Cognitive Conformal Antenna Array Exploiting Deep Reinforcement Learning Method

A cognitive antenna array, which is designed by using deep reinforcement learning (DRL) is proposed in this article to adapt to the complex electromagnetic environment. Specifically, the phased array antenna is utilized as the manipulatable component to achieve the characteristic of beam steering with the help of the DRL algorithm. We begin by establishing a DRL-based framework, which is comprised of a microprogrammed control unit, power divider, digital phase shifter, and the patch antenna array. In the DRL algorithm, the desired beam steering is obtained through trial-and-error interactions with the environment, which is required to observe predefined rewards based on the current state and action. Next, the system is trained to perform beam steering, and a set of hyperparameters of the deep neural network are obtained and stored for practical usage. A good agreement is achieved between the simulated and measured radiation patterns of the planar phased array antenna, which validates the DRL-based phase distribution regulation algorithm. Finally, the algorithm is implemented in the design process of a conformal phased array antenna, and it is shown that the measured radiation performance of the array is satisfactory for different beam scan angles.