Fast First-order Methods for the Massive Robust Multicast Beamforming Problem with Interference Temperature Constraints

Huikang Liu; Peng Wang; Anthony Man-Cho So

ICASSP 2019accepted0 citations

Fast First-order Methods for the Massive Robust Multicast Beamforming Problem with Interference Temperature Constraints

Huikang Liu, Peng Wang, Anthony Man-Cho So

Abstract

In this paper, we consider the large-scale case of the robust beamforming problem with interference temperature constraints. Previous semidefinite relaxation (SDR) method becomes impracticable because of its expensive computational cost. Even successive convex approximation (SCA) method, the state-of-the-art method, cannot tackle this problem efficiently. Thus, we are motivated to design two efficient first-order methods, multi-block alternating direction method of multipliers (ADMM) and linear programming-assisted subgradient descent (LPA-SD), to solve it. Numerical results demonstrate the potential of our proposed methods in terms of both computational efficiency and solution quality.

BibTeX

@inproceedings{icassp2019_fastfirstorderme,
  title = {Fast First-order Methods for the Massive Robust Multicast Beamforming Problem with Interference Temperature Constraints},
  author = {Huikang Liu and Peng Wang and Anthony Man-Cho So},
  booktitle = {ICASSP 2019},
  year = {2019}
}