Saturation Throughput Analysis of IEEE 802.11 in Presence of Non Ideal Transmission Channel and Capture Effects

In this paper, we provide a saturation throughput analysis of the IEEE 802.11 protocol at the data link layer by including the impact of both transmission channel and capture effects in Rayleigh fading environment. Impacts of both non-ideal channel and capture effects, specially in an environment of high interference, become important in terms of the actual observed throughput. As far as the 4-way handshaking mechanism is concerned, we extend the multi-dimensional Markovian state transition model characterizing the behavior at the MAC layer by including transmission states that account for packet transmission failures due to errors caused by propagation through the channel. This way, any channel model characterizing the physical transmission medium can be accommodated, including AWGN and fading channels. We also extend the Markov model in order to consider the behavior of the contention window when employing the basic 2-way handshaking mechanism. Under the usual assumptions regarding the traffic generated per node and independence of packet collisions, we solve for the stationary probabilities of the Markov chain and develop expressions for the saturation throughput as a function of the number of terminals, packet sizes, raw channel error rates, capture probability, and other key system parameters. The theoretical derivations are then compared to simulation results confirming the effectiveness of the proposed models.Comment: To appear on IEEE Transactions on Communications, 200

Unsaturated Throughput Analysis of IEEE 802.11 in Presence of Non Ideal Transmission Channel and Capture Effects

In this paper, we provide a throughput analysis of the IEEE 802.11 protocol at the data link layer in non-saturated traffic conditions taking into account the impact of both transmission channel and capture effects in Rayleigh fading environment. The impact of both non-ideal channel and capture become important in terms of the actual observed throughput in typical network conditions whereby traffic is mainly unsaturated, especially in an environment of high interference. We extend the multi-dimensional Markovian state transition model characterizing the behavior at the MAC layer by including transmission states that account for packet transmission failures due to errors caused by propagation through the channel, along with a state characterizing the system when there are no packets to be transmitted in the buffer of a station. Finally, we derive a linear model of the throughput along with its interval of validity. Simulation results closely match the theoretical derivations confirming the effectiveness of the proposed model.Comment: To appear on IEEE Transactions on Wireless Communications, 200

Modeling and analysis of slow CW decrease IEEE 802.11 WLAN

The IEEE 802.11 medium access control (MAC) protocol provides a contention-based distributed channel access mechanism for mobile stations to share the wireless medium, which may introduce a lot of collisions in case of overloaded active stations. Slow contention window (CW) decrease scheme is a simple and efficient solution for this problem. In this paper, we use an analytical model to compare the slow CW decrease scheme to the IEEE 802.11 MAC protocol. Several parameters are investigated such as the number of stations, the initial CW size, the decrease factor value, the maximum backoff stage and the coexistence with the RequestToSend and ClearToSend (RTS/CTS) mechanism. The results show that the slow CW decrease scheme can efficiently improve the throughput of IEEE 802.11, and that the throughput gain is higher when the decrease factor is larger. Moreover, the initial CW size and maximum backoff stage also affect the performance of slow CW decrease scheme

Backoff as Performance improvements Algorithms - A Comprehenssive Review

As a significant part of the Media Access Control protocol, the backoff algorithm purpose is to minimize number of collisions if not totally avoid any collision in Mobile Ad Hoc Networks, in the case of contention between nodes to access a channel. Researchers have proposed many algorithms for backoff to enhance the network performance and improve it. This paper aims at exploring the main and most studied backoff algorithms and how do these algorithms lead to an enhancement of the MANETs performance. This paper also compares between the algorithms proposed in the literature and evaluates to what extent they have affected the performance and enhance it

Contention-based learning MAC protocol for broadcast Vehicle-to-Vehicle Communication

Vehicle-to-Vehicle Communication (V2V) is an upcoming technology that can enable safer, more efficient transportation via wireless connectivity among moving cars. The key enabling technology, specifying the physical and medium access control (MAC) layers of the V2V stack is IEEE 802.11p, which belongs in the IEEE 802.11 family of protocols originally designed for use in WLANs. V2V networks are formed on an ad hoc basis from vehicular stations that rely on the delivery of broadcast transmissions for their envisioned services and applications. Broadcast is inherently more sensitive to channel contention than unicast due to the MAC protocol’s inability to adapt to increased network traffic and colliding packets never being detected or recovered. This paper addresses this inherent scalability problem of the IEEE 802.11p MAC protocol. The density of the network can range from being very sparse to hundreds of stations contenting for access to the channel. A suitable MAC needs to offer the capacity for V2V exchanges even in such dense topologies which will be common in urban networks. We present a modified version of the IEEE 802.11p MAC based on Reinforcement Learning (RL), aiming to reduce the packet collision probability and bandwidth wastage. Implementation details regarding both the learning algorithm tuning and the networking side are provided. We also present simulation results regarding achieved message packet delivery and possible delay overhead of this solution. Our solution shows up to 70% increase in throughput compared to the standard IEEE 802.11p as the network traffic increases, while maintaining the transmission latency within the acceptable levels

Enhancing IEEE 802.11MAC in congested environments

IEEE 802.11 is currently the most deployed wireless local area networking standard. It uses carrier sense multiple access with collision avoidance (CSMA/CA) to resolve contention between nodes. Contention windows (CW) change dynamically to adapt to the contention level: Upon each collision, a node doubles its CW to reduce further collision risks. Upon a successful transmission, the CW is reset, assuming that the contention level has dropped. However, the contention level is more likely to change slowly, and resetting the CW causes new collisions and retransmissions before the CW reaches the optimal value again. This wastes bandwidth and increases delays. In this paper we analyze simple slow CW decrease functions and compare their performances to the legacy standard. We use simulations and mathematical modeling to show their considerable improvements at all contention levels and transient phases, especially in highly congested environments

A Q-Learning approach with collective contention estimation for bandwidth-efficient and fair access control in IEEE 802.11p vehicular networks

Vehicular Ad hoc Networks (VANETs) are wireless networks formed of moving vehicle-stations, that enable safety-related packet exchanges among them. Their infrastructure-less, unbounded nature allows the formation of dense networks that present a channel sharing issue, which is harder to tackle than in conventional WLANs, due to fundamental differences of the protocol stack. Optimising channel access strategies is important for the efficient usage of the available wireless bandwidth and the successful deployment of VANETs. We present a Q-Learning-based approach to wirelessly network a big number of vehicles and enable the efficient exchange of data packets among them. More specifically, this work focuses on a IEEE 802.11p-compatible contention-based Medium Access Control (MAC) protocol for efficiently sharing the wireless channel among multiple vehicular stations. The stations feature algorithms that "learn" how to act optimally in a network in order to maximise their achieved packet delivery and minimise bandwidth wastage. Additionally, via a Collective Contention Estimation (CCE) mechanism which we embed on the Q-Learning agent, faster convergence, higher throughput and short-term fairness are achieved

On backoff mechanisms for wireless Mobile Ad Hoc Networks

Since their emergence within the past decade, which has seen wireless networks being adapted to enable mobility, wireless networks have become increasingly popular in the world of computer research. A Mobile Ad hoc Network (MANET) is a collection of mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure. MANETs have received significant attention in recent years due to their easiness to setup and to their potential applications in many domains. Such networks can be useful in situations where there is not enough time or resource to configure a wired network. Ad hoc networks are also used in military operations where the units are randomly mobile and a central unit cannot be used for synchronization. The shared media used by wireless networks, grant exclusive rights for a node to transmit a packet. Access to this media is controlled by the Media Access Control (MAC) protocol. The Backoff mechanism is a basic part of a MAC protocol. Since only one transmitting node uses the channel at any given time, the MAC protocol must suspend other nodes while the media is busy. In order to decide the length of node suspension, a backoff mechanism is installed in the MAC protocol. The choice of backoff mechanism should consider generating backoff timers which allow adequate time for current transmissions to finish and, at the same time, avoid unneeded idle time that leads to redundant delay in the network. Moreover, the backoff mechanism used should decide the suitable action to be taken in case of repeated failures of a node to attain the media. Further, the mechanism decides the action needed after a successful transmission since this action affects the next time backoff is needed. The Binary exponential Backoff (BEB) is the backoff mechanisms that MANETs have adopted from Ethernet. Similar to Ethernet, MANETs use a shared media. Therefore, the standard MAC protocol used for MANETs uses the standard BEB backoff algorithms. The first part of this work, presented as Chapter 3 of this thesis, studies the effects of changing the backoff behaviour upon a transmission failure or after a successful transmission. The investigation has revealed that using different behaviours directly affects both network throughput and average packet delay. This result indicates that BEB is not the optimal backoff mechanism for MANETs. Up until this research started, no research activity has focused on studying the major parameters of MANETs. These parameters are the speed at which nodes travel inside the network area, the number of nodes in the network and the data size generated per second. These are referred to as mobility speed, network size and traffic load respectively. The investigation has reported that changes made to these parameters values have a major effect on network performance. Existing research on backoff algorithms for MANETs mainly focuses on using external information, as opposed to information available from within the node, to decide the length of backoff timers. Such information includes network traffic load, transmission failures of other nodes and the total number of nodes in the network. In a mobile network, acquiring such information is not feasible at all times. To address this point, the second part of this thesis proposes new backoff algorithms to use with MANETs. These algorithms use internal information only to make their decisions. This part has revealed that it is possible to achieve higher network throughput and less average packet delay under different values of the parameters mentioned above without the use of any external information. This work proposes two new backoff algorithms. The Optimistic Linear-Exponential Backoff, (OLEB), and the Pessimistic Linear-Exponential Backoff (PLEB). In OLEB, the exponential backoff is combined with linear increment behaviour in order to reduce redundant long backoff times, during which the media is available and the node is still on backoff status, by implementing less dramatic increments in the early backoff stages. PLEB is also a combination of exponential and linear increment behaviours. However, the order in which linear and exponential behaviours are used is the reverse of that in OLEB. The two algorithms have been compared with existing work. Results of this research report that PLEB achieves higher network throughput for large numbers of nodes (e.g. 50 nodes and over). Moreover, PLEB achieves higher network throughput with low mobility speed. As for average packet delay, PLEB significantly improves average packet delay for large network sizes especially when combined with high traffic rate and mobility speed. On the other hand, the measurements of network throughput have revealed that for small networks of 10 nodes, OLEB has higher throughput than existing work at high traffic rates. For a medium network size of 50 nodes, OLEB also achieves higher throughput. Finally, at a large network size of 100 nodes, OLEB reaches higher throughput at low mobility speed. Moreover, OLEB produces lower average packet delay than the existing algorithms at low mobility speed for a network size of 50 nodes. Finally, this work has studied the effect of choosing the behaviour changing point between linear and exponential increments in OLEB and PLEB. Results have shown that increasing the number of times in which the linear increment is used increases network throughput. Moreover, using larger linear increments increase network throughput