Researchers have developed CRFCAN, a novel complex-valued residual FFT convolutional attention network designed for joint channel and phase noise estimation in sub-terahertz (sub-THz) communications. This end-to-end network integrates Fast Fourier Transform (FFT) modules to process features across time and frequency domains, effectively capturing fading and phase distortions. CRFCAN demonstrates superior performance over conventional methods and existing deep learning models in terms of normalized mean square error and bit error rate, offering robust and practical estimation for sub-THz receivers. AI
RANK_REASON The cluster contains a research paper detailing a new technical method for signal processing in communications. [lever_c_demoted from research: ic=1 ai=1.0]
- CRFCAN
- Fast Fourier Transform
- Orthogonal frequency-division multiplexing
- Sub-THz-range linearly chirped signals characterized using linear optical sampling technique to enable sub-millimeter resolution for optical sensing applications
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