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SELECT: Self-Learning Collision Avoidance for Wireless Networks

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4 Author(s)
Chun-cheng Chen ; Univ. of Illinois at Urbana-Champaign, Urbana ; Eunsoo Seo ; Hwangnam Kim ; Haiyun Luo

The limited number of orthogonal channels and autonomous installations of hot spots and home wireless networks often leave neighboring 802.11 basic service sets (BSSs) operating on the same or overlapping channels, therefore interfering with each other. However, the 802.11 medium access control (MAC) does not work well in resolving inter-BSS interference due to the well-known hidden/exposed-receiver problem, which has been haunting the research community for more than a decade. In this paper, we propose SELECT, an effective and efficient self-learning collision avoidance strategy to address the hidden/exposed-receiver problem in 802.11 wireless networks. SELECT is based on the observation that carrier sense with received signal strength (RSS) measurements at the sender and the receiver can be strongly correlated. A SELECT-enabled sender exploits such correlation using an automated online learning algorithm and makes an informed judgment of the channel availability at the intended receiver. SELECT achieves collision avoidance at packet-level time granularity, involves zero communication overhead, and easily integrates with the 802.11 distributed coordination function (DCF). Our evaluation in analysis, simulations, and prototype experiments show that SELECT addresses the hidden/exposed-receiver problem well. In typical hidden/exposed-receiver scenarios, SELECT improves the throughput by up to 140 percent and the channel access success ratio by up to 302 percent while almost completely eliminating contention-induced data packet drops.

Published in:

Mobile Computing, IEEE Transactions on  (Volume:7 ,  Issue: 3 )