@inproceedings{herzen2011measurement,
author = {Herzen, Julien and Aziz, Adel and Merz, Ruben and Shneer, Seva and Thiran, Patrick},
title = {A Measurement-based Algorithm to Maximize the Utility of Wireless Networks},
booktitle = {Proceedings of the 3rd ACM Workshop on Wireless of the Students, by the Students, for the Students},
series = {S3 '11},
year = {2011},
isbn = {978-1-4503-0868-7},
location = {Las Vegas, Nevada, USA},
pages = {13--16},
numpages = {4},
url = {http://doi.acm.org/10.1145/2030686.2030691},
doi = {10.1145/2030686.2030691},
acmid = {2030691},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {capacity region, fairness, optimal, performance, rate region, simulation, testbed, utility function, wireless},
}
The goal of jointly providing fairness and efficiency in wireless networks can be seen as the problem of maximizing a given utility function. The main difficulty when solving this problem is that the capacity region of wireless networks is typically unknown and time-varying, which prevents the usage of traditional optimization tools. As a result, scheduling and congestion control algorithms are either too conservative because they under-estimate the capacity region, or suffer from congestion collapse because they over-estimate it. We propose a new adaptive congestion control algorithm, called Enhance & Explore (E&E). It maximizes the utility of the network without requiring any explicit characterization of the capacity region. E&E works above the MAC layer and is decoupled from the underlying scheduling mechanism. It provably converges to a state of optimal utility. We evaluate the performance of the algorithm in a WLAN setting, using both simulations and measurements on a real testbed composed of IEEE 802.11 wireless routers.