Towards Near-Field 3D Spot Beamfocusing: Possibilities, Challenges, and Use-cases

TL;DR

The paper investigates near-field spot beamfocusing (SBF) using electronically reconfigurable, extremely large-scale apertures (ELAA) to enable 3D focal power concentration for nonoptical mmWave/sub-THz/THz waves. It analyzes beamforming implementations (favoring MRT in analog domains) and contrasts antenna technologies—CPAs, DMAs, and HMIMO—while addressing fundamental near-field CSI estimation challenges with ML-based and CSI-free strategies. It also introduces design concepts such as the Fraunhofer limit $D^F \approx 2D^2/\lambda$, the near-field region $D^N<r<D^F$, and the beamfocusing radius defined by a power fraction $\eta$, and discusses subarray DRL-based approaches to mitigate mobility and complexity. The paper identifies key applications in ultra-high-speed wireless communication, mid-range high-power WPT with safety, physical-layer security, EM neuromodulation, and THz switching, and outlines a roadmap of open challenges including medical tissue interactions, EM exposure, and optimization of system parameters for specific use cases.

Abstract

Spot beamfocusing (SBF) is the process of focusing the signal power in a small spot-like region in the 3D space, which can be either hard-tuned (HT) using traditional tools like lenses and mirrors or electronically reconfigured (ER) using modern large-scale intelligent surface phased arrays. ER-SBF can be a key enabling technology (KET) for the next-generation 6G wireless networks offering benefits to many future wireless application areas such as wireless communication and security, mid-range high-power and safe wireless chargers, medical and health, physics, etc. Although near-field HT-SBF and ER-beamfocusing have been studied in the literature and applied in the industry, there is no comprehensive study of different aspects of ER-SBF and its future applications, especially for nonoptical (mmWave, sub-THz, and THz) electromagnetic waves in the next generation wireless technology, which is the aim of this paper. The theoretical concepts behind ER-SBF, different antenna technologies for implementing ER-SBF, employing machine learning (ML)-based schemes for enabling channel-state-information (CSI)-independent ER-SBF, and different practical application areas that can benefit from ER-SBF will be explored.

Figures (5)

• Figure 1: (a) 2D beamforming is feasible for far-field in angular domain for UE1 with no radial domain beamfocusing in the $y$-axis, and 3D beamfocusing is feasible for UE2 in the near-field. (b): Near-field spacial power density on the $xy$ plane for an aperture located on $xz$ plane and a DFP at (0,1,0). The antenna elements are half wavelength apart and the frequency is 28GHz. The left and right figures correspond to $6 \times 6$ SPA and $60 \times 60$ ELAA respectively.
• Figure 2: Illustration of the physical layer security region for a $N$-element MIMO communication using UPA located on $xz$ plane centered at the origin.
• Figure 3: Three schemes for implementing EM-based neuromodulation and tissue thermal focusing.
• Figure 4: Realization of SBF through extremely large-scale UPA. (a): Power density versus distance ($y$) for $\Delta d/ \lambda \in\{0.5,1,1.5\}$ using a $N=60 \times 60$ ELAA. (b): Complexity/cost, as well as HPBW versus the number of antenna rows/cols (i.e., $\sqrt{N}$) of the UPA for $\Delta d=0.5 \lambda$.
• Figure 5: Implementation of ML-based SBF through an ELAA consisting of a set of SPAs.