Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
167 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

How Penalty Leads to Improvement: a Measurement Study of Wireless Backoff (1208.6318v3)

Published 30 Aug 2012 in cs.NI

Abstract: Despite much theoretical work, different modifications of backoff protocols in 802.11 networks lack empirical evidence demonstrating their real-life performance. To fill the gap we have set out to experiment with performance of exponential backoff by varying its backoff factor. Despite the satisfactory results for throughput, we have witnessed poor fairness manifesting in severe capture effect. The design of standard backoff protocol allows already successful nodes to remain successful, giving little chance to those nodes that failed to capture the channel in the beginning. With this at hand, we ask a conceptual question: Can one improve the performance of wireless backoff by introducing a mechanism of self-penalty, when overly successful nodes are penalized with big contention windows? Our real-life measurements using commodity hardware demonstrate that in many settings such mechanism not only allows to achieve better throughput, but also assures nearly perfect fairness. We further corroborate these results with simulations and an analytical model. Finally, we present a backoff factor selection protocol which can be implemented in access points to enable deployment of the penalty backoff protocol to consumer devices.

Citations (17)

Summary

We haven't generated a summary for this paper yet.