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doi:10.1016/j.peva.2005.05.002    
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Copyright © 2005 Elsevier B.V. All rights reserved.

Modeling the short-term unfairness of IEEE 802.11 in presence of hidden terminals

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Zhifei LiCorresponding Author Contact Information, E-mail The Corresponding Author, E-mail The Corresponding Author, Sukumar NandiE-mail The Corresponding Author and Anil K. GuptaE-mail The Corresponding Author

School of Computer Engineering, Nanyang Technological University, Singapore-639798, Singapore


Received 8 July 2003; 
revised 4 May 2005. 
Available online 23 June 2005.

Abstract

IEEE 802.11 exhibits both short-term and long-term unfairness. The short-term fairness automatically gives rise to long-term fairness, but not vice versa. When we thoroughly investigated a simple scenario with hidden terminals, we found it to be unfair on the short-term basis, though it provides fair access on a long-term basis. It implies that the protocol cannot be used to provide fair access for delay sensitive traffic even in a simple scenario. In this paper, we analyze the short-term behavior using the embedded-Markov chain method to answer the following two questions: (i) once a node gets control of the medium, what is the average number of packets this node can transmit consecutively without experiencing any collision, (ii) once a node loses its control of the medium, what is the average time the node has to wait before it gets control of the medium again. The first question reflects on how long a node can capture the medium, whereas the second question reflects on how long a node may be starved. The analytical model is validated by the simulation results. Our work is distinct from most of the work published in the literature in two aspects: we focus on the short-term behavior rather than the long-term, and the analytical method is adopted for the study.

Keywords: IEEE 802.11; Hidden-terminal; Short-term fairness; Embedded–Markov chain

Article Outline

1. Introduction
2. Short-term unfairness in IEEE 802.11
2.1. Basic techniques in IEEE 802.11
2.2. Explanation for the short-term unfairness
3. Analytical modeling
3.1. Markov chain model
3.2. Basic analysis of the model
3.3. Derivation of the metrics
4. Transition probability computation
4.1. Transition probabilities out of a Col state
4.2. Transition probabilities out of a T state
5. State holding time computation
6. Numerical results
6.1. Transition probabilities and state holding time
6.1.1. Transition probabilities in Col states
6.1.2. Transition probabilities in T states
6.1.3. State holding time: num(j, i)
6.2. Comparison between analytical and simulation results
6.2.1. State probabilities
6.2.2. Expected state holding time
6.2.3. Expected first passage time
6.2.4. Metrics
6.2.5. General applications of the model
7. Discussion and related work
7.1. Future work
7.2. Related work
8. Conclusions
References










Corresponding Author Contact InformationCorresponding author. Tel.: +65 67906579.

 
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