Intelligent reflecting surface‐assisted UAV inspection system based on transfer learning

Abstract

Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

Deep Learning-Based Multi-Feature Fusion for Communication and Radar Signal Sensing

“…As a CVC layer is implemented by two RVC layers, the time complexity of CVC is twice of that in Equation (13). Meanwhile, the time complexity of the DSC layer consists of two parts: channel-wise convolution and 1 × 1 convolution, i.e.,…”

“…Recently, data-driven DL methods have attracted a lot of attention of the researchers in academia [13][14][15][16][17] and strongly promoted the development of modulation sensing technology. In particular, O'Shea et al propose a convolution neural network (CNN) architecture and demonstrate that DL methods outperform several traditional methods in terms of sensing accuracy and inference speed [18].…”