper, we investigate the performance of the underlay Cognitive Radio-Inspired Non-Orthogonal Multiple Access (NOMA) system in a downlink transmission. In our proposed model, a primary transmitter (PT) operating over a weak channel shares its frequency band with a secondary transmitter (ST) to enhance overall spectrum utilization. This work employs a coordinated direct and relay transmission (CDRT) strategy, where the relay operates in amplify-and-forward (AF) mode. We considered co-channel interference affecting both primary and secondary users. We present a novel decoding technique for the symbols of the primary and secondary users based on a combined signal-to-interference plus noise ratio (SINR) framework. This technique significantly improves the outage probability for both user types, resulting in enhanced system performance overall. The main advantage of this method lies in utilizing the combined SINR from both transmission phases, which enables a more robust decoding process. In this study, we derived a closed-form analytical expression for the outage probability of both PU and secondary users (SU). Additionally, we provided an asymptotic approximation of the outage probability at high signal-to-noise ratio (SNR) regimes. Our analytical results show that, at a high SNR, the system's performance based on the asymptotic approximation matches the simulation results, which validates the accuracy of our model. An essential aspect of this study is analyzing power allocation coefficients, as they directly affect system efficiency and energy utilization. In this regard, we derived the power allocation coefficients for the primary and secondary users and presented the corresponding analyses. Based on these coefficients, we calculated the SU's throughput, and system's overall energy efficiency. Our findings indicate that the proposed model reduces outage probability and increases throughput while also enhancing energy efficiency, which is crucial for sustainability and optimal resource utilization. Ultimately, this work can significantly enhance the performance of NOMA and cognitive radio (CR) systems, making it more applicable to the next generation of communication networks.