Joint Scheduling and Resource Allocation in OFDMA Downlink Systems via ACK/NAK Feedback

TL;DR

This paper addresses joint scheduling and resource allocation in OFDMA downlink systems using ACK/NAK feedback, proposing a practical greedy algorithm with polynomial complexity that performs close to the optimal POMDP solution in slowly fading channels.

Contribution

It introduces a low-complexity greedy approach for joint scheduling and resource allocation based on posterior channel distributions updated via particle filtering.

Findings

Greedy approach performs close to the POMDP upper bound in slowly fading channels.

The proposed method significantly outperforms fixed-power random user scheduling.

Particle filtering enables efficient recursive updates of channel posteriors.

Abstract

In this paper, we consider the problem of joint scheduling and resource allocation in the OFDMA downlink, with the goal of maximizing an expected long-term goodput-based utility subject to an instantaneous sum-power constraint, and where the feedback to the base station consists only of ACK/NAKs from recently scheduled users. We first establish that the optimal solution is a partially observable Markov decision process (POMDP), which is impractical to implement. In response, we propose a greedy approach to joint scheduling and resource allocation that maintains a posterior channel distribution for every user, and has only polynomial complexity. For frequency-selective channels with Markov time-variation, we then outline a recursive method to update the channel posteriors, based on the ACK/NAK feedback, that is made computationally efficient through the use of particle filtering. To gauge the performance of our greedy approach relative to that of the optimal POMDP, we derive a POMDP performance upper-bound. Numerical experiments show that, for slowly fading channels, the performance of our greedy scheme is relatively close to the upper bound, and much better than fixed-power random user scheduling (FP-RUS), despite its relatively low complexity.