Multiuser MIMO with Large Intelligent Surfaces: Communication Model and Transmit Design

This paper proposes a communication model for multiuser multiple-input multiple-output (MIMO) systems based on large intelligent surfaces (LIS), where the LIS is modeled as a collection of tightly packed antenna elements. The LIS system is first represented in a circuital way, obtaining expressions for the radiated and received powers, as well as for the coupling between the distinct elements. Then, this circuital model is used to characterize the channel in a line-of-sight propagation scenario, rendering the basis for the analysis and design of MIMO systems. Due to the particular properties of LIS, the model accounts for superdirectivity and mutual coupling effects along with near field propagation, necessary in those situations where the array dimension becomes very large. Finally, with the proposed model, the matched filter transmitter and the weighted minimum mean square error precoding are derived under both realistic constraints: limited radiated power and maximum ohmic losses.

[1]  D. Dardari Communicating With Large Intelligent Surfaces: Fundamental Limits and Models , 2019, IEEE Journal on Selected Areas in Communications.

[2]  M.A. Jensen,et al.  Superdirectivity in MIMO systems , 2005, IEEE Transactions on Antennas and Propagation.

[3]  Emil Björnson,et al.  Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces , 2020, IEEE Open Journal of the Communications Society.

[4]  Michael A. Jensen,et al.  Mutual coupling in MIMO wireless systems: a rigorous network theory analysis , 2004, IEEE Transactions on Wireless Communications.

[5]  M. Uzsoky,et al.  Theory of super-directive linear arrays , 1956 .

[6]  Roger F. Harrington,et al.  Antenna excitation for maximum gain , 1965 .

[7]  John M. Cioffi,et al.  Weighted Sum-Rate Maximization Using Weighted MMSE for MIMO-BC Beamforming Design , 2008, 2009 IEEE International Conference on Communications.

[8]  Thomas L. Marzetta,et al.  Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas , 2010, IEEE Transactions on Wireless Communications.

[9]  Josef A. Nossek,et al.  Linear transmit processing in MIMO communications systems , 2005, IEEE Transactions on Signal Processing.

[10]  Fredrik Rusek,et al.  Beyond Massive MIMO: The Potential of Data Transmission With Large Intelligent Surfaces , 2017, IEEE Transactions on Signal Processing.

[11]  M. Salazar-Palma,et al.  Maximum power transfer versus efficiency , 2016, 2016 IEEE International Symposium on Antennas and Propagation (APSURSI).

[12]  Navrati Saxena,et al.  Next Generation 5G Wireless Networks: A Comprehensive Survey , 2016, IEEE Communications Surveys & Tutorials.

[13]  E. Gilbert,et al.  Optimum design of directive antenna arrays subject to random variations , 1955 .

[14]  S. Schelkunoff A mathematical theory of linear arrays , 1943 .

[15]  Yonina C. Eldar,et al.  Dynamic Metasurface Antennas for Uplink Massive MIMO Systems , 2019, IEEE Transactions on Communications.

[16]  A. Kishk,et al.  Theory and Applications of Infinitesimal Dipole Models for Computational Electromagnetics , 2007, IEEE Transactions on Antennas and Propagation.

[17]  Thomas L. Marzetta,et al.  Super-Directive Antenna Arrays: Fundamentals and New Perspectives , 2019, 2019 53rd Asilomar Conference on Signals, Systems, and Computers.

[18]  Emil Björnson,et al.  Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays , 2019, ArXiv.

[19]  Y. Lo,et al.  Optimization of directivity and signal-to-noise ratio of an arbitrary antenna array , 1966 .

[20]  Michael A. Jensen,et al.  The Relationship Between Antenna Loss and Superdirectivity in MIMO Systems , 2007, IEEE Transactions on Wireless Communications.

[21]  Mithat Idemen IEEE Press Series on Electromagnetic Wave Theory , 2011 .