7results about How to "Improve channel estimation performance" patented technology

The invention relates to a channel estimation method based on a deep residual learning network (ProDrnNet) in an OFDM (Orthogonal Frequency Division Multiplexing) system. The channel estimation method comprises the following steps of: firstly, carrying out channel estimation on the basis of the ProDrnNet; according to the method, firstly, channel estimation at a pilot frequency is preliminarily obtained at a receiving end through a least square method (LS), the information serves as a low-resolution noisy image to be input into a channel estimation model, the model learns a mapping relation from the low-resolution noisy image to high-resolution denoising information, and therefore a restored high-resolution denoising image is output from the model, and a channel estimation result is obtained. And accurate channel state information is obtained. Simulation results show that the provided ProDrnNet model has more advantages on the accuracy of restoring the channel state information compared with a traditional estimation method, and the channel information can still be accurately reconstructed in various channel environments.

The invention discloses a 4D tensor-based RIS-assisted millimeter wave MIMO-NOMA Internet of Vehicles system channel estimation method, and aims to reduce the influence of serious Doppler frequency shift and sight line path blockage on communication performance, first, a 5G subframe division scheme without changing a standard 5G frame structure is proposed by introducing concepts of aggregation time slot and half time slot, and a 5G subframe division scheme without changing a standard 5G frame structure is provided; modeling of the received signal is facilitated; then, modeling the received signal into a four-order tensor so as to separate channel parameters into four factor matrixes, thereby avoiding parameter coupling; then, the tensor is decomposed in a closed-form solution form; then, carrying out one-dimensional search on a factor matrix obtained by decomposition to extract channel parameters; after channel parameters are obtained, a least square problem is constructed to obtain channel path gains, and solving of a scaling matrix is avoided; finally, experiments verify that the method can accurately estimate the parameters of the channel, and excellent demodulation performance can be realized based on the parameters of the channel.

A DNN-based OTFS time-frequency domain situation awareness and channel estimation method comprises the following steps: modeling a frequency domain selective fading channel in a wireless communication process into an autoregression process, and further converting an estimation problem into an autoregression coefficient estimation process; the method comprises the following steps: acquiring training data by performing discrete sampling, DFT (Discrete Fourier Transform), characteristic matrix representation, channel prior information generation and other processes on a channel impulse response function, finishing a preprocessing process of the training data, designing a neural network based on data and model driving, training the neural network through a large amount of prior channel data, and obtaining the channel impulse response function. The optimal frequency domain related parameter estimation under the minimum mean square error of the network is solved through offline training and online estimation, then corresponding channel response is obtained, and the algorithm can achieve lower bit error rate and complexity under the condition of the same signal-to-noise ratio.

Various embodiments of the disclosure relate to a device and a method for cell coverage enhancement by an electronic device in a wireless communication system. An electronic device may include a communication circuit and a processor, wherein the processor identifies a channel variation of an external device configured to perform communication via the communication circuit, configures a frequency hopping interval corresponding to the external device, based on the channel variation of the external device, transmits information related to the frequency hopping interval to the external device, and transmits, based on the frequency hopping interval, at least one reference signal assigned to at least one slot corresponding to at least one frequency band to the external device. Other embodiments may be also possible.

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.

A long training field sequence transmission method and a communication apparatus are provided, to send an LTF sequence on a discrete RU, and reduce a PAPR. The method includes: A STA determines an LTF sequence, where the LTF sequence is denoted as a first LTF sequence, the first LTF sequence is a sequence segment that is in a total LTF sequence corresponding to a channel bandwidth and that corresponds to a first RU, N subcarriers in the channel bandwidth are in a one-to-one correspondence with N components of the total LTF sequence, the first RU is M consecutive first subcarriers in the N subcarriers, the first LTF sequence is M components in the N components, and there is a correspondence between the M consecutive first subcarriers and the M components; and the STA sends the first LTF sequence to an AP on a discrete RU, where the discrete RU includes M inconsecutive second subcarriers, and the M components of the first LTF sequence are respectively sent on the M inconsecutive second subcarriers.

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.