6 results about "Poisson point process" patented technology

The invention discloses a transmission optimization method of an NOMA-assisted LEO satellite communication system, and relates to the technical field of satellite communication and wireless networks. Comprising the following steps: (1) constructing a satellite-ground integrated network transmission model, modeling distribution of ground users and low earth orbit satellites into a spherical Poisson point process, and realizing efficient access of nearest users and farthest users based on an NOMA technology; (2) proposing a three-dimensional to two-dimensional topological conversion method, defining a two-dimensional annular region through a density equivalent condition, and simplifying the system analysis complexity; (3) deriving a closed expression of a user association probability and a moment of a conditional success probability, and quantifying link reliability through Beta distribution approximation signal to interference plus noise ratio element distribution; and (4) the satellite orbit height, the satellite density and the power distribution factor are jointly optimized, and the user coverage probability is maximized. According to the method, the analysis complexity of a large-scale LEO system is remarkably reduced, the spectrum efficiency is improved through the NOMA technology, and the method is suitable for performance optimization of a 6G global satellite communication network.

A residual life prediction method based on nonlinear wear and random jump comprises the following steps: firstly, constructing a health index of multi-source information fusion, and establishing a composite degradation model integrating a power law time scale function and a non-homogeneous Poisson process so as to simultaneously represent progressive accelerated wear and time-varying burst impact characteristics of equipment; then, designing an improved two-stage parameter estimation strategy, utilizing an expected condition maximization algorithm to process impact frequency hidden variables, identifying time-varying intensity parameters in combination with maximum likelihood estimation, and realizing robust identification of complex model parameters; deriving an approximate probability density function of the residual life based on a first passing time frame and a Gaussian approximation theory, and realizing probabilistic prediction through numerical integration; finally, a sliding window updating mechanism is established, and online self-adaptive adjustment and dynamic prediction of model parameters are achieved. The method is high in nonlinear fitting capability, sensitive in impact capture, high in prediction precision and accurate in uncertainty quantification under complex working conditions and limited observation data conditions.

The invention discloses an energy harvesting D2D communication system long-term energy efficiency optimization method and system based on reinforcement learning and Lyapunov optimization, and relates to the technical field of power control and energy efficiency optimization, and the method comprises the steps: firstly carrying out the modeling of energy arrival and task arrival according to a Poisson process; and calculating the time-varying channel gain according to the path loss and the Rayleigh fading characteristic. And constructing a Lyapunov function to derive a Lyapunov drift term, and embedding the Lyapunov drift term into a reward function of reinforcement learning. In the reinforcement learning part, a self-adaptive double-depth Q network structure based on a Dropout mechanism is adopted, a state vector is used as input, and self-adaptive balance of exploration and utilization is realized through randomization of Q value distribution. And the intelligent agent dynamically adjusts the transmission power of the EH-D2D system according to the optimal action output by the training network, realizes the stability of parameter updating through the target network, and realizes long-term energy efficiency optimization and queue stability in an energy collection environment.

The invention provides a neural mimicry retina-imitating sensing module bionic pulse coding method and system, and relates to the technical field of retinas, and the method comprises the steps: carrying out the asynchronous event extraction of a light intensity change signal based on a time difference mechanism, and obtaining a discrete event stream containing time-space coordinates and polarity information; performing space-time correlation analysis based on a local receptive field on each event, constructing a space-time neighborhood window, extracting a time interval distribution feature and a space topological relation feature, and generating a space-time feature vector; mapping the vector to pulse distribution probability distribution to form a bionic pulse coding sequence conforming to the statistical characteristics of the Poisson process; and dynamically adjusting space-time neighborhood window parameters according to the time domain density distribution of the pulse sequence to realize adaptive processing. According to the method, an information coding mechanism of a biological retina can be simulated, the bionic property and the calculation efficiency of a neural mimicry system are improved, and the self-adaptive processing capability on a dynamic scene is enhanced.

The application provides a radar radiation power control method, device and equipment based on MPC. The method comprises the following steps: establishing a target model; the target model comprises a target radiation model and a target state model, wherein the process of the target radiation signal is subject to a Poisson process; under a radar networking architecture, a measurement model is constructed through the target state model; at a preset decision time, the measurement model, an MPC algorithm and a PCRLB are used to measure tracking accuracy, and it is judged whether the center station in the radar networking architecture needs to be tracked actively and a radar radiation control result is obtained. In the application, the tracking accuracy and the anti-interference capability are effectively improved by combining the target radiation model, the target state model and the measurement model, the MPC algorithm and the PCRLB are used to measure the tracking accuracy on this basis, the optimization of the active tracking decision process is realized, and the resource utilization rate and the tracking effect are improved.