Approximation Algorithms for Online User Association in Multi-Tier Multi-Cell Mobile Networks

Abstract

The constantly growing wireless bandwidth demand is pushing wireless networks to multi-tier architectures consisting of a macrocell tier and a number of dense small cell deployment tiers. 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 designing static user-cell association algorithms, which, to achieve optimality, are periodically applied whenever there are new user arrivals, thus potentially inducing a large number of re-associations for previously arrived users. On the other hand, practical online algorithms that do not allow any such user re-association are often based on heuristics and may not have any performance guarantees. In this paper, we propose online algorithms for the multi-tier multi-cell user association problem that have provable performance guarantees, which improve previously known bounds by a sizable amount. The proposed algorithms are motivated by online combinatorial auctions, while capturing and leveraging the relative sparsity of choices in wireless networks as compared with auction setups. Our champion algorithm is a $\frac {1}{2-a^{-1}}$ approximation algorithm, where $a$ is the maximum number of feasible associations for a user and is, in general, small due to path loss. Our analysis considers the state-of-the-art wireless technologies, such as massive and multiuser MIMO, and practical aspects of the system such as the fact that highly mobile users have a preference to connect to larger cell tiers to keep the signaling overhead low. 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.