Exploiting Spatial Channel Occupancy Information in WLANs

Abstract

Professor Avideh Zakhor, Chair Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer optimization techniques designed to alleviate many causes of packet loss. A primary contributing factor is the difficulty in distinguishing between various causes of packet loss, including collisions caused by high network use, co-channel interference from neighboring networks, and errors due to poor channel conditions. In current 802.11 networks, there are two adaptations occurring simultaneously but independently each using packet loss as an indication of a different problem. The first is contention window adaptation, which implicitly assumes all losses are due to collision, and the second is rate adaptation, which implicitly assumes all losses are due to channel errors. Thus, in all high-loss scenarios, at least one of these two adaptations behaves incorrectly, unnecessarily increasing the impact of packet loss.In this thesis, we propose a novel method for estimating various collision type probabilities locally at a given node of an 802.11 network. The key to our approach is a signal we call the busy-idle (BI) signal, which is a binary-valued record of the channel occupancy over time as observed locally by a given node. We show that if the access point(AP) periodically broadcasts its BI signal to associated nodes at an overhead of less than 2%, the nodes can use this information in conjunction with locally observable quantities to obtain partial spatial information about the network traffic. With this spatial information, nodes can estimate their probabilities of various types of loss and make adaptations to improve throughput and/or network utility.We provide a systematic assessment and definition of the different types of collision, and show how to approximate each of them using local information and the AP's BI signal. We verify our methods using NS-2 simulations, as well as using the Ath5k open source wireless card driver in an experimental testbed. We show that our proposed methodology accurately estimates overall collision probability to within 5%. This experimental verification 1 demonstrates the feasibility of our collision probability estimation approach and the resulting throughput gains in practice.In addition, we describe a suite of methods which exploit these loss probability estimates and the BI signal to improve network throughput and utility. We show via NS-2 simulations that we can achieve gains of up to 400% via modulation rate adaptation, 350% via contention window adaptation, 25% via packet length adaptation, and 50% via carrier sense threshold adaptation. 2This thesis is dedicated to my parents, whose hard work provided me with both the opportunities and the inspiration to pursue my PhD.i

Efficient medium access protocol for Internet of things applications

“…The estimation mechanism used by such solution is able to distinguish the failure rates because of simultaneous channel accesses from the channel error corruptions. Another approach was used by Krishnan, which classifies the types of collisions that occur in WLANs. These collisions are estimated locally at any node via sharing spatial information about network traffic.…”

“…Simulations are performed using different random seed numbers to obtain a 95% confidence interval. The proposed solution is compared not only with DCF 802.11 but also with 2 other solutions proposed by Krishnan and Mirza and Almustafa, which we call protocol 1 and protocol 2. In fact, these 2 solutions use an approach similar to that exploited by the solution proposed, namely, the exploitation of error and collisions to bring improvement in performance.…”