Tootfinder

Near-Field Codebook-Based 3D Spherical Channel Estimation for UCA XL-MIMO Systems
Chenliang Yang, Guangchi Zhang, Miao Cui, Qingqing Wu, Yong Zeng
https://arxiv.org/abs/2507.03507

Near-Field Codebook-Based 3D Spherical Channel Estimation for UCA XL-MIMO Systems
Extremely large-scale multiple input multiple output (XL-MIMO), a key technology for 6G communications, faces challenges in near-field channel estimation due to spherical wavefronts and the need for three-dimensional (3D) spatial characterization, particularly with uniform circular arrays (UCAs). This letter proposes a spherical-domain simultaneous orthogonal matching pursuit (S-SOMP) based scheme tailored for near-field 3D channel estimation in UCA-equipped XL-MIMO systems. We establish a spar…

Efficient Seismic Data Interpolation via Sparse Attention Transformer and Diffusion Model
Xiaoli Wei, Chunxia Zhang, Baisong Jiang, Anxiang Di, Deng Xiong, Jiangshe Zhang, Mingming Gong
https://arxiv.org/abs/2506.07923

Efficient Seismic Data Interpolation via Sparse Attention Transformer and Diffusion Model
Seismic data interpolation is a critical pre-processing step for improving seismic imaging quality and remains a focus of academic innovation. To address the computational inefficiencies caused by extensive iterative resampling in current plug-and-play diffusion interpolation methods, we propose the diffusion-enhanced sparse attention transformer (Diff-spaformer), a novel deep learning framework. Our model integrates transformer architectures and diffusion models via a Seismic Prior Extraction …

This https://arxiv.org/abs/2503.04211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Adaptive Subarray Segmentation: A New Paradigm of Spatial Non-Stationary Near-Field Channel Estimation for XL-MIMO Systems
To address the complexities of spatial non-stationary (SnS) effects and spherical wave propagation in near-field channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems, this paper proposes an SnS-aware CE framework based on adaptive subarray partitioning. We first investigate spherical wave propagation and various SnS characteristics and construct an SnS near-field channel model for XL-MIMO systems. Due to the limitations of uniform array partitioning…

Deep Learning-Based Rate-Adaptive CSI Feedback for Wideband XL-MIMO Systems in the Near-Field Domain
Zhenyu Liu, Yi Ma, Rahim Tafazolli
https://arxiv.org/abs/2508.00626 https://…

Deep Learning-Based Rate-Adaptive CSI Feedback for Wideband XL-MIMO Systems in the Near-Field Domain
Accurate and efficient channel state information (CSI) feedback is crucial for unlocking the substantial spectral efficiency gains of extremely large-scale MIMO (XL-MIMO) systems in future 6G networks. However, the combination of near-field spherical wave propagation and frequency-dependent beam split effects in wideband scenarios poses significant challenges for CSI representation and compression. This paper proposes WideNLNet-CA, a rate-adaptive deep learning framework designed to enable effi…

Dynamic Coupling of Infiltration-Soil Moisture Feedback:Emergent Vegetation Patterns in a Water-Vegetation Model
Juan Yan, Xiaoli Wang, Guohong Zhang, Yuan Yuan
https://arxiv.org/abs/2508.01755

Dynamic Coupling of Infiltration-Soil Moisture Feedback:Emergent Vegetation Patterns in a Water-Vegetation Model
We present a modified water-vegetation model to investigate the mechanistic relationship between infiltration-soil moisture feedback and vegetation pattern in arid/semi-arid ecosystems. Employing Turing pattern formation theory, we drive conditions for diffusion-induced instability and analyze spatiotemporal dynamics near Turing-Hopf bifurcation points. Our key findings include: (i) The system exhibits rich dynamics including multiple stable equilibria, supercritical/subcritical Hopf bifurcatio…

Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems
Zhongnian Li, Chao Zheng, Jian Xiao, Ji Wang, Gongpu Wang, Ming Zeng, Octavia A. Dobre
https://arxiv.org/abs/2507.19936

Deep Learning Based Joint Channel Estimation and Positioning for Sparse XL-MIMO OFDM Systems
This paper investigates joint channel estimation and positioning in near-field sparse extra-large multiple-input multiple-output (XL-MIMO) orthogonal frequency division multiplexing (OFDM) systems. To achieve cooperative gains between channel estimation and positioning, we propose a deep learning-based two-stage framework comprising positioning and channel estimation. In the positioning stage, the user's coordinates are predicted and utilized in the channel estimation stage, thereby enhancing t…

Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage
Junius Pun, Xilai Dai, Grace Zgheib, Mahesh A. Iyer, Andrew Boutros, Vaughn Betz, Mohamed S. Abdelfattah
https://arxiv.org/abs/2507.11709

Double Duty: FPGA Architecture to Enable Concurrent LUT and Adder Chain Usage
Flexibility and customization are key strengths of Field-Programmable Gate Arrays (FPGAs) when compared to other computing devices. For instance, FPGAs can efficiently implement arbitrary-precision arithmetic operations, and can perform aggressive synthesis optimizations to eliminate ineffectual operations. Motivated by sparsity and mixed-precision in deep neural networks (DNNs), we investigate how to optimize the current logic block architecture to increase its arithmetic density. We find that…