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SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks
November 2008 (vol. 19 no. 11)
pp. 1579-1590
ASCII Text
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Tarun Joshi, Disha Ahuja, Damanjit Singh, Dharma P. Agrawal, "SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 11, pp. 1579-1590, November, 2008.
 
 
BibTex
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@article{ 10.1109/TPDS.2007.70814,
author = {Tarun Joshi and Disha Ahuja and Damanjit Singh and Dharma P. Agrawal},
title = {SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks},
journal ={IEEE Transactions on Parallel and Distributed Systems},
volume = {19},
number = {11},
issn = {1045-9219},
year = {2008},
pages = {1579-1590},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPDS.2007.70814},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Parallel and Distributed Systems
TI - SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks
IS - 11
SN - 1045-9219
SP1579
EP1590
EPD - 1579-1590
A1 - Tarun Joshi,
A1 - Disha Ahuja,
A1 - Damanjit Singh,
A1 - Dharma P. Agrawal,
PY - 2008
KW - Automatic Fall Back
KW - IEEE 802.11 MAC
KW - Packet Success Rate
KW - Rate Adaptation
KW - Reinforcement Learning
KW - Stochastic Learning Automata.
VL - 19
JA - IEEE Transactions on Parallel and Distributed Systems
ER -
 
Existing rate adaptation algorithms can broadly be classified under two categories: (a) Signal to Noise Ratio measurement based and (b) Statistical Count based. While the former suffers from inaccurate estimations, the latter utilizes pre-defined thresholds for dynamically varying the rate. In this paper, we first analyze the impact of transmission rate on the performance of a wireless link as the performance may either improve or deteriorate with increasing transmission rates. Based on this observation, we then propose Stochastic Automata Rate Adaptation Algorithm (SARA). SARA is inspired by Stochastic Learning Automata (SLA), a machine learning technique for adaptation in random environments. SARA assigns a selection probability to each of the transmission rates. It then randomly selects a rate for a transmission attempt and dynamically updates the probabilities based on the obtained feedback from the receiver (ACK/NACK), thereby obviating the need for explicit channel estimation or predefined thresholds. As opposed to the previous work, SARA is ideally suited for both stationary and non-stationary channel environments and is fully compatible with the existing IEEE 802.11 MAC standard. We compare the performance of our proposed protocol with Automatic Rate Fallback (ARF) thoroughly and Adaptive ARF (AARF) and other existing protocols, under different channel scenarios.

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Index Terms:
Automatic Fall Back, IEEE 802.11 MAC, Packet Success Rate, Rate Adaptation, Reinforcement Learning, Stochastic Learning Automata.
Citation:
Tarun Joshi, Disha Ahuja, Damanjit Singh, Dharma P. Agrawal, "SARA: Stochastic Automata Rate Adaptation for IEEE 802.11 Networks," IEEE Transactions on Parallel and Distributed Systems, vol. 19, no. 11, pp. 1579-1590, Nov. 2008, doi:10.1109/TPDS.2007.70814
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