Distributed Downlink Power Control by Message-Passing for Very Large-Scale Networks

Abstract

Downlink power control is revisited by assuming very large-scale networks. In very large-scale networks, conventional centralized power control schemes quickly become impractical owing to the huge computational burden and limited backhaul capacity. Alternative distributed power control schemes have been proposed; however, these schemes suffer from poor performance when compared with the centralized power control. In this work, a completely new approach to distributed downlink power control is proposed using a belief-propagation (BP) framework. The proposed BP approach includes two tasks: first, the sum rate maximizing power control problem is modeled as a factor graph representation. Second, a message-passing algorithm is constructed on the basis of the factor graph, which efficiently computes a near-optimal solution in a distributed manner. The practical issues for implementing the proposed BP approach are extensively discussed in terms of the computational complexity, signaling overhead, convergence, and latency. Surprisingly, the simulation results verify that the average sum rate performance of the proposed BP-based power control is nearly equivalent to that of centralized power control schemes. The proposed BP-based power control even outperforms the centralized binary on/off power control and approaches the performance of geometric programming power control, which is the best-known centralized power control, within only 0.8% of the average sum rate.