Application of genetic algorithm for downlink NOMA resource allocation

Non-Orthogonal Multiple Access (NOMA) as a multiple access scheme can be instrumental to satisfy the high system capacity requirement of the 5G. NOMA increases the spectral efficiency by providing simultaneous transmission of multiple users at the same radio resource at the transmitter and employing more sophisticated signal processing techniques at the receiver such as successive interference cancellation (SIC). The user group selection is one of the important elements that affects the performance of NOMA. In this thesis, the Genetic Algorithm (GA) approach is proposed to determine the user pair selection for multi-user OFDM based NOMA downlink system. GA is a powerful meta-heuristic to explore a huge search space when there is no polynomial time solution. The proposed GA procedure can be applied to find a reasonable solution for any underlying objective function as long as the corresponding fitness function is appropriately set. We employ two different objectives as the GA fitness function: the first one maximizes the total system throughput while the second one maximizes the system fairness. The simulations are performed to evaluate the performance of the GA approach under different scenarios, where there is a base station at the center of rectangular area for 2 km by 2 km. Users are randomly distributed over this area. The results demonstrate that the GA tool can be successfully used to converge the desired objectives within a reasonable time. However, a slight performance degradation in the GA approach is observed when the search space is relatively high.