Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments

• URL: http://arxiv.org/abs/2511.03632v1
• Date: Wed, 05 Nov 2025 16:51:26 GMT
• Title: Neural Beamforming with Doppler-Aware Sparse Attention for High Mobility Environments
• Authors: Cemil Vahapoglu, Timothy J. O'Shea, Wan Liu, Sennur Ulukus,
• Abstract summary: Deep learning-based beamforming offers an alternative with nonlinear mappings from channel state information to beamforming weights.<n> Transformer-based models are particularly effective due to their ability to model long-range dependencies across time and frequency.<n>Recent studies address this issue through sparse attention mechanisms that reduce complexity while maintaining, yet often employ patterns that disregard channel dynamics.
• Score: 35.90167049594104
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Beamforming has significance for enhancing spectral efficiency and mitigating interference in multi-antenna wireless systems, facilitating spatial multiplexing and diversity in dense and high mobility scenarios. Traditional beamforming techniques such as zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) beamforming experience performance deterioration under adverse channel conditions. Deep learning-based beamforming offers an alternative with nonlinear mappings from channel state information (CSI) to beamforming weights by improving robustness against dynamic channel environments. Transformer-based models are particularly effective due to their ability to model long-range dependencies across time and frequency. However, their quadratic attention complexity limits scalability in large OFDM grids. Recent studies address this issue through sparse attention mechanisms that reduce complexity while maintaining expressiveness, yet often employ patterns that disregard channel dynamics, as they are not specifically designed for wireless communication scenarios. In this work, we propose a Doppler-aware Sparse Neural Network Beamforming (Doppler-aware Sparse NNBF) model that incorporates a channel-adaptive sparse attention mechanism in a multi-user single-input multiple-output (MU-SIMO) setting. The proposed sparsity structure is configurable along 2D time-frequency axes based on channel dynamics and is theoretically proven to ensure full connectivity within p hops, where p is the number of attention heads. Simulation results under urban macro (UMa) channel conditions show that Doppler-aware Sparse NNBF significantly outperforms both a fixed-pattern baseline, referred to as Standard Sparse NNBF, and conventional beamforming techniques ZFBF and MMSE beamforming in high mobility scenarios, while maintaining structured sparsity with a controlled number of attended keys per query.

Related papers

• FADPNet: Frequency-Aware Dual-Path Network for Face Super-Resolution [70.61549422952193]
  Face super-resolution (FSR) under limited computational costs remains an open problem.<n>Existing approaches typically treat all facial pixels equally, resulting in suboptimal allocation of computational resources.<n>We propose FADPNet, a Frequency-Aware Dual-Path Network that decomposes facial features into low- and high-frequency components.
  arXiv  Detail & Related papers  (2025-06-17T02:33:42Z)
• FLEX: A Backbone for Diffusion-Based Modeling of Spatio-temporal Physical Systems [51.15230303652732]
  FLEX (F Low EXpert) is a backbone architecture for generative modeling of-temporal physical systems.<n>It reduces the variance of the velocity field in the diffusion model, which helps stabilize training.<n>It achieves accurate predictions for super-resolution and forecasting tasks using as few features as two reverse diffusion steps.
  arXiv  Detail & Related papers  (2025-05-23T00:07:59Z)
• AdaFortiTran: An Adaptive Transformer Model for Robust OFDM Channel Estimation [22.40154714677385]
  We introduce the Adaptive Fortified Transformer (AdaFortiTran) to enhance channel estimation in challenging environments.<n>AdaFortiTran achieves up to 6 dB reduction in mean squared error (MSE) compared to state-of-the-art models.
  arXiv  Detail & Related papers  (2025-05-14T02:22:37Z)
• Transformer-Driven Neural Beamforming with Imperfect CSI in Urban Macro Wireless Channels [27.445841110148674]
  We introduce a novel unsupervised deep learning framework that integrates depthwise separable convolutions and transformers to generate beamforming weights under imperfect channel state information.<n>The primary goal is to enhance throughput by maximizing sum-rate while ensuring reliable communication.
  arXiv  Detail & Related papers  (2025-04-15T23:41:24Z)
• FreSca: Scaling in Frequency Space Enhances Diffusion Models [55.75504192166779]
  This paper explores frequency-based control within latent diffusion models.<n>We introduce FreSca, a novel framework that decomposes noise difference into low- and high-frequency components.<n>FreSca operates without any model retraining or architectural change, offering model- and task-agnostic control.
  arXiv  Detail & Related papers  (2025-04-02T22:03:11Z)
• Graph Neural Network Based Hybrid Beamforming Design in Wideband Terahertz MIMO-OFDM Systems [11.887537452826622]
  6G wireless technology is projected to adopt higher and wider frequency bands, enabled by highly directional beamforming.<n>The vast bandwidths available also make the impact of beam squint in massive multiple input and multiple output (MIMO) systems non-negligible.<n>Traditional approaches such as adding a true-time-delay line (TTD) on each antenna are costly due to the massive antenna arrays required.<n>This paper puts forth a signal processing alternative, specifically adapted to the multicarrier structure of OFDM systems, through an innovative application of Graph Neural Networks (GNNs) to optimize hybrid beamforming.
  arXiv  Detail & Related papers  (2025-01-27T18:45:54Z)
• 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)
• Joint Superposition Coding and Training for Federated Learning over Multi-Width Neural Networks [52.93232352968347]
  This paper aims to integrate two synergetic technologies, federated learning (FL) and width-adjustable slimmable neural network (SNN) FL preserves data privacy by exchanging the locally trained models of mobile devices. SNNs are however non-trivial, particularly under wireless connections with time-varying channel conditions. We propose a communication and energy-efficient SNN-based FL (named SlimFL) that jointly utilizes superposition coding (SC) for global model aggregation and superposition training (ST) for updating local models.
  arXiv  Detail & Related papers  (2021-12-05T11:17:17Z)
• 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)
• Harnessing Wireless Channels for Scalable and Privacy-Preserving Federated Learning [56.94644428312295]
  Wireless connectivity is instrumental in enabling federated learning (FL) Channel randomnessperturbs each worker inversions model update while multiple workers updates incur significant interference on bandwidth. In A-FADMM, all workers upload their model updates to the parameter server using a single channel via analog transmissions. This not only saves communication bandwidth, but also hides each worker's exact model update trajectory from any eavesdropper.
  arXiv  Detail & Related papers  (2020-07-03T16:31:15Z)

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.