On MMSE vector-perturbation precoding for MIMO broadcast channels with per-antenna-group power constraints

Recently, studies on sub-optimal precoding techniques for multiple-input multiple-output broadcast channels (MIMO-BC), which achieve performance near to that of the dirty paper coding (DPC), have drawn attention to vector perturbation (VP). In practice, each antenna or more generally each antenna group has its own limit on the transmitted power, which makes per-antenna-group (per-AG) power constraints more meaningful than the sum power constraint. In this paper, we assume the per-antenna-group constraints and first investigate antenna-group power-constrained VP inspired by the p-sphere encoder for the case when channel inversion is used by the front-end linear precoder. Furthermore, we consider joint optimization of the front-end precoder and the perturbing vector subject to per-AG power constraints and propose precoding based on a minimum mean square error (MMSE) criterion. The results show that the proposed algorithm outperforms conventional VP and the p-sphere encoding in the case of per-AG power constraints.

[1]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[2]  Reinaldo A. Valenzuela,et al.  Network coordination for spectrally efficient communications in cellular systems , 2006, IEEE Wireless Communications.

[3]  Gerald Matz,et al.  Vector Perturbation Precoding Revisited , 2011, IEEE Transactions on Signal Processing.

[4]  Giuseppe Caire,et al.  The $p$ -Sphere Encoder: Peak-Power Reduction by Lattice Precoding for the MIMO Gaussian Broadcast Channel , 2006, IEEE Transactions on Communications.

[5]  T. Sälzer,et al.  From Single User to Multiuser Communications : Shifting the MIMO Paradigm , 2007 .

[6]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[7]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[8]  Witold A. Krzymien,et al.  Vector perturbation precoding and user scheduling for network MIMO , 2011, 2011 IEEE Wireless Communications and Networking Conference.

[9]  Alexander Vardy,et al.  Closest point search in lattices , 2002, IEEE Trans. Inf. Theory.

[10]  John Stillwell.  Elements of Number Theory , 2002 .

[11]  Witold A. Krzymien,et al.  On MMSE Vector-Perturbation Precoding for MIMO Broadcast Channels With Per-Antenna-Group Power Constraints , 2013, IEEE Transactions on Signal Processing.

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

[13]  Erik Dahlman,et al.  4G: LTE/LTE-Advanced for Mobile Broadband , 2011 .

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

[15]  Giuseppe Caire,et al.  On achievable rates in a multi-antenna broadcast downlink , 2000 .

[16]  Robert W. Heath,et al.  Shifting the MIMO Paradigm , 2007, IEEE Signal Processing Magazine.

[17]  Wolfgang Utschick,et al.  Minimum Mean Square Error Vector Precoding , 2005, 2005 IEEE 16th International Symposium on Personal, Indoor and Mobile Radio Communications.

[18]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[19]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part II: perturbation , 2005, IEEE Transactions on Communications.

[20]  A. Lee Swindlehurst,et al.  A vector-perturbation technique for near-capacity multiantenna multiuser communication-part I: channel inversion and regularization , 2005, IEEE Transactions on Communications.