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Abstract:Most works on pilot-aided orthogonal time frequency space (OTFS) channel estimation operate in the delay-Doppler (DD) domain, where embedded-pilot guard regions must cover the unknown maximum delay-Doppler spread, causing high overhead that worsens with antenna scaling: per-antenna guard regions increase overhead with the number of transmit antennas, while shared guard regions avoid this but introduce pilot contamination. Fractional Doppler further reduces DD-domain sparsity and increases estimation complexity. This paper shows that the time-frequency (TF) representation provides a low-overhead, scalable framework for OTFS channel estimation based on physical scatterer parameters (angle, delay, Doppler, and gain). We derive exact TF- and DD-domain input-output relationships under fractional Doppler. The TF expression decomposes the received signal into a desired component, identical in form for integer and fractional Doppler, and an explicitly characterized interference term; this Doppler-invariant structure enables scalable TF-domain estimation. This motivates a TF-domain pilot design using private TF bins protected by TF/DD guard bins, preserving cross-antenna pilot orthogonality with overhead that depends only on the number of pilots, not the array size. We then develop a low-complexity coarse-to-fine estimation method combining discrete Fourier transform (DFT)-based coarse estimation with dimensionality-reduced sparse recovery. The exact DD-domain expression provides the signal model for data-symbol recovery and explains why DD-domain estimation complexity grows substantially under fractional Doppler. Simulations show accurate channel state information (CSI) estimation under integer and fractional Doppler, robustness to pilot pollution, and scalability to large arrays with substantially lower overhead and complexity than existing schemes.

Submission history

From: Kailong Wang [view email]
[v1] Tue, 11 Nov 2025 17:39:22 UTC (478 KB)
[v2] Sun, 29 Mar 2026 23:29:34 UTC (199 KB)
[v3] Sat, 22 Aug 2026 21:55:36 UTC (372 KB)