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Title of Journal: Wireless Netw

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Abbravation: Wireless Networks

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Springer US

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DOI

10.1007/s00580-013-1730-8

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ISSN

1572-8196

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Analysis and optimization of a crosslayer adaptat

Authors: Dionysia Triantafyllopoulou Nikos Passas Evangelos Zervas Lazaros Merakos
Publish Date: 2011/05/21
Volume: 17, Issue: 5, Pages: 1339-1354
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Abstract

The theoretical analysis of a crosslayer mechanism for improving the quality of service of realtime applications in wireless networks is presented The mechanism coordinates adaptations of the modulation order at the Physical layer and the media encoding mode at the Application layer to improve packet loss rate throughput and mean delay With the use of Continuous Flow Modeling the system is considered as a “fluid” queue with inflow and outflow rates representing its traffic generation and service rates respectively Each data source is modeled as a Markov chain from the steadystate of which the optimal adaptation thresholds of the crosslayer mechanism are derived Extensive performance evaluation results show that the optimized operation of the mechanism attains a significant performance improvement compared to both the suboptimal case and a legacy system which adjusts the modulation order and encoding mode separately and independently of each other th delta low=1left 1fracfracL ialphaicdot T fcdot delta low1delta lowcdotleft 1fracL ialpha icdot T fright rightfrac1b m i so that δ  δ low if BER i   th delta low th delta med=1left 1fracfracL ialphaicdot T fcdot delta med1delta medcdotleft 1fracL ialpha icdot T fright rightfrac1b m i so that δ  δ med if BER i   th delta medIn any other case where either m i  = 1 or m i  = M or d i  = 1 or d i  = D the state i is absorbing if all the above conditions are met except the ones that refer to impossible transitions for example the condition of zero probability of a transition to a state with lower modulation order in case m i  = 1Due to the structure of the Markov chain and the calculation of its transition matrix some of its states are transient For example although the state i left d i m i 0right with fracmu d ib m ivarphi can be considered as the initial state the Markov chain cannot return to it due to the fact that the queue cannot empty when the inflow rate is higher that the outflow rate Thus in order to calculate the mean loss rate of the system modeled by the Markov chain the problem is limited to its minimum closed set that is irreducible as it has a finite number of states


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  1. A review of industrial wireless networks in the context of Industry 4.0
  2. Spectrally efficient communication for wireless sensor networks using a cooperative MIMO technique
  3. Optimal joint utility based load balancing algorithm for heterogeneous wireless networks
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  9. Stochastic approximation based on-line algorithm for fairness in multi-rate wireless LANs
  10. D2D-based Survival on Sharing for critical communications
  11. Supporting voice and video applications over IEEE 802.11n WLANs
  12. Energy-efficient scheduling with individual packet delay constraints over a fading channel
  13. Guest editorial
  14. Time-frequency hopping sequences with three no hit zones
  15. Characterizing 802.11 wireless link behavior
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  17. Research on routing protocol facing to signal conflicting in link quality guaranteed WSN
  18. Design and analysis of novel quorum-based sink location service scheme in wireless sensor networks
  19. Efficient cluster head selection using Naïve Bayes classifier for wireless sensor networks
  20. Guest editorial on selected papers from WiOpt’05
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  22. The fundamental limits of broadcasting in dense wireless mobile networks
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  24. Effective Location Based Services with Dynamic Data Management in Mobile Environments
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