Tensor-Based Channel Estimation Considering Beam Squint Effects in LEO Satellite Communications
In this paper, we propose a tensor-based channel estimation framework for beam squint-aware low Earth orbit (LEO) downlink communication at a ground station. We first demonstrate that the downlink signals received at the ground station from multiple LEO satellites can be represented as a structured...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11115052/ |
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| Summary: | In this paper, we propose a tensor-based channel estimation framework for beam squint-aware low Earth orbit (LEO) downlink communication at a ground station. We first demonstrate that the downlink signals received at the ground station from multiple LEO satellites can be represented as a structured tensor using mode-n products. Based on this tensor representation, we formulate the channel estimation problem as a tensor decomposition problem to mitigate beam squint effects and inter-satellite interference in downlink communication. To solve this problem, we propose constrained versions of CANDECOMP/PARAFAC (CP) and Tucker decompositions. Specifically, our modified approach incorporates additional constraints, such as a power constraint, which are not considered in the standard CP and Tucker decompositions. Finally, we validate the proposed methods through numerical simulations. Results show that the Tucker-based method achieves lower computational error than the CP-based method, albeit with higher computational cost. Furthermore, the proposed methods incorporating power constraints demonstrate comparable convergence performance to conventional approaches while requiring fewer iterations. |
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| ISSN: | 2169-3536 |