Energy efficient user association and resource allocation in active array aided HetNets. Issue 6 (15th March 2018)

• Record Type: Journal Article
• Authors: Li, Qing; Yang, Qinghai; Qin, Meng; Kwak, Kyung‐Sup
• Is Part Of: IET communications. Volume 12:Issue 6(2018)
• Page Start: 672
• Page End: 679
• Publication Date: 2018-03-15
• Subjects: energy conservation -- resource allocation -- multicast communication -- nonlinear programming -- integer programming -- quality of service -- cellular radio -- iterative methods; energy efficient user association -- active array‐aided HetNet -- sustainable wireless networks -- system spectrum efficiency -- active array system -- heterogeneous networks -- resource allocation problem -- preference‐aware multicast service -- AAS‐aided HetNet -- spectrum efficiency -- mixed‐integer nonlinear fractional programming -- generalised fractional programming theory -- Lagrangian dual decomposition -- iterative algorithm -- quality of service‐guaranteed user association; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2017.0755
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : To enable sustainable wireless networks, though new technologies have been proposed to improve the system spectrum efficiency, the energy efficiency (EE) is also of vital importance due to the increasing users and devices. Active array system (AAS) and heterogeneous networks (HetNets) have been reckoned as an enormous enhancement in spectrum efficiency and EE. In this study, the energy efficient user association and resource allocation problem for preference‐aware multicast service in AAS aided HetNets is investigated, formulated as a mixed‐integer non‐linear fractional programming. By generalised fractional programming theory and Lagrangian dual decomposition, an iterative algorithm is devised to determine user association and resource allocation. Further, an efficient solution is proposed to perform quality of service‐guaranteed user association and resource allocation to maximise the EE. Simulation results demonstrate the convergence performance and potential gain of the proposed algorithms in terms of EE.