Joint power allocation and user association in non-orthogonal multiple access networks: An evolutionary approach

Joint power allocation and user association in non-orthogonal multiple access networks: An evolutionary approach. Goudos, S. Physical Communication, 2019.
doi abstract bibtex

© 2019 Elsevier B.V. In this paper, the problem of joint power allocation and user association is studied for non-orthogonal multiple access (NOMA) downlink networks with multiple base stations (BSs). We consider that users are grouped into orthogonal clusters to allocate into different physical resource blocks (PRBs). The problem is formulated using two different utility functions. The first is the maximization of the weighted sum rate and the other is the maximization of the minimum achievable user rate. We apply two different evolutionary algorithms in order to solve this problem. Namely, the recently introduced Salp Swarm Algorithm (SSA) and the popular Particle Swarm Optimization (PSO). The simulation results show that the above-described problem can be effectively solved by both algorithms. SSA is more efficient in average than PSO. The effect of increasing the number of user is also studied. In this case the problem becomes more difficult to solve, which indicates that more network resources are required.

@article{
 title = {Joint power allocation and user association in non-orthogonal multiple access networks: An evolutionary approach},
 type = {article},
 year = {2019},
 keywords = {5G,Evolutionary algorithms,NOMA,Non-orthogonal multiple access,Power control,User association},
 volume = {37},
 id = {763fb5d0-03c4-3c3e-8fa0-d7fef5616e9c},
 created = {2020-02-29T16:57:41.756Z},
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 last_modified = {2022-05-12T20:32:45.805Z},
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 abstract = {© 2019 Elsevier B.V. In this paper, the problem of joint power allocation and user association is studied for non-orthogonal multiple access (NOMA) downlink networks with multiple base stations (BSs). We consider that users are grouped into orthogonal clusters to allocate into different physical resource blocks (PRBs). The problem is formulated using two different utility functions. The first is the maximization of the weighted sum rate and the other is the maximization of the minimum achievable user rate. We apply two different evolutionary algorithms in order to solve this problem. Namely, the recently introduced Salp Swarm Algorithm (SSA) and the popular Particle Swarm Optimization (PSO). The simulation results show that the above-described problem can be effectively solved by both algorithms. SSA is more efficient in average than PSO. The effect of increasing the number of user is also studied. In this case the problem becomes more difficult to solve, which indicates that more network resources are required.},
 bibtype = {article},
 author = {Goudos, S.K.},
 doi = {10.1016/j.phycom.2019.100841},
 journal = {Physical Communication}
}

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