Improved MAC layer protocol of Wifi for satellite network

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.

This paper proposes enhancements to the current IEEE 802.11 medium access control (MAC) distributed coordination function (DCF) through the coordinated adaptation of several MAC configuration parameters and the introduction of a traffic management function. MAC parameter adaptation is based upon the many novel methods that exist in literature. The proposed framework, referred to in this paper as the network efficiency enhancement device (NEED), aims to integrate several proposed adaptive methods into a coordinated framework along with an additional traffic management function to provide traffic conditioning and enable quality of service (QoS) both at the traffic class and platform levels. The adaptively controlled parameters include minimum and maximum contention window (CW) sizes; interframe spacing (IFS); maximum number of retries; fragmentation; and request to send/clear to send (RTS/CTS). Each of these individual methods has been shown in literature to provide, often significantly, enhanced MAC performance. However, these individual methods are typically scenario-specific, and can degrade performance if improperly applied. The goal of the NEED is to integrate these and other methods. These adaptations are based upon the statistics of the offered traffic, the wireless channel conditions, and the perceived network topology

Since the IEEE 802.11 DCF adopted the Binary Exponential Back-off (BEB) algorithm in its contention window adjustment scheme, many alternative back-off algorithms were proposed in order to enhance the performance. Among them is an Exponential Increase Exponential Decrease (EIED) back-off algorithm, whose performance was simulated and analyzed in a special case of doubling or halving the contention window size. This paper provides an analytical analysis for the general EIED algorithm. A closed form saturation throughput and a closed form saturation delay are derived. Our analytical results further establish the performance improvement of the general EIED algorithm over the BEB algorithm and the basic EIED 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.

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).

A new flexible multiple access control (MAC) scheme embedding channelization in multi-band carrier sense multiple access / collision avoidance (CSMA/CA) systems is proposed to provide different priority classes among users, and its performance is investigated. In the proposed scheme, two priority classes of users, primary and secondary users, are considered; the classical CSMA/CA protocol is modified to multiband operation for secondary users, whereas channelization is provided for primary users to ensure the quality of service (QoS). The performance of the proposed MAC scheme is analyzed using a new multi-band CSMA/CA model based on a Markov chain capturing the primary user channel activity and the number of bands. The throughput of secondary users is obtained as a function of the primary user activity as well as other CSMA/CA parameters. It is shown that this mixed MAC scheme can guarantee the throughput of primary users certainly with minimal performance degradation to secondary users, especially when the number of available bands is large or the maximum contention window (CW) size for the CSMA/CA secondary users is small compared with the number of secondary users.

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.

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.

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.

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.

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

In uplink wireless local area networks (WLANs) with multi-packet reception (MPR) which can be implementable with multi-user multiple-input multiple-output techniques, partial successes among simultaneous transmissions may frequently occur due to frame errors. This causes an increase in the contention window size only for the failed stations (STAs) under the binary exponential backoff algorithm, and a severe unfairness problem in terms of channel access opportunity may occur among the STAs with different frame error rates. In this letter, we propose a novel medium access control (MAC) protocol to support fair channel access in uplink WLANs with MPR. Analysis and simulation results show that our proposed MAC protocol can significantly improve the fairness in terms of both channel access opportunity and throughput.

Guest editorial IJSCN special issue on 3GPP NTN standards for future satellite communications

From experience, it has been well known that the performance of IEEE 802.11 wireless local area networks significantly drops in dense settings, largely driven by poor capacity of the medium access control (MAC) layer. The current 802.11 MAC mechanism utilizes a contention window (CW) for random access, and the CW size is dictated to take discrete values from a bounded finite set. In this paper, with focus on a saturated network scenario, we closely investigate the impacts of the aforementioned constraint and show that the limitations on the CW size are the main MAC bottleneck in dense conditions. Then, in order to tackle this bottleneck, we propose a new backoff scheme that does not attempt to tune the CW but makes a small modification in the backoff freezing process to determine the optimal configuration. We prove that our backoff scheme can achieve the maximum network throughput even under densely populated networks (i.e., many contenders), while a maximal compliance with the mentioned constraints is maintained. Then, we turn our attention to minimizing the channel access delay, and propose a variant of our backoff algorithm to accomplish this aim. We further present a new result on the theoretical relation between the optimal configurations for which the throughput is maximized and the delay is minimized. Finally, we note that our modified backoff mechanism does not penalize legacy users and is, therefore, practical for implementation in existing networks, which removes co-existence concerns.

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.