Artificial intelligence based directional mesh network design for spectrum efficiency

The paper presents a novel directional mesh network (DMN) design that can distribute the limited radio spectrum resources more efficiently for a DMN by applying artificial intelligence machine learning (ML) techniques. The proposed DMN framework analyzes time-sensitive signal data close to the signal source using fog computing with different types of ML techniques. Depending on the computational capabilities of the fog nodes, different feature extraction methods such as energy detection, match filter, and cyclostationary detection are selected to optimize spectrum allocation. The proposed system also takes the antenna power gain into consideration, which can further reduce probability of detection and interference of the DMN system. Also, the computing nodes send the periodic signal summary which is much smaller than the original signal to the cloud so that the overall system spectrum source allocation strategies are dynamically updated. Instead of just detecting the spectrum holes for secondary users to transmit the signal, the proposed system can optimize the signal transmission path from the cloud to the end user under the interference and relay constraints. The distributed nodes can further improve the strategy based on the sensing information from the fog. Applying fog computing, the system is more adaptive to the local environment and robust to spectrum changes. It will significantly improve the network reliability, resiliency, and flexibility. Designing the proposed system doesn't necessary need change much of the current communication network platform. As most of the signal data is processed at the fog level, it further strengthens the system security by reducing the communication burden of the communications network.