Particle swarm optimization of the spectral and energy efficiency of an SCMA‐based heterogeneous cellular network

Abstract Background The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)‐based heterogeneous cellular networks (HCNs) is still mostly unknown. Aim This research study seeks to provide insight into the interaction between SE and EE in SBS sleep‐mode enabled SCMA‐based HCNs. Methodology A model that characterizes the energy‐spectral‐efficiency (ESE) of a two‐tier SBS sleep‐mode enabled SCMA‐based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA‐based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA‐based HCN is unoptimized. Results The Pareto‐optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA‐based HCN at high traffic levels and a convex front that allows network operators to select the SE‐EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA‐based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA‐based HCN. The optimum SE and EE achieved by the SCMA‐based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion In sleep‐mode enabled SCMA‐based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.