432 results about "Channel models" patented technology

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 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 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.

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 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 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 discloses a 5G-TSN network resource scheduling method based on a near-end strategy optimization algorithm, which completes 5G-TSN network resource scheduling by establishing a 5G wireless channel model, constructing a Markov decision process model and realizing action value output and strategy optimization through the near-end strategy optimization algorithm. According to the network resource scheduling method provided by the invention, a dynamic resource allocation strategy is realized based on gating state sensing, ultralow delay services such as robot control instructions and the like can be preferentially scheduled when gating of a time-sensitive network is started, and non-real-time services such as resource transmission video monitoring and the like can be intelligently multiplexed when the gating of the time-sensitive network is closed; the wireless resource utilization rate is effectively improved, meanwhile, it is ensured that data with high real-time performance can be transmitted as soon as possible in a short time, reliable transmission of equipment data in an industrial scene is ensured, scheduling mechanisms of a 5G wireless network and a time-sensitive network are fused, resource scheduling is efficiently carried out, and the real-time performance, reliability and resource utilization rate of data transmission are balanced.

The invention discloses a relay unmanned aerial vehicle track optimization method, which comprises the following steps of: firstly, constructing a lossy relay unmanned aerial vehicle communication system model under finite length coding, and covering communication time slot division, unmanned aerial vehicle flight constraint and a channel model; deducing outage probability expressions of two communication strategies under finite length coding, wherein the outage probability expressions are an independent decoding strategy and a joint decoding strategy respectively; then, a trajectory optimization problem is converted into a Markov decision process (MDP), and a state space, an action space, a reward function and a state transition mechanism are clarified; solving by adopting a joint optimization algorithm based on depth deterministic strategy gradient (DDPG) to realize joint optimization of the unmanned aerial vehicle track and the communication strategy; and finally, outputting an optimal unmanned aerial vehicle track and strategy configuration to provide reference for actual system deployment. The method can effectively reduce the outage probability of the system and improve the communication reliability under the condition of limited length coding, and is suitable for various scenes in 6G and future air-ground cooperative communication networks.

The invention belongs to the technical field of communication, and relates to an unmanned aerial vehicle user association and 3D flight path optimization method for anti-interference communication. The method comprises the steps of firstly obtaining information of the unmanned aerial vehicle, each ground node and an interference node to establish a system model, then establishing a 3D trajectory operation model, a user association strategy model and a sight distance probability channel model of the unmanned aerial vehicle, defining a communication rate, and finally establishing an optimization problem of maximizing the total throughput of the system. An optimization problem is modeled as a Markov decision process, finally a user association and unmanned aerial vehicle 3D trajectory planning network is constructed, and an unmanned aerial vehicle learning optimal strategy and an unmanned aerial vehicle 3D trajectory and user association strategy are obtained through a deep learning framework. According to the method, while the lightweight characteristic is maintained, user scheduling and unmanned aerial vehicle 3D trajectory control are dynamically realized, and the communication performance of an unmanned aerial vehicle auxiliary communication system in a malicious interference environment is remarkably improved.

Aspects of the disclosure relate to computing platforms that utilize machine learning to perform output generation based on intent identification. The computing platform may train intent orchestration models (e.g., intent identification, output generation, or communication channel) using historical data. The computing platform may data corresponding to an individual. Based on the data, the computing platform may select intent identification models, and may use them to identify an intent. Based on the intent of the individual, the computing platform may select engagement output generation models, and may use them to generate a customer engagement output. The computing platform may use a communication channel model to identify a communication channel. The computing platform may send commands directing display of the customer engagement output, which may cause a user device to display the customer engagement output using the communication channel.

The invention discloses a hybrid far and near field communication beam and power joint optimization method based on rate splitting, and relates to the technical field of wireless communication and novel multiple access. The designed hybrid near-far field communication beam and power joint optimization method comprises the following four steps: S1, establishing a far-field user and a near-field user channel model in a hybrid near-far field communication scene; s2, splitting data of near-field users into public information streams and private information streams by using a rate splitting multiple access technology; s3, pre-configuring beams of the public information flow and the private information flow of the near-field user by using a zero-forcing algorithm, so that the far-field user occupies the beams of the near-field user to carry out information transmission; and S4, establishing an optimization problem by taking maximization of the reachable sum rate as a target, and designing an iterative optimization algorithm to jointly process beam allocation of the near-field users and transmission power allocation variables of the near-field users and the far-field users and the near-field users. According to the invention, the throughput and connectivity of the communication network can be obviously improved.

The application discloses a scene adaptive channel modeling method based on 6G full coverage scene classification. By optimizing the channel model parameters using measurement or simulation data, a new architecture design and adaptive channel modeling for 6G full coverage scene conversion are realized. Including 1) based on the physical definition of 6G space-air-ground-sea full coverage scene, using the combination scheme of environmental parameters and statistical characteristics for scene conversion classification. 2) according to the scene classification result, a scene model parameter database is constructed. 3) input the model parameters corresponding to the scene into the 6G universal channel model to generate the real-time channel model of scene conversion. 4) according to the measured data, the parameters of the universal channel model are adjusted by adopting closed-loop feedback, and the optimized model of scene conversion is obtained by iteration. 5) verify the accuracy of specific scene adaptive channel modeling. For complex and diverse 6G communication environment, adaptive construction of high-precision channel model lays an important foundation for wireless communication research and development.

The invention discloses an emergency multi-unmanned aerial vehicle distributed service quality assurance method and system based on private flow sensing integrated design. The method comprises the following steps: establishing an equivalent Nakagami-channel model for a physical channel of a base station-unmanned aerial vehicle link in an emergency scene, establishing a signal model based on rate division multiple access, and calculating effective capacity; establishing a sensing signal model, and constructing an extended effective capacity of the fusion communication and sensing performance indexes; constructing a joint optimization problem; designing a dynamic weighted federated learning (DW-FL) algorithm, and solving a joint optimization problem; according to the method, the private flow of rate segmentation multiple access is designed into the sensing dual-function signal, and the federated learning DW-FL distributed algorithm for dynamic weighting based on the performance contribution degree is combined, so that the sensing resources of the multi-unmanned aerial vehicle network can be subjected to high-time-efficiency collaborative optimization and distribution in an emergency scene with limited resources, and the resource utilization rate of the multi-unmanned aerial vehicle network is improved. And the total effective capacity of the system is maximized while the dual service quality of communication delay and perception probability is ensured.

Disclosed in the present application are a resource scheduling method and apparatus, and a device, a medium and a product. The method comprises: on the basis of a pre-established channel model of a network-assisted free duplex architecture under a cellular access network, obtaining channel state information between each user equipment and an edge distribution unit (EDU), channel state information between an uplink user equipment and a downlink user equipment, and channel state information between an uplink access point and a downlink access point; with the aim of maximizing the total system spectral efficiency, determining an association condition between the EDU and an access point by using a discrete differential evolution algorithm; determining the operating mode of each access point by using a game theory algorithm; and determining an association state between the EDU and a user equipment on the basis of a backhaul capacity constraint and a reinforcement learning algorithm. The present application solves the technical problems in the prior art of small coverage and poor communication quality that are caused by centralized processing of APs in NAFD, a waste of resources and interference that are caused by the incapability of flexibly expanding operating modes of the APs, and high backhaul consumption caused by centralized processing of all data in a CPU.

The invention provides a deep learning channel estimation method based on spatial perception interpolation, and belongs to the technical field of wireless communication and artificial intelligence fusion. The method comprises the following steps: firstly, constructing a pilot frequency index set according to sparse distribution of pilot frequency points, and mapping the pilot frequency index set to a two-dimensional subcarrier-symbol grid; then generating a centrosymmetric and edge-attenuated space weighting matrix alpha (x, y), and adjusting interpolation weight distribution by introducing a position-related Gaussian weighting function; complex field interpolation is carried out on the pilot frequency observation value in combination with a space weighting radial basis function, and a complete channel initial estimation matrix is constructed; and then decomposing the interpolation result into a real part, an imaginary part and a spatial weighting coefficient, constructing a three-channel tensor as input, sending the three-channel tensor into a convolutional neural network for refined estimation, and outputting a final complex channel estimation result. In a preferred embodiment, the convolutional neural network adopts an SRCNN structure, and inputs a real part, an imaginary part and a spatial weighting matrix alpha (x, y) including an interpolation channel to enhance the spatial perception ability of the model. Simulation results show that under a VehA standard channel model, the method is always superior to a traditional LS method under the sparse pilot frequency condition, the performance of the method is close to that of an MMSE method, higher estimation precision and higher robustness are shown, and the method is suitable for a channel recovery task in a high-speed dynamic wireless communication system.

The invention discloses an RIS-assisted millimeter wave satellite communication system channel estimation method based on PARAFAC. The method comprises the following steps: constructing an RIS-assisted millimeter wave satellite communication system cascade channel model; the method comprises the following steps: modeling received signals of an RIS-assisted satellite communication system into a third-order parallel factor (PARAFAC) model through data reconstruction based on a multi-dimensional structure of an RIS-assisted millimeter wave satellite communication system channel; and performing parallel factorization on received signals of the RIS-assisted satellite communication system by using an alternating optimization method to obtain an estimated value of a model factor matrix, and finally reconstructing an RIS-assisted satellite communication channel by using the estimated value of the factor matrix to obtain a channel estimated value. The RIS-assisted millimeter wave satellite communication system channel estimation method can accurately achieve RIS-assisted millimeter wave satellite communication system channel estimation, and the performance of the RIS-assisted millimeter wave satellite communication system channel estimation method is superior to that of a traditional least square method (LS)-based channel estimation method.

The invention relates to a time-varying channel modeling method oriented to a low-orbit satellite scene, and belongs to the technical field of low-orbit satellite communication. In order to solve the problem that low-orbit satellite channel modeling accurately represents time-varying characteristics, a propagation model is provided, and a channel is fitted into a process that a signal reaches a receiving end from a satellite terminal through multiple times of cluster bounce; a parameter drift mechanism is introduced to simulate the trend of channel parameters changing along with time, natural transition and connection between newly generated parameters and parameters in the previous stage are guaranteed, and the continuity of channel parameter change is improved; the dynamic process of generation, development and extinction of the cluster is considered from the perspective of the channel cluster through the birth and death process of the dynamic change of the cluster, and the dynamic change of the cluster in the channel is reflected more truly. On the basis of the method, the characterization capability of a channel simulation framework on the time-varying characteristic can be remarkably enhanced, and a more reliable and more accurate channel model is provided for a low-orbit satellite scene.

The invention discloses a low-voltage power line power frequency communication interference suppression and adaptive demodulation method and system, and the method comprises the steps: firstly, detecting the voltage zero crossing of a power grid in real time, and dynamically calibrating a communication time slot based on an actually measured power frequency, thereby achieving the precise power frequency synchronization; injecting a pilot signal into the synchronous time slot window, and judging line topology mutation through coupling of pilot response and a preset signal-to-noise ratio threshold, thereby completing topology perception and verification; triggering channel reestimation in combination with a topological change result, rejecting fundamental wave and multi-order harmonic interference from channel response of least square estimation, and updating a channel model by adopting a time-varying smoothing algorithm; then, a low-noise time window is determined according to the corrected zero cross point, key data packets are scheduled preferentially, and high-reliability anti-interference transmission is achieved; and finally, dynamically and cooperatively adjusting the transmitting power, the receiving filter and the phase compensation based on the newest channel and topology state to form closed-loop self-adaption. According to the scheme, the intelligent degree is high, and the communication stability and the system maintainability are greatly improved.

The invention discloses an Internet of Vehicles-oriented MLE and UKF combined speed sensing and prediction beam forming method, which comprises the following steps of: establishing a two-dimensional coordinate model of a uniform linear array in a near-field uplink narrow-band transmission scene, and accurately describing a relative position and a motion state between a target and a base station; according to a near-field channel model, calculating propagation characteristics of echo signals received by each antenna unit; a speed estimation problem is converted into an unconstrained optimization problem to be solved by taking minimization of a loss function of observation data as a target, and the speed of a target vehicle is estimated by using received echo signals and target expected position information; taking the speed estimated by the MLE from the echo signal as an observed value of the UKF, and iteratively optimizing the estimation precision of the state of the target vehicle in combination with a state prediction and updating mechanism of the UKF; and according to the optimized motion state of the target vehicle, a beam forming scheme is designed, data transmission is carried out, and communication rates under different conditions are evaluated. According to the invention, prediction precision and communication performance in a mobile scene are effectively improved.

The invention belongs to the technical field of unmanned aerial vehicle millimeter wave communication channel modeling, and discloses an urban low-altitude unmanned aerial vehicle millimeter wave communication channel modeling method under sand and dust weather conditions. According to the method, a three-dimensional geometric random channel model is provided, and scatterers are divided into three types: near-field scatterers at the transmitting end and the receiving end adopt bicylindrical models, suspended dust adopts a complete ellipsoid model, and ground fixed scatterers adopt a lower half ellipsoid model with a larger semi-major axis. On the basis of the model, channel impulse response containing sight distance, primary scattering components and secondary scattering components is derived, and a space-time correlation function and Doppler power spectral density are calculated. And finally, establishing a joint path loss model: on the basis of the two-path fading model, introducing an additional attenuation factor related to the visibility of the sand and dust and the particle charge mass ratio, and determining specific parameters of the factor according to the grade of the sand and dust. According to the method, the channel characteristics can be accurately predicted, and theoretical support is provided for anti-interference design and link optimization of the millimeter wave communication system of the unmanned aerial vehicle.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned communication over multi-input-multi-output (MIMO) channels. One of the methods includes: determining a transmitter and a receiver, at least one of which implements a machine-learning network; determining a MIMO channel model; determining first information; using the transmitter to process the first information and generate first RF signals representing inputs to the MIMO channel model; determining second RF signals representing outputs of the MIMO channel model, each second RF signal representing aggregated reception of the first RF signals altered by transmission through the MIMO channel model; using the receiver to process the second RF signals and generate second information as a reconstruction of the first information; calculating a measure of distance between the second and first information; and updating the machine-learning network based on the measure of distance between the second and first information.

This invention provides a method for planning a wireless network within a high-speed train, belonging to the field of train communication. The method extracts key internal structural factors affecting radio wave propagation within the train by establishing a geometric model of the target scenario, and then constructs a segmented path loss model. It establishes a communication system model and a channel model based on the asymmetrical structural features inside the carriages, deriving mathematical expressions for wireless network coverage performance indicators. Combining the communication quality requirements within the train and relevant model formulas, it determines the maximum coverage distance of radio waves, determines the initial deployment locations of base stations based on the actual layout within the train, optimizes the size of cell overlap areas using wireless network coverage performance indicators, adjusts base station locations, and obtains the final deployment plan. This invention solves the problem that wireless communication technologies with random fluctuation characteristics cannot meet the high communication quality requirements of next-generation train communication networks.

Side-channel analysis systems and related methods are disclosed. A side-channel analysis system is configured to generate a synthetic side-channel signal for a code for a software program using a side-channel model. The code includes human-readable instructions of a human-readable computing language. The synthetic side-channel signal includes an estimate of an actual side-channel signal that would be generated by a computer executing the code for the software program. The synthetic side-channel signal is generated based, at least in part, on token values for the human-readable instructions of the code and the training data. The side-channel analysis system is also configured to analyze the synthetic side-channel signal to detect one or more of anomalies or vulnerabilities of the software program. A method includes training the side-channel model.

The embodiment of the invention provides a steerable antenna-based covert communication system transmission rate enhancement method and related equipment, and belongs to the technical field of wireless communication. The method comprises the following steps: introducing an antenna pointing vector and a deflection angle pair to represent the three-dimensional direction of a single steerable antenna, establishing a channel model related to the antenna deflection angle pair, and deducing a hidden rate expression of a legal user; deducing a hidden constraint condition expression of the illegal user side by using the channel model; according to the concealment rate expression and the concealment constraint condition expression, constructing a joint optimization problem taking maximization of the concealment rate of the legal user as a target and taking concealment constraint, transmitting power constraint and antenna steering angle constraint as conditions; and solving the joint optimization problem to obtain an optimized transmitting beam forming vector and a deflection angle pair of the steerable antenna. The antenna orientation is dynamically adjusted, the spatial degree of freedom is fully excavated, and the transmission rate of the system can be remarkably improved on the premise of strictly ensuring the communication concealment.

This invention discloses an intelligent resource scheduling and interference coordination optimization method and apparatus for unmanned aerial vehicle (UAV) swarms. The method includes determining a channel model and a rate model based on a system model of the UAV swarm serving user system. The channel model represents the free path loss between the UAV and the users it serves. The rate model represents the transmission rate obtained by the users after being allocated a channel. Based on the channel model and the rate model, a time-frequency resource allocation optimization problem is constructed. Using a near-end policy optimization algorithm, the time-frequency resource allocation optimization problem is solved with the maximum transmit power and limited bandwidth of each UAV as constraints, and the goal of maximizing the total transmission rate of users as the optimization objective. This yields a UAV swarm resource allocation scheme, which improves the efficiency of policy improvement while enhancing the stability of convergence, effectively avoiding the curse of dimensionality problem that traditional optimization algorithms may face in high-dimensional environments.

The invention relates to the field of power line channel modeling, in particular to a power line channel modeling method, system, equipment and medium, power line channel data is actually measured and collected and preprocessed, channel characteristics are extracted, parameters such as characteristic impedance and propagation constant of a power line are calculated by using a transmission line theory, and the power line channel is modeled. Constructing an enhanced conditional generative adversarial network comprising a generator and a discriminator, inputting parameters obtained by theoretical calculation of a transmission line as conditional information in a training process, optimizing structures and loss functions of the generator and the discriminator, enabling the generator to generate a high-quality analog channel sample, fusing a deep learning model with a transmission line theoretical model, and obtaining a high-quality analog channel sample; according to the method, a mixed channel model is formed, model parameters are alternately trained and optimized, accurate prediction of transmission characteristics of the power line channel is realized, complex characteristics of the power line channel can be effectively modeled, and a reliable basis is provided for design optimization of a power line communication system.