Scalable Spectrum Allocation for Large Networks Based on Sparse Optimization

TL;DR

This paper introduces a scalable method for spectrum allocation in large cellular networks by leveraging sparse optimization techniques, significantly reducing complexity and improving throughput.

Contribution

It proposes a novel sparse optimization framework that reduces the problem size from exponential to linear in the number of access points, enabling efficient spectrum allocation.

Findings

Achieves substantial throughput increase over benchmarks

Reduces problem complexity from exponential to linear in network size

Uses iterative reweighted l1 algorithm for approximate solutions

Abstract

Joint allocation of spectrum and user association is considered for a large cellular network. The objective is to optimize a network utility function such as average delay given traffic statistics collected over a slow timescale. A key challenge is scalability: given $n$ Access Points (APs), there are $O(2^n)$ ways in which the APs can share the spectrum. The number of variables is reduced from $O(2^n)$ to $O(nk)$, where $k$ is the number of users, by optimizing over local overlapping neighborhoods, defined by interference conditions, and by exploiting the existence of sparse solutions in which the spectrum is divided into $k+1$ segments. We reformulate the problem by optimizing the assignment of subsets of active APs to those segments. An $\ell_0$ constraint enforces a one-to-one mapping of subsets to spectrum, and an iterative (reweighted $\ell_1$) algorithm is used to find an approximate solution. Numerical results for a network with 100 APs serving several hundred users show the proposed method achieves a substantial increase in total throughput relative to benchmark schemes.