Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 7 references
Abstract
In millimeter-wave Integrated sensing and communication (ISAC) systems, a high-resolution Doppler velocity estimation is crucial for localizing multiple tightly coupled mobile users in the environment for subsequent communication. Conventional subspace methods, such as the Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), provide high resolution but incur high complexity and latency, limiting their use in real-time hardware. This work proposes a lowcomplexity end-to-end multi-layer perceptron (MLP) for Doppler estimation for IEEE 802.11ad ISAC waveform. The proposed MLP achieves up to 59.8% lower RMSE than classical ESPRIT while using fewer slow-time packets and up to $20.4 \times$ lower latency on the system-on-chip (SoC). A multi-model extension further improves robustness across signal-to-noise ratio (SNR), providing an additional 56% gain at low SNR by adaptively selecting model parameters. These results demonstrate that MLPbased estimation provides a scalable, efficient alternative to classical subspace methods for real-time ISAC systems.
Simulation results across six Acosta-Marum vehicular channel models show that DPA-RDCNN with multi-channel mixed-SNR training surpasses RNN-based baselines in accuracy while reducing per-frame processing time by a factor of 16 to 32.
During superior solar conjunction, deep-space communication links are susceptible to solar scintillation, Doppler shifts, and low signal-to-noise ratio (SNR), which make accurate estimation of the complete channel response challenging. To address this issue, this work proposes a two-stage channel estimation method base...
Accurate channel estimation is essential for reliable communication in OFDM-based wireless sensor networks (WSNs), where resource constraints and dynamic environments pose significant challenges. In this paper, we propose a low-complexity deep residual convolutional neural network (CNN) for channel estimation, using...
Link-level evaluations confirm improved error vector magnitude and maintained bit/block error rates relative to the RT baseline, validating that the proposed physical-data collaborative paradigm not only enhances estimation accuracy but also preserves communication reliability, while offering a promising foundation for...
An autoencoder (AE)-based framework for ISAC waveform design in noncoherent settings is proposed and results show that the proposed design outperforms conventional schemes in both communication reliability and sensing accuracy, especially under short blocklength and fading conditions.
Muah Kim, Shuang-Yang Li, Tayyebeh Jahani-Nezhad et al.· 0 citations
The emergence of low-altitude networks (LANs) necessitates high-rate communication and high-accuracy localization to overcome the channel dynamics caused by unmanned aerial vehicle (UAV) mobility. To address these requirements, this paper proposes a novel sparsity-aware learning framework for joint channel estimation a...
Dai-Xin Xie, Chen-Xi Liu, Wei Wang et al.· IEEE Transactions on Communi...· 0 citations
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