Online Algorithms for Basestation Allocation

TL;DR

This paper develops online algorithms for assigning users to basestations to maximize total data rates, operating without future knowledge and inspired by classic online problems, achieving near-optimal performance.

Contribution

It introduces novel online algorithms for basestation allocation that do not require future information, with performance guarantees close to offline optimal solutions.

Findings

Achieves constant factor approximation to offline optimal

Algorithms inspired by online k-secretary and maximum weight matching

Provides theoretical performance bounds for online basestation assignment

Abstract

Design of {\it online algorithms} for assigning mobile users to basestations is considered with the objective of maximizing the sum-rate, when all users associated to any one basestation equally share each basestation's resources. Each user on its arrival reveals the rates it can obtain if connected to each of the basestations, and the problem is to assign each user to any one basestation irrevocably so that the sum-rate is maximized at the end of all user arrivals, without knowing the future user arrival or rate information or its statistics at each user arrival. Online algorithms with constant factor loss in comparison to offline algorithms (that know both the user arrival and user rates profile in advance) are derived. The proposed online algorithms are motivated from the famous online k-secretary problem and online maximum weight matching problem.