Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition

• URL: http://arxiv.org/abs/2602.04083v1
• Date: Tue, 03 Feb 2026 23:38:05 GMT
• Title: Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition
• Authors: Alexandre Barbosa de Lima,
• Abstract summary: 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.
• Score: 51.56484100374058
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Channel estimation in wideband multiple-input multiple-output (MIMO) systems faces fundamental pilot overhead limitations in high-dimensional beyond-5G and sixth-generation (6G) scenarios. This paper presents a hybrid tensor-neural architecture that formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations -- a fundamentally different setting from prior tensor methods that assume fully observed received signal tensors. A canonical polyadic (CP) baseline implemented via a projection-based scheme (Tucker completion under partial observations) and Tucker decompositions are compared under varying signal-to-noise ratio (SNR) and scattering conditions: CP performs well for specular channels matching the multipath model, while Tucker provides greater robustness under model mismatch. A lightweight three-dimensional (3D) U-Net learns residual components beyond the low-rank structure, bridging algebraic models and realistic propagation effects. Empirical recovery threshold analysis shows that sample complexity scales approximately with intrinsic model dimensionality $L(N_r + N_t + N_f)$ rather than ambient tensor size $N_r N_t N_f$, where $L$ denotes the number of dominant propagation paths. Experiments on synthetic channels demonstrate 10-20\,dB normalized mean-square error (NMSE) improvement over least-squares (LS) and orthogonal matching pursuit (OMP) baselines at 5-10\% pilot density, while evaluations on DeepMIMO ray-tracing channels show 24-44\% additional NMSE reduction over pure tensor-based methods.

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