RIS-Aided Channel Construction in Random Opportunistic Networks

Abstract

Random opportunistic networks are dynamic, resulting in nodes not being able to sense the state of the network, and the network topology of nodes changes all the time. Therefore, this paper proposes a RIS-aided channel construction algorithm, which can be used to maintain and change the topology of random opportunistic networks. A machine learning algorithm with spatio-temporal feature fusion is first used to predict the current position of the node, and finally the RIS-aided channel construction is implemented based on the predicted position. The simulation experiments show that the algorithm can find the optimal path between the target node and the source node in the presence of errors in the target node.