Quasi-Static Multiple-Antenna Fading Channels at Finite Blocklength

This paper investigates the maximal achievable rate for a given blocklength and error probability over quasi-static multiple-input multiple-output fading channels, with and without channel state information at the transmitter and/or the receiver. The principal finding is that outage capacity, despite being an asymptotic quantity, is a sharp proxy for the finite-blocklength fundamental limits of slow-fading channels. Specifically, the channel dispersion is shown to be zero regardless of whether the fading realizations are available at both transmitter and receiver, at only one of them, or at neither of them. These results follow from analytically tractable converse and achievability bounds. Numerical evaluation of these bounds verifies that zero dispersion may indeed imply fast convergence to the outage capacity as the blocklength increases. In the example of a particular 1 × 2 single-input multiple-output Rician fading channel, the blocklength required to achieve 90% of capacity is about an order of magnitude smaller compared with the blocklength required for an AWGN channel with the same capacity. For this specific scenario, the coding/decoding schemes adopted in the LTE-Advanced standard are benchmarked against the finite-blocklength achievability and converse bounds.

[1]  Giuseppe Caire,et al.  Finite-blocklength channel coding rate under a long-term power constraint , 2014, 2014 IEEE International Symposium on Information Theory.

[2]  I. Olkin,et al.  Inequalities: Theory of Majorization and Its Applications , 1980 .

[3]  Romain Couillet,et al.  Bounds on the second-order coding rate of the MIMO Rayleigh block-fading channel , 2013, 2013 IEEE International Symposium on Information Theory.

[4]  R. Rajendiran,et al.  Topological Spaces , 2019, A Physicist's Introduction to Algebraic Structures.

[5]  Stefania Sesia,et al.  LTE - The UMTS Long Term Evolution, Second Edition , 2011 .

[6]  N. L. Johnson,et al.  Continuous Univariate Distributions. , 1995 .

[7]  H. Vincent Poor,et al.  Channel coding: non-asymptotic fundamental limits , 2010 .

[8]  Yury Polyanskiy,et al.  Saddle Point in the Minimax Converse for Channel Coding , 2013, IEEE Transactions on Information Theory.

[9]  D. Owen Handbook of Mathematical Functions with Formulas , 1965 .

[10]  P. Koev,et al.  On the largest principal angle between random subspaces , 2006 .

[11]  Hiroki Koga,et al.  Information-Spectrum Methods in Information Theory , 2002 .

[12]  Adi Ben-Israel,et al.  On principal angles between subspaces in Rn , 1992 .

[13]  S. Afriat Orthogonal and oblique projectors and the characteristics of pairs of vector spaces , 1957, Mathematical Proceedings of the Cambridge Philosophical Society.

[14]  H. Luetkepohl The Handbook of Matrices , 1996 .

[15]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[16]  W. Rudin Real and complex analysis, 3rd ed. , 1987 .

[17]  Sergio Verdú,et al.  Scalar coherent fading channel: Dispersion analysis , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[18]  J. Jost Riemannian geometry and geometric analysis , 1995 .

[19]  John M. Lee Riemannian Manifolds: An Introduction to Curvature , 1997 .

[20]  H. Vincent Poor,et al.  Dispersion of the Gilbert-Elliott Channel , 2009, IEEE Transactions on Information Theory.

[21]  A. V. Skorohod,et al.  Sums of Independent Random Variables , 1991 .

[22]  W. Rudin Principles of mathematical analysis , 1964 .

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

[24]  Ingram Olkin,et al.  Inequalities: Theory of Majorization and Its Application , 1979 .

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

[26]  Feng Qi (祁锋) BOUNDS FOR THE RATIO OF TWO GAMMA FUNCTIONS-FROM WENDEL'S AND RELATED INEQUALITIES TO LOGARITHMICALLY COMPLETELY MONOTONIC FUNCTIONS , 2009, 0904.1048.

[27]  Alexander Barg,et al.  Bounds on packings of spheres in the Grassmann manifold , 2002, IEEE Trans. Inf. Theory.

[28]  Shao-Lun Huang,et al.  Proof of the Outage Probability Conjecture for MISO Channels , 2013, IEEE Transactions on Information Theory.

[29]  Bhaskar D. Rao,et al.  Design and Analysis of MIMO Spatial Multiplexing Systems With Quantized Feedback , 2006, IEEE Transactions on Signal Processing.

[30]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[31]  H. Vincent Poor,et al.  Channel Coding Rate in the Finite Blocklength Regime , 2010, IEEE Transactions on Information Theory.

[32]  Shlomo Shamai,et al.  Information theoretic considerations for cellular mobile radio , 1994 .

[33]  Ali Esmaili,et al.  Probability and Random Processes , 2005, Technometrics.

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

[35]  C. Shannon Probability of error for optimal codes in a Gaussian channel , 1959 .

[36]  Vincent Yan Fu Tan,et al.  " -Capacities and Second-Order Coding Rates for Channels with General State , 2013 .

[37]  Vincent Yan Fu Tan,et al.  The third-order term in the normal approximation for the AWGN channel , 2014, 2014 IEEE International Symposium on Information Theory.

[38]  Giuseppe Durisi,et al.  Quasi-static SIMO fading channels at finite blocklength , 2013, 2013 IEEE International Symposium on Information Theory.

[39]  Patrick Robertson,et al.  A comparison of optimal and sub-optimal MAP decoding algorithms operating in the log domain , 1995, Proceedings IEEE International Conference on Communications ICC '95.

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

[41]  David Tse,et al.  Fundamentals of Wireless Communication , 2005 .

[42]  A. Edelman Eigenvalues and condition numbers of random matrices , 1988 .

[43]  A. Krall Applied Analysis , 1986 .

[44]  Andrea J. Goldsmith,et al.  Generalizing Capacity: New Definitions and Capacity Theorems for Composite Channels , 2010, IEEE Transactions on Information Theory.

[45]  Giuseppe Durisi,et al.  Diversity versus channel knowledge at finite block-length , 2012, 2012 IEEE Information Theory Workshop.

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

[47]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[48]  Sergio Verdú,et al.  A general formula for channel capacity , 1994, IEEE Trans. Inf. Theory.

[49]  I. Chavel Riemannian Geometry: Subject Index , 2006 .

[50]  A. Goldsmith,et al.  Capacity definitions and coding strategies for general channels with receiver side information , 1998, Proceedings. 1998 IEEE International Symposium on Information Theory (Cat. No.98CH36252).

[51]  Milton Abramowitz,et al.  Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables , 1964 .

[52]  Giuseppe Caire,et al.  Optimum power control over fading channels , 1999, IEEE Trans. Inf. Theory.

[53]  M. Marias Analysis on Manifolds , 2005 .

[54]  M. Abramowitz,et al.  Handbook of Mathematical Functions With Formulas, Graphs and Mathematical Tables (National Bureau of Standards Applied Mathematics Series No. 55) , 1965 .

[55]  Adam Panagos,et al.  On Achievable Rates for MIMO Systems with Imperfect Channel State Information in the Finite Length Regime , 2013, IEEE Transactions on Communications.

[56]  Dennis D. Boos,et al.  A Converse to Scheffe's Theorem , 1985 .

[57]  A. James Distributions of Matrix Variates and Latent Roots Derived from Normal Samples , 1964 .