Regularized phase alignment precoding for the MISO downlink

With the knowledge of both channel and data information at the base station prior to downlink transmission, we can increase the received signal-to-noise ratio (SNR) of each user without the need to increase the transmitted power. Achievability is based on the idea of phase alignment (PA) precoding where instead of removing the destructive interference, it judiciously rotates the phases of the transmitted symbols. In this way, for each user, the received interference from the other users add up coherently, and consequently we can glean higher received SNRs at all mobile terminals. In addition, it is well-known that the regularized channel inversion (RCI) improves the performance of channel inversion (CI). In line with this and similar to the RCI precoding, in this paper we propose the idea of regularized PA (RPA) which is shown to improve the performance of original PA precoding. To do so, we first rectify the original PA precoding by deriving a closed-form expression of its scaling factor. We then use this new analysis to select an appropriate regularization factor for the proposed RPA scheme. Finally, we drive an explicit formula regarding its received SNR. Since the focus of this work is on linear precoders, we show that the proposed RPA precoding outperforms CI, RCI, and PA precoders from both symbol-error rate (SER) and sum rate perspectives. We also consider the performance of RPA under imperfect channel state information at transmit side. We show that even in this case, RPA precoding is as sensitive as other linear precoders to channel imperfections.

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

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

[3]  M. Sellathurai,et al.  Computationally Efficient Vector Perturbation Precoding Using Thresholded Optimization , 2013, IEEE Transactions on Communications.

[4]  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.

[5]  Luca Sanguinetti,et al.  Non-Linear Pre-Coding for Multiple-Antenna Multi-User Downlink Transmissions with Different QoS Requirements , 2007, IEEE Transactions on Wireless Communications.

[6]  Tharmalingam Ratnarajah,et al.  Interference Optimization for Transmit Power Reduction in Tomlinson-Harashima Precoded MIMO Downlinks , 2012, IEEE Transactions on Signal Processing.

[7]  Tharmalingam Ratnarajah,et al.  Interference-Driven Linear Precoding in Multiuser MISO Downlink Cognitive Radio Network , 2012, IEEE Transactions on Vehicular Technology.

[8]  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.

[9]  Y. Shao,et al.  Error performance analysis of linear zero forcing and MMSE precoders for MIMO broadcast channels , 2007, IET Commun..

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

[11]  Volker Jungnickel,et al.  Performance of MIMO systems with channel inversion , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

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

[13]  Christos Masouros,et al.  Correlation Rotation Linear Precoding for MIMO Broadcast Communications , 2011, IEEE Transactions on Signal Processing.

[14]  Tharmalingam Ratnarajah,et al.  Interference as a Source of Green Signal Power in Cognitive Relay Assisted Co-Existing MIMO Wireless Transmissions , 2012, IEEE Transactions on Communications.

[15]  Tharmalingam Ratnarajah,et al.  Performance Analysis of Interference Alignment Under CSI Mismatch , 2014, IEEE Transactions on Vehicular Technology.

[16]  Andrea J. Goldsmith,et al.  Capacity and power allocation for fading MIMO channels with channel estimation error , 2006, IEEE Trans. Inf. Theory.

[17]  Andrea J. Goldsmith,et al.  On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming , 2006, IEEE Journal on Selected Areas in Communications.