737 results about "Channel models" patented technology

The invention relates to a control method of a digital ultrasonic flaw detector calibrating device. The control method comprises the following steps: acquiring shape data and material parameters of a to-be-detected material, calling a matched standard test block, calculating adaptive ultrasonic frequency and flaw detector parameters, creating and solving a multi-objective optimization model, and planning a detection path of the to-be-detected material based on an optimal solution; emitting ultrasonic waves with matched frequency to implant the quantum dot mark array to carry out fluorescence-ultrasonic dual-mode signal correlation calibration; scanning according to a planned detection path, establishing a time-varying channel model to predict a current time delay variation, extracting a fundamental frequency and a harmonic component of a probe excitation pulse by adopting an autocorrelation algorithm to serve as a reference, constructing a state vector of a filter based on the predicted current time delay variation, and performing phase correction on a received signal to obtain a phase correction signal; and analyzing the defect of the to-be-detected material based on the corrected receiving signal. By integrating material parameter dynamic modeling, multi-modal signal collaborative calibration and adaptive path optimization, the detection precision and efficiency under complex working conditions are improved.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

The invention belongs to the field of flight path prediction, and discloses a flight path stability optimization method and system, a storage medium and equipment, and the method comprises the steps: responding to aircraft kinetic parameters, pilot physiological state parameters and three-dimensional space environment parameters which are synchronously collected from multiple sources, and constructing a dynamic fusion data set through timestamp alignment; based on a pre-constructed dual-channel feature extraction architecture, dual-channel features of the dynamic fusion data set are extracted, a first channel analyzes a dynamic coupling relationship in a time dimension through a sparse attention mechanism, and a second channel models a spatial correlation topology of environmental parameters through a graph neural network; a deep reinforcement learning decision engine is constructed by fusing dual-channel features, an environment state-control action-flight path stability mapping strategy is established, and control strategy optimization is driven through a reward mechanism; and generating a multi-parameter collaborative optimization instruction of the flight control plane based on the real-time decision engine. According to the method, the efficiency and precision during flight path prediction can be improved, and then the flight path is optimized.

The invention discloses a 6G digital twinning online channel modeling method, which comprises the following steps of: based on information acquired from an environment data source, carrying out communication scene online identification by utilizing a machine learning model; matching channel parameters of a 6G universal channel model according to the identified communication scene, and mapping the channel parameters to the communication scene; reconstructing the digital twin online channel of the static scene in combination with the obtained channel parameters; dynamically sensing the physical environment, acquiring environment change information, and obtaining dynamic sensing data; and in combination with a static-dynamic mixed channel modeling algorithm and dynamic sensing data, dynamically updating the digital twin online channel of the reconstructed static scene to obtain a new real-time twin channel, and constructing a visual platform of the digital twin online channel. According to the method, the channel characteristics of the existing network communication environment can be reflected in real time, real-time sensing, prediction and decision making can be realized, the pilot frequency overhead is reduced, and the system performance is improved.

The invention provides a channel modeling method and device suitable for satellite-ground communication, and the method comprises the steps: generating a satellite motion track, and setting ground environment parameters and simulation parameters; converting the satellite motion trail from a global coordinate system to a local coordinate system; acquiring a satellite position from the converted satellite motion trail, and performing ray tracing calculation based on plane wave hypothesis to obtain all effective ray paths between the satellite position and the position of a ground receiver; for each effective ray path, calculating large-scale fading caused by a meteorological environment and small-scale fading caused by ground end multipath propagation and multi-end movement; and a channel model is generated by combining large-scale fading and small-scale fading. According to the method, large-scale fading caused by the meteorological environment is modeled, the influence of meteorological attenuation on the multipath receiving power is considered, small-scale fading caused by multipath propagation and multi-end movement at the ground end is modeled, and the modeling accuracy is improved.

The invention discloses a node mode selection and beam forming method in a cooperative communication integrated scene, and belongs to the field of wireless communication. The invention provides a system node mode selection and beamforming scheme capable of obtaining optimal balance between communication and sensing performance, which comprises the following steps of: in the first stage, firstly establishing a channel model and a data transmission model of a system, and deducing an uplink and downlink communication signal to interference plus noise ratio expression and a sensing signal to interference plus noise ratio expression; in the second stage, an expression of maximum ratio transmission (MRT) beam forming, an expression of zero forcing (ZF) beam forming and an expression of conjugate sensing beam forming are derived; in the third stage, a node mode selection and beam forming algorithm based on a multi-agent layered reinforcement learning algorithm is designed; and in the fourth stage, an algorithm is called to perform duplex mode selection and beam vector design on each access node. According to the method, the system efficiency can be improved, and the actual deployment of the cellular-free cooperative communication integrated system is promoted, so that the method has a certain actual value.

Disclosed in the present invention are an age-of-sensing analysis and optimization method for a wireless power supply sensor network. The method comprises the following steps: S1, determining a network model; S2, determining a MAC layer model; S3, determining a channel model; S4, on the basis of a multi-segment formula, fitting a nonlinear energy-receiving model, and calculating the decoding success probability of a single sensor; S5, calculating the possibility of information being successfully received in a fusion center under a sensing information requirement threshold; S6, using a hypothesis testing theory to model a fusion-sensing reliability model, so as to obtain the probability of credible fusion; S7, by means of a joint sensing-transmission success possibility, acquiring a closed-form expression of age of sensing, and formulating an information timeliness optimization problem; and S8, respectively optimizing a sensor power supply duration, a fusion information demand threshold, and sensor deployment, so as to give a minimum age of sensing. By means of the present invention, age of sensing is optimized on the basis of the analysis thereof, thereby improving the timeliness of sensing information.

The invention relates to the technical field of microwave communication, and discloses a microwave communication data transmission method, which comprises the following steps: S1, sensing real-time rainfall intensity and atmospheric turbulence intensity parameters through combination of a multi-band radar and a meteorological sensor; s2, establishing a fractional order random channel model of a rainfall and turbulence coupling effect; s3, generating a dynamic coded modulation control strategy based on deep fractional order reinforcement learning; s4, optimizing a programmable metasurface reflection coefficient matrix according to the dynamic coded modulation control strategy and a channel state to reconstruct an electromagnetic propagation path; s5, executing power-codebook joint optimization of space-time coding and non-orthogonal multiple access; and S6, the system stability is monitored on line, and a degradation fault-tolerant mechanism is triggered. According to the invention, a Ku / W dual-band radar and meteorological sensor variational Kalman fusion technical scheme is adopted, and compared with a single-band radar or sparse meteorological station isolated observation scheme in the prior art, the problem of signal sudden drop misjudgment in a rainstorm scene is solved.

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 discloses a digital twinning synchronization-oriented queue sensing cloud edge collaborative reasoning method, which comprises the following steps of: S1, establishing a reasoning task model according to a processing requirement of data acquired by sensing equipment; s2, establishing a wireless channel model according to a wireless communication process between the sensing device and the edge server and a wireless communication process between the edge server and the cloud server; s3, establishing a task queue model according to the processing process of each task in the queue buffer area of the edge server; s4, establishing a collaborative reasoning model according to the collaborative reasoning process of the reasoning task on the edge server and the cloud server; s5, constructing an optimized objective function and a Markov model according to the low-delay demand of digital twin synchronization; and S6, a sensing device intelligent agent and an edge server intelligent agent are established by using an MAPPO algorithm, the two intelligent agents are trained based on a Markov model, and a current strategy is updated. According to the invention, the waiting time delay of priority tasks can be reduced, and the digital twinning synchronization efficiency is improved.

The invention discloses a rate splitting assisted near-field flux-sensing integrated hybrid beam forming method, and relates to the technical field of wireless communication and novel multiple access. The designed hybrid beam forming method comprises the following four steps: S1, constructing a near-field communication and sensing integrated network with a hybrid beam forming architecture, and establishing a near-field communication and near-field sensing channel model; s2, the base station sends required data to a communication user by using a rate splitting multiple access technology; s3, the base station collects echo signals reflected by the sensing target to carry out target detection; s4, establishing an optimization problem aiming at meeting the target sensing requirement and maximizing the communication rate, and designing a double-layer rotation optimization algorithm by using penalty dual decomposition, weight minimum mean square error and quadratic transformation methods to jointly process variables such as a receiving filter, digital beam forming, analog beam forming and common rate allocation; according to the invention, the communication and sensing rate of the communication and sensing integrated network can be effectively improved.

The invention relates to the technical field of intelligent communication, and discloses an end-to-end optimization method, a signal constellation geometry and probability joint shaping optimization method in an end-to-end intelligent communication system, and a communication device based on a neural network. According to the method, a transmitter is constructed by using a neural network, and a received signal passes through a two-stage equalization processing system to form an end-to-end optimization framework without assistance of a channel model. The transmitter adopts a modular design, can realize joint optimization of constellation probability shaping (PS) and geometric shaping (GS), and performs joint updating on all trainable parameters of the system based on a total loss function containing an equilibrium error and a decoding error through a back propagation algorithm. According to the invention, computing resources are effectively saved; the two-stage equalization system can accurately compensate channel damage, the realized PS and GS joint optimization can significantly improve the performance of the communication system and reduce the bit error rate, and the method has good performance advantages and practical value in an actual optical fiber communication system.

The invention provides a low-orbit satellite beam scheduling optimization method and system based on a simulated annealing algorithm, and belongs to the technical field of satellite communication. Firstly, modeling is carried out on a determined satellite communication system model and communication service distribution in a satellite coverage range, then a channel model between a satellite and a communication terminal is established, then constraint conditions of a problem are determined, mathematical modeling is carried out on the optimal beam scheduling problem, and the optimal beam scheduling problem is obtained. In order to simultaneously improve the coverage communication service volume and reduce the inter-beam interference, the beam scheduling problem is solved by using the simulated annealing algorithm, the optimal design of the beam pattern is realized, and the comprehensive performance of the low-orbit satellite communication system is improved.

The invention relates to the technical field of three-dimensional model design of molds, and provides a mold three-dimensional high-precision modeling method and system based on multi-modal data fusion, and the method comprises the steps: collecting an RGB image, a high dynamic range image and a structured light depth image of an entity mold, and carrying out the reconstruction to obtain a globally consistent dense point cloud; denoising and semantic segmentation are carried out on the point cloud, and a cavity function area, a core function area and a runner function area are recognized; performing differential parametric modeling based on an identification result: performing geometric primitive fitting on regular geometric features, performing NURBS curved surface reconstruction on a free-form surface, and performing swept volume or rotator fitting on a flow channel to obtain a parameterized geometry, a parameterized curved surface and a parameterized channel model; and applying boundary continuity constraint to fuse the parameterized geometry, the parameterized curved surface and the parameterized channel model, and constructing a hybrid parameterized three-dimensional model. According to the method, rapid reconstruction of a high-precision, high-semantic and full-parameterized three-dimensional model of an entity mold can be realized.

The invention discloses a communication data transmission method and system for an intelligent charging pile, and relates to the technical field of intelligent charging pile communication, and the method comprises the steps: constructing a reverse power line transmission channel model based on an equivalent channel response matrix, extracting channel frequency response, generating a channel equalization configuration parameter set through a joint optimization equalization algorithm, and transmitting the channel equalization configuration parameter set to a communication module. Subcarrier modulation orders are dynamically allocated according to the signal-to-noise ratio parameter, and a pre-balanced OFDM time domain signal is generated; on the basis of the channel equalization configuration parameter set and the pre-equalization OFDM time domain signal, dynamically monitoring power frequency harmonic noise of the cable, and inhibiting interference through a filtering algorithm to generate a clean power line carrier signal; and generating a verified V2G communication control instruction through a cyclic redundancy check method based on the clean power line carrier signal in combination with the main polarization direction feature vector. According to the method, an accurate channel model is constructed by combining an equivalent channel response matrix and cable characteristics, and dynamic subcarrier distribution is realized based on signal-to-noise ratio parameters.

The invention discloses an RIS division and resource optimization method for an RIS-assisted millimeter wave NOMA system. The method comprises the following steps: constructing a downlink millimeter wave NOMA multi-user system model; wherein the system model comprises a base station, an RIS and K terminal devices, and the K terminal devices are divided into I clusters; constructing an RIS model, a channel model and a transmission model according to the system model; determining an optimization problem according to the constructed model; and on the basis of the optimization problem, performing joint optimization on terminal equipment grouping, RIS division, active beam forming, passive beam forming and power distribution so as to maximize the total achievable rate of the terminal equipment in downlink transmission. The method overcomes the problems of limited transmission distance and signal shielding of millimeter wave communication, meets the large-scale connection requirement, improves the system performance, and can be widely applied to the technical field of wireless communication.

The invention discloses a dynamic channel transmission characteristic optimization method for a boundary layer meteorological observation aircraft, and the method comprises the steps: supposing that a direct path between a transmitting end and a receiving end is shielded by an obstacle in the air-to-ground communication in a boundary layer in which the meteorological observation aircraft participates; and one part of the signal sent by the transmitting end is reflected to the receiving end through the moving scatterer, and the other part of the signal directly reaches the receiving end without interference. According to the invention, a communication environment between a mobile transmitting terminal and a mobile receiving terminal is described by adopting a geometric stochastic modeling mode, a DRL enabling boundary layer meteorological observation aircraft channel transmission characteristic dynamic optimization method based on geometric characteristics is established, and a nonlinear relationship between motion characteristics of a meteorological observation aircraft and a channel model is revealed. The method has important application value for evaluating the performance of the air-to-ground wireless communication system in the DRL auxiliary boundary layer.

The invention relates to an Internet of Vehicles channel selection method based on a multi-agent depth deterministic strategy gradient algorithm, and belongs to the technical field of communication. The method comprises the following steps: establishing an Internet of Vehicles system model, and setting local and global environment variables, a vehicle action space and a reward function of the Internet of Vehicles system model; establishing a dynamic channel model of the Internet of Vehicles based on the system model of the Internet of Vehicles, and determining a state space and steady-state probability distribution under multi-channel combination; a graph neural network GNN is introduced into a multi-agent depth deterministic strategy gradient algorithm, and a GNN-Critic network is established to process dynamic vehicle input information; the intelligent body vehicle learns a channel selection strategy by using a global environment state so as to complete centralized training; and after training is completed, the intelligent body vehicle observes local channel state distribution and executes channel selection. According to the method, the channel utilization rate can be remarkably improved, the vehicle can make a better channel selection decision according to the global environment information, the channel idle time is reduced, and the overall network performance is improved.

According to the large-scale real-time data link layer simulation method, a data link layer simulation system based on an FPGA (Field Programmable Gate Array) is composed of a wireless channel simulation module, a media access control module and a logic link control module; the wireless channel simulation module is used for carrying out modeling simulation on a physical channel transmitted in reality in an FPGA (Field Programmable Gate Array), and the channel distributes control signals to different nodes according to the control signals of the media access control module and transmits high-speed data transmitted by an upper-layer transmitting end back to the media access control module after passing through the simulated channel; the wireless channel simulation module has two functions of quantizing a channel model and quantizing an error rate model; in the quantization implementation of channel simulation, the quantization implementation of a channel model is completed, and the FPGA calculates the SNR in the transmission process through the sending frequency, the distance and the system transmission loss parameters transmitted by the upper layer; and randomly carrying out packet loss on the data according to the PER in quantization implementation of the error rate model so as to simulate real communication.

The invention discloses an optical fiber terahertz communication system optimization method based on artificial intelligence, and belongs to the technical field of communication. According to the method, four-dimensional constellation shaping is constructed by utilizing additional dimensions provided by two independent sidebands, and an end-to-end optimization framework is divided into a training stage and a deployment stage; in the training stage, firstly, an equalizer based on a pruning neural network and an equalized channel model based on a mixed density network are trained in sequence through receiving and transmitting of a random training sequence; then connecting a constellation shaper at a transmitting end with a demapper at a receiving end through an equalized channel model with fixed parameters, thereby considering full-link residual distortion and noise after equalization, and carrying out back propagation gradient to learn a globally optimal four-dimensional joint geometric probability constellation diagram to further approach the Shannon limit; and in the deployment stage, transmission of the bit stream is realized through coordination of the four-dimensional geometric shaping constellation table and the probability distribution obtained in the training stage and an equalizer at the receiving end and a demapper.

The invention discloses a multi-antenna joint precoding Gbit wireless transmission system, which relates to the technical field of wireless communication and comprises a frequency spectrum channel acquisition module, a signal preprocessing module, a precoding joint optimization module, a transmission performance evaluation module and a closed-loop parameter control module. The spectrum channel acquisition module acquires signal data and evaluates a transmission bit error rate and a signal-to-noise ratio by using a spectrum analyzer; the signal preprocessing module performs denoising and normalization and constructs a channel model; the precoding joint optimization module dynamically adjusts precoding and suppresses bit error diffusion; the transmission performance evaluation module calculates a data rate and optimizes protocol design; and the closed-loop parameter control module dynamically configures system parameters to guarantee closed-loop optimization. According to the method, the transmission efficiency and the stability of a Gbit wireless transmission system are remarkably improved, a complex channel environment is effectively dealt with, the bit error rate is reduced, the data rate is improved, a transmission protocol is optimized, intelligent dynamic adjustment of system parameters is realized, and the optimal performance of a multi-antenna joint precoding technology is ensured to be exerted.

The invention provides a multi-user near-field secure communication method based on stacked intelligent metasurfaces, and relates to the technical field of wireless communication. The method comprises the following steps: obtaining an equivalent beamforming matrix and a first-layer channel transmission matrix of an SIM according to parameters of the SIM in a base station; according to the transmission distance between the SIM and the user and the transmission distance between the SIM and the eavesdropper and the parameters of the SIM, modeling a near-field channel to obtain a near-field channel model; obtaining a data transmission rate in combination with a beamforming vector of the base station, and further determining the security and the rate; and by taking maximization of the safety and the rate as a target, constructing an optimization model and decomposing the optimization model into two sub-problems to carry out alternate iterative optimization so as to obtain an optimized equivalent beam forming matrix and an optimized beam forming vector. An SIM is introduced, the freedom degree of beam regulation and control is increased, optimization is carried out on the basis of a near-field channel model by taking maximization of safety and rate as targets, legal users and eavesdroppers are effectively distinguished, signal energy is accurately guided to the legal users, and the communication safety is improved.

The invention discloses an Internet of Vehicles channel modeling method considering antenna attitude vibration, and the method comprises the steps: building an accurate vibration model of an antenna three-dimensional attitude through precisely modeling vehicle body tiny vibration caused by bumping, steering and other factors in vehicle driving into micro-motion of an antenna attitude at three time-varying Euler angles of pitching, rolling and yawing; the time-varying attitude is introduced into a coordinate system rotation matrix, the random vibration characteristic parameterization of the antenna in a three-dimensional space is realized, and then the random vibration characteristic parameterization is coupled to the evolution of key parameters of a channel model, so that the channel rapid fluctuation and beam mismatch effect caused by the antenna attitude jitter can be more accurately reproduced; the method can effectively solve the problems that in the prior art, due to the fact that microcosmic vibration of an antenna is ignored, the fidelity of a model is low, and characterization of a key beam mismatch effect in millimeter wave communication is seriously insufficient, and can provide theoretical basis and data support for performance evaluation and beam management algorithm design of a high-dynamic and high-reliability car networking system.

The invention relates to the technical field of radar reflectivity factor calibration, and discloses a terahertz cloud measurement radar calibration method based on channel perception, which comprises the following steps: S1, acquiring two types of input data; s2, constructing a multi-scale channel model; s3, constructing a distance error estimation model; s4, constructing a dynamic time correction model; and S5, constructing a calibration module. According to the method, a high-precision physical channel model is constructed to independently predict path attenuation of signals, the concepts of gain deviation and path attenuation factors are put forward, the influence of thermal noise attenuated by radar on radar waves in the atmospheric environment is reflected respectively, and a Kalman filtering algorithm is used to estimate the path attenuation of the signals. And filtering errors generated in the time direction, and finally, calibrating the radar reflectivity factor by using the channel data by means of a calibration formula.

Disclosed are a channel estimation method, apparatus and device, and a storage medium, which relate to the technical field of communications, comprising: acquiring a channel parameter and a channel coefficient matrix of a pilot symbol based on received signal data, the channel parameter being a channel model parameter of non-ground network communication; determining a first wiener filter tap coefficient from a preset typical wiener filter tap coefficient set according to the position combination of the channel parameter and the pilot symbol; and determining a time domain channel estimation result of the received signal data according to the first wiener filter tap coefficient and a channel coefficient matrix of a pilot symbol. According to the technical scheme, the channel estimation process in a non-ground network communication scene is simplified, and the real-time performance of time domain channel estimation is improved on the premise of ensuring the accuracy of a channel estimation result.

The invention discloses a large-scale antenna for RFID (Radio Frequency Identification) tag inventory, which relates to the technical field of wireless communication, extracts channel state information by using a communication link, extracts main characteristics from a sparse channel through a compressed sensing technology, constructs a channel model, effectively captures a signal path, a multi-path attenuation characteristic and noise intensity, and improves the performance of the antenna in a dynamic environment. According to the signal strength and return delay of the tag, dynamically dividing the inventory area, and adjusting the phase and amplitude distribution of the antenna array through real-time feedback, so that the beam coverage is matched with the channel characteristics, and the signal focusing capability of the target area is enhanced; a recurrent neural network (RNN) is introduced to analyze CSI information and feedback signals, multi-path interference and effective signals are separated by learning time sequence features, independent responses of tags are extracted, and the influence of reflected signal interference on tag identification is reduced; the identification success rate of the weak signal label is improved by preferentially processing the label with the weak signal or the shielded label and combining a dynamic recording and alarming mechanism.

The invention relates to the technical field of beam forming, in particular to a flow antenna assisted communication sensing integrated beam forming method and device, and the method comprises the steps: building a flow antenna FAS communication channel model, and constructing a radar sensing model employing multiple input multiple output (MIMO) at a base station end; the method comprises the following steps of: establishing an optimization problem of combining port selection and beamforming under the constraint of meeting a minimum communication SNR (Signal to Noise Ratio) requirement, a minimum sensing SNR requirement and a limited port activation number by taking minimization of system transmitting power as a target; and setting constraint conditions, solving a target problem, and obtaining a communication-sensing integrated beam forming matrix. Therefore, the problems that performance coupling is aggravated and the joint design complexity is high due to the fact that the tradeoff of the communication rate and the sensing precision in the ISAC and optimization variables are increased through FAS dynamic port switching, and the performance potential is difficult to play due to the fact that the FAS extra freedom degree is not effectively combined with ISAC beam forming and waveform design are solved.

The invention discloses a target detection and channel estimation method for a near-field sensing integrated system. The method comprises the following steps: a base station sends a downlink pilot signal to a sensing target and receives a corresponding echo signal; then, a user sends an uplink pilot signal to estimate a user channel; according to the characteristics of a near-field channel, a sensing and communication signal model based on a spherical wavefront hypothesis is established; according to the characteristics of a near-field channel, firstly, a near-field channel model is approximated by adopting Fresnel approximation, then a low-overhead dictionary based on near-field spherical wave approximation is provided, and on the basis, an improved orthogonal matching pursuit algorithm is designed for near-field communication perception to estimate parameters of a perception target and a communication channel. According to the invention, parameters of a near-field sensing target and a channel can be estimated with low overhead, and the estimation precision of the parameters in a near-field scene is improved.

The invention belongs to the technical field of wireless communication, and particularly relates to a communication method and device based on MQAM-OFDM modulation, a medium and equipment, and the method comprises the steps: generating a random data source, carrying out MQAM modulation, generating a serial modulation signal, carrying out serial-parallel conversion and subcarrier mapping, and obtaining frequency domain subcarrier data; performing inverse fast Fourier transform on the frequency domain subcarrier data to generate a time domain OFDM symbol, and adding a cyclic prefix to obtain a sending signal; transmitting the sending signal based on a multipath channel model; receiving a sending signal and removing a cyclic prefix part in the sending signal to obtain an effective OFDM signal; performing fast Fourier transform on the effective OFDM signal to recover frequency domain data, and obtaining a frequency domain receiving symbol sequence; performing channel estimation and equalization on the frequency domain receiving symbol sequence to obtain a frequency domain symbol estimation value; and performing MQAM demodulation on the frequency domain symbol estimation value, and recovering the parallel data stream into the serial bit stream to complete data recovery.

The invention belongs to the technical field of wireless communication, and discloses a task unloading and wireless energy transmission method and system, and the method comprises the steps: building a communication and calculation model between a UAV (Unmanned Aerial Vehicle) and a ground Internet of Things user, including a time-varying channel model for communication, a WPT (Wireless Power Transfer) channel model for modeling by using a free space loss model, and a WPT channel model for modeling by using a free space loss model; a calculation and transmission model of UAV and ground Internet of Things users; the method comprises the following steps: designing a basic framework for performing task unloading and WPT on a ground Internet of Things user by using UAV under an edge computing scene assisted by the UAV, and performing system modeling according to the framework to obtain a mathematical model of the whole system; designing an optimization problem, and defining a target function as the weighted sum of the total throughput of the system and the energy consumption of the UAV; modeling the whole framework as a Markov process, designing a reasonable state action space, and designing a reasonable reward function by using a target function; and a task unloading decision and wireless energy transmission are optimized while the total throughput of the system and the UAV energy consumption are constrained by using a double-delay depth deterministic strategy gradient algorithm.