Mobile Information Systems

Volume 2016 (2016), Article ID 8967281, 11 pages

http://dx.doi.org/10.1155/2016/8967281

## Adaptive Backoff Algorithm for Contention Window for Dense IEEE 802.11 WLANs

Department of Computer Engineering, Ajou University, Suwon-si, Gyeonggi-do 16499, Republic of Korea

Received 5 December 2015; Accepted 15 May 2016

Academic Editor: Pedro M. Ruiz

Copyright © 2016 Ikram Syed and Byeong-hee Roh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The performance improvement in IEEE 802.11 WLANs in widely fluctuating network loads is a challenging task. To improve the performance in this saturated state, we develop an adaptive backoff algorithm that maximizes the system throughput, reduces the collision probability, and maintains a high fairness for the IEEE 802.11 DCF under dense network conditions. In this paper, we present two main advantages of the proposed ABA-CW algorithm. First, it estimates the number of active stations and then calculates an optimal contention window based on the active station number. Each station calculates the channel state probabilities by observing the channel for the total backoff period. Based on these channel states probabilities, each station can estimate the number of active stations in the network, after which it calculates the optimal CW utilizing the estimated active number of stations. To evaluate the proposed mechanism, we derive an analytical model to determine the network performance. From our results, the proposed ABA-CW mechanism achieved better system performance compared to fixed-CW (BEB, EIED, LILD, and SETL) and adaptive-CW (AMOCW, Idle Sense) mechanisms. The simulation results confirmed the outstanding performance of the proposed mechanism in that it led to a lower collision probability, higher throughput, and high fairness.

#### 1. Introduction

Wireless local area networks (WLANs) are becoming the most popular and widely deployed networks worldwide. The reason for this success is its low deployment cost and the robust and flexible medium access control (MAC) protocol with coexistence capabilities. The IEEE 802.11-based WLANs have become the most popular wireless network standard, and the IEEE 802.11 standard [1] has two basic operation modes: distributed coordination function (DCF) and optional point coordination function (PCF). PCF is a centralized MAC protocol in which an access point (AP) coordinates with different stations by sending polling messages, with the aim of providing collision-free services. However, DCF is a contention-based access scheme and is based on the carrier sensing multiple access/collision avoidance (CSMA/CA) mechanism using the binary exponential backoff (BEB) algorithm.

The DCF is the fundamental MAC mechanism employed in the IEEE 802.11 [1] to enable random access to wireless channels. If a station wishes to transmit, it is required to listen for the channel status for an interval called the DCF interframe space (DIFS) interval. If the status is busy during the interval, the station defers its access to the channel for a backoff period, which is determined by , where and denote the time for the backoff period and slot time, respectively. Further, is the random-number generation function. Thus, the performance of the IEEE 802.11 DCF depends mainly on the CW adjustment and backoff strategy [2–5].

The BEB algorithm is the main backoff scheme employed in the IEEE 802.11 DCF. However, in terms of the throughput and collision rate of the BEB algorithm, its performance decreases dramatically when the number of stations increases beyond a certain limit. Cali et al. [3] reported that the actual throughput is lower than the analytical throughput because the CW varies depending on the network status. An improper CW adjustment increases the collision probability such that the stations experience many collisions before reaching the optimal CW. Several studies have been carried out to calculate the optimal CW that will increase the throughput and decrease the collision probability. Next, we further classify the related works into two categories based on the CW adjustment schemes employed: the fixed-CW ( and ) mechanism and adaptive-CW adjustment mechanism.

In fixed-CW adjustment schemes, several algorithms have been proposed, such as exponential increase exponential decrease (EIED), exponential increase linear decrease (EILD), multiplicative increase multiplicative decrease (MIMD), and smart exponential threshold linear (SETL), to improve the network performance using different increment factors to adjust the CW size [6–14]. Unfortunately, none of these algorithms can cope with the significant fluctuations in the network state and consider the fixed initial CW, that is, , regardless of the network conditions [15]. All of the abovementioned algorithms increase their CWs exponentially or linearly when a collision occurs and decrease the CW when a successful transmission occurs. The CW adjustment rule is usually based on the status of the last transmission attempt, which increases the collision probability and decreases the aggregated throughput when the number of stations increases. Thus, we deduce that both the fixed-CW and improper CW adjustment rules based on the states of the last transmission are the main causes for the reduced throughput, and they increase the collision probability of the network.

In adaptive-CW adjustment schemes, the CW adjusts dynamically according to the network conditions (number of active stations or network traffic load) regardless of the last transmission. The stations receive the channel-state information and coordinate with other stations to reduce the collisions and improve the network throughput. In [16–20], a lot of local channel-state information (e.g., idle slot intervals, slot utilization, and collision rate) was utilized to characterize the dynamic network conditions (number of active stations or network traffic load). In [21], the authors utilized the idle slot interval to adjust the CW in order to obtain a higher throughput. However, these methods do not estimate the number of stations, which may lead to decreased fairness. Further, some studies [22–33] estimated the number of active stations and then adjusted the access parameters to realize further performance improvements. However, in DCF, it is difficult to estimate the number of stations because each station can enter or leave the network at any time.

In [25], the authors estimated the number of stations by considering the signs of activity originating from other stations. In [26], the authors observed the channel events to estimate the number of stations by using the channel-event probability, and they tuned the network to obtain high throughput with good fairness according to the number of stations. In [27], the authors proposed a mechanism to tune the CW size based on the number of active stations, and the number was estimated by observing the channel status. Peng et al. [28] proposed a method to determine the CW by optimizing the ratio between the idle period and the collision period in terms of the throughput. Bianchi et al. [7] proposed a method to determine the CW based on the number of active stations. The number of stations was estimated by the number of slot times that were observed to be busy because of transmissions by other stations. Cali et al. [15] proposed a method that estimates the number of stations using the number of empty slots and tunes the CW accordingly.

In [32], the authors proposed an Idle Sense algorithm, where each host observes the mean number of idle slots between two transmission attempts, and the hosts adjust their CW by comparing the estimate with a theoretically derived value. In [33], the authors reported an Idle Sense mechanism with three thresholds for estimating the number of active stations in the network, and they developed a linear CW adjustment rule based on that number. However, the aggregated throughput of these mechanisms can be further improved.

In this paper, we aim to develop a new adaptive and robust backoff algorithm that improves the performance in terms of maximizing the system throughput, reducing the collision probability, and ensuring good fairness for the DCF in an IEEE 802.11 WLAN under dense network conditions. In this paper, we present two main contributions of the proposed adaptive backoff algorithm for the contention window, named ABA-CW. First, it estimates the number of active stations and then calculates an optimal CW based on the number of active stations. Each station has to observe the channel for the total backoff period and calculate the channel-state probabilities for the backoff period. Based on these channel-state probabilities, each station can estimate the number of active stations on the network, after which it then calculates the optimal CW utilizing the estimated number of active stations. For the estimated number of active stations, the proposed scheme achieved an optimal CW value that gives a good network performance in terms of high throughput, low collision probability, better channel utilization, and good fairness as the number of stations on the network varies.

The remainder of the paper is organized as follows. In Section 2, we discuss the DCF mechanism and the motivation for the proposed work. In Section 3, we estimate the channel status and propose a simple but novel algorithm for estimating the number of active stations based on the channel-state probabilities. We also derived an optimal CW for the number of active stations. In Section 4, we present an analysis of the proposed scheme. The simulation results and discussion are presented in Section 5, and we conclude the paper in Section 6.

#### 2. DCF Optimization

In this section, we briefly describe the DCF mechanism, as detailed in the IEEE 802.11 standard [1]. According to the DCF mechanism, when a station has a packet to transmit, it senses the channel for an idle period that is greater than a DIFS interval. If the channel remains idle, the station transmits the packet; otherwise, the transfer is deferred until the ongoing transmission is terminated. The station continues to monitor the channel until it is measured as idle for a DIFS interval, and it then generates a random backoff interval. Random backoff intervals are slotted, and stations are only allowed to transmit their packets at the beginning of each slot. The random backoff interval value is uniformly selected from the range , interval, where CW is the current CW. The contention window size doubles with each collision, and this occurs when two or more stations transmit their packets in the same time slot. The backoff counter is frozen when activities are detected on the channel, and it is reactivated after it is sensed that the channel is again idle for more than a DIFS interval; the counter is decremented as long as it is sensed that the channel is idle. When the backoff timer reaches zero, the station attempts to transmit a packet at the beginning of the next time slot. If the packet is successfully received, the receiver sends an acknowledgment (ACK) after a short interframe space (SIFS), which is less than DIFS. If the packet transmission is unsuccessful as indicated by an ACK timeout, a retransmission is scheduled, and the CW increased by two with each unsuccessful transmission until it reaches its maximum value; that is, , where is the maximum backoff stage and its value could be 5 or 7, depending on the maximum and minimum contention window size.

The main reason for the throughput degradation in DCF is the exponential backoff, which changes the CW based on the last transmission status. As shown in Figure 1, the BEB algorithm works well for a small number of stations. However, as the number of stations increases, the collision rate increases and the throughput significantly decreases. By making the CW dynamically adjust irrespective of the last transmission attempt, our proposed algorithm adjusts the CW based on the network conditions. It estimates the number of stations and then adjusts the CW based on the number of active stations in the network, which improves the network performance in terms of the throughput and collision probability.