An efficient approximation algorithm for online multi-tier multi-cell user association

The ever growing wireless bandwidth demand is pushing WiFi and cellular networks to dense multi-cell deployments, as well as to multi-tier architectures consisting of macrocells and small cells. In such a multi-tier multi-cell environment, the classic problem of associating users to base stations becomes both more challenging and more critical to the overall network performance. Most previous analytical work is focused on offline/static user-cell association, where the users' arrivals and their rates are assumed to be known in advance and thus has little practical relevance. On the other hand, practical online algorithms based on heuristics are often suboptimal and may not provide any performance guarantees. In this paper, we propose an online algorithm for the multi-tier multi-cell user association problem that has a provable performance guarantee which improves previously known bounds by a sizable amount. The proposed algorithm is motivated by online combinatorial auctions, while capturing and leveraging the relative sparsity of choices in wireless networks as compared to auction setups. Specifically, it is a 1/2−a₋₁ approximation algorithm, where a is the maximum number of feasible associations for a user and is, in general, small due to path loss. In addition to establishing formal performance bounds, we also conduct simulations under realistic assumptions which establish the superiority of the proposed algorithm over existing approaches under real-world scenarios.