On outage probability and throughput performance of cognitive radio inspired NOMA relay system

To improve the throughput and the outage probability of the Cognitive Radio (CR) inspired system, a novel Non-Orthogonal Multiple Access (NOMA) can be deployed to adapt multiple services in 5G wireless communication. In this scheme, after the reception of the superposition coded symbol with a predefined power allocation factors from the primary source, the relay decodes and forwards with a new superposition coded symbol (i.e. allocating another power factors) to the destination. By employing twin antenna design at the relay, the bandwidth efficiency in such scheme will be improved. Assuming Rayleigh fading channels, the closed-form expressions in terms of throughput and the outage performance are derived. Through numerical results, they showed that the outage performance of the proposed scheme using a Single Antenna (SA) scheme at the relay is better than a Twin Antenna (TA) scheme because SA scheme is not affected by its own antenna interference

Energy-efficient non-orthogonal multiple access for wireless communication system

Non-orthogonal multiple access (NOMA) has been recognized as a potential solution for enhancing the throughput of next-generation wireless communications. NOMA is a potential option for 5G networks due to its superiority in providing better spectrum efficiency (SE) compared to orthogonal multiple access (OMA). From the perspective of green communication, energy efficiency (EE) has become a new performance indicator. A systematic literature review is conducted to investigate the available energy efficient approach researchers have employed in NOMA. We identified 19 subcategories related to EE in NOMA out of 108 publications where 92 publications are from the IEEE website. To help the reader comprehend, a summary for each category is explained and elaborated in detail. From the literature review, it had been observed that NOMA can enhance the EE of wireless communication systems. At the end of this survey, future research particularly in machine learning algorithms such as reinforcement learning (RL) and deep reinforcement learning (DRL) for NOMA are also discussed