A Primer on Near-Field Beamforming for Arrays and Reconfigurable Intelligent Surfaces (2110.06661v2)

Abstract: Wireless communication systems have almost exclusively operated in the far-field of antennas and antenna arrays, which is conventionally characterized by having propagation distances beyond the Fraunhofer distance. This is natural since the Fraunhofer distance is normally only a few wavelengths. With the advent of active arrays and passive reconfigurable intelligent surfaces (RIS) that are physically large, it is plausible that the transmitter or receiver is located in between the Fraunhofer distance of the individual array/surface elements and the Fraunhofer distance of the entire array. An RIS then can be configured to reflect the incident waveform towards a point in the radiative near-field of the surface, resulting in a beam with finite depth, or as a conventional angular beam with infinity focus, which only results in amplification in the far-field. To understand when these different options are viable, an accurate characterization of the near-field behaviors is necessary. In this paper, we revisit the motivation and approximations behind the Fraunhofer distance and show that it is not the right metric for determining when near-field focusing is possible. We obtain the distance range where finite-depth beamforming is possible and the distance where the beamforming gain tapers off.

• The paper challenges the conventional use of the Fraunhofer distance for large arrays and RIS, proposing new metrics like the Fraunhofer array distance (d_FA) and Björnson distance (d_B) to better define the radiative near-field boundary.
• Numerical analysis shows that normalized array gain reaches near-maximum at distances greater than d_B, demonstrating the potential for near-field operation even within classically defined far-field ranges.
• The findings suggest re-designing communication channels to leverage finite-depth beamforming, impacting future wireless systems (beyond 5G), and opening avenues for AI optimization and novel spatial networking.

Overview of Near-Field Beamforming for Arrays and Reconfigurable Intelligent Surfaces

This paper explores the field of near-field beamforming within the context of communication systems utilizing active arrays and reconfigurable intelligent surfaces (RIS). The central thesis posits a departure from traditional far-field antenna operations, which are typically dictated by the Fraunhofer distance. As arrays and RIS become physically larger, there emerges a compelling case for operations in what the authors label the radiative near-field.

A Re-evaluation of Near-Field Metrics

The motivation for re-evaluating near-field conceptual frameworks is prompted by the realization that the Fraunhofer distance, commonly used to partition far-field operations, fails to account for scenarios where finite-depth beamforming becomes viable. This paper introduces the concept of the Fraunhofer array distance, $d_{FA} = N d_F$, to meaningfully delineate the far-field boundary in massive antenna arrays, whilst introducing the Björnson distance, $d_B$, as a more practical metric. The relationship between these distances provides a guideline for distinguishing the near-field operational mode when the depth-of-focus (DF) is finite and can be exploited for beamforming gain.

Numerical Results and Methodologies

The researchers provide rigorous numerical analyses demonstrating that the normalized antenna array gain approaches its theoretical maximum at distances greater than $d_B$, even when $z \leq d_{FA}$. This is contrasted with classical antenna descriptions through comprehensive evaluations considering parameters such as depth-of-focus and beam widths for both arrays and RIS.

Implications and Future Directions

The theoretical implications of this work challenge the conventional usage of the Fraunhofer distance in large-scale antennas, suggesting that communication channels might need re-design to leverage finite-depth beamforming. On a practical level, these findings bear potential impacts on future telecommunications infrastructure, particularly beyond 5G technologies, which might markedly advance wireless communication efficiency and capacity.

From a forward-looking perspective, this paper suggests potential developments in AI-driven optimizations for RIS configurations, focusing on spatial multiplexing and signal processing adaptations to maximize gains in near-field conditions. Furthermore, the conservation of angular beam width highlighted in the paper may open avenues for novel applications in spatial networking and adaptive beamforming algorithms.

In summary, the investigation into near-field beamforming mechanisms as articulated in this paper provides a fresh lens through which wireless communication and algorithmic design can be viewed, posing implications for future research and practical deployments in advanced communication systems.