Cross-layer optimisation of network performance over multiple-input multipleoutput wireless mobile channels

In this study a wireless multiple-input multiple-output (MIMO) communication system operating over a fading channel is considered. Data packets are stored in a finite size buffer before being released into the time-varying MIMO wireless channel. The main objective of this work is to satisfy a specific quality of service (QoS) requirement, i.e. the probability of data loss because of both erroneous wireless transmission and buffer overflow, as well as to maximise the system throughput. The theoretical limit of ergodic capacity in MIMO time-variant channels can be achieved by adapting the transmission rate to the capacity evolving process. In this study, the channel capacity evolving process has been described by a suitable autoregressive model based on the capacity time correlation and a finite state Markov chain (FSMC) has been derived. The joint effect of channel outage at the physical layer and the buffer overflow at the medium access control layer has been considered to describe the probability of data loss in the system. The optimal transmission strategy must minimise that probability of data loss and has been derived analytically through the Markov decision process (MDP) theory. Analytical results show the significant improvements of the proposed optimal transmission strategy in terms of both system throughput and probability of data loss.

[1]  A. Shami,et al.  On the Capacity of MIMO Channels and Its Effect on Network Performance , 2008, 2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications.

[2]  E. Gilbert Capacity of a burst-noise channel , 1960 .

[3]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[4]  R. Bellman,et al.  Dynamic Programming and Markov Processes , 1960 .

[5]  Abdallah Shami,et al.  Two dimensional cross-layer optimization for packet transmission over fading channel , 2008, IEEE Transactions on Wireless Communications.

[6]  Norman C. Beaulieu,et al.  On first-order Markov modeling for the Rayleigh fading channel , 2000, IEEE Trans. Commun..

[7]  John G. Proakis,et al.  Digital Communications , 1983 .

[8]  Kareem E. Baddour,et al.  Channel estimation using DPSS based frames , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[10]  Georgios B. Giannakis,et al.  Adaptive PSAM accounting for channel estimation and prediction errors , 2005, IEEE Transactions on Wireless Communications.

[11]  A. Robert Calderbank,et al.  MIMO Wireless Communications , 2007 .

[12]  Abdallah Shami,et al.  A New Perspective of Cross-layer Optimization for Wireless Communication over Fading Channel , 2007, 2007 16th International Conference on Computer Communications and Networks.

[13]  A.R.K. Sastry,et al.  Models for channels with memory and their applications to error control , 1978, Proceedings of the IEEE.

[14]  Georgios B. Giannakis,et al.  Queuing with adaptive modulation and coding over wireless links: cross-Layer analysis and design , 2005, IEEE Transactions on Wireless Communications.

[15]  Ralf R. Müller,et al.  MIMO channel modeling and the principle of maximum entropy , 2005, IEEE Transactions on Information Theory.

[16]  Dimitri P. Bertsekas,et al.  Data Networks , 1986 .

[17]  Antonia Maria Tulino,et al.  Random Matrix Theory and Wireless Communications , 2004, Found. Trends Commun. Inf. Theory.

[18]  Thomas L. Marzetta,et al.  Multiple-antenna channel hardening and its implications for rate feedback and scheduling , 2004, IEEE Transactions on Information Theory.

[19]  S. Haykin,et al.  MIMO channel capacity modeling using Markov models , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[20]  E. O. Elliott Estimates of error rates for codes on burst-noise channels , 1963 .

[21]  Laurence B. Milstein,et al.  Error statistics in data transmission over fading channels , 1998, IEEE Trans. Commun..

[22]  Hong Shen Wang,et al.  Finite-state Markov channel-a useful model for radio communication channels , 1995 .

[23]  Mansoor Shafi,et al.  Capacity of MIMO systems with semicorrelated flat fading , 2003, IEEE Trans. Inf. Theory.

[24]  T. Zemen,et al.  Time-variant channel estimation using discrete prolate spheroidal sequences , 2005, IEEE Transactions on Signal Processing.