Communicating over nonstationary nonflat wireless channels

We develop the concept of joint time-frequency estimation of wireless channels. The motivation is to optimize channel usage by increasing the signal-to-noise ratio (SNR) after demodulation while keeping training overhead at a moderate level. This issue is important for single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems but particularly so for the latter. Linear operators offer a general mathematical framework for symbol modulation in channels that vary both temporally and spectrally within the duration and bandwidth of one symbol. In particular, we present a channel model that assumes first-order temporal and spectral fluctuations within one symbol or symbol block. Discrete prolate spheroidal sequences (Slepian sequences) are used as pulse-shaping functions. The channel operator in the Slepian basis is almost tridiagonal, and the simple intersymbol interference pattern can be exploited for efficient and fast decoding using Viterbi's algorithm. To prove the concept, we use the acoustic channel as a meaningful physical analogy to the radio channel. In acoustic 2 /spl times/ 2 MIMO experiments, our method produced estimation results that are superior to first-order time-only, frequency-only, and zeroth-order models by 7.0, 9.4, and 11.6 db. In computer simulations of cellular wireless channels with realistic temporal and spectral fluctuations, time-frequency estimation gains us 12 to 18 db over constant-only estimation in terms of received SNR when signal-to-receiver-noise is 10 to 20 db. The bit error rate (BER) decreases by a factor of two for a binary constellation.

[1]  Andrew J. Viterbi,et al.  Error bounds for convolutional codes and an asymptotically optimum decoding algorithm , 1967, IEEE Trans. Inf. Theory.

[2]  Jerry D. Gibson,et al.  Mobile Communications Handbook , 1999 .

[3]  Tadashi Matsumoto,et al.  Multicarrier signal detection and parameter estimation in frequency-selective Rayleigh fading channels , 1997 .

[4]  Partha P. Mitra,et al.  Tripling the capacity of wireless communications using electromagnetic polarization , 2001, Nature.

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

[6]  Gerard J. Foschini,et al.  Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas , 1996, Bell Labs Technical Journal.

[7]  D. Slepian.  Prolate spheroidal wave functions, fourier analysis, and uncertainty — V: the discrete case , 1978, The Bell System Technical Journal.

[8]  David J. Thomson,et al.  Nonstationary fluctuations in stationary time-series , 1993, Optics & Photonics.

[9]  William J. Thompson,et al.  Spheroidal wave functions , 1999, Comput. Sci. Eng..

[10]  Guohua Pan,et al.  Local Regression and Likelihood , 1999, Technometrics.

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

[12]  H. Pollak,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — III: The dimension of the space of essentially time- and band-limited signals , 1962 .

[13]  D. Slepian,et al.  Prolate spheroidal wave functions, fourier analysis and uncertainty — II , 1961 .

[14]  Donald B. Percival,et al.  Spectral Analysis for Physical Applications , 1993 .

[15]  V.W.S. Chan,et al.  Principles of Digital Communication and Coding , 1979 .

[16]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[17]  D. Thomson.  Quadratic-inverse spectrum estimates: applications to palaeoclimatology , 1990, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.

[18]  M. Wand.  Local Regression and Likelihood , 2001 .

[19]  Babak Hassibi,et al.  How much training is needed in multiple-antenna wireless links? , 2003, IEEE Trans. Inf. Theory.

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