Learning Oriented Cross-Entropy Approach to User Association in Load-Balanced HetNet

• Published in: IEEE wireless communications letters Vol. 7; no. 6; pp. 1014 - 1017
• Main Authors: Huang, Xietian, Xu, Wei, Xie, Guo, Jin, Shi, You, Xiaohu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Combinatorial analysis; Computational geometry; Computer simulation; Convex functions; Convexity; Cross-entropy (CE); Entropy; Entropy (Information theory); heterogeneous network (HetNet); Load balancing; Machine learning; Optimization; Probabilistic models; Probability density function; Probability distribution; Random variables; Sampling; Statistical analysis; Stochastic processes; stochastic sampling; user association
• ISSN: 2162-2337; 2162-2345
• DOI: 10.1109/LWC.2018.2846610

This letter considers optimizing user association in a heterogeneous network via utility maximization, which is a combinatorial optimization problem due to integer constraints. Different from existing solutions based on convex optimization, we alternatively propose a cross-entropy (CE)-based algorithm inspired by a sampling approach developed in machine learning. Adopting a probabilistic model, we first reformulate the original problem as a CE minimization problem which aims to learn the probability distribution of variables in the optimal association. An efficient solution by stochastic sampling is introduced to solve the learning problem. The integer constraint is directly handled by the proposed algorithm, which is robust to network deployment and algorithm parameter choices. Simulations verify that the proposed CE approach achieves near-optimal performance quite efficiently.