Performance evaluation of a state-aware backoff algorithm in congested wireless local area networks

In this paper, we propose a state-aware backoff (SAB) algorithm for IEEE 802.11 to improve the performance of wireless local area networks (WLANs) by reducing the collision rate when WLANs are heavily congested. Since congestion tends to last for a while in WLANs once it occurs, the conventional binary exponential backoff (BEB) algorithm wastes a lot of time and bandwidth before reaching the appropriate contention window (CW) size whenever it sends a new frame in crowded WLANs. SAB pessimistically adjusts its CW in two different ways from BEB. Firstly, SAB starts from the CW size at the last successful transmission, not from the prefixed initial CW size for transmitting new frames. Secondly, SAB adjusts its CW in an exponential increase and linear decrease (EILD) way differently from the BEB algorithm that changes the CW in exponential increase exponential decrease (EIED) way. The SAB’s Markov chain and ns-2 simulation results confirmed that SAB outperformed BEB by 40% on average in congested WLANs.