Joint Unitary Triangularization for Gaussian Multi-User MIMO Networks

The problem of transmitting a common message to multiple users over the Gaussian multiple-input multiple-output broadcast channel is considered, where each user is equipped with an arbitrary number of antennas. A closed-loop scenario is assumed, for which a practical capacity-approaching scheme is developed. By applying judiciously chosen unitary operations at the transmit and receive nodes, the channel matrices are triangularized so that the resulting matrices have equal diagonals, up to a possible multiplicative scalar factor. This, along with the utilization of successive interference cancellation, reduces the coding and decoding tasks to those of coding and decoding over the single-antenna additive white Gaussian noise channel. Over the resulting effective channel, any off-the-shelf code may be used. For the two-user case, it was recently shown that such joint unitary triangularization is always possible. In this paper, it is shown that for more than two users, it is necessary to carry out the unitary linear processing jointly over multiple channel uses, i.e., space-time processing is employed. It is further shown that exact triangularization, where all resulting diagonals are equal, is still not always possible, and appropriate conditions for the existence of such are established for certain cases. When exact triangularization is not possible, an asymptotic construction is proposed, that achieves the desired property of equal diagonals up to edge effects that can be made arbitrarily small, at the price of processing a sufficiently large number of channel uses together.

[1]  Yuval Kochman,et al.  Physical-layer MIMO relaying , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[2]  P. Vijay Kumar,et al.  Perfect Space–Time Codes for Any Number of Antennas , 2007, IEEE Transactions on Information Theory.

[3]  Christopher Holden,et al.  Perfect Space-Time Block Codes , 2004 .

[4]  A. Robert Calderbank,et al.  Space-Time Codes for High Data Rate Wireless Communications : Performance criterion and Code Construction , 1998, IEEE Trans. Inf. Theory.

[5]  Babak Hassibi,et al.  High-rate codes that are linear in space and time , 2002, IEEE Trans. Inf. Theory.

[6]  Yi Jiang,et al.  The generalized triangular decomposition , 2007, Math. Comput..

[7]  Yuval Kochman,et al.  Joint Unitary Triangularization for MIMO Networks , 2012, IEEE Transactions on Signal Processing.

[8]  Pramod Viswanath,et al.  Approximately universal codes over slow-fading channels , 2005, IEEE Transactions on Information Theory.

[9]  Alexei Gorokhov,et al.  Signaling Over Arbitrarily Permuted Parallel Channels , 2008, IEEE Transactions on Information Theory.

[10]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[11]  John M. Cioffi,et al.  MMSE decision-feedback equalizers and coding. I. Equalization results , 1995, IEEE Trans. Commun..

[12]  Uri Erez,et al.  Transmission over arbitrarily permuted parallel Gaussian channels , 2012, 2012 IEEE International Symposium on Information Theory Proceedings.

[13]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[14]  J. Wolfowitz Simultaneous channels , 1959 .

[15]  Frédérique E. Oggier,et al.  Perfect Space–Time Block Codes , 2006, IEEE Transactions on Information Theory.

[16]  Babak Hassibi,et al.  An efficient square-root algorithm for BLAST , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[17]  Xue-Bin Liang,et al.  Orthogonal designs with maximal rates , 2003, IEEE Trans. Inf. Theory.

[18]  Zhi-Quan Luo,et al.  An efficient design method for vector broadcast systems with common information , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[19]  Yuval Kochman,et al.  Improved rates and coding for the MIMO two-way relay channel , 2014, 2014 International Symposium on Information Theory and its Applications.

[20]  W. Marsden I and J , 2012 .

[21]  Gregory W. Wornell,et al.  Rateless Coding for Gaussian Channels , 2007, IEEE Transactions on Information Theory.

[22]  Emanuele Viterbo,et al.  The golden code: a 2 x 2 full-rate space-time code with non-vanishing determinants , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

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

[24]  D. Blackwell,et al.  The Capacity of a Class of Channels , 1959 .

[25]  Jian-Kang Zhang,et al.  Equal-diagonal QR decomposition and its application to precoder design for successive-cancellation detection , 2005, IEEE Transactions on Information Theory.

[26]  A. Robert Calderbank,et al.  Space-Time block codes from orthogonal designs , 1999, IEEE Trans. Inf. Theory.

[27]  Curt M. White Multiplexing , 2002, Encyclopedia of Information Systems.

[28]  G. David Forney,et al.  Multidimensional constellations. II. Voronoi constellations , 1989, IEEE J. Sel. Areas Commun..

[29]  Ying-Chang Liang,et al.  Block diagonal geometric mean decomposition (BD-GMD) for MIMO broadcast channels , 2008, IEEE Transactions on Wireless Communications.

[30]  H. Weyl Inequalities between the Two Kinds of Eigenvalues of a Linear Transformation. , 1949, Proceedings of the National Academy of Sciences of the United States of America.

[31]  Alicja Smoktunowicz,et al.  On Constructing Unit Triangular Matrices with Prescribed Singular Values , 2000, Computing.

[32]  Gregory W. Wornell,et al.  Incremental coding over MIMO channels , 2011, 2011 IEEE Information Theory Workshop.

[33]  C. Loan Generalizing the Singular Value Decomposition , 1976 .

[34]  Y. Jiang,et al.  Uniform channel decomposition for MIMO communications , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..

[35]  G. Forney,et al.  Generalized Decision-Feedback Equalization for Packet Transmission with ISI and Gaussian Noise , 1997 .

[36]  Siavash M. Alamouti,et al.  A simple transmit diversity technique for wireless communications , 1998, IEEE J. Sel. Areas Commun..

[37]  John M. Cioffi,et al.  MMSE decision-feedback equalizers and coding. II. Coding results , 1995, IEEE Trans. Commun..

[38]  Shlomo Shamai,et al.  Polar coding for reliable communications over parallel channels , 2010, 2010 IEEE Information Theory Workshop.

[39]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[40]  A. Horn On the eigenvalues of a matrix with prescribed singular values , 1954 .

[41]  Yi Jiang,et al.  MIMO Transceiver Design via Majorization Theory , 2007, Found. Trends Commun. Inf. Theory.

[42]  Jian Li,et al.  The geometric mean decomposition , 2005 .

[43]  Michael J. Lopez Multiplexing, scheduling, and multicasting strategies for antenna arrays in wireless networks , 2004 .

[44]  Emanuele Viterbo,et al.  The golden code: a 2×2 full-rate space-time code with nonvanishing determinants , 2004, IEEE Trans. Inf. Theory.

[45]  Gregory W. Wornell,et al.  Decode-and-forward for the Gaussian relay channel via standard AWGN coding and decoding , 2012, 2012 IEEE Information Theory Workshop.