A new method of hybrid optimization of small cell range development and density for energy efficient ultra‐dense networks

Abstract

Ultra-dense networks (UDNs) which comprise of small and macro cells, are considered the principal solution for increasing demands of data. However, making energy efficient networks will be a challenge for researchers. In this article, a hybrid optimization technique is proposed to enhance the energy efficiency (EE) of the UDNs, considering the user correlation and hybrid optimization of the density and transmitting power of the small-cell-base-station (SBS). More precisely, the problem framed is a problem of convex-linear programming and hence is divided into two sub-problems: cell correlation and hybrid optimization. The expression for the EE of the system is derived in the closed-form as a factor of the density of SBSs and small cell-range-development (CRD) bias dependent on the Poisson point process (PPP) followed by the sequential-search-algorithm to improve the small CRD bias and SBS density correspondingly. Additionally, to realize hybrid optimization of the small CRD bias and density of SBSs, a heuristic algorithm is put forward to accomplish the EE of the system. It is revealed from the simulation outcomes that the proposed small CRD bias and SBS density hybrid optimization significantly enhances the EE of the system with low computational intricacy.

DCC: CF-MIMO- A new Dynamic Cooperation Cluster Driven Cell-Free Massive MIMO Architecture Network for Future Networks

“…They also indicated that connecting photonics well-defined radio with machine learning (ML) will be a major development of artificial intelligencein 6G. The 6G technologies, scenarios, and system architecture were also described [6].…”