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A distributed implementation of mobility load balancing with power control and cell reselection in 4G network
Author(s) -
Kang Hye J.,
Kang Chung G.
Publication year - 2014
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.1860
Subject(s) - computer science , load balancing (electrical power) , greedy algorithm , node (physics) , transmission (telecommunications) , computer network , small cell , throughput , distributed computing , cellular network , distributed algorithm , scheme (mathematics) , base station , channel (broadcasting) , power control , information exchange , power (physics) , algorithm , wireless , telecommunications , mathematical analysis , geometry , mathematics , physics , structural engineering , quantum mechanics , engineering , grid
SUMMARY We consider a self‐organizing network (SON) capability of mobility load balancing in a 4G network, which determines the transmission power level for individual base stations and cell reselection for individual mobile stations such that the network‐wide load is minimized while satisfying the minimum signal‐to‐noise and interference ratio (SINR) requirement of individual users. Both centralized and distributed schemes are proposed. The centralized scheme is based on the greedy algorithm, serving as a performance bound to the distributed scheme. The distributed scheme is to solve the system‐wide optimization problem in the flat network model, i.e. no central control node. Furthermore, it requires relatively low inter‐cell exchange information among neighboring cells over an inter‐cell channel, e.g. X2 interface in the LTE network. The proposed design objective is to minimize the number of mobile users that do not satisfy the specified average throughput, while distributing the user traffic load as uniformly as possible among the neighboring cells. Our simulation results for a uniform user distribution demonstrate that the proposed scheme can achieve up to almost 80% of a load balancing gain that has been achieved by a greedy algorithm in the centralized optimization. Copyright © 2014 John Wiley & Sons, Ltd.