2results about How to "Improve channel estimation performance" patented technology

The invention provides a communication method, a communication node, a storage medium and a program product. The method comprises the steps of determining a first time domain reference position, determining a first time domain length corresponding to N transmission units according to the first time domain reference position, N being a positive integer, determining a time domain position of a demodulation reference signal according to the first time domain length, the N transmission units being capable of sharing the demodulation reference signal, effectively reducing the load of the demodulation reference signal, and improving the user experience. In the embodiment of the invention, the more reasonable time domain position of the demodulation reference signal is obtained according to the time domain length corresponding to the N transmission units, even if N is equal to 1, DMRS time domain position alignment of a plurality of transmission units or a plurality of user equipment can be realized through the first time domain reference position, interference management of the demodulation reference signal is facilitated, and demodulation performance is ensured.

This invention relates to a channel estimation method based on a Time-Frequency Joint Sparse Attention Network (TF-JSANet) in OFDM systems. The method first obtains a preliminary estimate of the pilot position at the receiver using the least squares (LS) criterion, forming a low-resolution noisy input. Then, a TF-JSANet network structure is constructed, extracting channel features in the time and frequency dimensions through parallel time-domain and frequency-domain branches, respectively. An Adaptive Sparse Attention Module (ASAM) is introduced to achieve dynamic soft-threshold denoising and feature enhancement. Finally, an upsampling module reconstructs the complete channel state information. Simulation results show that this method outperforms traditional algorithms and existing typical deep learning channel estimation methods, demonstrating good generalization ability and robustness.