Effect of service differentiation on QoS in IEEE 802.11e enhanced distributed channel access: a simulation approach

The enhanced distributed channel access (EDCA) protocol is a supplement to IEEE 802.11 medium access control (MAC), ratified by IEEE 802.11e task group to support quality of service (QoS) requirements of both data and real-time applications. Previous research show that it supports priority scheme for multimedia traffic but strict QoS is not guaranteed. This can be attributed to inappropriate tuning of the medium access parameters. Thus, an in-depth analysis of the EDCA protocol and ways of tuning medium access parameters to improve QoS requirements for multimedia traffic is presented in this work. An EDCA model was developed and simulated using MATLAB to assess the effect of differentiating contention window (CW) and arbitration inter-frame space (AIFS) of different traffic on QoS parameters. The optimal performance, delay, and maximum sustainable throughput for each traffic type were computed under saturation load. Insight shows that traffic with higher priority values acquired most of the available channels and starved traffic with lower priority values. The AIFS has more influence on the QoS of EDCA protocol. It was also observed that small CW values generate higher packet drops and collision rate probability. Thus, EDCA protocol provides mechanism for service differentiation which strongly depends on channel access parameters: CW sizes and AIFS.

An Intelligent Method for Bandwidth Utilization in IEEE 802.11e Wireless LAN

IEEE 802.11 Wireless LAN, popularly known as WiFi, has become the admired source of internet connectivity for most of the offices as well as organizations. Due to the rapid growth of multimedia data and VoIP and also to provide better quality of service (QoS), bandwidth and management of bandwidth have become important factors in 802.11 wireless LAN. In 802.11 wireless LAN, Enhanced Distributed Coordination Access (EDCA) mechanism provides QoS with service differentiation by setting different priority to different traffic types. Some of the available methods provide priority based on user category also. In this paper we have proposed an intelligent bandwidth management technique based on the user priority value, traffic priority value and the bandwidth requirement value. By combining these three values, a new metric called request weight value is calculated. Based on this request weight value, dynamically bandwidth is allocated to the users. From the simulation results, we have seen that the proposed method performs well in proper utilization of bandwidth among the available users.

Controlling Contention Window to Ensure QoS for Multimedia Data in Wireless Network

The IEEE-802.11e standard was published withthe goal of ensuring quality of service, especially with mul-timedia data. However, this standard only assigns differentpriorities for different types of data but does not controlthe sharing of bandwidth among different data flows. In thispaper, we will propose a method of sharing bandwidth forproportional data flows and controlling Contention Windowof each priority flow to achieve that ratio.

Introducing a Mobility Access Category for IEEE 802.11e

With traffic increase in a wireless network beyond its capacity and as the number of connected devices continue to grow, the quality of service (QoS) degrades. In this paper we study the impact of mobility on throughput in the case of an infrastructure wireless network using IEEE 802.11 Wi-Fi standard. Since we found in the literature that the mobility of stations can have an impact on the quality of service, we try to remedy to this by implementing a new access category reserved for mobile stations. First we compare the throughput between static and mobile nodes, both connected to a QoS station. Then we propose our new model that consists of adding a new access category used by mobile nodes regardless of their traffic category. The study was made by simulating different scenarios using Network Simulator-3 (NS-3). We found that the throughput may vary depending on the simulation scenario. The simulation results show that with the proposed solution the mobile nodes can have a better throughput.