Enabling Near-Field Communications: Beam Shaping, Optimization, and Channel/Data Estimation

Abstract

Recent advances in high-frequency communications and the growing demand for large-scale antenna arrays have led to significantly expanded antenna apertures, bringing near-field propagation effects to the forefront of wireless system design.

This talk focuses on these near-field effects and presents a comprehensive study of beam shaping and channel/data estimation technique for near-field communications. In the first part, we investigate near-field beams that can mitigate signal attenuation and blockage effects using a uniform linear array (ULA). In particular, closed-form expressions for phase distributions in a ULA are derived to generate Bessel beams and curving beams based on the desired propagation directions and trajectories. Based on the phase distributions, the maximum steering angle and propagation distance of Bessel beams with a ULA are revealed. In addition, from the sampling theorem in the spatial domain, the requirements for ULAs to properly generate Bessel beams are clarified. For curving beams, trajectories to reach a user while avoiding one obstacle are designed via the Lagrangian method. Electromagnetic wave simulations confirm the effectiveness of the analyses for Bessel beams and the curving beam designs. and the characteristics of Gaussian beams, beamfocusing, Bessel beams, and curving beams are summarized in terms of the statistical behavior of their intensity and signal processing.

In the second part, we study an efficient channel estimation method for near-field communications. The initial channel estimation is formulated as a sparse reconstruction problem based on the angle and distance sparsity under the near-field propagation condition. This problem is solved using non-orthogonal pilots through an efficient low complexity two-stage compressed sensing algorithm. Furthermore, the estimates are refined by a bilinear framework driven by both non-orthogonal pilots and estimated data. Numerical simulations reveal that the proposed initial channel estimation and joint channel and data estimation (JCDE) algorithm outperforms the state-ofthe-art approaches in terms of channel estimation, data detection, and computational complexity.

Biography

Koji Ishibashi received the B.E. and M.E. degrees in engineering from The University of Electro-Communications, Tokyo, Japan, in 2002 and 2004, respectively, and the Ph.D. degree in engineering from Yokohama National University, Yokohama, Japan, in 2007. From 2007 to 2012, he was an Assistant Professor at the Department of Electrical and Electronic Engineering, Shizuoka University, Hamamatsu, Japan. Since April 2012, he has been with the Advanced Wireless and Communication Research Center (AWCC), The University of Electro-Communications, Tokyo, Japan where he is currently a Professor.

From 2010 to 2012, he was a Visiting Scholar at the School of Engineering and Applied Sciences, Harvard University, Cambridge, MA. He is a senior member of IEICE and IEEE.  He is a recipient of Takayanagi Research Encouragement Award in 2009 and KDDI Foundation Award in 2023. He was certified as an Exemplary Reviewer of IEEE Communications Wireless Letters in 2015 and awarded by the Telecommunication Technology Committee (TTC) for his devotion to standardization activities in 2020. He has been an Associate Editor for IEEE Internet of Things Journal since 2025.

He in the past served as an Associate Editor for IEICE Transactions on Communications, an Associate Editor for IEEE Journal on Selected Areas in Communications (JSAC) and a Guest Editor for IEEE Open Journal of the Communications Society. His current research interests are beamforming design, grant-free access, energy-harvesting, compressed sensing, coding, and MIMO technologies.