Mobility assisted spectrum aware routing protocol for cognitive radio ad hoc networks

We propose a mobility assisted spectrum aware routing(MASAR) protocol for cognitive radio ad hoc networks(CRAHNs),providing robustness to primary user activity and node mobility.This protocol allows nodes to collect spectrum information during a spectrum management interval followed by a transmission period.Cognitive users discover next hops based on the collected spectrum and mobility information.Using a beaconless mechanism,nodes obtain the mobility information and spectrum status of their neighbors.A geographical routing scheme is adopted to avoid performance degradation specially due to the mobility of the nodes and the activity of the primary users.Our scheme uses two approaches to fnd either short or stable routes.Since mobility metrics have a signifcant role in the selection of the next hop,both approaches use a reactive mobility update process assisted by mobility prediction to avoid location errors.MASAR protocol performance is investigated through simulations of diferent scenarios and compared with that of the most similar protocol,CAODV.The results indicate that MASAR can achieve signifcant reduction in control overhead as well as improved packet delivery in highly mobile networks.

Radio spectrum awareness using deep learning: Identification of fading channels, signal distortions, medium access control protocols, and cellular systems

Radio spectrum awareness,including understanding radio signal activities,is crucial for improving spectrum utilization,detecting security vulnerabilities,and supporting adaptive transmissions.Related tasks include spectrum sensing,identifying systems and terminals,and understanding various protocol layers.In this paper,we investigate various identification and classification tasks related to fading channel parameters,signal distortions,Medium Access Control(MAC)protocols,radio signal types,and cellular systems.Specifically,we utilize deep learning methods in those identification and classification tasks.Performance evaluations demonstrate the effectiveness of deep learning in those radio spectrum awareness tasks.