2317results 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.

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.

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.

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 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.

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.

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.

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.

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.

The invention discloses a channel estimation method and device, a communication system and a storage medium, relates to the technical field of communication, and is used for improving the multiplexing capability of reference signals and solving the problem that uplink reference signals cannot be transmitted to a base station in time when the number of the uplink reference signals is too large. The method comprises the following steps: terminal equipment reports first indication information of channel characteristics of a channel between the terminal equipment and network equipment to the network equipment, obtains a characteristic weight of the channel determined by the network equipment based on the first indication information, and then determines a first uplink reference signal based on a pilot frequency base sequence and the characteristic weight of the terminal equipment, and sending the first uplink reference signal to a network device.

Methods, apparatus and systems for wireless monitoring and tracking are described. In one example, a described method of a wireless monitoring system comprises: transmitting a wireless signal that is impacted by a wireless multipath channel in a venue and a modulation of an object undergoing a motion in the venue, to obtain a set of channel information (CI) of the wireless multipath channel; performing a monitoring task by monitoring the object and the motion; determining a plurality of admissible system states of the wireless monitoring system, wherein each admissible system state is associated with a respective setting of at least one of: the wireless signal, a series of sounding signals in the wireless signal, or the monitoring task; choosing one of the admissible system states to be a system state of the wireless monitoring system based on the monitoring task; and applying a setting associated with the chosen admissible system state to the wireless monitoring system.

The invention is suitable for the technical field of unmanned aerial vehicle information transmission, and provides a self-adaptive multi-unmanned aerial vehicle information transmission system and method based on channel estimation, and the system comprises a channel modeling module, a communication scheduling module, a topology control module and a terminal sensing layer. The channel modeling module is used for estimating and predicting a wireless link state in a complex environment in real time through an improved Kalman filtering method; the communication scheduling module constructs a transmission decision module based on a reinforcement learning strategy, and realizes joint dynamic optimization of a communication link and a task execution path by introducing a Q-learning algorithm; the topology control module introduces a multi-objective optimization strategy based on a utility function to realize dynamic reconstruction of the network topology; the terminal sensing layer is used for multi-unmanned aerial vehicle distributed cooperative detection and multi-modal sensing data fusion; according to the invention, the edge collaborative fusion perception of multiple unmanned aerial vehicles replaces the traditional single-point detection, and the target recognition precision and the shielding robustness are significantly improved.

A communication device for providing a channel state information, CSI, feedback in a wireless communication system includes a transceiver to receive, from a transmitter a radio signal via a time-variant, frequency-selective MIMO channel, the radio signal including downlink reference signals according to a reference signal configuration including a number of antenna ports, and downlink signals including the reference signal configuration; and a processor. The processor estimates an explicit CSI in the frequency domain using measurements on the downlink reference signals on the radio channel, selects a Doppler-delay precoder matrix (W) for a composite Doppler-delay-beam three-stage precoder, calculates either one or more of a channel quality indicator, CQI, and / or a precoder matrix indicator, PMI, and / or a rank indicator, RI, and reports to the transmitter the CSI feedback including either one or more of the CQI, and / or the PMI and / or the RI.

A method of MIMO channel estimation is provided that involves the transmission of MIMO reference signals that use a non-uniform pattern of resources in space, time, frequency, and / or code and is calculated from a prior knowledge. The prior knowledge is obtained from a plurality of MIMO channel samples associated to a region. In a receiver, MIMO reference signal are received based on a receiver-MIMO-reference-signal-placement is also a non-uniform pattern of resources in space, time, frequency, and / or code and is calculated from the prior knowledge. A MIMO channel is estimated from channel measurement on the reference signals and the prior knowledge.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, a user equipment (UE) may receive a configuration for a reference signal used for data collection and / or a first message that indicates that data collection based on the reference signal is allowed. The UE may receive the reference signal. The UE may collect data based at least in part on the reference signal. The UE may transmit the data that is collected. Numerous other aspects are described.

The invention belongs to the technical field of information and communication, and relates to an active user detection and data decoding method of an asynchronous massive machine type communication system. Aiming at the problem that the detection and estimation performance is reduced due to unknown user time delay in asynchronous transmission, the method comprises the following steps: initial estimation and preprocessing: detecting initial active users and transmission time delay thereof through correlation peaks, and whitening received signals; the iterative joint detection and channel estimation module is used for iteratively updating channel information and data symbol information for multiple rounds based on a factor graph and a message passing algorithm; the data decoding module is used for decoding data based on a soft decision device; the detection and decoding performance is improved by transmitting external information among the modules; and the time delay parameter updating adopts a greedy search algorithm to obtain the optimal time delay. According to the method, the active user detection rate, the channel estimation precision and the data decoding accuracy under the condition that the receiving and transmitting ends are completely asynchronous are improved.

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). The present disclosure provides a method performed by a second node in a communication system, comprising: receiving first configuration information for first data from a first node; obtaining first predicted data signals of various layers of the first data based on the first configuration information; determining a first set including the first predicted data signal of at least one layer of the various layers and a second set including the first predicted data signals of one or more layers of multiple layers based on an accuracy of the first predicted data signals of the various layers; obtaining a third set based on the first configuration information and the first set, wherein the third set includes second predicted data signals corresponding to the one or more layers; obtaining a fourth set based on the second set and the third set, wherein the fourth set includes third predicted data signals corresponding to the one or more layers; obtaining soft bits of the first data based on the first set and the fourth set.

The invention provides an unmanned aerial vehicle dual-polarized antenna multipath interference suppression and polarization mismatch joint optimization algorithm, and relates to the technical field of low-altitude communication, and the algorithm carries out the coupling modeling of a multipath interference problem and a polarization mismatch problem through building a unified dual-polarized channel model which considers multipath propagation and time-varying polarization characteristics; an adaptive polarization state estimation method based on received signal covariance matrix feature analysis is provided; a combined space-time-polarization domain adaptive filter structure is designed; a joint optimization objective function containing a minimum mean square error and a maximum polarization matching degree (MPM) is constructed; a low-complexity adaptive weight updating rule based on a recursive least square (RLS) thought and integrated with polarization constraints is deduced. According to the algorithm, interference suppression and polarization matching are collaboratively optimized through a unified framework, the complexity is reduced by adopting recursive calculation, rapid convergence and strong environmental adaptability are realized, and the low-altitude communication performance of the unmanned aerial vehicle is remarkably improved.

The invention discloses an inter-body area network and intra-network joint optimization interference mitigation method based on channel prediction, which comprises the following steps of: firstly, predicting channel quality between each node and a coordinator in a body area network under different frequencies by using an autoregression model, classifying the body area network according to neighbor information among the body area networks, and dividing the channel quality of each node in the body area network according to neighbor information; secondly, distributing channels with different frequencies to various body area networks according to the principle of maximizing the average signal receiving strength of the nodes so as to avoid inter-network interference; in each body area network, the number of required time slots is calculated according to the service volume of each node, and when the number of time slots is not enough, the number of time slots of the nodes is reduced according to the principle that the total utility of the nodes is maximized; and then, according to the predicted channel quality condition of the nodes in the network under the allocated frequency, allocating time slots to the nodes by taking the minimum average packet loss probability as a principle. The method provided by the invention can be used for the scene of coexistence interference of the semi-dynamic body area network, and inter-network and intra-network interference can be avoided.

A method is disclosed for enhancing the performance of a Multi-User Multiple Input Multiple Output (MU-MIMO) system, comprising: measuring, by a User Equipment (UE), a plurality of ports including dedicated ports and additional ports beyond the dedicated ports; characterizing, by the UE, interfering streams based on the measurements of the additional ports; and sending, from the UE, a message to a gNB to relax stream separation requirements, thereby increasing cell throughput and efficiency.

The embodiment of the invention provides a communication method, a communication device and a storage medium, relates to the technical field of communication, and is used for improving the data transmission efficiency in a communication process. The method comprises the following steps: receiving a first communication signal without a pilot symbol; processing the first communication signal through a first model to obtain a second communication signal; and performing signal analysis processing on the second communication signal to obtain communication data.

The present application provides a channel estimation method and a related apparatus, improving the accuracy of channel estimation. The method comprises: a terminal device receiving a first signal, the first signal being a signal received by the terminal device after a second signal sent by a network device passes through a wireless channel; receiving first information, the first information being used for indicating a first channel estimation parameter, and the first channel estimation parameter including at least one of the following: a matrix norm of a first expression and an iteration step size, wherein the first expression is determined by a precoding matrix, the first signal, and a kth channel estimation value, at least one of the matrix norm and the iteration step size is determined on the basis of the precoding matrix, k is a positive integer less than or equal to T, T is a positive integer, and T is the number of iterations of channel estimation; and on the basis of the first signal and the first channel estimation parameter, performing channel estimation T times to obtain a channel estimation result.

The disclosure provides a data collection method for beam management based on machine learning (ML). At least one wireless communication device includes a combination of base stations and user equipment (UE). The least one wireless communication device executes the data collection method to collect data units for beam management based on machine learning. The data units associate at least one beam with at least one attribute, and each data unit in the data units associates a beam with at least one attribute. A machine learning model is trained by the data units to a machine learning model and outputs a beam operation advice. The machine learning model operates in relation to a channel-estimation-based beam management function that outputs a channel estimation result. A beam operation is performed based on the beam operation advice and the channel estimation result.