Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications

• URL: http://arxiv.org/abs/2509.06967v1
• Date: Sat, 23 Aug 2025 14:50:36 GMT
• Title: Cross-field SNR Analysis and Tensor Channel Estimation for Multi-UAV Near-field Communications
• Authors: Tianyu Huo, Jian Xiong, Yiyan Wu, Songjie Yang, Bo Liu, Wenjun Zhang,
• Abstract summary: This paper investigates channel estimation for distributed near-field multi-UAV communication systems.<n>We propose two channel estimation algorithms: the spherical-domain matching pursuit (SD-OMP) and the tensor-OMP.
• Score: 12.979004533235845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extremely large antenna array (ELAA) is key to enhancing spectral efficiency in 6G networks. Leveraging the distributed nature of multi-unmanned aerial vehicle (UAV) systems enables the formation of distributed ELAA, which often operate in the near-field region with spatial sparsity, rendering the conventional far-field plane wave assumption invalid. This paper investigates channel estimation for distributed near-field multi-UAV communication systems. We first derive closed-form signal-to-noise ratio (SNR) expressions under the plane wave model (PWM), spherical wave model (SWM), and a hybrid spherical-plane wave model (HSPWM), also referred to as the cross-field model, within a distributed uniform planar array (UPA) scenario. The analysis shows that HSPWM achieves a good balance between modeling accuracy and analytical tractability. Based on this, we propose two channel estimation algorithms: the spherical-domain orthogonal matching pursuit (SD-OMP) and the tensor-OMP. The SD-OMP generalizes the polar domain to jointly consider elevation, azimuth, and range. Under the HSPWM, the channel is naturally formulated as a tensor, enabling the use of tensor-OMP. Simulation results demonstrate that tensor-OMP achieves normalized mean square error (NMSE) performance comparable to SD-OMP, while offering reduced computational complexity and improved scalability.

Related papers

• Learning to Separate RF Signals Under Uncertainty: Detect-Then-Separate vs. Unified Joint Models [53.79667447811139]
  We show that a single deep neural architecture learns to jointly detect and separate when applied directly to the received signal.<n>These findings highlight UJM as a scalable and practical alternative to DTS, while opening new directions for unified separation under broader estimation.
  arXiv  Detail & Related papers  (2026-02-04T15:25:02Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• DiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems [42.56832361766872]
  This paper introduces DiffPace, a diffusion-based plug-and-play method for channel estimation.<n>It provides high estimation precision and enhanced computational efficiency.
  arXiv  Detail & Related papers  (2025-10-21T06:22:24Z)
• Unsupervised Radio Map Construction in Mixed LoS/NLoS Indoor Environments [34.91945910235526]
  This paper aims to recover the data collection trajectory directly from the channel propagation sequence.<n>The proposed method achieves an average localization accuracy of 0.65 meters in an indoor environment.
  arXiv  Detail & Related papers  (2025-10-09T09:53:24Z)
• Fluid Antenna System-assisted Physical Layer Secret Key Generation [64.92952968689636]
  This paper investigates physical-layer generation (PLKG) in multiant base station systems by leveraging a fluid antenna system (FAS) to dynamically radio environments.<n>We propose an assisted PLKG model that integrates transmit beamforming and port selection under independent and spatially correlated environments.<n>It is shown that the sliding window-based port selection method introduced in this paper achieves higher KGR with fewer chains through dynamic port selection.
  arXiv  Detail & Related papers  (2025-09-19T03:01:29Z)
• RF-LSCM: Pushing Radiance Fields to Multi-Domain Localized Statistical Channel Modeling for Cellular Network Optimization [26.457933013666253]
  We propose RF-LSCM, a novel framework that models the channel APS by jointly representing large-scale signal attenuation and multipath components within a radiance field.<n>RF-LSCM significantly outperforms state-of-the-art methods, achieving up to a 30% reduction in mean absolute error (MAE) for coverage prediction and a 22% MAE improvement by effectively fusing multi-frequency data.
  arXiv  Detail & Related papers  (2025-09-17T04:31:18Z)
• Joint Multi-Target Detection-Tracking in Cognitive Massive MIMO Radar via POMCP [42.99053410696693]
  This correspondence presents a power-aware cognitive radar framework for joint detection and tracking of multiple targets.<n>Building on a previous single-target algorithm based on Partially Observable Monte Carlo Planning (POMCP), we extend it to the multi-target case by assigning each target an independent POMCP tree.<n>The proposed framework for the cognitive radar improves detection probability for low-SNR targets and achieves more accurate tracking compared to approaches using uniform or waveforms.
  arXiv  Detail & Related papers  (2025-07-23T13:43:29Z)
• Near-Field Channel Estimation for XL-MIMO: A Deep Generative Model Guided by Side Information [70.25632840894272]
  This paper investigates the near-field (NF) channel estimation for large-scale multiple-input multiple-output (XL-MIMO) systems.<n>We propose a GenAI-based approach to refine the estimated channel.<n> Experimental results indicate that the proposed approach is capable of offering substantial performance gain in CE.
  arXiv  Detail & Related papers  (2025-05-11T08:35:36Z)
• Symmetric Mean-field Langevin Dynamics for Distributional Minimax Problems [78.96969465641024]
  We extend mean-field Langevin dynamics to minimax optimization over probability distributions for the first time with symmetric and provably convergent updates. We also study time and particle discretization regimes and prove a new uniform-in-time propagation of chaos result.
  arXiv  Detail & Related papers  (2023-12-02T13:01:29Z)
• Random Orthogonalization for Federated Learning in Massive MIMO Systems [85.71432283670114]
  We propose a novel communication design for federated learning (FL) in a massive multiple-input and multiple-output (MIMO) wireless system. Key novelty of random orthogonalization comes from the tight coupling of FL and two unique characteristics of massive MIMO -- channel hardening and favorable propagation. We extend this principle to the downlink communication phase and develop a simple but highly effective model broadcast method for FL.
  arXiv  Detail & Related papers  (2022-10-18T14:17:10Z)
• Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks [15.498866529344275]
  Terahertz ultra-massive multiple-input multiple-output (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless systems. We develop an efficient deep learning based channel estimator with adaptive complexity and linear convergence guarantee. A major algorithmic innovation involves applying fixed point to compute the channel estimate while modeling neural networks with arbitrary depth and adapting to the hybrid-field channel conditions.
  arXiv  Detail & Related papers  (2022-05-10T14:57:56Z)
• Radio-Frequency Multi-Mode OAM Detection Based on UCA Samples Learning [19.3368485421533]
  Orbital angular momentum (OAM) at radio-frequency provides a novel approach of multiplexing a set of modes on the same frequency channel. classical phase gradient-based OAM mode detection methods require perfect alignment of transmit and receive antennas.
  arXiv  Detail & Related papers  (2021-11-29T15:55:29Z)
• Neural Calibration for Scalable Beamforming in FDD Massive MIMO with Implicit Channel Estimation [10.775558382613077]
  Channel estimation and beamforming play critical roles in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We propose a deep learning-based approach that directly optimize the beamformers at the base station according to the received uplink pilots. A neural calibration method is proposed to improve the scalability of the end-to-end design.
  arXiv  Detail & Related papers  (2021-08-03T14:26:14Z)
• Distributed Conditional Generative Adversarial Networks (GANs) for Data-Driven Millimeter Wave Communications in UAV Networks [116.94802388688653]
  A novel framework is proposed to perform data-driven air-to-ground (A2G) channel estimation for millimeter wave (mmWave) communications in an unmanned aerial vehicle (UAV) wireless network. An effective channel estimation approach is developed, allowing each UAV to train a stand-alone channel model via a conditional generative adversarial network (CGAN) along each beamforming direction. A cooperative framework, based on a distributed CGAN architecture, is developed, allowing each UAV to collaboratively learn the mmWave channel distribution.
  arXiv  Detail & Related papers  (2021-02-02T20:56:46Z)
• 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.