Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks

TL;DR

The paper addresses resource allocation for near-field XL-MIMO, highlighting how spherical wavefronts, non-stationary channels, and enhanced EM effects create non-convex, high-dimensional optimization problems. It provides a taxonomy of resources (beamforming, power control, antenna selection, scheduling, channel estimation) and surveys optimization tools across numerical, heuristic, and ML-based methods, illustrating their applicability through XL-MIMO and cell-free use cases. Key contributions include linking near-field channel characteristics to appropriate optimization strategies, comparing traditional methods with ML and diffusion-based approaches, and demonstrating scenarios where near-field beam focusing yields increased DoF and SE/EE. The work offers practical guidance for deploying near-field resources in 6G-era networks and points to future directions like electromagnetic information theory and semantic communications to further leverage near-field capabilities for high-capacity, energy-efficient wireless systems.

Abstract

Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising technology to achieve high spectral efficiency (SE) and energy efficiency (EE) in future wireless systems. The larger array aperture of XL-MIMO makes communication scenarios closer to the near-field region. Therefore, near-field resource allocation is essential in realizing the above key performance indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily depends on the channel characteristics of the selected users, eliminating interference between users through beamforming, power control, etc. The above resource allocation issue constitutes a complex joint multi-objective optimization problem since many variables and parameters must be optimized, including the spatial degree of freedom, rate, power allocation, and transmission technique. In this article, we review the basic properties of near-field communications and focus on the corresponding "resource allocation" problems. First, we identify available resources in near-field communication systems and highlight their distinctions from far-field communications. Then, we summarize optimization tools, such as numerical techniques and machine learning methods, for addressing near-field resource allocation, emphasizing their strengths and limitations. Finally, several important research directions of near-field communications are pointed out for further investigation.

Figures (5)

• Figure 1: The major differences in resource allocation between near and far-field include beamforming, power control, and channel estimation. We consider a communication system with a frequency of $28$ GHz and an array aperture of $1$ m. Accordingly, the Rayleigh distance is $187$ m, and users are more likely located in the near-field region. The major application scenarios for near-field resource allocation include XL-MIMO systems, RIS-aided communication systems, and cell-free communication systems.
• Figure 2: Comparison of near-field and far-field channel characteristics. Figure (a) compares the differences between near- and far-field from the perspective of channel sparsity. Moreover, Figure (b) compares the differences between near- and far-field from the perspective of antenna array gain at spatial positions.
• Figure 3: At the base station, the signal arrives at the receiver through resource allocation strategies such as precoding and power control, passing through the near-field channel. At the receiver, user grouping and scheduling strategies improve efficiency. Besides, we introduce three major challenges and solutions for near-field resource allocation.
• Figure 4: Near-field resource allocation problems and corresponding optimization problems and tools. (a) User grouping and scheduling based on near-field beam focusing can be modeled as a mixed-integer non-convex problem and solved using search class methods li2023multiuser. (b) The antenna selection design based on the non-stationarity of near-field channels can be modeled as a combinatorial optimization problem and solved using methods such as ML 10475888. (c) The beamforming problem in the near-field is similar to the far-field problem. The distinction arises from channel vector variations, which can be solved using traditional alternating optimization algorithms [68].
• Figure 5: Scenario 1: Beamforming design in XL-MIMO systems. The BS has $N = 512$ antennas and the carrier frequency is $f = 28$ GHz. The number of RF chains is $4$, serving $8$ receivers. Scenario 2: Power control in cell-free communication systems. The number of access points is $9$, serving $6$ receivers. The communication bandwidth is $20$ MHz and the noise power is $\sigma^{2}=-69$ dBm. All transmitters transmit with a transmission power of no more than $200$ mW.