Variable Traffic Conditions Research Articles

Providing Throughput Guarantees in 802.11e WLAN Through a Dynamic Priority Assignment Mechanism

Supporting real-time and interactive traffic in addition to traditional data traffic with a best-effort nature represents a constantly rising need in any kind of telecommunications environment. The IEEE 802.11 based WLAN (Wireless Local Area Network) environment does not represent an exception. This is why at different protocol layers, and primarily at the MAC layer, many efforts are being put by both the research community and the standardization bodies to design effective mechanisms for user QoS (Quality of Service) differentiation. Although early results are coming into sight, such as, for example, the IEEE 802.11e standard release, still a thorough research activity is required. Aim of the present paper is to contribute to the cited research issue by proposing an improvement to the "static" traffic prioritisation mechanism foreseen by the IEEE 802.11e MAC (Medium Access Control) protocol. This latter shows a twofold drawback. First, there is no certainty that QoS requirements relevant to a given application are always fulfilled by the "statically" associated priority. Second, resource requests of the applications are not adapted to the (usually highly) variable traffic conditions of a distributed WLAN environment. The algorithm we propose is specifically tailored to "dynamically" assign 802.11e MAC priorities, depending on both application QoS requirements and observed network congestion conditions. It is carefully designed, implemented into a system simulation tool, and its highly effective behaviour assessed under variable traffic and system conditions.

Improving QoS and throughput in single- and multihop WLANs through dynamic traffic prioritization

Nowadays, research efforts are being put into the design of effective mechanisms to provide service quality differentiation in IEEE 802.11-based wireless LANs. In this article we analyze the effects of augmenting the contention-based channel access mechanism of IEEE 802.11e through the assignment of dynamic traffic priorities, that adapt to either the application's quality requirements or the network congestion status. The aim is to show the effectiveness and robustness of the proposed dynamic mechanism in a wireless ad hoc network, in both single-hop and multihop scenarios, under variable traffic and network load conditions.

Call admission control in cellular networks: A reinforcement learning solution

AbstractIn this paper, we address the call admission control (CAC) problem in a cellular network that handles several classes of traffic with different resource requirements. The problem is formulated as a semi‐Markov decision process (SMDP) problem. We use a real‐time reinforcement learning (RL) [neuro‐dynamic programming (NDP)] algorithm to construct a dynamic call admission control policy. We show that the policies obtained using our TQ‐CAC and NQ‐CAC algorithms, which are two different implementations of the RL algorithm, provide a good solution and are able to earn significantly higher revenues than classical solutions such as guard channel. A large number of experiments illustrates the robustness of our policies and shows how they improve quality of service (QoS) and reduce call‐blocking probabilities of handoff calls even with variable traffic conditions. Copyright © 2004 John Wiley & Sons, Ltd.

Reinforcement Learning for True Adaptive Traffic Signal Control

The ability to exert real-time, adaptive control of transportation processes is the core of many intelligent transportation systems decision support tools. Reinforcement learning, an artificial intelligence approach undergoing development in the machine- learning community, offers key advantages in this regard. The ability of a control agent to learn relationships between control actions and their effect on the environment while pursuing a goal is a distinct improvement over prespecified models of the environment. Prespecified models are a prerequisite of conventional control methods and their accuracy limits the performance of control agents. This paper contains an introduction to Q-learning, a simple yet powerful reinforcement learning algorithm, and presents a case study involving application to traffic signal control. Encouraging results of the application to an isolated traffic signal, particularly under variable traffic conditions, are presented. A broader research effort is outlined, including extension to linear and networked signal systems and integration with dynamic route guidance. The research objective involves optimal control of heavily congested traffic across a two-dimensional road network—a challenging task for conventional traffic signal control methodologies.

Contention-TDMA protocol: performance evaluation

A hybrid access protocol known, as contention time-division multiple access (C-TDMA), is presented and analyzed in a radio cellular multiuser system scenario. C-TDMA shows some features of contention-based (slotted-ALOHA) and reservation-based (packet reservation multiple access, PRMA) protocols. It has been recommended for use in the uplink of future European multimedia distribution systems. A simple Markov model is proposed to describe C-TDMA behavior. A complete statistical analysis of the model has been made in order to evaluate the performance of the protocol. However, due to the long computation time required in the presence of a large number of users, a simpler approach known as equilibrium point analysis (EPA) is used. Moreover, on the basis of the EPA analysis and the C-TDMA design parameters, a fast algorithm has been developed to improve the achievable throughput of C-TDMA. Results in terms of throughput and delay under variable traffic conditions indicate that C-TDMA is able to grant optimum throughput/delay figures for typical multiuser systems. Moreover, for a digital speech scenario, a performance comparison with PRMA demonstrates that C-TDMA yields equivalent performance to PRMA in terms of number of users supported by the system with a limited packet dropping rate.