13 results about "Ofdm channel estimation" patented technology

The present invention discloses a MIMO-OFDM channel estimation method and device based on natural interpolation. The method comprises: obtaining a channel coefficient at a pilot signal based on least squares channel estimation; assigning weights to the channel coefficients at several pilot signals near each interpolation point according to the size of the Voronoi diagram grid area based on the natural interpolation method, and obtaining an interpolation formula at the interpolation point by weighted summation; and performing natural interpolation on each interpolation point using the interpolation formula to obtain a MIMO-OFDM channel estimation result. The method applies natural interpolation to channel estimation, and obtains a channel frequency response estimate at the interpolation point by weighting the values ​​at several pilot signals near the interpolation point. This method can simultaneously utilize the time and frequency correlation of the channel, significantly improve the channel estimation accuracy, reduce the channel estimation error, and have a smoother interpolation effect and better interpolation performance.

The application belongs to the field of OFDM channel estimation, and relates to a data-assisted short-wave OFDM sparse channel Turbo iteration estimation method, which comprises the following steps: S1, receiving a signal transmitted through a channel and processing the signal to obtain a receiving end symbol matrix Y; S2, performing channel estimation and equalization on Y to obtain an estimated channel and an OFDM signal pair, and performing Turbo iteration on the estimated channel and the OFDM signal pair to obtain a symbol variance; S3, selecting a virtual pilot in the signal according to the symbol variance; S4, performing channel estimation and equalization on Y according to the virtual pilot to obtain a current estimated channel and an OFDM signal; S5, performing Turbo iteration on the signal to obtain a current symbol variance; and S6, if a preset maximum iteration number is reached, obtaining a final estimated channel; otherwise, returning to step S3 to select a virtual pilot in the signal according to the symbol variance; and the application selects a virtual pilot in the signal according to the symbol variance, and uses the virtual pilot to perform estimation again, so that the spectral utilization rate is improved and the channel estimation performance is improved.

The invention discloses a design method for ISTA sparse channel estimation based on a single-transmitting and single-receiving OFDM (Orthogonal Frequency Division Multiplexing) system. According to the method, the signal can be recovered under the condition of unknown sparseness, accurate reconstruction of the signal is realized, and a sparse channel is reconstructed through a small amount of training data, so that the training overhead is reduced, the spectrum efficiency is improved, and signal reconstruction is carried out by utilizing gradient updating and a soft threshold threshold function. A simulation experiment shows that the algorithm can realize better channel reconstruction, and meanwhile, the algorithm also has higher convergence speed and lower calculation complexity.

When an OFDM radio system which uses a wide frequency band is interfered with by another narrow-band radio system, the interference can frequently be compensated but the transmission quality decreases drastically. Thus, narrow-band interferers in an OFDM radio system are determined according to the invention whereby none of the subscribers of the radio system transmits in a defined time slot or scan slot but all switch at the same time into the receiving mode. If there is interference (P1, P2), it is detected in this time slot. Countermeasures are taken individually in all the mobile devices, in particular the detection of the frequency and strength of the narrowband interference (P1, P2) and the configuration of a flexible notch filter (140) in the time range to the detected frequency and strength. The scanned received signal (RXS) is then filtered in the time range, i.e. before the FFT (120) and the OFDM channel estimation (130) by the correspondingly configured notch filter (140). The notch (S1, S2) of the notch filter thereby acts in the transmission function like a natural break when receiving data.

Methods, systems, and devices for wireless communications are described. In some cases, a user equipment (UE) may transmit an indication of a capability of the UE to support channel estimation for wideband using signaling via a narrowband. The UE may receive a control signal indicating a resource occasion for communication of a frequency modulated continuous waveform (FMCW) signal via a first bandwidth part (BWP) of multiple BWPs. Additionally, the UE may receive, via the resource occasion and the first BWP of the multiple BWPs, the FMCW signal. As such, the UE may transmit a report indicating one or more second BWPs of the multiple BWPs of the wideband based on a channel estimation procedure associated with the multiple BWPs of the wideband, where the channel estimation is based on the FMCW signal.

The invention discloses a lightweight OFDM (Orthogonal Frequency Division Multiplexing) channel estimation method and system based on an attention mechanism. The method comprises the steps that an improved LMMSE algorithm is adopted to carry out preliminary estimation on a pilot frequency matrix, and adversarial noise disturbance is introduced to enhance estimation robustness; lMMSE estimation is used as input, and global and local features of a frequency domain and a time domain are captured through processing of a Transform encoder and a lightweight residual network in sequence; and super-resolution-level channel recovery is realized based on up-sampling. Compared with a traditional MMSE and an existing attention model, the method has the advantages that more than 81.14% of performance improvement is kept under the condition of a high signal-to-noise ratio, and better robustness and generalization ability are shown under different Doppler frequency shift scenes; the method has the advantages of being simple in structure, low in calculation overhead and suitable for the mobile terminal with limited resources, and the requirements of 5G and subsequent mobile communication systems for real-time high-precision channel estimation are met.

The application provides an OFDM channel estimation method and device, and a signal receiving method and device, which comprises: obtaining LS channel estimation and background noise of OFDM according to OFDM pilot time domain symbol sequences uniformly distributed in subcarriers of OFDM pilot time slots; performing mirror image extension on the LS channel estimation of a first number of subcarriers on two edges of the frequency domain to obtain extended channel estimation, and performing IDFT transformation on the extended channel estimation to obtain an extended time domain symbol sequence of the pilot time domain symbol sequence; filtering the extended time domain symbol sequence by using a cosine window function obtained according to the background noise and the extended time domain symbol sequence to obtain a noise reduction time domain symbol sequence of the pilot time domain symbol sequence; and obtaining the channel estimation of OFDM according to the noise reduction time domain symbol sequence. The technical scheme of the embodiment of the application improves the problem of deterioration of edge subcarrier channel estimation values in the DFT channel estimation method of OFDM, and improves the channel estimation performance of the system.

The invention relates to an OFDM (Orthogonal Frequency Division Multiplexing) channel estimation method based on a mamba neural network, and belongs to the technical field of channel estimation methods. According to the method, the current channel state is estimated by utilizing the correlation of the OFDM channel state data time sequence based on the mamba model, and aiming at channel state estimation in a high-baud-rate data transmission scene, window limitation of an attention mechanism can be broken through, internal correlation of a long-time channel state sequence is efficiently utilized, and accurate estimation of the channel state is realized; in the process of training the neural network, a parallel computing mode can be adopted to improve the training efficiency; when the current channel state is estimated, the estimation output of the channel state can be realized only by considering the current input and the hidden state at the previous moment, so that the calculation complexity of channel state estimation is effectively reduced, and rapid estimation is realized. Therefore, a channel environment with rapid change and dual selective fading can be more effectively coped with.

The invention belongs to the field of OFDM (Orthogonal Frequency Division Multiplexing) channel estimation, and relates to a data-assisted short-wave OFDM sparse channel Turbo iterative estimation method, which comprises the following steps of: S1, receiving and processing a signal transmitted by a channel to obtain a receiving end symbol matrix Y; s2, performing channel estimation and equalization on the Y to obtain an estimated channel and an OFDM (Orthogonal Frequency Division Multiplexing) signal # imgabs0 #, performing Turbo iteration on the # imgabs1 # to obtain a symbol variance # imgabs2 #; S3, selecting a virtual pilot frequency in a signal # imgabs4 # according to the # imgabs3 #; s4, performing channel estimation and equalization on the Y according to the virtual pilot frequency to obtain a currently estimated channel and an OFDM signal # imgabs5 # S5, performing Turbo iteration on the signal # imgabs6 # to obtain a current symbol variance # imgabs7 # S6, and if a preset maximum number of iterations is reached, obtaining a final estimated channel; otherwise, returning to the step S3, and selecting the virtual pilot frequency in the signal # imgabs9 # according to the # imgabs8 #; according to the method, the virtual pilot frequency is selected from the signal # imgabs10 # according to the symbol variance, and the virtual pilot frequency is used for estimation again, so that the spectrum utilization rate is improved, and the channel estimation performance is improved.

The present invention relates to the field of high-speed wireless communication, and in particular to an OFDM channel estimation and signal detection method based on a self-normalizing network. The present invention adopts an innovative logical design. First, a data set required for training the SNN network is generated through an OFDM system, and the data set is divided into a training set and a test set and data preprocessing is performed. Secondly, an SNN network is built. Subsequently, the data is input into the SNN network, the model is trained and learned, and the Adam optimizer is used to optimize the network parameters. The present invention can be applied to an OFDM system with a large number of subcarriers and a small number of pilots, effectively solving the problems of low channel estimation and signal detection accuracy and serious inter-carrier interference in existing and traditional algorithms for such systems. At the same time, a high channel estimation and signal detection accuracy and a low bit error rate can be achieved with a small number of pilot frequencies and cyclic prefixes, greatly saving spectrum resources.

The invention discloses an OFDM (Orthogonal Frequency Division Multiplexing) channel estimation method based on a shallow neural network, which comprises the following steps of: 1, preprocessing: obtaining the most basic channel LS (Least Squares) estimation, transforming the estimation value to an image feature domain by using a DCT (Discrete Cosine Transformation) technology, and performing dimension compression; step 2, interpolation: inputting the channel estimation of the compressed version output in the step 1 into a shallow neural network, completing a channel estimation interpolation function by using the neural network, and outputting a corresponding estimated value; and step 3, reduction: carrying out DCT inverse transformation on the channel estimation value output in the step 2, and transforming the channel estimation value to a signal frequency domain to obtain a full-dimensional channel estimation value. Compared with a traditional channel estimation method, the channel estimation method based on the deep neural network provided by the invention uses a data-oriented optimization algorithm to learn channel characteristics, thereby having more excellent channel estimation performance.

The present invention belongs to the field of communication technologies, and particularly relates to an OFDM channel estimation method based on a spatial specific neural network. The present invention constructs a channel estimation neural network for a fast time-varying OFDM channel, solves the problem of limited ICI suppression performance of OFDM channel estimation by a deep learning method based on implicit ICI suppression. The constructed network includes a preprocessing network and a channel refinement network composed of spatial specific neural network operators. The preprocessing network eliminates ICI from the input initial channel estimate and its auxiliary data, and the channel refinement network further suppresses the influence of residual noise to improve the accuracy of the channel estimate value. At the same time, a loss weighting mechanism is applied during the training process to significantly improve the adaptability of the network.

The application relates to the technical field of wireless communication, and particularly provides an OFDM channel estimation method combining a super-resolution and denoising joint model. The method comprises the following steps: generating a data set through a constructed multipath Rayleigh fading channel model; converting a channel estimation problem in an OFDM system into an image super-resolution problem; constructing an improved super-resolution module SRCNN; adopting a residual learning mode to construct a denoising module DnCNN to predict noise between an output of the SRCNN module and an actual channel; adopting a feature fusion layer to connect the super-resolution module and the denoising module to share information and construct a joint model; adopting an end-to-end training strategy to optimize parameters of the super-resolution module and the denoising module; and simulating and verifying, evaluating an algorithm performance through a mean square error, setting multiple groups of signal-to-noise ratio parameters, and verifying performances of different models under different signal-to-noise ratios. The method realizes high-precision and low-overhead channel estimation, and improves applicability and engineering practice value.