采用神经网络及梯度下降反向传播算法，实现了周期性安全消息发送间隔和发送

Neural network and gradient descent backpropagation algorithm are used to realize the joint adaptive adjustment of periodic safety message transmission interval and transmission power, which can improve the performance of periodic safety message transmission in high-density scenarios and improve the fairness of the system . For traditional data services on traffic channels, this paper proposes a frame length selection transmission strategy to reduce the bandwidth waste and system performance degradation caused by channel switching.

Using neural network and gradient drop reverse propagation algorithm, the adaptive adjustment of periodic security message transmission interval and sending power can be realized, which can improve the performance of periodic secure message transmission in high density scenarios and improve the fairness of the system. For traditional data services on the business channel, this paper proposes a frame length selection sending strategy to reduce bandwidth waste and system performance due to channel switching.

The neural network and gradient descent back propagation algorithm are used to realize the joint adaptive adjustment of the periodic security message transmission interval and transmission power, which can improve the performance of periodic security message transmission in high-density scenarios and improve the fairness of the system. For the traditional data traffic on the traffic channel, this paper proposes a frame length selection transmission strategy to reduce the bandwidth waste and system performance degradation caused by channel switching.<br>