Distributed Pricing-Based User Association for Downlink Heterogeneous Cellular Networks

TL;DR

This paper introduces distributed algorithms for user association in heterogeneous cellular networks, optimizing for fairness, power control, and beamforming, with improved performance guarantees and reduced handovers.

Contribution

It proposes novel distributed price update and joint optimization algorithms for user association, power control, and beamforming, with rigorous performance guarantees and practical implementation advantages.

Findings

The price update algorithm is parameter-independent and asynchronous.

Joint user association and power control significantly outperform existing methods.

The beamforming approach achieves near-benchmark performance with fewer handovers.

Abstract

This paper considers the optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power, and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem, and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to the existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem, and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm, and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association), while avoiding excessive BS handover.