Adaptive Beamforming Design for mmWave RIS-Aided Joint Localization and Communication

The concept of reconfigurable intelligent surface (RIS) has been proposed to change the propagation of electromagnetic waves, e.g., reflection, diffraction, and refraction. To accomplish this goal, the phase values of the discrete RIS units need to be optimized. In this paper, we consider RIS-aided millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems for both accurate positioning and high data-rate transmission. We propose an adaptive phase shifter design based on hierarchical codebooks and feedback from the mobile station (MS). The benefit of the scheme lies in that the RIS does not require deployment of any active sensors and baseband processing units. During the update process of phase shifters, the combining vector at the MS is sequentially refined. Simulation results show the performance improvement of the proposed algorithm over the random phase design scheme, in terms of both positioning accuracy and data rate. Moreover, the performance converges to that of the exhaustive search scheme even in the low signal-to-noise ratio regime.

[1]  Henk Wymeersch,et al.  Localization Optimal Multi-user Beamforming with multi-carrier mmWave MIMO , 2018, 2018 IEEE 29th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC).

[2]  Ahmed Alkhateeb,et al.  Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning , 2019, IEEE Access.

[3]  Henk Wymeersch,et al.  Position and Orientation Estimation Through Millimeter-Wave MIMO in 5G Systems , 2017, IEEE Transactions on Wireless Communications.

[4]  Nikos D. Sidiropoulos,et al.  Fast Unit-Modulus Least Squares With Applications in Beamforming , 2016, IEEE Transactions on Signal Processing.

[5]  Josef A. Nossek,et al.  5G Downlink Multi-Beam Signal Design for LOS Positioning , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[6]  Xiangyun Zhou,et al.  Error Bounds for Uplink and Downlink 3D Localization in 5G Millimeter Wave Systems , 2017, IEEE Transactions on Wireless Communications.

[7]  Henk Wymeersch,et al.  Large Intelligent Surface for Positioning in Millimeter Wave MIMO Systems , 2019, 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring).

[8]  Chau Yuen,et al.  Large Intelligent Surfaces for Energy Efficiency in Wireless Communication , 2018, ArXiv.

[9]  Mohamed-Slim Alouini,et al.  Wireless Communications Through Reconfigurable Intelligent Surfaces , 2019, IEEE Access.

[10]  Khaled Ben Letaief,et al.  Alternating Minimization Algorithms for Hybrid Precoding in Millimeter Wave MIMO Systems , 2016, IEEE Journal of Selected Topics in Signal Processing.

[11]  Robert W. Heath,et al.  Channel Estimation and Hybrid Precoding for Millimeter Wave Cellular Systems , 2014, IEEE Journal of Selected Topics in Signal Processing.

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

[13]  Yuan Shen,et al.  On the Optimal Beamspace Design for Direct Localization Systems , 2018, 2018 IEEE International Conference on Communications (ICC).