NDLI: Adaptive biasing scheme for load balancing in backhaul constrained small cell networks

Content Provider	IET Digital Library
Author	Xu, Yang Yin, Rui Yu, Guanding
Abstract	In this study, a distributed biasing scheme is designed to achieve load balancing for heterogeneous networks. Based on the limited backhaul capacity and user distribution in the system, each small cell base station adaptively and distributively changes its cell range by setting the bias value, to effectively utilise the wireless resource and achieve load balancing as well. The Q-learning algorithm is adopted to design the biasing scheme in each small cell base station. The tradeoff between the backhaul resource utilisation and the quality-of-service of users is considered in the reward function of the Q-learning model. To examine the performance of the distributed scheme, a centralised scheme aiming at maximising the backhaul resource utilisation is also proposed for comparison, whose performance lower bound is derived. Numerical results show that the proposed distributed scheme can effectively utilise the backhaul resource for load balancing, and achieve a close performance to the centralised one.
Starting Page	999
Ending Page	1005
Page Count	7
ISSN	17518628
Volume Number	9
e-ISSN	17518636
Issue Number	Issue 7, May (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-com/9/7
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-com.2014.0749
Journal	IET Communications
Publisher Date	2015-03-17
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Distributed Biasing Scheme Backhaul Constrained Small Cell Network Backhaul Resource Utilisation Cellular Radio Learning in AI Load Balancing Mobile Radio System Numerical Analysis Numerical Method Q-learning Algorithm Quality of Service Resource Allocation Small Cell Base Station User Distribution System Wireless Resource
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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