Accurate channel estimation for pulse-shaped AFDM systems over doubly selective channels with fractional normalized delay and Doppler remains challenging. This paper proposes a low-complexity ambiguity-function-assisted newtonized channel (AFNC) estimation method. Specifically, we first present a closed-form input-output relation for pulse-shaped affine frequency division multiplexing (AFDM) under fractional normalized delay and Doppler. As a further step, we demonstrate that the input-output relation admits a low-complexity representation by offline precomputing and storing the discretized ambiguity function of the shaping pulse, followed by tailored cyclic-shift and stacking operations. Building on this representation, AFNC performs fractional delay-Doppler channel estimation through Newtonized refinement, where the required Jacobian and Hessian updates are computed efficiently using the low-complexity input-output representation. Simulation results confirm the effectiveness of the proposed approach.
Accurate full-band channel acquisition in frequency-hopping time-division duplex (TDD) systems is challenging because each sounding slot observes only a limited frequency subband, while conventional single-slot recovery cannot fully exploit historical observations. We propose ST-…
This letter proposes an off-grid variational Bayesian (OVB) method for fractional delay-Doppler (DD) estimation in OTFS-based integrated sensing and communication (ISAC) systems. To enable off-grid parameter estimation, the OTFS channel is reformulated using separable delay and D…
Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for next-generation integrated sensing and communication (ISAC) systems. However, it becomes challenging to improve spectral efficiency while simultaneously obtaining accurate channel and sensing-re…
Accurate channel estimation in orthogonal frequency division multiplexing (OFDM) systems remains challenging when demodulation reference signal (DMRS) observations are sparse and noisy, and when DMRS configurations vary across deployment scenarios. This paper proposes DANCE (Diff…
Channel estimation in extra-large multiple-input multiple-output systems is challenging due to near-field propagation, where the array response depends on both the angle and distance of the propagation paths. Existing near-field channel estimation methods typically rely either on…
Artificial Intelligence(AI) methods have shown strong channel estimation performance in terrestrial networks, but they typically rely on substantial computational resources. As 6G moves toward a unified architecture that will include Non-Terrestrial Networks (NTN) from day 0, ava…