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The integration of radar and communication system is attracting considerable interest due to spectral scarcity and increasing demand for unified radar and communication applications. In this paper, four novel schemes combining channel estimation, radar detection, and information exchange using power splitting (PS) and time division (TD) are investigated: channel estimation PS (EPS), communication information exchange PS (CPS), channel estimation TD (ETD), and radar detection TD (RTD). Their achievable rate and tradeoff measure are derived and examined for different parameters. New radar detectors for EPS, CPS, and ETD are also proposed. Numerical results show that the optimal time ratio and power ratio for channel estimation, radar detection, and information exchange exist. Also, the CPS schemehas the best overall performance.

A method based on the deep learning algorithm is proposed to accurately capture the posture of the human body. It is one of the important means to improve athletes’ competitive level in modern sports to accurately analyze the posture of sports training by technical means. Aiming at the application demand of using artificial intelligence technology to accurately analyze and predict the motion training posture, a motion posture analysis and prediction system based on deep learning is designed in this paper. Based on the Arduino embedded development board and equipped with multiple IMU sensors, the scheme established a system to collect accurate human movement data such as speed and acceleration by using stepper motors and obtained accurate human movement data. The experimental results show that these models have been trained with H3.6 m data sets. The sampling frequency was reduced to 25 Hz, and the joint angles were converted into exponential graphs. When the time window covers approximately 1 660 ms, the loop network will be initialized to 40 frames, equivalent to 1 600 ms. For each action, a separate pretrained recursive model is used. It is proved that the method based on deep learning can reduce the prediction error of fine-tuning specific movements and effectively classify and predict the movements not included in the original training data.