A Quantile-Based Sequential Feedback Scheme via Overhearing in Multicarrier Access Networks

TL;DR

This paper introduces a quantile-based feedback scheme for multicarrier networks that reduces CSI feedback overhead by leveraging overhearing and probabilistic inference, improving efficiency in opportunistic scheduling.

Contribution

It formulates feedback overhead minimization as a Bayesian network problem and proposes a greedy heuristic for efficient inference in complex network structures.

Findings

Significant reduction in feedback overhead demonstrated in simulations

Effective inference methods for tree-like and certain complex network structures

Enhanced opportunistic scheduling performance with the proposed scheme

Abstract

We propose a scheme to reduce the overhead associated with channel state information (CSI) feedback required for opportunistic scheduling in multicarrier access networks. We study the case where CSI is partially overheard by mobiles and one can suppress transmitting CSI reports for time varying channel of inferior quality. As a means to assess channel quality and exploit multiuser diversity we adopt maximum quantile (MQ) scheduling. We show that the problem of minimizing the average feedback overhead can be formulated as a Bayesian network problem. A greedy heuristic using probabilistic inference is proposed to deal with the NP-hardness of the problem. Leveraging properties of MQ scheduling we first show that networks having tree-like overhearing graphs admit simple inference. We then present a class of more general network structures for which exact inference is computationally tractable. Simulation results are provided to demonstrate the improvements offered by the proposed heuristic.