Joint Opportunistic Scheduling in Multi-Cellular Systems

TL;DR

This paper investigates joint opportunistic scheduling in multi-cellular systems with partial channel information, proposing a framework that combines channel learning, interference mitigation, and optimal policy derivation using Restless Multiarmed Bandit models.

Contribution

It introduces a novel joint scheduling framework that integrates channel learning and interference mitigation, and studies the indexability of the problem within a bandit process context.

Findings

Optimal joint scheduling policy established under system constraints.

Numerical evidence supports the indexability conjecture.

Partial structure of the index policy derived.

Abstract

We address the problem of multiuser scheduling with partial channel information in a multi-cell environment. The scheduling problem is formulated jointly with the ARQ based channel learning process and the intercell interference mitigating cell breathing protocol. The optimal joint scheduling policy under various system constraints is established. The general problem is posed as a generalized Restless Multiarmed Bandit process and the notion of indexability is studied. We conjecture, with numerical support, that the multicell multiuser scheduling problem is indexable and obtain a partial structure of the index policy.