Near-field Beam Research Articles

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping

Multi-User Modular XL-MIMO Communications: Near-Field Beam Focusing Pattern and User Grouping

Near-field wideband beam training for ELAA with uniform circular array

Near-field wideband beam training for ELAA with uniform circular array

Optical vector-eigenmode decomposition for few-mode fibers through deep learning networks

For the optical vector-eigenmode (VE) decomposition, we introduce a novel deep learning-based optical-mode decomposition (OMD) technique for accurate retrieval of near-field beams generated from few-mode fiber (FMF) channel, in which this residual network (Resnet) scheme could obtain complete and accurate amplitude and phase coefficients from the near-field beams. For the case of 3 intra-module modes in the FMF, the correlation coefficient of the OMD reaches 0.9808, while the modal weight error and modal phase error are as low as 0.0287 and 0.0331, respectively. And for the case of 10 inter-module modes in the FMF, the correlation coefficient of the OMD achieves 0.9878, while the mode weight error and mode phase error are recorded down to 0.0071 and 0.0205, respectively. Moreover, the noise-added near-field beams show a good robustness of the OMD based on deep learning. Finally, we also investigate the relationship between modal differences and correlation coefficients, revealing the nature of VE-based OMD functions.

Base Station Beamforming Design in Near-Field XL-IRS Beam Training

Base Station Beamforming Design in Near-Field XL-IRS Beam Training

Plasmonic-nanowire near-field beam analyzer

Abstract Experimental near-field analysis of the output beams from the end faces of micro/nano-waveguide is very necessary, because important information such as spatial intensity distributions, mode orders, and divergence angles can be obtained, and are very important for investigating and designing nanophotonic devices. However, as far as we know, it has not been demonstrated yet. In this work, we experimentally demonstrate a plasmonic-nanowire near-field beam analyzer, utilizing a single Au nanowire (AuNW) as the probe to scan the spatial near-field distributions of emitted beams from micro/nano-waveguide end-faces. Our analyzer can resolve the trade-off between high measurement resolution and light collection efficiency in conventional beam analyzers by a reverse nanofocusing process, achieving a probe resolution of 190 nm (<λ/8) and a simulated collection efficiency of ∼47.4 % at λ = 1596 nm. These attractive advantages allow us to obtain three‐dimensional (3D) scanning in a large range from the plasmonic hotspot region to the far-field region, characterizing the 3D spatial distribution evolution from a metal nanowire output beam for the first time, with an M 2 factor lower than that of the ideal Gaussian beam (M 2 = 1). In addition, the analyzer also demonstrates simultaneous characterization of multimodes in irregular and large-sized nanoribbons, further verifying its ability to selectively explore complex multimodes that are difficult to be predicted by numerical simulations. Our results suggest that this plasmonic-nanowire beam analyzer may hold promise for diverse near-field applications for micro/nano-waveguides such as nanolasers and biosensing, and offer a new method for understanding nanophotonic structures.

Wideband directional-fed holographic metasurface with integrated monopole and parabolic reflector for far-/near-field beams manipulations

Holographic metasurface has been widely investigated, but suffers from the trade-off between bandwidth, gain, and integration. In this article, a wideband high-gain holographic metasurface with miniaturized integrated monopole is proposed. It employs the geometrical characteristics of parabolic reflector to realize directional-fed metasurface, and the footprint is reduced to only one-third of the conventional center-fed designs. By varying modulation index and paraboloid height, the bandwidth and gain are improved simultaneously. To validate the proposed idea, two prototypes are analyzed and fabricated to demonstrate flexible beam manipulation in respective far-field radiation and near-field propagation. Pencil beam pays more attention to the far-field and directionality of the beam, while Bessel beam focuses more on the near-field and non-diffractive of the beam. In both cases, the measured results are well-matched with the simulation. The proposed holographic metasurface for far-field applications features wide bandwidth, high gain, and planar integration simultaneously. Moreover, to the best of the authors’ knowledge, this is the first Bessel beam generator using directional-fed holographic metasurface with parabolic reflector.

Side Angle Information Assisted Near-Field Beam Training for XL-Array Communications

Side Angle Information Assisted Near-Field Beam Training for XL-Array Communications

Near-Field Beam Training: Joint Angle and Range Estimation with DFT Codebook

Near-Field Beam Training: Joint Angle and Range Estimation with DFT Codebook

NEAR FIELD BEAM FORMING IN TERAHERTZ WIDEBAND MASSIVE MIMO SYSTEMS USING CODEBOOK LEARNING

Terahertz (THz) communication technology holds immense potential for enabling ultra-high data rates in wireless communication networks. However, the highly directional nature of THz waves poses significant challenges for achieving precise beam focusing, especially in near-field scenarios. In this study, we propose a novel approach leveraging deep learning techniques for near-field beam focusing in Terahertz Wideband Massive Multiple Input, Multiple Output (MIMO) systems. Our methodology's main novelty is the combination of codebook learning with deep neural networks. We use a convolutional neural network (CNN) architecture to learn complex spatial properties of THz channels and optimise beamforming weights. During the training phase, the codebook, which represents a discrete collection of beamforming vectors, is adaptively modified, allowing for dynamic response to changing channel circumstances. Extensive tests are being carried out to confirm the efficacy of our strategy, employing cutting-edge THz transceivers and MIMO technology. The proposed deep learning model is trained and evaluated using real-world channel data. Comparative comparisons with traditional beamforming approaches demonstrate our methodology's better performance, revealing significant advances in spectral efficiency and signal quality. This paper highlights the transformational influence of codebook learning linked with deep neural networks in boosting the performance of near-field beam focusing in Terahertz Wideband Massive MIMO systems. It not only provides a major achievement in the field of THz communications. Key Words: THz Communication, deep neural networks, MIMO, beamforming, deep learning.

Multi-Beam Design for Near-Field Extremely Large-Scale RIS-Aided Wireless Communications

As the energy-saving array composed of passive elements, reconfigurable intelligent surface (RIS) will evolve to the extremely large-scale RIS (XL-RIS) to overcome serious path loss. This change leads to the near-field propagation becoming dominant. There are some works to explore the near-field beam design via beam training. Unfortunately, due to the constant modulus constraint for XL-RIS, most of works in the near-field scenario focus on single-beam design. For massive connectivity requirement scenario, these works will face a serious loss of beam gains, resulting in a decrease in transmission rate. To solve this problem, we propose a block coordinate descent-based scheme with majorization-minimization (MM) algorithm for multi-beam design. The proposed scheme handles constant modulus constraint from two aspects. Firstly, under this constraint, the multi-beam design is an intractable non-convex quadratic programming problem. We utilize MM algorithm to solve this problem as several iterative sub-problems which are easily to be sloved. Secondly, the solution space for multi-beam optimization is confined to a limited space due to this constraint, so we introduce the phases for beam gains as an extra optimizable variable to enrich the degree of freedom for optimization. Simulation results show that the proposed scheme could achieve a superior rate 50% higher than the existing schemes.

Reduction of depolarization loss in a Tm-doped sesquioxide ceramic laser using a ring-shaped pump beam.

We report on a simple method for reduction of the depolarization loss in an end-pumped Tm:Y2O3 ceramic laser by using a near-field ring-shaped pump beam. Initially, we theoretically derive the expression of the depolarization loss in a bulk laser end-pumped with a near-field flat-top-hat or ring-shaped beam, where a significant reduction of depolarization loss in the latter case is presented. Experimental verification is thereafter carried out with a Tm:Y2O3 ceramic laser employing these two different pump configurations. It shows that the experimentally measured depolarization losses are close to the simulated values; the loss in the case of the annular-beam pump is almost 18 times lower than that with a quasi-top-hat beam at a same absorption pump power of 7.4 W. This work, as a proof-of-principle study, indicates that depolarization loss in the end-pumped bulk lasers can be significantly reduced simply by using a ring-shaped pump beam.

The Far-/Near-Field Beam Squint and Solutions for THz Intelligent Reflecting Surface Communications

Terahertz (THz) and intelligent reflecting surface (IRS) have been regarded as two promising technologies to improve the capacity and coverage for future 6G networks. Generally, IRS is usually equipped with large-scale elements when implemented at THz frequency. In this case, the near-field model and beam squint should be considered. Therefore, in this paper, we investigate the far-field and near-field beam squint problems in THz IRS communications for the first time. The far-field and near-field channel models are constructed based on the different electromagnetic radiation characteristics. Next, we first analyze the far-field beam squint and its effect for the beam gain based on the cascaded base station (BS)-IRS-user channel model, and then the near-field case is studied. To overcome the far-field and near-field beam squint effects, we propose to apply delay adjustable metasurface to IRS, and develop a scheme of optimizing the reflecting phase shifts and time delays of IRS elements, which effectively eliminates the beam gain loss caused by beam squint. Finally, simulations are conducted to demonstrate the effectiveness of our proposed schemes in combating the far-field and near-field beam squint.

Low-Overhead Beam Training Scheme for Extremely Large-Scale RIS in Near Field

Extremely large-scale reconfigurable intelligent surface (XL-RIS) has recently been proposed and is recognized as a promising technology that can further enhance the capacity of communication systems and compensate for severe path loss. However, the pilot overhead of beam training in XL-RIS-assisted wireless communication systems is enormous because the near-field channel model needs to be taken into account, and the number of candidate codewords in the codebook increases dramatically. To tackle this problem, we propose two deep learning-based near-field beam training schemes in XL-RIS-assisted communication systems, where deep residual networks are employed to determine the optimal near-field RIS codeword. Specifically, we first propose a far-field beam-based beam training (FBT) scheme in which the received signals of all far-field RIS codewords are fed into the neural network to estimate the optimal near-field RIS codeword. In order to further reduce the pilot overhead, a partial near-field beam-based beam training (PNBT) scheme is proposed, where only the received signals corresponding to the partial near-field XL-RIS codewords are input to the neural network. Moreover, we further propose an improved PNBT scheme to enhance the performance of beam training by fully exploring the neural network’s output. Finally, simulation results show that the proposed schemes outperform the existing beam training schemes and can reduce the beam sweeping overhead by approximately 95%.

Near-Field Beam Training Based on Deep Learning for Extremely Large-Scale MIMO

Near-Field Beam Training Based on Deep Learning for Extremely Large-Scale MIMO

Near-Field Rainbow: Wideband Beam Training for XL-MIMO

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) plays an important role in boosting the data rate for 6G networks. Because of the huge bandwidth and the large number of antennas, wideband XL-MIMO introduces a significant near-field beam split effect, where beams at different frequencies are focused on different locations. This effect results in a severe array gain loss, and existing works mainly consider to compensate for this loss by utilizing time-delay (TD) beamforming. This paper demonstrates that despite degrading the array gain, the near-field beam split effect can also contribute to the fast near-field beam training. Specifically, we first reveal the controllable near-field beam split effect. This effect indicates that TD beamforming can control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, we also call this effect as “near-field rainbow”. Then, by taking advantage of the near-field rainbow, a fast wideband beam training scheme is proposed to generate beams focusing on multiple locations at multiple frequencies with the help of TD beamforming. Finally, simulation results demonstrate that the proposed scheme is able to realize efficient near-field beam training with low training overheads.

Multiple Access for Near-Field Communications: SDMA or LDMA?

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.

Near-Field Beam Training in Terahertz Communications with Hybrid Beamforming Architecture

Terahertz (THz) communication has a large available bandwidth, which is expected to be deployed in future communication networks. As THz wave suffers from severe propagation loss in wireless transmission, we consider a THz near-field scenario where a base station (BS) is equipped with a large-scale antenna array with a low-cost hybrid beamforming architecture to serve mobile users nearby. However, the large-scale array and the user mobility incur difficulty in channel estimation. To tackle this issue, we propose a near-field beam training scheme that can align a beam to the user in a fast way by searching the codebook. Specifically, the BS employs a uniform circular array (UCA), and the radiation pattern of the beams in our proposed codebook appears as ellipsoids. To cover the serving zone with the minimum codebook size, we develop a near-field codebook by tangent arrangement approach (TAA). To reduce the time overhead, we leverage the hybrid beamforming architecture to realize multi-beam training concurrently since each RF chain can enable a codeword whose element has a constant magnitude. Numerical results validate that our proposed UCA near-field codebook achieves less time cost while achieving a comparable coverage performance compared to the conventional near-field codebook.

Terahertz metasurface for near-field beam conversion.

A uniform illumination over a screen is crucial for terahertz imaging. As such, conversion from a Gaussian beam to a flattop beam becomes necessary. Most of the current beam conversion techniques rely on bulky multi-lens systems for collimated input and operate in the far-field. We present a single metasurface lens to efficiently convert a quasi-Gaussian beam from the near-field region of a WR-3.4 horn antenna to a flattop beam. The design process is divided into three sections to minimize simulation time, and the conventional Gerchberg-Saxton (GS) algorithm is supplemented with the Kirchhoff-Fresnel diffraction equation. Experimental validation confirms that a flattop beam with an efficiency of 80% has been achieved at 275 GHz. Such high-efficiency conversion is desirable for practical terahertz systems and the design approach can be generally used for beam shaping in the near-field.

Near-Field Beam Training in THz Communications: The Merits of Uniform Circular Array

As the line-of-sight propagation component is dominant in Terahertz (THz) channels, beam training is regarded as an efficient beamforming strategy for THz communications. In the Fresnel region, the near-field effect boosts the codebook size for the beam training since each near-field beam merely concentrates the radiation in a small area, rather than along a direction regardless of distance. In this letter, we propose to use the uniform circular array (UCA) in THz near-field communications, benefiting from its geometric advantage in beam training. Specifically, an explicit model is proposed for characterizing the beam pattern of UCA. Based on this model, we propose a codebook design for the near-field beam training, where the predefined training beams aim at covering the user’s area. Compared to the conventional uniform linear array (ULA), the UCA theoretically shows the advantage of reducing the codebook size in near-field communications. Numerical results validate the effectiveness of the beam-shape model and show the merits of our proposed near-field codebook of UCA.