3030results about "Channel estimation" patented technology

The invention relates to a multi-channel signal integrity optimization method and system for a passive copper cable, and the method comprises the steps: obtaining channel characteristic parameters and signal transmission requirements of the passive copper cable, and carrying out the impedance matching, and obtaining an optimized transmission channel; acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; acquiring crosstalk interference data acquired by the passive copper cable, and carrying out shielding analysis on the crosstalk interference data and the optimized transmission channel to obtain a crosstalk suppression measure; performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain a frequency compensation parameter; and performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimization signal. According to the invention, complex and changeable transmission environments can be coped, and stable transmission of signals is ensured.

The invention discloses a pilot optimization method for large-scale MIMO channel estimation based on structural compressed sensing. The method comprises the steps of establishing a channel estimation model for a large-scale MIMO-OFDM (Multiple-Input-Multiple-Output-Orthogonal Frequency Division Multiplexing) system when pilots are placed in an overlapping mode; simplifying the channel estimation model for the large-scale MIMO-OFDM system, thereby enabling the channel estimation model to correspond to a structural compressed sensing model; and obtaining an optimum pilot matrix through utilization of a pilot optimization algorithm. Through adoption of the optimum pilot matrix, according to the channel estimation of the large-scale MIMO system based on structural compressed sensing, the mean square errors MSEs of the channel estimation are clearly reduced, and the channel estimation performance is improved.

Certain aspects of the present disclosure provide techniques for channel estimation based on transmission spatial information. An example method of wireless communication by a user equipment includes receiving an indication of transmission spatial information associated with a network entity; receiving a reference signal from the network entity based on the transmission spatial information; and transmitting, to the network entity, channel state information (CSI) based on the received reference signal and the transmission spatial information.

An apparatus may include a receiver configured to receive a reference signal using a channel, and receive restriction information relating to the channel, a processing circuit configured to determine channel information based on the reference signal, and generate, using a machine learning model, a representation based on the channel information and the restriction information, a transmitter configured to transmit the representation.

The invention relates to the technical field of channel estimation, and discloses an OTFS sparse channel estimation method based on unitary transformation and sparse Bayesian, which comprises the following steps: S1, forming a 2D data block in a time delay Doppler domain, and dividing the time delay Doppler domain; s2, acquiring a receiving signal of a time delay Doppler domain; s3, constructing a sparse sensing matrix and performing singular value decomposition; s4, setting a judgment condition for successful channel recovery; s5, calculating posterior distribution of a transmission channel by adopting a generalized approximate message passing strategy based on unitary transformation; s6, obtaining an optimal updating rule of the hyper-parameter in the estimation channel and the hyper-parameter in the noise signal; and S7, judging whether iteration is continued or not according to a set judgment condition. According to the invention, the approximation of the channel posterior distribution information is completed by using the generalized approximation message passing framework based on unitary transformation through the information carried by the pilot frequency position, so that the calculation burden is reduced, and the calculation resource is saved.

The invention discloses an intelligent metasurface channel optimization method and system based on a beam forming algorithm, and relates to the technical field of intelligent metasurfaces, and the method comprises the steps: collecting real-time environment data, monitoring channel state data, and evaluating the influence of the environment and the channel state on a signal; evaluating the gain strategy of the intelligent metasurface, and evaluating the performance of the current beam forming strategy by combining the influence of the environment and the channel state on the signal; and calculating an optimal beam forming parameter by using a gradient descent method to realize channel optimization. According to the method, through comprehensive utilization of real-time environment monitoring, channel state evaluation, an advanced data analysis technology and a mathematical optimization method, dynamic adjustment and real-time optimization of intelligent metasurface channel optimization are achieved, efficient and stable operation of a communication system in a complex environment is ensured, the speed and reliability of data transmission are remarkably improved, and the method is suitable for popularization and application. The invention provides a more efficient, flexible and adaptive beam forming strategy optimization method, and the performance of a wireless communication system is remarkably improved.

The invention discloses a self-adaptive modulation method and device for a power line carrier communication system, and the method comprises the following steps: S1, obtaining the channel information of the power line carrier communication system, and calculating the signal-to-noise ratio of each data subcarrier; and S2, based on a preset mapping relationship between different data subcarrier signal-to-noise ratios and corresponding modulation modes, according to the signal-to-noise ratio of each data subcarrier calculated in the step S1, adaptively allocating a corresponding modulation mode to each data subcarrier for modulation. According to the channel information of the power line carrier communication system, under the condition that the constraint of the target bit error rate is met, a proper modulation mode can be adaptively selected for each data subcarrier, and the system throughput is improved while the communication reliability is ensured.

A reconfigurable intelligent surface (RIS) includes one or more radio frequency identification (RFID) tags. The one or more RFID tags may perform functions of a controller of the RIS. The one or more RFID tags receive a reference signal from a wireless communications device. The RIS may process the received reference signal and the one or more RFID tags backscatter a response signal to the wireless communications device. The wireless communications device may also perform processing of the backscattered response. Processing includes measuring channels from the wireless communication to the RFID tags. Processing also includes estimating channels from the wireless communication to elements of the RIS. Based on the estimated channels, a beam matrix corresponding to optimized weights of the RIS elements is determined and transmitted to the RIS.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment may receive a demodulation reference signal (DMRS) configuration that indicates a code-division multiplexing (CDM) group configured for the UE; receive a first indication of an antenna port, included in the CDM group, assigned to the UE for reception of a first set of DMRS transmissions; receive a second indication of whether a second set of DMRS transmissions for another antenna port, included in the CDM group, is present or absent for another UE; and perform channel estimation based at least in part on the first indication of the antenna port and the second indication of whether the second set of DMRS transmissions for the other antenna port is present or absent. Numerous other aspects are provided.

An apparatus includes at least one processor; and at least one memory including computer program code; wherein the at least one memory and the computer program code are configured to, with the at least one processor, cause the apparatus at least to: train a machine learning model to learn a configuration matrix that defines a reconfigurable intelligent surface; configure the reconfigurable intelligent surface for channel estimation during runtime, using the learned configuration matrix; perform channel estimation on an uplink channel using the reconfigurable intelligent surface; and reconfigure the reconfigurable intelligent surface after the channel estimation to improve coverage within the uplink channel.

Disclosed are a method and apparatus for transmitting / receiving a wireless signal in a wireless communication system. A method performed by a UE in a wireless communication system, according to an embodiment of the present disclosure may comprise: receiving configuration information including a plurality of resources for CSI reporting; receiving a reference signal by skipping at least one resource from among the plurality of resources; at least one of determining first CSI for a resource excluding the at least one resource from the plurality of resources, based on the received reference signal, and determining second CSI for the at least one resource based on a CSI prediction-related algorithm; and performing the CSI reporting for at least one of the first CSI and the second CSI.

A system and method of targeting spatialized audio to ears of a listener, comprising sensing characteristics of an environment comprising at least one human by receiving radio frequency signals with a radio receiver; using the sensed characteristics in at least one automated processor to analyze at least one human dynamic physiological pattern of each human and estimate a body pose and head position within the environment of each human based on the sensed characteristics and the at least one human dynamic physiological pattern; and using an estimated position of the ears of each human in conjunction with a head-related transfer function in a spatial audio system to generate spatialized audio.

The invention relates to the technical field of channel estimation, and discloses an efficient OTFS sparse channel estimation method based on fast sparse Bayesian, which comprises the following steps: S1, forming a 2D data block in a time delay Doppler domain, and dividing the time delay Doppler domain; s2, acquiring a receiving signal of a time delay Doppler domain; s3, constructing a sparse sensing matrix, and optimizing the sensing matrix by using an adaptive particle swarm optimization algorithm; s4, setting a judgment condition for successful channel recovery; s5, channel posterior distribution and hyper-parameters are updated by adopting rapid sparse Bayesian based on Laplacian prior; and S6, judging whether iteration is continued or not according to a set judgment condition. According to the method, the sensing matrix is optimized by using the adaptive particle swarm optimization algorithm, the system performance is improved, the posterior distribution and the hyper-parameter of the channel are updated by using the fast sparse Bayesian method, the calculation complexity is reduced, and the calculation resources are saved.

Methods, systems, and devices for spectral sharing wireless systems, wherein multiple user devices share time and frequency resources for uplink and / or downlink transmissions, are described. One example method includes transmitting transmission symbols from the network station to at least one user device by processing through a first precoder and a pre-compensation stage, wherein the pre-compensation stage is selected to have the transmission symbols receivable at the at least one user device to appear as if the transmission symbols are processed by a second precoder different from the first precoder.

A first UE may receive an indication to operate in at least one of a TD beam prediction mode or an SD beam prediction mode. The indication may be received from a first network entity. A second UE may obtain side information associated with the second UE for at least one of the TD beam prediction mode or the SD beam prediction mode. The second UE may transmit the side information associated with the second UE. The first UE may receive the side information from at least one of the first network entity, the second UE, or a second network entity. The first UE may estimate, based on the side information, one or more channel characteristics for a first set of DL RSs based on at least one of the TD beam prediction mode or the SD beam prediction mode.

The present disclosure provides a wireless receiver having a Delay Doppler channel detector. The Delay Doppler channel detector includes a Delay Dopper domain channel estimator and a detector and demodulator. The Delay Doppler domain channel estimator is configured for estimating a channel gain, a delay, and a Doppler frequency shift of a path of a channel of data received via the channel. The detector and demodulator is configured for extracting a symbol sequence of the received data based on the estimated channel gain, the estimated delay, and the estimated Doppler frequency shift of the path of the channel.

Techniques and apparatuses are described for machine-learning architectures for broadcast and multicast communications. A network entity processes broadcast or multicast communications using a deep neural network (DNN) to direct the one or more broadcast or multicast communications to a targeted group of user equipments (UEs) using the wireless communication system. The network entity receives feedback from at least one user equipment (UE) of the targeted group of UEs. The network entity determines a modification to the DNN based on the feedback. The network entity transmits an indication of the modification to the targeted group of UEs. The network entity updates the DNN with the modification to form a modified DNN. The network entity processes the broadcast or multicast communications using the modified DNN to direct the broadcast or multicast communications to the targeted group of UEs using the wireless communication system.

The apparatus may be a wireless device configured to estimate, for a first transmission in a first slot, a first channel associated with the first transmission, wherein the first transmission is associated with a first precoding, receive, in a second slot following the first slot, a second transmission associated with a second precoding, and estimate, based on the received second transmission and at least one of the received first transmission or the estimated first channel, a second channel associated with the second transmission.

Methods, systems, and devices for wireless communication are described. A user equipment (UE) may receive a carrier aggregation (CA) configuration including at least a primary component carrier (PCC) and a secondary component carrier (SCC), where the PCC is configured for frequency domain duplexed (FDD) communications and the SCC is configured for time domain duplexed (TDD) communications. To determine whether sounding reference signal (SRS) carrier switching (e.g., between the PCC and the SCC) is appropriate, the UE may monitor performance metrics. If the performance metrics fail to satisfy corresponding thresholds, the UE may reduce an SRS periodicity. For instance, the UE may request that a network entity reduce the SRS periodicity. Alternatively, the UE may reduce a quantity of antennas used for the SRS transmissions. In other examples, the UE may initiate an interchange between the PCC and the SCC such that SRS carrier switching is no longer utilized.

The present disclosure relates to a 5G communication system or a 6G communication system for supporting higher data rates beyond a 4G communication system such as long term evolution (LTE). A method of transmitting or receiving a signal by a user equipment (UE) in a mobile communication system is provided. The method may include: identifying a neural network model for transmitting first information to a base station (BS); learning a connection weight of the neural network model using the first information; transmitting, to the base station, second information for updating a weight of a second partial neural network corresponding to the base station based on a result of the learning; and updating a weight of a first partial neural network corresponding to the UE based on the result of the learning.

The invention discloses a wireless radio frequency data monitoring and distribution platform system and method based on data analysis, and belongs to the technical field of wireless communication. Wireless radio frequency signal parameters are acquired, data processing is performed on the signal parameters, a signal evaluation model is constructed, and the signal parameters are imported into the signal evaluation model to evaluate the signal-to-noise ratio. Constructing a channel occupation and multipath time delay extension evaluation model, importing the channel occupation time and the power and time delay of the multipath channel into the channel occupation and multipath time delay extension evaluation model to evaluate the signal condition, and constructing an equipment interference model; the method comprises the following steps: importing signal parameters and position distances of field working equipment into an equipment interference model to evaluate the interference degree of the working equipment on a wireless radio frequency transmission signal, constructing an optimal frequency band scoring model, and importing a signal-to-noise ratio, a channel occupancy rate, multipath time delay extension and equipment interference conditions into the optimal frequency band scoring model to perform frequency band selection. And the reliability and the real-time performance of wireless communication in an industrial scene are improved.

Embodiments are disclosed for capability reporting for antenna switching, AS, sound reference signals, SRS. A user equipment (UE) can transmit a capability report for AS SRS that includes a plurality of receive antenna (e.g., six or eight receive antenna), and receive SRS instructions based on the capability report. The UE can transmit a first temporary capability reduction for a duration, and transmit a first SRS according to the first temporary capability reduction and the SRS instructions, while maintaining phase continuity. To maintain phase continuity, the UE can calibrate one or more SRS ports participating in the AS SRS to transmit with substantially a same transmit power. The UE can determine that a second SRS overlaps an other transmission in a time domain, determine that the other transmission has an absolute priority over the second SRS, and transmit the other transmission but not the second SRS.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention belongs to the technical field of digital pre-distortion in satellite communication transmission, and provides a digital pre-distorter model design method based on a neural network for solving the technical problems that an existing digital pre-distortion algorithm lacks utilization of effective information and phase deviation, and the digital pre-distorter model design method based on the neural network is designed by combining the amplitude / phase and orthogonal / in-phase shunt isolation and interaction characteristics of communication data. A digital predistorter model based on set vector decomposition and multi-expert judgment is designed on the basis of an LSTM model, in-phase / orthogonal components and amplitude / phase information are comprehensively considered, cross-layer splicing is performed on original distortion signals and phase recovery signals through a vector splicing module, and isolation and interaction among different features are learned.

Radio receiver devices and related methods and computer programs are disclosed. A radio signal comprising a pilot signal is received at a radio receiver device over a radio channel. Raw channel estimation of the radio channel is performed based on the received pilot signal, thereby obtaining a raw channel estimate. The raw channel estimate is processed. The processing of the raw channel estimate comprises applying a neural network, NN, to the raw channel estimate. The NN comprises at least one fully connected layer and at least one convolutional layer. The NN is executable to encode the raw channel estimate by mapping the raw channel estimate into an alternate domain, thereby obtaining an encoded channel estimate. The NN is further executable to decode the encoded channel estimate by mapping the encoded channel estimate to a frequency-interpolated channel estimate.

Wireless sensing using measurement enhancement is described. In one example, a described method comprises: receiving a wireless signal transmitted by a transmitter through a wireless multipath channel of a venue, wherein the received wireless signal differs from the transmitted wireless signal due to the wireless multipath channel and a motion of an object in the venue; obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the received wireless signal, wherein each channel information (CI) comprises N1 CI components (CIC); for each respective CI having N1 CIC of the TSCI, computing a respective enhanced CI (ECI) having N2 CIC; computing a motion information (MI) based on the ECI of the TSCI; and performing a sensing task based on the MI.

A communication method and device are applied to the technical field of communication, can be applied to a wireless local area network system of an IEEE 802.11 ax next generation Wi-Fi protocol such as 802.11 be, Wi-Fi 7 or EHT, and 802.11 series protocols such as 802.11 be next generation, Wi-Fi 8, UHR, Wi-Fi AI and millimeter wave (MMW), and can also be applied to a WPAN system supporting UWB, a sensing system and the like. A first communication device generates a PPDU and transmits the PPDU. The second communication device receives the PPDU and performs channel estimation based on the LTF sequence. Wherein the PPDU comprises an LTF bearing an LTF sequence, the LTF sequence corresponds to the bandwidth of the PPDU, the LTF sequence is determined based on a basic LTF sequence, and the LTF sequence is a sequence in a 2x mode. The LTF sequence can effectively reduce the error of channel estimation.

The invention relates to a channel estimation method based on frequency domain deviation compensation and dynamic DCT denoising, and belongs to the technical field of wireless communication. The method comprises the following steps: establishing a wireless channel model and an OFDM system model; estimating the frequency offset in the received signal by using the phase rotation characteristic of the pilot signal; calculating a preliminary channel frequency domain response # imgabs2 # at a pilot frequency position through a least square criterion based on the compensation signal # imgabs1 #; obtaining a receiving pilot frequency signal # imgabs4 # according to the sending pilot frequency Xp and the roughly estimated channel frequency response # imgabs3 #; estimating a current signal-to-noise ratio SNRest based on the pilot frequency signal # imgabs5 # and a noise power ratio; and establishing an adaptive DCT denoising strategy based on a signal-to-noise ratio threshold, and selecting different denoising strategies according to different signal-to-noise ratios to perform denoising processing on # imgabs6 # to obtain final channel estimation # imgabs7 #. The adaptive denoising strategy design of the invention effectively balances the relationship between noise suppression and channel structure fidelity, and improves the precision and stability of overall channel estimation.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a signal processing method, device, equipment and medium based on mixed experts. And generating routing information based on the channel information, dynamically fusing expert output, generating a first task processing result by using the first task processing network, generating a second task processing result by using the second task processing network, jointly optimizing all network parameters, and generating a hybrid expert model for a to-be-processed signal. According to the method, the hybrid expert architecture aiming at the multi-channel characteristic is introduced, and the gating network is combined to dynamically allocate the characteristic processing path, so that the self-adaptive optimization aiming at different channel environments is realized, the accuracy of cross-channel identification and the resource utilization efficiency of the whole model are effectively improved, and the robustness of the cross-channel identification is improved. And high-precision, high-robustness and efficient signal identification can be realized in a complex and changeable environment.

The invention relates to a multi-device ad hoc network communication method and device and a storage medium, the polarization phenomenon can be accurately monitored and analyzed by collecting the magnetic field intensity vector of the device in real time, calculating the magnetic field gradient tensor and generating a medium polarization intensity distribution diagram, and when it is detected that the polarization angle deviation exceeds a threshold value, the system can quickly respond, and the communication efficiency is improved. A polarization compensation instruction is automatically sent to related equipment, by extracting a multipath component and a historical polarization residual component, the failure of a traditional channel estimation method is avoided, the prediction and compensation capability of a channel is optimized, the establishment of a dynamic compensation codebook is combined with a phase pre-compensation technology, and a phase adjustment process is optimized by using a symbol error fed back by a receiving end. By constructing the polarization memory attenuation prediction model, the channel state change can be learned and predicted in real time, an efficient compensation strategy is provided for the system, the communication requirement of each device is met, stable transmission of signals is ensured, and the communication performance and reliability of the system are improved.