Non-orthogonal multiple access with imperfect successive interference cancellation in spectrum-sharing network under interference power constraints
A dissertation submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2020 === In this research, a non-orthogonal multiple access (NOMA) scheme is investigated with suc...
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| Format: | Others |
| Language: | en |
| Published: |
2021
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| Online Access: | https://hdl.handle.net/10539/31116 |
| Summary: | A dissertation submitted to the Faculty of Engineering and the Built Environment, University of Witwatersrand, in fulfilment of the requirements for the degree of Master of Science in Engineering, 2020 === In this research, a non-orthogonal multiple access (NOMA) scheme is investigated with successive interference cancellation (SIC) error in an underlay cognitive radio network. This system has an intermediate set of relays in the secondary network to improve network reliability. The research examines the outage probability of a dual-hop NOMA system with SIC error exposed to Rayleigh fading. The secondary network is allowed to access the licensed primary network frequency band if it does not exceed the maximum allowable transmit power. Concurrent transmission of the primary and secondary network will only occur if the secondary network transmissions do not exceed preset interference temperature. The inclusion of relays increases network coverage and the research analyses the effect of the number of relays in the system and makes a comparison of outage probability of the secondary network in decode and forward (DF) and amplify and forward (AF) relaying scheme. Considering interference power constraints, a relay that maximises the received signal-to-interference-and-noise ratio (SINR) is selected among the secondary set of relays. We derive analytical expressions for the asymptotic analysis of the NOMA system and then perform simulations to verify the accuracy of the model === CK2021 |
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