Distributed Opportunistic Scheduling For Ad-Hoc Communications Under Noisy Channel Estimation

TL;DR

This paper extends distributed opportunistic scheduling in wireless ad-hoc networks to scenarios with noisy channel estimation, proposing a threshold-based policy and a practical linear backoff scheme to optimize throughput.

Contribution

It generalizes the optimal scheduling policy to noisy estimation conditions and introduces a suboptimal linear backoff method with an iterative algorithm for parameter tuning.

Findings

Optimal threshold policy remains threshold-based under noisy estimation.

Proposed linear backoff scheme effectively balances estimation accuracy and probing efficiency.

Simulation demonstrates tradeoffs between training time and throughput.

Abstract

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be threshold-based, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency.