Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics.

ABSTRACT: Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature e

SUBMITTER: Xu S 

PROVIDER: S-EPMC10866915 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature