A scheme of high-resolution inverse synthetic aperture radar (ISAR) imaging based on photonic receiving is demonstrated. In the scheme, the linear frequency modulated (LFM) pulse echoes with 8 GHz bandwidth at the center frequency of 36 GHz are directly sampled with the photonic analog-to-digital converter (PADC). The ISAR images of complex targets can be constructed without detection range swath limitation due to the fidelity of the sampled results. The images of two pyramids demonstrate that the two-dimension (2D) resolution is 3.3 cm × 1.9 cm. Furthermore, the automatic target recognition (ATR) is employed based on the high-resolution experimental dataset under the assistance of deep learning. Despite of the small training dataset containing only 50 samples for each model, the ATR accuracy of three complex targets is still validated to be 95% on a test dataset with the equal number of samples.
Channel estimation is a key technology in MIMO-OFDM wireless communication systems. Increasingly extensive application scenarios and exponentially growing data volumes of MIMO-OFDM systems have imposed greater challenges on the speed, latency, and parallelism of channel estimation based on electronic processors. Here, we propose a photonic parallel channel estimation (PPCE) architecture which features radio-frequency direct processing. Proof-of-concept experiment is carried out to demonstrate the general feasibility of the proposed architecture at different frequency bands (100 MHz, 4 GHz, and 10 GHz). The mean square errors (MSEs) between the experimental channel estimation results and the theoretically simulated ones lie on the order of 10−3. The bit error rates (BERs) are below the pre-forward error correction (pre-FEC) threshold. Besides, we analyze the performance of PPCE under different signal-to-noise ratios (SNRs), baseband symbol forms, and weight tuning precisions. The proposed PPCE architecture has the potential to achieve high-speed, highly parallel channel estimation in large-scale MIMO-OFDM systems after the photonic-electronic chip integration.
VANETs (Vehicular Ad-hoc NETworks) were deemed most suitable communication network for supporting the dissemination of alert messages due to their low dissemination delays as well as extensive vehicle coverage in vicinity of an emergency. With the introduction of cooperative ITS services, it is envisaged that emerging vehicular networks will progressively rely on Vehicle to Infrastructure (V2I) communication lines, which are expected to be nominally accessible with certain temporary as well as time-limited connectivity losses. This study proposes a novel method for VANET-based efficient vehicle clustering and routing based on network infrastructure for high-performance smart transportation. the vehicle clustering using infrastructure-based fuzzy K-means convolutional neural networks. then the energy-efficient cluster-based multi-hop distributed routing. the experimental analysis in terms of latency, network lifetime, throughput, QoS, energy efficiency, and packet delivery ratio. In addition, empirical equations that can be used to predict speed recommendations for drivers are derived from the result.
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