Dynamic Clustering and on/off Strategies for Wireless Small Cell Networks

Abstract

In this paper, a novel cluster-based approach for maximizing the energy efficiency of wireless small cell networks is proposed. A dynamic mechanism is proposed to group locallycoupled small cell base stations (SBSs) into clusters based on location and traffic load. Within each formed cluster, SBSs coordinate their transmission parameters to minimize a cost function which captures the tradeoffs between energy efficiency and flow level performance, while satisfying their users' quality-of-service requirements. Due to the lack of inter-cluster communications, clusters compete with one another in order to improve the overall network's energy efficiency. This inter-cluster competition is formulated as a noncooperative game between clusters that seek to minimize their respective cost functions. To solve this game, a distributed learning algorithm is proposed using which clusters autonomously choose their optimal transmission strategies based on local information. It is shown that the proposed algorithm converges to a stationary mixed-strategy distribution which constitutes an epsilon-coarse correlated equilibrium for the studied game. Simulation results show that the proposed approach yields significant performance gains reaching up to 36% of reduced energy expenditures and up to 41% of reduced fractional transfer time compared to conventional approaches. Index Terms-Smallcell networks; energy efficiency; learning; game theory; 5G. Sumudu Samarakoon received his B. Sc. (Hons.) degree in Electronic and Telecommunication Engineering from the University of Moratuwa, Sri Lanka and the M. Eng. degree from the Asian Institute of Technology, Thailand in 2009 and 2011, respectively. He is currently persuading Dr. Tech degree in Communications Engineering in University of Oulu, Finland. Sumudu is also a member of the research staff of the Centre for Wireless Communications (CWC), Oulu, Finland. His main research interests are in heterogeneous networks, radio resource management, machine learning, and game theory.

A distributed learning–based user association for heterogeneous networks

“…For comparison purposes, we consider an additional UEA scheme in which the UEs select their serving BSs based on the RSSI and the BS's estimated load. 22 It is referred to hereinafter as "baseline UEA".…”

“…In this respect, each BS periodically broadcasts its estimated load through a control message. 22,26 Our adaptive proposed scheme is based on the learning algorithm. In this regard, we use historical information on the UE's performance instead of completely relying on the instantaneous measurement.…”

“…To estimate the load, the learning tool used in Equation (9) considers the long-term history and the instantaneous load, and it balances between them. 22 Let η k ≥ 0 be a private UE-dependent parameter that characterizes the personal preference of UE k for the effect of the BS's estimated load to the UEA metric. It balances the trade-off between the probability of UE outages and received SINR.…”

“…To solve the problem, a gradient descent method is adopted. A UEA scheme based on the BS's estimated load is considered in the work of Samarakoon et al, 22 in which each BS broadcasts its estimated load through a beacon signal in downlink transmission. In the work of Ye et al, 23 to implement UE-BS association, a logarithmic utility maximization problem is developed, in which a fractional association assumption is used to convert the problem into a convex optimization problem.…”

20 Years of Evolution From Cognitive to Intelligent Communications

“…For example, in a large-scale network with multidimensional radio resource, it is challenging to manage the radio resource in a centralized manner due to high signalling costs. Alternatively, nearby communication nodes can be clustered into the same group, in which the radio resource can be coordinated among the communication nodes [57]- [60]. The intuition behind is two-fold.…”