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In this paper we develop a framework for user association in infrastructure-based wireless networks, specifically focused on flow-level cell load balancing under spatially inhomogeneous traffic distributions. Our work encompasses several different user association policies: rate-optimal, throughput- optimal, delay-optimal, and load-equalizing, which we collectively denote α-optimal user association. We prove that the optimal load vector ρ∗ that minimizes a generalized system performance function is the fixed point of a certain mapping. Based on this mapping we propose and analyze an iterative distributed user association policy that adapts to spatial traffic loads and converges to a globally optimal allocation.
Kim et al. (2010) studied this question.