Joint Beamforming and Power Control in Coordinated Multicell: Max-Min Duality, Effective Network and Large System Transition

TL;DR

This paper develops a distributed beamforming and power control algorithm for multicell systems that maximizes the minimum SINR, leveraging duality, nonlinear Perron-Frobenius theory, and random matrix theory for asymptotic optimality without requiring real-time backhaul communication.

Contribution

It introduces a novel asymptotically optimal distributed algorithm that uses only statistical information, eliminating the need for instantaneous backhaul updates.

Findings

Algorithm achieves fast convergence in distributed settings.

No real-time backhaul communication needed for power updates.

Effective primal and dual networks characterize asymptotic solutions.

Abstract

This paper studies joint beamforming and power control in a coordinated multicell downlink system that serves multiple users per cell to maximize the minimum weighted signal-to-interference-plus-noise ratio. The optimal solution and distributed algorithm with geometrically fast convergence rate are derived by employing the nonlinear Perron-Frobenius theory and the multicell network duality. The iterative algorithm, though operating in a distributed manner, still requires instantaneous power update within the coordinated cluster through the backhaul. The backhaul information exchange and message passing may become prohibitive with increasing number of transmit antennas and increasing number of users. In order to derive asymptotically optimal solution, random matrix theory is leveraged to design a distributed algorithm that only requires statistical information. The advantage of our approach is that there is no instantaneous power update through backhaul. Moreover, by using nonlinear Perron-Frobenius theory and random matrix theory, an effective primal network and an effective dual network are proposed to characterize and interpret the asymptotic solution.