5 results about "Cyclic correlation" patented technology

This invention discloses a low-power DTMB transmitter carrier recovery and constellation mapping enhancement method and platform, belonging to the field of terrestrial digital television broadcasting technology. The transmitter samples feedback signals from the intermediate frequency (IF), RF preamplifier, or power amplifier output. Based on the PN420, PN595, or PN945 frame header characteristics, it performs cyclic correlation, sliding correlation, or segmented correlation to obtain frequency offset, phase error, and correlation confidence, and drives a digital phase-locked loop to complete transmitter carrier self-calibration. The system generates non-uniform pre-scaling and pre-rotation parameters based on the feedback constellation error, causing the antenna port constellation to converge to a standard DTMB constellation; enhanced constellation mapping is enabled when supported by a dedicated receiver. The low-power control module reduces power consumption and improves transmission quality based on correlation confidence, EVM scheduling correlator, phase calculation, constellation pre-compensation, digital predistortion, and Doherty power amplifier bias.

The application discloses a kind of integrated signal generation and multi-target clustering identification method and device of feeling.This application uses unified AFDM waveform as bottom transmission mechanism, realizes full diversity gain in time-frequency plane by adjusting affine parameter, effectively suppresses the inter-subcarrier interference under high dynamic scene.In the receiving end, the distance-Doppler heat map is reconstructed by fast cyclic correlation radar algorithm, and the original detection point cloud is extracted combined with 2D-CFAR detection.Introduce the DBSCAN clustering algorithm based on dimensionless weight, and the scattered point is condensed into an independent entity target in RDM physical space, and the radial velocity characteristics of clustering centroid are used to construct automatic decision engine, to realize the real-time classification and identification of static obstacles and dynamic moving targets.The application solves the technical pain points of traditional ISAC system in high frequency, high mobility scene, such as sensing point fragmentation and confusion of static and dynamic targets.

A method of detecting multiple radar targets based on joint radar and communication using OTFS signals is provided. The method includes: generating a range-Doppler map (RDM) based on a first delay-Doppler (DD) domain signal and a second DD domain signal, whereby a first time domain signal is generated from an OTFS modulation based on the first DD domain signal and transmitted to multiple radar targets, and the second DD domain signal is generated from an OTFS demodulation based on a second time domain signal received and reflected from the multiple radar targets from the first time domain signal transmitted; and detecting a dominant radar target amongst the multiple radar targets based on the RDM. In particular, the method further includes, for each iteration of a plurality of iterations: modifying a correlation intermediate term associated with the RDM associated with the iteration based on the detected dominant radar target to obtain a modified correlation intermediate term, the correlation intermediate term relating to a cyclic correlation between the first and second DD domain signals; modifying the RDM associated with the iteration based on the modified correlation intermediate term; and detecting a dominant radar target (i.e., a next dominant radar target) amongst the multiple radar targets based on the RDM modified based on the modified correlation intermediate term. There is also provided a corresponding system for detecting multiple radar targets and a corresponding joint radar and communication system for detecting multiple targets.

This application discloses an enhanced high-precision search and capture method for Rowland signals based on convolutional neural networks, comprising: acquiring a target signal sequence within the GRI range of the group repetition period; simulating and generating training data and training a convolutional neural network to obtain a signal search and capture model; using the model to determine whether the signal exists, and if it exists, generating a local pulse group sequence; calculating the maximum value and sampling position of the correlation value sequence through cyclic correlation to determine the signal starting point and complete the search and capture. This method achieves high-precision signal capture under low signal-to-noise ratio by combining neural networks and cyclic correlation.

This invention belongs to the field of large-scale model training and push cluster communication optimization technology, specifically disclosing a network traffic timing orchestration method and system for large-scale model parallel training tasks. The system comprises a topology and traffic acquisition module, a square wave construction module, a conflict calculation module, a scheduling decision module, and an execution module. The method uses sampled cluster communication link occupancy and a link task square wave constructed using the periodicity and temporal dependence characteristics of large-scale model training as input. It utilizes the cyclic correlation between cluster communication link occupancy and the link task square wave to characterize the link conflict state under different timing offsets. By introducing a signal change rate index, it selects the optimal timing offset to orchestrate the training task, thereby improving the utilization rate of link resources. This system can be integrated as a pluggable module into the cluster physical resource scheduler to further reduce cross-node communication conflicts.