Delay‐sensitive user scheduling and power control in heterogeneous networks. Issue 3 (1st May 2015)

• Record Type: Journal Article
• Authors: Cheng, Aolin; Li, Jian; Yu, Yuling; Jin, Hao
• Is Part Of: IET networks. Volume 4:Issue 3(2015)
• Page Start: 175
• Page End: 184
• Publication Date: 2015-05-01
• Subjects: power control -- scheduling -- cellular radio -- delays -- infinite horizon -- Markov processes -- wireless channels -- minimisation -- computational complexity -- learning (artificial intelligence) -- adaptive control; Markov decision process -- optimal user scheduling -- QSI dynamics -- CSI dynamics -- distributive stochastic learning algorithm -- distributive implementation -- computational complexity reduction -- heterogeneous base stations -- power constraints -- average delay minimisation -- power control policy -- adaptive user scheduling -- queue state information -- channel state information -- MDP -- infinite horizon -- delay sensitive traffic service -- heterogeneous networks -- delay sensitive user scheduling; Computer network architectures -- Periodicals; Computer network protocols -- Periodicals; Information networks -- Periodicals; Telecommunication systems -- Periodicals; 004.605
• DOI: 10.1049/iet-net.2014.0026
• Languages: English
• ISSNs: 2047-4954

Abstract

Abstract : Delay‐sensitive traffic services, such as live streaming video, voice over IP and multimedia teleconferencing, requires low end‐to‐end delay in order to maintain its interactive and streaming nature. In recent years, the popularity of delay‐sensitive applications has been rapidly growing. In this study, the authors consider the delay‐sensitive user scheduling and power control design for heterogeneous networks (HetNets). They model the problem as an infinite‐horizon average cost Markov decision process MDP. Based on the dynamics of channel state information (CSI) and queue state information (QSI), they focus on finding an adaptive user scheduling and power control policy to minimise the average delay of network subjected to the power constraints of heterogeneous base stations. In order to reduce the computational complexity and facilitate the distributive implementation, the authors derive a distributive stochastic learning algorithm which only requires local CSI and local QSI to determine the optimal user scheduling and power control policy. The simulation results confirm that significant delay performance gain can be achieved compared with various baseline schemes.