Topological Interference Management with User Admission Control via Riemannian Optimization

TL;DR

This paper introduces a Riemannian optimization-based method for user admission control in topological interference management, maximizing admitted users while ensuring network feasibility using sparse and low-rank techniques.

Contribution

It proposes a novel approach combining sparse and low-rank optimization with Riemannian trust-region algorithms for user admission in TIM, which is a new application of these methods.

Findings

The Riemannian algorithm effectively maximizes admitted users.

The approach achieves near-optimal performance in simulations.

It provides a scalable solution for user admission in TIM.

Abstract

Topological interference management (TIM) provides a promising way to manage interference only based on the network connectivity information. Previous works on the TIM problem mainly focus on using the index coding approach and graph theory to establish conditions of network topologies to achieve the feasibility of topological interference management. In this paper, we propose a novel user admission control approach via sparse and low-rank optimization to maximize the number of admitted users for achieving the feasibility of topological interference management. To assist efficient algorithms design for the formulated rank-constrained (i.e., degrees-of-freedom (DoF) allocation) l0-norm maximization (i.e., user capacity maximization) problem, we propose a regularized smoothed l1- norm minimization approach to induce sparsity pattern, thereby guiding the user selection. We further develop a Riemannian trust-region algorithm to solve the resulting rank-constrained smooth optimization problem via exploiting the quotient manifold of fixed-rank matrices. Simulation results demonstrate the effectiveness and near-optimal performance of the proposed Riemannian algorithm to maximize the number of admitted users for topological interference management.