Capacity of Channels With Frequency-Selective and Time-Selective Fading

This paper finds the capacity of single-user discrete-time channels subject to both frequency-selective and time-selective fading, where the channel output is observed in additive Gaussian noise. A coherent model is assumed where the fading coefficients are known at the receiver. Capacity depends on the first-order distributions of the fading processes in frequency and in time, which are assumed to be independent of each other, and a simple formula is given when one of the processes is independent identically distributed (i.i.d.) and the other one is sufficiently mixing. When the frequency-selective fading coefficients are known also to the transmitter, we show that the optimum normalized power spectral density is the waterfilling power allocation for a reduced signal-to-noise ratio (SNR), where the gap to the actual SNR depends on the fading distributions. Asymptotic expressions for high/low SNR and easily computable bounds on capacity are also provided.

[1]  Antonia Maria Tulino,et al.  Capacity-achieving input covariance for single-user multi-antenna channels , 2006, IEEE Transactions on Wireless Communications.

[2]  John Riordan,et al.  Introduction to Combinatorial Analysis , 1958 .

[3]  Sergio Verdú,et al.  Spectral efficiency in the wideband regime , 2002, IEEE Trans. Inf. Theory.

[4]  Shlomo Shamai,et al.  Fading Channels: Information-Theoretic and Communication Aspects , 1998, IEEE Trans. Inf. Theory.

[5]  Shlomo Shamai,et al.  Intersymbol interference with flat fading: Channel capacity , 2008, 2008 IEEE International Symposium on Information Theory.

[6]  J. Sherman,et al.  Adjustment of an Inverse Matrix Corresponding to a Change in One Element of a Given Matrix , 1950 .

[7]  James L. Massey,et al.  Capacity of the discrete-time Gaussian channel with intersymbol interference , 1988, IEEE Trans. Inf. Theory.

[8]  R. Stanley What Is Enumerative Combinatorics , 1986 .

[9]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[10]  John Riordan,et al.  Introduction to Combinatorial Analysis , 1959 .

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

[12]  Shlomo Shamai,et al.  The impact of frequency-flat fading on the spectral efficiency of CDMA , 2001, IEEE Trans. Inf. Theory.

[13]  C.E. Shannon,et al.  Communication in the Presence of Noise , 1949, Proceedings of the IRE.

[14]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[15]  Shlomo Shamai,et al.  The Gaussian Erasure Channel , 2007, 2007 IEEE International Symposium on Information Theory.

[16]  Sergio VerdÂ,et al.  Fading Channels: InformationTheoretic and Communications Aspects , 2000 .

[17]  Giuseppe Caire,et al.  Capacity of the Gaussian Erasure Channel , 2007 .

[18]  Shlomo Shamai,et al.  Estimation of non-Gaussian random variables in Gaussian noise: Properties of the MMSE , 2008, 2008 IEEE International Symposium on Information Theory.

[19]  R. Olshen Asymptotic properties of the periodogram of a discrete stationary process , 1967, Journal of Applied Probability.

[20]  Daniel Cygan,et al.  The land mobile satellite communication channel-recording, statistics, and channel model , 1991 .

[21]  Robert M. Gray,et al.  Toeplitz and Circulant Matrices: A Review , 2005, Found. Trends Commun. Inf. Theory.