Dual-functional microwave photonic system for concurrent radar and secure communication via radar signal masking

Integrating functions such as radar and communication into a single system is of great significance for the miniaturization and functional integration of future electronic warfare and 6G systems. Here, we show a dual-functional microwave photonic system for concurrent radar and secure communication. The scheme utilizes microwave photonic frequency multiplying and frequency conversion techniques to shift both the intermediate frequency radar and communication signals to the same frequency band, enabling radar and communication operations at the same time and frequency. The high-power radar signal is also used to mask the communication signal, increasing the difficulty of signal interception and thus enhancing security. By employing de-chirping at the radar receiver and self-interference cancelation at the communication receiver, the radar function can be implemented and the communication signal can also be correctly demodulated after removing the radar masking. An experiment is performed. A 0.3-GHz bandwidth linearly frequency-modulated signal is quadrupled and superimposed with two up-converted 0.5-Gbaud orthogonal frequency-division multiplexing signals. A communication data rate of 2 Gbit/s, a radar ranging measurement error of less than $\pm$ 0.3 cm, and a radar inverse synthetic aperture radar imaging resolution of 12.5$\times$10.2 cm are achieved.

Principle Driven Parameterized Fiber Model based on GPT-PINN Neural Network

In cater the need of Beyond 5G communications, large numbers of data driven artificial intelligence based fiber models has been put forward as to utilize artificial intelligence's regression ability to predict pulse evolution in fiber transmission at a much faster speed compared with the traditional split step Fourier method. In order to increase the physical interpretabiliy, principle driven fiber models have been proposed which inserts the Nonlinear Schodinger Equation into their loss functions. However, regardless of either principle driven or data driven models, they need to be re-trained the whole model under different transmission conditions. Unfortunately, this situation can be unavoidable when conducting the fiber communication optimization work. If the scale of different transmission conditions is large, then the whole model needs to be retrained large numbers of time with relatively large scale of parameters which may consume higher time costs. Computing efficiency will be dragged down as well. In order to address this problem, we propose the principle driven parameterized fiber model in this manuscript. This model breaks down the predicted NLSE solution with respect to one set of transmission condition into the linear combination of several eigen solutions which were outputted by each pre-trained principle driven fiber model via the reduced basis method. Therefore, the model can greatly alleviate the heavy burden of re-training since only the linear combination coefficients need to be found when changing the transmission condition. Not only strong physical interpretability can the model posses, but also higher computing efficiency can be obtained. Under the demonstration, the model's computational complexity is 0.0113% of split step Fourier method and 1% of the previously proposed principle driven fiber model.

Fiber Transmission Model with Parameterized Inputs based on GPT-PINN Neural Network

In this manuscript, a novelty principle driven fiber transmission model for short-distance transmission with parameterized inputs is put forward. By taking into the account of the previously proposed principle driven fiber model, the reduced basis expansion method and transforming the parameterized inputs into parameterized coefficients of the Nonlinear Schrodinger Equations, universal solutions with respect to inputs corresponding to different bit rates can all be obtained without the need of re-training the whole model. This model, once adopted, can have prominent advantages in both computation efficiency and physical background. Besides, this model can still be effectively trained without the needs of transmitted signals collected in advance. Tasks of on-off keying signals with bit rates ranging from 2Gbps to 50Gbps are adopted to demonstrate the fidelity of the model.