SELECT: Self-learning Collision Avoidance for Wireless Networks

Abstract — The limited number of orthogonal channels and autonomous installations of hotspots 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 MAC does not work well in resolving inter-BSS interference due to the well-known hidden/exposed receiver problem, which has been haunting in 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 automated on-line learning algorithm, and makes informed judgment of the channel availability at the intended receiver. SE-LECT achieves collision avoidance at packet-level time granularity, involves zero communication overhead, and easily integrates with the 802.11 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 % and channel access success ratio by up to 302%, while almost completely eliminating contention-induced data packet drops. Index Terms — Wireless communication, Access schemes, Algorithm/protocol design and analysi