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. In such cases, the transmission rate needs to be backed off from the estimated rate to reduce outages. It is shown that the optimal scheduling policy remains threshold-based, and that the rate threshold turns out to hinge on the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, we devise suboptimal linear backoff schemes that back off the estimated signal-to-noise ratio (SNR) and hence the rate. The corresponding optimal backoff ratios and rate thresholds can be obtained via iterative algorithms. Finally, simulation results are provided to illustrate the tradeoff between increased training time to improve channel estimation and probing efficiency.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics
- Ad hoc networks.
- Channel Estimation
- Distributed opportunistic scheduling
- Optimal stopping theory