Distributed Optimization with Incomplete Information for Heterogeneous Cellular Networks

Haibo DAI
Chunguo LI

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A    No.7    pp.1578-1582
Publication Date: 2017/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.1578
Type of Manuscript: LETTER
Category: Numerical Analysis and Optimization
heterogeneous cellular networks,  incomplete information,  game theory,  robust learning algorithms,  

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In this letter, we propose two robust and distributed game-based algorithms, which are the modifications of two algorithms proposed in [1], to solve the joint base station selection and resource allocation problem with imperfect information in heterogeneous cellular networks (HCNs). In particular, we repeatedly sample the received payoffs in the exploitation stage of each algorithm to guarantee the convergence when the payoffs of some users (UEs) in [1] cannot accurately be acquired for some reasons. Then, we derive the rational sampling number and prove the convergence of the modified algorithms. Finally, simulation results demonstrate that two modified algorithms achieve good convergence performances and robustness in the incomplete information scheme.