Practical Interference Exploitation Precoding Without Symbol-by-Symbol Optimization: A Block-Level Approach

In this paper, we propose a constructive interference (CI)-based block-level precoding (CI-BLP) approach for the downlink of a multi-user multiple-input single-output (MU-MISO) communication system. Contrary to existing CI precoding approaches which have to be designed on a symbol-by-symbol level, here a constant precoding matrix is applied to a collection of symbols within a given transmission block, thus significantly reducing the computational costs over traditional CI-based symbol-level precoding (CI-SLP) as the CI-BLP optimization problem only needs to be solved once per block. For both PSK and QAM modulation, we formulate an optimization problem to maximize the minimum CI effect over the block subject to a block- rather than symbol-level power budget. We mathematically derive the optimal precoding matrix for CI-BLP as a function of the Lagrange multipliers in closed form. By formulating the dual problem, the original CI-BLP optimization problem is further shown to be equivalent to a quadratic programming (QP) optimization. Numerical results validate our derivations, and show that the proposed CI-BLP scheme achieves improved performance over the traditional CI-SLP method, thanks to the relaxed power constraint over the considered block of symbol slots.

[1]  Christos Masouros,et al.  A Memory-Efficient Learning Framework for Symbol Level Precoding With Quantized NN Weights , 2021, IEEE Open Journal of the Communications Society.

[2]  A. Lee Swindlehurst,et al.  Dual-Functional Radar-Communication Waveform Design: A Symbol-Level Precoding Approach , 2021, IEEE Journal of Selected Topics in Signal Processing.

[3]  Christos Masouros,et al.  An Unsupervised Deep Unfolding Framework for Robust Symbol-Level Precoding , 2021, IEEE Open Journal of the Communications Society.

[4]  C. Masouros,et al.  An Unsupervised Learning-Based Approach for Symbol-Level-Precoding , 2021, 2021 IEEE Global Communications Conference (GLOBECOM).

[5]  Wing-Kin Ma,et al.  Symbol-Level Precoding Through the Lens of Zero Forcing and Vector Perturbation , 2021, IEEE Transactions on Signal Processing.

[6]  Christos Masouros,et al.  1-Bit Massive MIMO Transmission: Embracing Interference with Symbol-Level Precoding , 2020, IEEE Communications Magazine.

[7]  Christos Masouros,et al.  Symbol-Level Precoding Made Practical for Multi-Level Modulations via Block-Level Rescaling , 2020, 2021 IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[8]  Branka Vucetic,et al.  A Tutorial on Interference Exploitation via Symbol-Level Precoding: Overview, State-of-the-Art and Future Directions , 2020, IEEE Communications Surveys & Tutorials.

[9]  A. Lee Swindlehurst,et al.  Joint Symbol-Level Precoding and Reflecting Designs for IRS-Enhanced MU-MISO Systems , 2019, IEEE Transactions on Wireless Communications.

[10]  Victor C. M. Leung,et al.  Secure Interference Exploitation Precoding in MISO Wiretap Channel: Destructive Region Redefinition With Efficient Solutions , 2019, IEEE Transactions on Information Forensics and Security.

[11]  Fan Liu,et al.  Interference Exploitation 1-Bit Massive MIMO Precoding: A Partial Branch-and-Bound Solution With Near-Optimal Performance , 2019, IEEE Transactions on Wireless Communications.

[12]  Pinyi Ren,et al.  Rethinking Secure Precoding via Interference Exploitation: A Smart Eavesdropper Perspective , 2019, IEEE Transactions on Information Forensics and Security.

[13]  G. Consigli,et al.  Optimization Methods in Finance , 2019, Quantitative Finance.

[14]  Branka Vucetic,et al.  Interference Exploitation Precoding for Multi-Level Modulations: Closed-Form Solutions , 2018, IEEE Transactions on Communications.

[15]  Bjorn Ottersten,et al.  Handbook of Antennas in Wireless Communications , 2018 .

[16]  Christos Masouros,et al.  Interference Exploitation Precoding Made Practical: Optimal Closed-Form Solutions for PSK Modulations , 2018, IEEE Transactions on Wireless Communications.

[17]  Björn Ottersten,et al.  Power Minimizer Symbol-Level Precoding: A Closed-Form Suboptimal Solution , 2018, IEEE Signal Processing Letters.

[18]  Symeon Chatzinotas,et al.  Symbol-Level and Multicast Precoding for Multiuser Multiantenna Downlink: A State-of-the-Art, Classification, and Challenges , 2018, IEEE Communications Surveys & Tutorials.

[19]  Wing-Kin Ma,et al.  Symbol-Level Precoding is Symbol-Perturbed zf When Energy Efficiency is Sought , 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[20]  Wei Yu,et al.  One-Bit Precoding and Constellation Range Design for Massive MIMO With QAM Signaling , 2018, IEEE Journal of Selected Topics in Signal Processing.

[21]  A. L. Swindlehurst,et al.  Quantized Constant Envelope Precoding With PSK and QAM Signaling , 2018, IEEE Transactions on Wireless Communications.

[22]  Christos Masouros,et al.  Massive MIMO 1-Bit DAC Transmission: A Low-Complexity Symbol Scaling Approach , 2017, IEEE Transactions on Wireless Communications.

[23]  Christos Masouros,et al.  An Efficient Manifold Algorithm for Constructive Interference Based Constant Envelope Precoding , 2017, IEEE Signal Processing Letters.

[24]  Tharmalingam Ratnarajah,et al.  Interference Exploitation for Radar and Cellular Coexistence: The Power-Efficient Approach , 2017, ArXiv.

[25]  Christos Masouros,et al.  Exploiting Constructive Mutual Coupling in P2P MIMO by Analog-Digital Phase Alignment , 2017, IEEE Transactions on Wireless Communications.

[26]  Symeon Chatzinotas,et al.  Symbol-Level Multiuser MISO Precoding for Multi-Level Adaptive Modulation , 2016, IEEE Transactions on Wireless Communications.

[27]  Christos Masouros,et al.  Exploiting Known Interference as Green Signal Power for Downlink Beamforming Optimization , 2015, IEEE Transactions on Signal Processing.

[28]  Symeon Chatzinotas,et al.  Energy-Efficient Symbol-Level Precoding in Multiuser MISO Based on Relaxed Detection Region , 2015, IEEE Transactions on Wireless Communications.

[29]  Christos Masouros,et al.  Rethinking the role of interference in wireless networks , 2014, IEEE Communications Magazine.

[30]  Symeon Chatzinotas,et al.  Constructive Multiuser Interference in Symbol Level Precoding for the MISO Downlink Channel , 2014, IEEE Transactions on Signal Processing.

[31]  Feng Wang,et al.  Transmit beamforming for multiuser downlink with per-antenna power constraints , 2014, 2014 IEEE International Conference on Communications (ICC).

[32]  Mathini Sellathurai,et al.  Vector Perturbation Based on Symbol Scaling for Limited Feedback MISO Downlinks , 2014, IEEE Transactions on Signal Processing.

[33]  Tharmalingam Ratnarajah,et al.  Known interference in the cellular downlink: a performance limiting factor or a source of green signal power? , 2013, IEEE Communications Magazine.

[34]  Iva,et al.  Quantized CSI-Based Tomlinson-Harashima Precoding in Multiuser MIMO Systems , 2013, IEEE Transactions on Wireless Communications.

[35]  Stephen P. Boyd,et al.  Convex Optimization , 2010, IEEE Transactions on Automatic Control.

[36]  Ralf R. Müller,et al.  On Convex Vector Precoding for Multiuser MIMO Broadcast Channels , 2009, IEEE Transactions on Signal Processing.

[37]  Christos Masouros,et al.  Dynamic linear precoding for the exploitation of known interference in MIMO broadcast systems , 2009, IEEE Transactions on Wireless Communications.

[38]  J. Cioffi,et al.  Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design , 2008, IEEE Transactions on Wireless Communications.

[39]  Christos Masouros,et al.  A Novel Transmitter-Based Selective-Precoding Technique for DS/CDMA Systems , 2007, 2007 IEEE International Conference on Communications.

[40]  Nikos D. Sidiropoulos,et al.  Transmit beamforming for physical-layer multicasting , 2006, IEEE Transactions on Signal Processing.

[41]  Ami Wiesel,et al.  Linear precoding via conic optimization for fixed MIMO receivers , 2006, IEEE Transactions on Signal Processing.

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

[43]  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.

[44]  Holger Boche,et al.  Solution of the multiuser downlink beamforming problem with individual SINR constraints , 2004, IEEE Transactions on Vehicular Technology.

[45]  Lizhong Zheng,et al.  Diversity and multiplexing: a fundamental tradeoff in multiple-antenna channels , 2003, IEEE Trans. Inf. Theory.

[46]  L. Ghaoui,et al.  Static arbitrage bounds on basket option prices , 2003, Math. Program..

[47]  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).

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

[49]  P. Wolfe THE SIMPLEX METHOD FOR QUADRATIC PROGRAMMING , 1959 .

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