Large system analysis of projection based algorithms for the MIMO broadcast channel

Analytical results for the average sum rate achievable in the Multiple-Input Multiple-Output (MIMO) broadcast channel with algorithms relying on full channel state information at the transmitter are hard to obtain in practice. In the large system limit, when the number of transmit and receive antennas goes to infinity at a finite fixed ratio, however, the eigenvalues of many random matrices become deterministic and analytical expressions for the sum rate can be derived in some cases. In this paper we will present large system expressions for the sum rate for three sub-optimum algorithms, namely the Successive Encoding Successive Allocation Method (SESAM), Block Diagonalization and Block Diagonalization with Dirty Paper Coding. In case the large system limit of the sum rate does not exist, we derive lower bounds. By simulation results it is shown that the asymptotic results serve as a good approximation of the system performance with finite system parameters of reasonable size.

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

[2]  F. B. Hildebrand,et al.  Introduction To Numerical Analysis , 1957 .

[3]  Martin Haardt,et al.  Linear MMSE Multi-User MIMO Downlink Precoding for Users with Multiple Antennas , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[4]  Jamie S. Evans,et al.  Multiuser Transmit Beamforming via Regularized Channel Inversion: A Large System Analysis , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[5]  Antonia Maria Tulino,et al.  Impact of antenna correlation on the capacity of multiantenna channels , 2005, IEEE Transactions on Information Theory.

[6]  Shlomo Shamai,et al.  The Capacity Region of the Gaussian Multiple-Input Multiple-Output Broadcast Channel , 2006, IEEE Transactions on Information Theory.

[7]  Harish Viswanathan,et al.  Downlink capacity evaluation of cellular networks with known-interference cancellation , 2003, IEEE J. Sel. Areas Commun..

[8]  Michael L. Honig,et al.  Advances in Multiuser Detection , 2009 .

[9]  Martin Haardt,et al.  Low-Complexity Space–Time–Frequency Scheduling for MIMO Systems With SDMA , 2007, IEEE Transactions on Vehicular Technology.

[10]  V. Marčenko,et al.  DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES , 1967 .

[11]  Walid Hachem,et al.  A Central Limit Theorem for the SINR at the LMMSE Estimator Output for Large-Dimensional Signals , 2008, IEEE Transactions on Information Theory.

[12]  Matthew R. McKay,et al.  Largest Eigenvalue Statistics of Double-Correlated Complex Wishart Matrices and MIMO-MRC , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[13]  B.L. Evans,et al.  Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization , 2005, IEEE Transactions on Signal Processing.

[14]  Josef A. Nossek,et al.  Subchannel Allocation in Multiuser Multiple-Input–Multiple-Output Systems , 2006, IEEE Transactions on Information Theory.

[15]  Josef A. Nossek,et al.  Sum-Rate Maximizing Decompositon Approaches for Multiuser MIMO-OFDM , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[16]  Iain B. Collings,et al.  Performance with Random Signatures , 2009 .

[17]  Michael L. Honig,et al.  Capacity of a Multiple-Antenna Fading Channel With a Quantized Precoding Matrix , 2007, IEEE Transactions on Information Theory.

[18]  R. Speicher Free Convolution and the Random Sum of Matrices , 1993 .

[19]  Martin Haardt,et al.  Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels , 2004, IEEE Transactions on Signal Processing.

[20]  Jamie S. Evans,et al.  Large system performance of linear multiuser receivers in multipath fading channels , 2000, IEEE Trans. Inf. Theory.