Propagation Mechanism-Aware Near-Field Spatially Non-Stationary Channel Estimation and Environment Mapping
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866913083206664192 |
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| author | Liu, Yuan Cai, Xuesong Saha, Dipankar Shankar, M. R. Bhavani Ottersten, Björn |
| author_facet | Liu, Yuan Cai, Xuesong Saha, Dipankar Shankar, M. R. Bhavani Ottersten, Björn |
| contents | Extremely large aperture arrays (ELAAs) benefit the dual functions of integrated sensing and communication (ISAC) systems by enabling high-throughput data streams and high angular resolution with near-field spatial diversity. However, near-field spherical wavefront effects and spatial non-stationarity (SNS) bring challenges to both communication and sensing. This paper studies near-field spatially non-stationary channel estimation and environment mapping by jointly accounting for multi-bounce, blockage-induced partial visibility, and hybrid reflection-scattering propagation. We propose a unified parametric sensing channel model that represents the SNS phenomenon (due to partial array blockage, diffraction, and specular reflection) through spatially varying visibility and amplitude of each multipath across the array. To regularize the spatially varying delays caused by propagation mechanisms, we incorporate geometric constraints (GCs) based on environmental interaction points, embedding them into the model as absolute propagation delays. We then develop a GC-space-alternating generalized expectation-maximization (GC-SAGE) algorithm to estimate near-field channel parameters and locate environment scatterers/reflectors. Moreover, the GC-SAGE calculates per antenna path amplitudes based on the delays determined by the coordinates of scatterers/reflectors and transceivers, thereby effectively detecting channel SNS. Both ray-based simulation and field measurement are used to validate the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01162 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Propagation Mechanism-Aware Near-Field Spatially Non-Stationary Channel Estimation and Environment Mapping Liu, Yuan Cai, Xuesong Saha, Dipankar Shankar, M. R. Bhavani Ottersten, Björn Signal Processing Extremely large aperture arrays (ELAAs) benefit the dual functions of integrated sensing and communication (ISAC) systems by enabling high-throughput data streams and high angular resolution with near-field spatial diversity. However, near-field spherical wavefront effects and spatial non-stationarity (SNS) bring challenges to both communication and sensing. This paper studies near-field spatially non-stationary channel estimation and environment mapping by jointly accounting for multi-bounce, blockage-induced partial visibility, and hybrid reflection-scattering propagation. We propose a unified parametric sensing channel model that represents the SNS phenomenon (due to partial array blockage, diffraction, and specular reflection) through spatially varying visibility and amplitude of each multipath across the array. To regularize the spatially varying delays caused by propagation mechanisms, we incorporate geometric constraints (GCs) based on environmental interaction points, embedding them into the model as absolute propagation delays. We then develop a GC-space-alternating generalized expectation-maximization (GC-SAGE) algorithm to estimate near-field channel parameters and locate environment scatterers/reflectors. Moreover, the GC-SAGE calculates per antenna path amplitudes based on the delays determined by the coordinates of scatterers/reflectors and transceivers, thereby effectively detecting channel SNS. Both ray-based simulation and field measurement are used to validate the proposed approach. |
| title | Propagation Mechanism-Aware Near-Field Spatially Non-Stationary Channel Estimation and Environment Mapping |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2605.01162 |