57 research outputs found
Application of Neural Networks for Achieving 802.11 QoS in Heterogeneous Channels
No full text[[abstract]]In error-prone IEEE 802.11 WLAN (Wireless Local Area Network) environments, heterogeneous link qualities can signi?cantly a?ect channel utilizations of mobile stations and consequently the user-perceived QoS (Quality of Services) of
multimedia applications. In this paper we propose a novel optimization framework which provides QoS by adjusting IWSs
(Initial Window Size) according to current channel states and QoS requirements. It is a table-driven approach which o?-
line pre-establishes the table of the best IWSs based on a cost-reward function. Neural networks are utilized to learn the
mapping correlation and then to generalize that to other situations of interest. At runtime, the IWS of each user can thus
be determined optimally with a simple table lookup rapidly without much time spent on learning about the nonlinear and
complicated correlation. A video streaming transmission scenario is used to evaluate the performance of our scheme. The
simulation results demonstrate that the proposed mechanism can e?ectively provide QoS for each user when the capacity
of the network is su?cient for the requirements of all users.
On Fairness in Heterogeneous WLAN Environments
No full text[[abstract]]We analyze the fairness of IEEE 802.11 DCF in heterogeneous wireless LAN environments where users experience unequal channel conditions due to the mobility and fading effects. Previous works [3] [4] show that the 802.11 CSMA/CA can present fairness characteristics in both long- term and short-term. However, the conclusion is only valid under the condition of homogeneous link qualities, which may be impractical. In this paper, we consider heterogeneous channel conditions based on an analytical approach of extending a verified two dimensional Markov chain model of DCF proposed by Bianchi [10]. From our analytical results, it is shown that 802.11 CSMA/CA can present fairness among hosts with identical link qualities regardless of equal or different data rates applied, which is consistent with the observations of previous works. Our analytical results also demonstrate that the presence of heterogeneous channel conditions can pose significant unfairness of channel sharing even with a link adaptation mechanism since the MCSs (modulation and coding schemes) available are limited.
A Cross-Layer Adaptive Handoff Algorithm in Wireless Multimedia Environments
No full text[[abstract]]Providing multimedia services in wireless networks is concerned about the performance of handoff algorithms because of the irretrievable property of real-time data delivery. To lessen unnecessary handoffs and handoff latencies which can cause media disruption perceived by users, we present in this paper a cross-layer handoff algorithm base on link quality. Neural networks are used to learn the cross-layer correlation between the link quality estimator such as packet success rate and the corresponding context metric indictors, e.g. the transmitting packet length, received signal strength, and signal to noise ratio. Based on a pre-processed learning of link quality profile, our approach makes handoff decisions intelligently and efficiently with the evaluations of link quality instead of the comparisons between relative signal strength. The experiment and simulation results show that the proposed method outperforms RSS-based handoff algorithms in a transmission scenario of VoIP applications.
A Neural Network Based Adaptive Algorithm for Multimedia Quality Fairness in WLAN Environments
No full text[[abstract]]This paper investigates multimedia quality fairness in wireless LAN environments where channel are error-prone due to mobility and fading. The experimental results show that using fixed MAC arguments for nodes in heterogeneous channel conditions leads to unequal throughput performance and that may incur the degradation of multimedia QoS. To overcome the unfairness problem for provisioning QoS, we propose a cross-layer adaptation scheme by on-line adapting the multidimensional MAC-layer backoff parameters depending on the application-layer QoS requirements and PHY-layer channel conditions. Our solution is based on an optimization approach which utilizes neural networks to learn the cross-layer function. Simulation results demonstrate that our adaptive scheme can tackle a variety of channel condition to provide fair throughput for nodes in heterogeneous channel conditions
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