Performance Analysis of Exponential Increase Exponential Decrease Back-Off Algorithm

The IEEE 802.15.4 standard utilizes the Binary Exponential Backoff (BEB) algorithm to control nodes' access to the shared wireless medium. The main drawback of BEB is that it updates the size of the contention window (CW) without taking into consideration the number of competing nodes and the conditions in the communications medium. Therefore, BEB has been shown to be inefficient in terms of channel utilization and fairness among the contending nodes. In this paper, we propose Adaptive Backoff Algorithm (ABA), a new backoff algorithm that adaptively determines the appropriate size of CW based on the collisions experienced by the nodes. That is, while BEB updates CW in a deterministic fashion, we introduce a probabilistic methodology to achieve that update. Our simulations compare the performance of ABA with that of BEB as well as three other algorithms proposed in the literature, namely, NO-BEB, KEB, and IBEB. The performance is studied in terms of power consumption, reliability, and channel utilization. Our results show that ABA outperforms the aforementioned algorithms while granting each node a fair access to the wireless medium.

SUMMARYThe medium access control protocol determines system throughput in wireless mobile ad hoc networks following the ieee 802.11 standard. Under this standard, asynchronous data transmissions have a defined distributed coordination function that allows stations to contend for channel usage in a distributed manner via the carrier sensing multiple access with collision avoidance protocol. In distributed coordination function, a slotted binary exponential backoff (BEB) algorithm resolves collisions of packets transmitted simultaneously by different stations. The BEB algorithm prevents packet collisions during simultaneous access by randomizing moments at stations attempting to access the wireless channels. However, this randomization does not eliminate packet collisions entirely, leading to reduced system throughput and increased packet delay and drop. In addition, the BEB algorithm results in unfair channel access among stations. In this paper, we propose an enhanced binary exponential backoff algorithm to improve channel access fairness by adjusting the manner of increasing or decreasing the contention window based on the number of the successfully sent frames. We propose several configurations and use the NS2 simulator to analyze network performance. The enhanced binary exponential backoff algorithm improves channel access fairness, significantly increases network throughput capacity, and reduces packet delay and drop. Copyright © 2013 John Wiley & Sons, Ltd.

The distributed coordination function (DCF) of IEEE 802.11 standard adopts the binary exponential backoff (BEB) for collision avoidance. In DCF, the contention window is reset to an initial value, i.e., CWmin, after each successful transmission. Much research has shown that this dramatic change of window size may degrade the network performance. Therefore, backoff algorithms, such as gentle DCF (GDCF), multiplicative increase-linear decrease (MILD), exponential increase-exponential decrease (EIED), etc., have been proposed that try to keep the memory of congestion level by not resetting the contention window after each successful transmission. This paper proposes a multichain backoff (MCB) algorithm, which allows stations to adapt to different congestion levels by using more than one backoff chain together with collision events caused by stations themselves as well as other stations as indications for choosing the next backoff chain. The performance of MCB is analyzed and compared with those of 802.11 DCF, GDCF, MILD, and EIED backoff algorithms. Simulation results show that, with multiple backoff chains and collision events as reference for chain transition, MCB can offer a higher throughput while still maintaining fair channel access than the existing backoff algorithms

In this paper we propose a new Markovian model for p-persistent carrier sense multiple access (CSMA) systems with a finite population of unsaturated single-buffered terminals. Focused on the distribution of the number of backlogged terminals in the steady state, our model allows the optimal persistent probability p from the number of backlogged terminals, which enables us to determine the throughput upper-bound (or mean access delay lower-bound) of slotted CSMA systems. We compare the performance of slotted CSMA systems with binary exponential backoff (BEB) algorithm and with p-persistent protocol against the throughput upper-bound and examine the stability of these systems. We show how closely slotted CSMA systems with BEB algorithm or p-persistent protocol approaches the throughput upper-bound in accordance with the minimum contention window size or the persistent probability p. Further, we propose a generalized Bertsekas' (backoff) algorithm (GBA) based on backlog size estimation, which is a generalization of the existing algorithm proposed by Bertsekas, in order to achieve the throughout upper-bound. Our study shows that in slotted CSMA systems, the access fairness of BEB algorithm is worse than those of p-persistent protocol and GBA algorithm, while the BEB and GBA algorithms show throughput performance close to optimality.

The Low Rate Wireless Personal Area network (LR-WPAN), that is IEEE 802.15.4 is extensively used for many areas of applications. The Slotted CSMA/CA mechanism is used by the standard in its contention access period (CAP) in beacon enabled mode. The protocol supported a binary exponential backoff (BEB) algorithm. In this paper, we investigate the saturation throughput, mean frame service time or delay and energy of this standard using the existing BEB algorithm and compares it with three other backoff schemes-exponential increase exponential decrease (EIED), exponential increase linear decrease (EILD), and exponential increase multiplicative decrease (EIMD) algorithm. From simulation results, it is obtained that EIED, EILD, EIMD performs better than BEB for higher loads that is EIED, EILD, EIMD has better throughput, less delay, using minimal power. The EIED propagates the highest throughput, less delay and minimal energy used among all of these schemes.

The IEEE 802.11's binary exponential backoff (BEB) algorithm plays a critical role in the throughput performance and fair channel allocation of IEEE 802.11 networks. In particular, one of BEB algorithm's parameters, the Contention Window determines how long a node needs to wait before it (re)transmits data. Consequently, choosing adequate values of the Contention Window is crucial for IEEE 802.11's performance. In this paper, we introduce a simple, yet effective machine learning approach to adjust the value of IEEE 802.11's Contention Window based on present- as well as recent past network contention conditions. Using a wide range of network scenarios and conditions, we show that our approach outperforms both 802.11's BEB as well as an existing contention window adjustment technique that only considers the last two transmissions. Our results indicate that our contention window adaptation algorithm is able to deliver consistently higher average throughput, lower end-to-end delay, as well as improved fairness.

In this paper, a backoff mechanism, Exponential Linear Backoff Algorithm (ELBA), is proposed to improve system performance over contention-based wireless networks. In the ELBA, the variation of contention window size is combined both exponentially and linearly, dependent on the network load, as indicated by the number of consecutive collisions. In the ELBA scheme, a threshold is set to determine the network load. If the contention window size is smaller than the threshold, a light network load, the contention window is tuned exponentially. Conversely, if the contention window size is larger than the threshold, a heavy network load, the contention window size is tuned linearly. The numerical results show that the ELBA provides a better system throughput and collision rate in both light and heavy network loads than the related backoff schemes, including binary exponential backoff (BEB), exponential increase exponential decrease (EIED) and linear increase linear decrease (LILD).

In IEEE 802.11 standard, distributed coordination function (DCF) uses binary exponential backoff (BEB) algorithm to resolve collisions. However, performance of DCF with BEB decreases as the number of contending stations increases. To solve this problem, enhanced gentle DCF (EGDCF) and binary negative-exponential backoff algorithm (BNEB) have been proposed. In EGDCF algorithm, each station counts the number of consecutive successful transmissions in present backoff stage. If the number of consecutive successful transmissions reaches the predefined maximum value, station decreases its backoff stage. BNEB algorithm maintains a maximum contention window size during collisions and reduces a contention window size into half after complete transmission without retransmission. Since these two algorithms control backoff stage and contention window based on estimation of the number of stations, they improve performance of DCF. However, DCF cannot support differential quality of services (QoS) So IEEE 802.11e has adopted EDCA for contention-based medium access mechanism. In this paper, we adopt EGDCF and BNEB algorithms into EDCA to effectively support differentiated services in wireless LAN. We evaluate performance of conventional EDCA and EDCA with EGDCF and BNEB under saturation environment.

Wireless networks manage performance by adjusting the contention window, as they cannot directly detect collisions. Traditional contention window adjustment algorithms, such as the Binary Exponential Backoff (BEB) algorithm, may lead to lower throughput when multiple services with varying bandwidth demands coexist. To address this issue, this study aims to enhance network throughput by enabling differentiated bandwidth allocation for various services. Using deep reinforcement learning, the state space, action space, and reward functions are defined to optimize this differentiation. These definitions are integrated into the Deep Deterministic Policy Gradient (DDPG) technique, implemented in the Access Point (AP) to intelligently adjust the contention window. Leveraging DDPG’s capability for continuous actions, the proposed method provides Quality of Service (QoS) differentiation, ensuring that each service at its respective priority level meets its transmission requirements. Compared to the BEB algorithm, the proposed approach offers improved traffic allocation and higher network bandwidth utilization.

It is difficult for wireless local area networks (WLANs), IEEE 802.11ax high-efficiency WLAN (HEW), to join next-generation innovations such as 5th generation (5G) and Internet of Things (IoT) because they still have their conventional channel access mechanism as their essential medium access control (MAC) protocol. The MAC protocol uses a traditional binary exponential backoff (BEB) algorithm to access channel resources that depend on the noncognitive increment of contention parameters for collision avoidance. In BEB, the collision issue increases with the increase in connected devices in the network due to a fixed contention window size. The larger the size of the network, the larger the collision in the network. To avoid such a circumstance, in this paper, we propose a sliding group window (sGW) mechanism dependent on collision-point assessment in order to improve the performance of MAC protocol for HEW. The proposed algorithm additionally presents a rebacking off for collision avoidance (ReBOCA) system for sGW, which combines the uniform dispersion of the contention parameters. This variation of an ordinary backoff algorithm permits the reasonable sliding of the user groups in the case of collision. The algorithm explicitly accounts for the peculiarities of dense environments and backward compatibility. Key aspects of the proposed solution include collision-point estimation, rebacking off for collision distribution convergence for fair treatment, and adaptive sliding of group windows to mitigate contention unfairness. We further formulated a closed-form Markov chain model for the performance analysis of our proposed sGW with ReBOCA scheme. Theoretical and practical results prove that our proposed scheme achieved maximal efficiency, even under dense environments. An increase in throughput with a lower packet collision probability was achieved with the proposed mechanism, and the efficiency increased as the number of contending stations increased than compared to traditional BEB performance. Our proposed ReBOCA mechanism enhanced network throughput by 38.18% than compared to the conventional BEB mechanism.

With the development and universal application of satellite technology, an important way to expand the function of satellites is setting up inter-satellite networks to make them work together. Traditional satellite networking methods generally adopt a fixed time slot allocation method, which is not suitable for small satellite groups with low latency and high throughput requirements. In order to solve this problem, it has been proposed to apply the traditional Wifi protocol in satellite networking. As there are differences between satellite networks and terrestrial networks, it’s necessary to improve the traditional 802.11 protocol. The Media Access Control (MAC) protocol in 802.11 is improved in this paper, which mainly includes the adaptive algorithm of maximum contention window size and the growth algorithm of Contention Window (CW) size. The maximum contention window is adjusted according to the conflict state of the current network, which makes the network accommodate more satellite nodes. The CW growth algorithm improves the traditional Binary Exponential Back-off (BEB) algorithm, where the CW is designed according to the priority of the data frame or the network load. In this way, high-priority satellite accusation information will have higher reliability or tolerate greater network load.

Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved Adaptive Backoff Algorithm (I-ABA), this paper proposes an Attack-Aware Adaptive Backoff Indicator (AA-BI) mechanism to enhance the security and robustness of the two-step random access process in LEO SI. The mechanism constructs a composite threat intensity indicator that incorporates collision probability, Denial-of-Service (DoS) attack strength, and replay attack intensity. This quantified threat level is smoothly mapped to a dynamic backoff window to achieve adaptive backoff adjustment. Simulation results demonstrate that, with 200 pieces of user equipment (UE), the AA-BI mechanism significantly improves the access success rate (ASR) and jamming resistance rate (JRR) under various attack scenarios compared to the I-ABA and Binary Exponential Backoff (BEB) algorithms. Notably, under high-attack conditions, AA-BI improves ASR by up to 25.1% and 56.6% over I-ABA and BEB, respectively. Moreover, under high-load conditions with 800 users, AA-BI still maintains superior performance, achieving an ASR of 0.42 and a JRR of 0.68, thereby effectively ensuring the access performance and reliability of satellite Internet in malicious environments.

In the IEEE 802.11 standard, network nodes experiencing collisions on the shared medium need a mechanism that can prevent collisions and improve the throughput. Furthermore, a backoff mechanism is used that uniformly selects a random period of time from the contention window (cw) that is dynamically controlled by the Binary Exponential Backoff (BEB) algorithm. Prior research has proved that the BEB scheme suffers from a fairness problem and low throughput, especially under high traffic load. In this paper, we present a new backoff control mechanism that is used with the IEEE 802.11 distributed coordination function (DCF). In particular, we propose a dynamic, deterministic contention window control (DDCWC) scheme, in which the backoff range is divided into several small backoff sub-ranges. In the proposed scheme, several network levels are introduced, based on an introduced channel state vector that keeps network history. After successful transmissions and collisions, network nodes change their cw based on their network levels. Our extensive simulation studies show that the DDCWC scheme outperforms four other well-known schemes: Multiplicative Increase and Linear Decrease, Double Increment Double Decrement, Exponential Increase Exponential Decrease, and Linear/Multiplicative Increase and Linear Decrease. Moreover, the proposed scheme, compared with the IEEE 802.11 DCF, gives 30.77% improvement in packet delivery ratio, 31.76% in delay, and 30.81% in throughput.

We investigate the potential for high throughput when combining random backoff schemes with a robust cross-layer tree algorithm (TA) for wireless random access. We first develop a BEB-SICTA/FS protocol which combines the binary exponential backoff (BEB) algorithm with a recently proposed SICTA/FS protocol saturation throughput analysis of BEB-SICTA/FS motivates the combined protocol herein because: 1) by using the practically feasible SICTA/FS to resolve collisions in a conventional BEB based protocol for wireless random access, we can achieve high throughput; and 2) BEB can sufficiently reduce the collision size and thus enhance the efficiency of SICTA/FS, since SICTA/FS is more efficient when the number of initially collided packets is small. Guided by our analysis, we further put forth a GBEB-SICTA/FS protocol which is capable of higher and more steadfast saturation throughput than BEB-SICTA/FS. Finally, we tailor our protocols for the IEEE 802.16 broadband wireless access (BWA) networks and test their performance through simulations

A new medium access control (MAC) scheme for mobile ad-hoc networks (MANETs) has been proposed with the goal of achieving fairness at the MAC layer. Most of the recently proposed contention-based MAC for MANET still use binary exponential backoff (BEB) algorithm to solve the contention problem, which contributes partially to instability and unfair medium sharing. In this paper, we propose to combine the exponential increase exponential decrease backoff algorithm (EIED) with an existing MANET MAC called fair MAC with cooperation between sender and receiver (FMAC/CSR), aiming to achieve fairer medium sharing between contending connections and more stable behavior. Preliminary simulation results show that our scheme outperforms existing MAC schemes, with respect to stability and fairness