Ant‐colony algorithm with interference cancellation for cooperative transmission

Abstract

Despite the large bandwidth available for all users in high-speed wireless networks, resources allocation and user scheduling remain essential to combat interference, increase throughput and reduce complexity. As the number of users increases, the computational complexity tends to increase significantly. The trade-off between the complexity reduction and capacity improvement is the challenge. Hence a unique ant-colony optimisation method is implemented with successive interference cancellation to reduce complexity and provide higher capacity to more users. The incurred complexity is at least 50% less than other schemes. The average mean square error achieved is around 4 dB smaller than that of the existing scheme.

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

“…Several methods are proposed for the formation of user pairs such as round robin [28]. As these approaches tend to require significantly higher computational loads when the number of users increases, new computationally lower schemes have been proposed in literature to determine the user pairs such as the heuristic models which are inspired by artificial intelligence approaches [29][30][31][32][33][34] which include drosophila optimization algorithm [35], particle swarm optimization algorithm [36][37][38], firefly optimization algorithm [39], dolphin echolocation algorithm [40], genetic algorithm [20,41] and ant-colony optimization algorithm [19,42]. These models have the ability to solve problems in varying fields such as, but not limited to, transportation, signal processing, image processing and biomedical engineering.…”

“…An ant generally moves in a random fashion prior finding the food. When the food is found, the ant will return to its colony leaving markers, known as pheromones, that instigate other ants to take the same path for finding the food [19]. The concentration of pheromone in a particular (optimal) path depends on the number of ants using the same particular path to reach the food location.…”

“…For grouping and scheduling the users in NOMA, the best heuristic algorithm presented in literature so far is ACO. It has been demonstrated to be able to group the users in cellular networks [19]. Other heuristic algorithms are suitable for other applications.…”

“…Firefly optimization and particle swarm optimization require a smaller number of parameters but so far implemented with a small number of users per sub channel. The practical solution provided by ACO [19,42,51] method for NOMA is useful as the number of mobile users using the cellular network services keeps increasing in this age. It is a method worth to be implemented and further developed and presented in the research and academic community.…”

“…While reducing the computational complexity, the achievable throughput and spectral efficiency of NOMA should be maintained or improved to be better than OMA, which is the prior access technique implemented in cellular networks. To tackle these issues, scheduling methods with lower computational complexity including ACO-based schemes [19] and heuristic methods such as particle swarm optimization (PSO) schemes, genetic algorithms [20] are proposed for cellular and wireless networks [21] to improve the throughput, increase the spectral efficiency, and reduce the complexity and interference [22]. Other scheduling schemes have also been proposed in literature to obtain the best user pair such as round robin (RR) Scheduling but it has high computational complexity [18].…”