An HMM approach to adaptive modulation and coding with multicodes for fading channels

In this paper, the impact of channel state estimation errors in a system employing AMC and multicodes is studied. The channel is modelled as a finite-state Markov chain (FSMC), and a hidden Markov model (HMM) formulation for the above problem is presented. An HMM filter is used and shown to yield an improved estimate of the channel state. Since the transition probabilities of the FSMC are required by the HMM filter, the problem of estimating these probabilities is also addressed. The performance of the proposed scheme is evaluated using computer simulation. The results show that the HMM filter provides a significant throughput improvement over the unfiltered case, especially when the channel state information (CSI) is quite noisy or the normalized Doppler rate (defined as the product of the Doppler rate of the channel and the transmission period) is small

[1]  V Krishnamurthy,et al.  Adaptive processing techniques based on hidden Markov models for characterizing very small channel currents buried in noise and deterministic interferences. , 1991, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[2]  John B. Moore,et al.  Hidden Markov Models: Estimation and Control , 1994 .

[3]  John B. Moore,et al.  Adaptive HMM filters for signals in noisy fading channels , 1994, Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Saleem A. Kassam,et al.  Finite-state Markov model for Rayleigh fading channels , 1999, IEEE Trans. Commun..

[5]  R. Kwan,et al.  Scheduling for the downlink in a CDMA network with imperfect channel estimation , 2004, IEEE Global Telecommunications Conference Workshops, 2004. GlobeCom Workshops 2004..

[6]  Fulvio Babich,et al.  Statistical analysis and characterization of the indoor propagation channel , 2000, IEEE Trans. Commun..

[7]  R. Kwan,et al.  Gamma variate ratio distribution with application to CDMA performance analysis , 2005, IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication, 2005..

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

[9]  Bernhard Raaf,et al.  Hybrid ARQ and adaptive modulation and coding schemes for high speed downlink packet access , 2002, The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications.

[10]  Antti Toskala,et al.  WCDMA for UMTS: Radio Access for Third Generation Mobile Communications , 2000 .

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

[12]  Dan Keun Sung,et al.  Capacities of single-code and multicode DS-CDMA systems accommodating multiclass services , 1999 .

[13]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[14]  R. Kwan,et al.  Channel-based downlink scheduling schemes for CDMA networks [3G wireless networks] , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[15]  Lars K. Rasmussen,et al.  Linear interference cancellation in CDMA based on iterative techniques for linear equation systems , 2000, IEEE Trans. Commun..