An Information-Theoretic Measure for the Selection of Effective Memory Order in Flat-Fading Channels

In this paper, we consider an information-theoretic measure for the selection of effective memory order in time-varying flat-fading (FF) channels in the Jakes-Clarke’s model. The measure is based on how many past realisations of the fading channel actively contribute to an achievable information rate lower bound through the channel. The lower bound signifies the achievable information rate when the receiver is equipped with a sup-optimal mismatched decoding rule, which would have only been optimal for a finite-state Markov channel (FSMC) with a finite memory order. The sensitivity of the mismatched decoding rule to memory order is used to find the optimum channel memory order for decoding at the receiver side, with a negligible information rate loss. Our case study shows that the FF channel has an effective information-theoretic memory order of M = 1 for the normalised fading rates fDT . 0.01, where fD is the Doppler spread and T is symbol period. For faster fading rates, the use of first-order models at the receiver incurs a non-negligible information rate loss. The results are, generally, in accordance with our previous findings.

[1]  Fulvio Babich,et al.  Generalized Markov modeling for flat fading , 2000, IEEE Trans. Commun..

[2]  Wayne E. Stark,et al.  On the design of Markov models for fading channels , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[3]  Fulvio Babich,et al.  A context-tree based model for quantized fading , 1999, IEEE Communications Letters.

[4]  Andrea J. Goldsmith,et al.  Low-complexity maximum-likelihood detection of coded signals sent over finite-state Markov channels , 2002, IEEE Trans. Commun..

[5]  H. Kong,et al.  Sequence detection and channel state estimation over finite state Markov channels , 1999 .

[6]  D. A. Bell,et al.  Information Theory and Reliable Communication , 1969 .

[7]  Parastoo Sadeghi,et al.  The effect of memory order on the capacity of finite-state Markov and flat-fading channels , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[8]  Kareem E. Baddour,et al.  Autoregressive models for fading channel simulation , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[9]  Andrea Goldsmith,et al.  Wireless Communications , 2005, 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS).

[10]  Parastoo Sadeghi Modelling, information capacity, and estimation of time-varying channels in mobile communication systems , 2006 .

[11]  Emre Telatar,et al.  Mismatched decoding revisited: General alphabets, channels with memory, and the wide-band limit , 2000, IEEE Trans. Inf. Theory.

[12]  J.E. Mazo,et al.  Digital communications , 1985, Proceedings of the IEEE.

[13]  Pao-Chi Chang,et al.  On verifying the first-order Markovian assumption for a Rayleigh fading channel model , 1996 .

[14]  E. Shwedyk,et al.  Communication receivers based on Markov models of the fading channel , 2002, IEEE CCECE2002. Canadian Conference on Electrical and Computer Engineering. Conference Proceedings (Cat. No.02CH37373).

[15]  R. Clarke A statistical theory of mobile-radio reception , 1968 .

[16]  P. Rapajic,et al.  Capacity performance analysis of coherent detection in correlated fading channels using finite state Markov models , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[17]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[18]  W. C. Jakes,et al.  Microwave Mobile Communications , 1974 .

[19]  Richard D. Wesel,et al.  Joint iterative channel estimation and decoding in flat correlated Rayleigh fading , 2001, IEEE J. Sel. Areas Commun..

[20]  Hans-Andrea Loeliger,et al.  Computation of Information Rates from Finite-State Source/Channel Models , 2002 .