Antenna Array Calibration Via Gaussian Process Models

• URL: http://arxiv.org/abs/2301.06582v2
• Date: Wed, 18 Jan 2023 14:51:21 GMT
• Title: Antenna Array Calibration Via Gaussian Process Models
• Authors: Sergey S. Tambovskiy, G\'abor Fodor, Hugo Tullberg
• Abstract summary: Antenna array calibration is necessary to maintain the high fidelity of beam patterns across a wide range of advanced antenna systems. We formulate antenna calibration in an alternative way, namely as a task of functional approximation, and address it via Bayesian machine learning. Our contributions are three-fold. Firstly, we define a parameter space, that captures the underlying hardware impairments corresponding to each radiating element, their positional offsets, as well as the mutual coupling effects between antenna elements. Once deployed, the learned non-parametric models effectively serve to continuously transform the beamforming weights of the system, resulting in corrected beam patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Antenna array calibration is necessary to maintain the high fidelity of beam patterns across a wide range of advanced antenna systems and to ensure channel reciprocity in time division duplexing schemes. Despite the continuous development in this area, most existing solutions are optimised for specific radio architectures, require standardised over-the-air data transmission, or serve as extensions of conventional methods. The diversity of communication protocols and hardware creates a problematic case, since this diversity requires to design or update the calibration procedures for each new advanced antenna system. In this study, we formulate antenna calibration in an alternative way, namely as a task of functional approximation, and address it via Bayesian machine learning. Our contributions are three-fold. Firstly, we define a parameter space, based on near-field measurements, that captures the underlying hardware impairments corresponding to each radiating element, their positional offsets, as well as the mutual coupling effects between antenna elements. Secondly, Gaussian process regression is used to form models from a sparse set of the aforementioned near-field data. Once deployed, the learned non-parametric models effectively serve to continuously transform the beamforming weights of the system, resulting in corrected beam patterns. Lastly, we demonstrate the viability of the described methodology for both digital and analog beamforming antenna arrays of different scales and discuss its further extension to support real-time operation with dynamic hardware impairments.

Related papers

• Learning Radio Environments by Differentiable Ray Tracing [56.40113938833999]
  We introduce a novel gradient-based calibration method, complemented by differentiable parametrizations of material properties, scattering and antenna patterns. We have validated our method using both synthetic data and real-world indoor channel measurements, employing a distributed multiple-input multiple-output (MIMO) channel sounder.
  arXiv  Detail & Related papers  (2023-11-30T13:50:21Z)
• NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions [93.02515761070201]
  We present a novel type of neural fields that uses general radial bases for signal representation. Our method builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.
  arXiv  Detail & Related papers  (2023-09-27T06:32:05Z)
• Sionna RT: Differentiable Ray Tracing for Radio Propagation Modeling [65.17711407805756]
  Sionna is a GPU-accelerated open-source library for link-level simulations based on. Since release v0.14 it integrates a differentiable ray tracer (RT) for the simulation of radio wave propagation.
  arXiv  Detail & Related papers  (2023-03-20T13:40:11Z)
• Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward? [85.06664206117088]
  Multi-user wireless systems can meet metaverse requirements by utilizing terahertz bandwidth with massive number of antennas. Existing solutions lack proper modeling of channel dynamics, resulting in inaccurate beamforming solutions in high-mobility scenarios. Herein, a dynamic, semantically aware beamforming solution is proposed for the first time, utilizing novel artificial intelligence algorithms in variational causal inference.
  arXiv  Detail & Related papers  (2023-03-14T16:02:46Z)
• Implementation of a Three-class Classification LS-SVM Model for the Hybrid Antenna Array with Bowtie Elements in the Adaptive Beamforming Application [1.2183405753834562]
  bowtie elements allow for a significant improvement in the beamforming performance of the hybrid antenna array. The proposed hybrid antenna array has shown a 3D uniform directivity, which is accompanied by its superior performance in the 3D uniform beam-scanning capability.
  arXiv  Detail & Related papers  (2022-10-01T16:43:44Z)
• DEFORM: A Practical, Universal Deep Beamforming System [4.450750414447688]
  We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX beamforming achieves significant gain for multi antenna RF receivers.
  arXiv  Detail & Related papers  (2022-03-18T03:52:18Z)
• A Machine Learning Generative Method for Automating Antenna Design and Optimization [9.438718097561061]
  We introduce a flexible geometric scheme with the concept of mesh network that can form any arbitrary shape by connecting different nodes. For a dual resonance antenna design with wide bandwidth, our proposed method is in par with Trust Region Framework and much better than the other mature machine learning algorithms.
  arXiv  Detail & Related papers  (2022-02-28T21:30:37Z)
• HyperHyperNetworks for the Design of Antenna Arrays [91.3755431537592]
  We present deep learning methods for the design of arrays and single instances of small antennas. In the case of a single antenna, the solution is based on a composite neural network that combines a simulation network, a hypernetwork, and a refinement network. The learning objective is based on measuring the similarity of the obtained radiation pattern to the desired one.
  arXiv  Detail & Related papers  (2021-05-09T05:21:28Z)
• Learning to Beamform in Heterogeneous Massive MIMO Networks [48.62625893368218]
  It is well-known problem of finding the optimal beamformers in massive multiple-input multiple-output (MIMO) networks. We propose a novel deep learning based paper algorithm to address this problem.
  arXiv  Detail & Related papers  (2020-11-08T12:48:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.