AbstractNon-orthogonal multiple access (NOMA) is a promising candidate for future wireless networks due to its improved spectral efficiency (SE), massive connectivity and low latency. This thesis focuses on studying the error rate and throughput performances of NOMA systems considering various applications including Internet of Things (IoT) and mobile communications. Exact and asymptotic bit error rate (BER)/block error rate (BLER) expressions are derived considering different fading channels such as Rayleigh, Rician and Nakagami-m. Various system parameters such as the number of users, modulation orders, number of receiving antennas, power assignments and labelling schemes are taken into account in these expressions. The derived expressions are utilised to optimise the power assignments in order to achieve specific objectives while satisfying quality of service (QoS) constraints. Moreover, the power coefficients' bounds , which ensure users' fairness, and solve the constellation ambiguity problem, are derived for two and three user cases. In addition, the power assignment achieving equally spaced constellation points is derived for an arbitrary number of users and arbitrary modulation orders. The derived BER expressions are further used in the design cognitive NOMA, adaptive modulation NOMA and automatic repeat request (ARQ)- based non-orthogonal multiplexing (NOM) systems. While the throughput is derived analytically for the first two systems, it is only evaluated using Monte-Carlo simulations for the latter. Despite NOMAâs degraded BER performance compared to the orthogonal multiple access (OMA) schemes, it gains its superiority from the significant throughput performance gains at moderate and high signal to noise ratio (SNR) values, where the throughput can be as high as the number of multiplexed users/streams per communications resource. These results offer valuable insight into the system design, where the derived expressions can be utilised by the system scheduler to make informed decisions in selecting appropriate system parameters.
|Date of Award
|31 Dec 2023
|Emad Alsusa (Supervisor) & Zhiguo Ding (Supervisor)
- Performance Analysis
- Physical Layer