Research On Neighbor Discovery Protocol In Unmanned Aerial Vehicle Ad Hoc Networks Based On Joint Radar And Communication

Since unmanned aerial vehicles(UAVs)have the characteristics of concealment,high flexibility and low cost,UAVs are widely used in military and civilian fields.In these communication scenarios,due to the limited energy,high mobility and dynamic topology,there is a strong demand for energy-saving and fast networking to realize collaboration among UAVs.Neighbor discovery is a key forst step in networking,and it’s essential to improve its energy and time effectiveness.Based on the UAV communication under joint radar and communication(JRC),this thesis studies and designs neighbor discovery protocols to improve the efficiency.The specific contents are as follows:1)First,this thesis studies the energy consumption problem of neighbor discovery.Based on the JRC network,a radar detection mechanism is designed,and a sleep state is increased for the nodes.A JRC scan-based neighbor discovery algorithm is proposed.By designing a novel scanning scheme,the JRC-SBA algorithm is extended to three-dimensional(3D)environment.Simulations prove that the JRC-SBA algorithm reduces energy consumption by at least 46.8%,and significantly improves the energy efficiency in 3D.2)Next,this thesis introduces reinforcement learning,designs two learning mechanisms based on Q-value and direct change strategy,gives a computer system for the integrated signal,adjusts the scan strategy,and improves the efficiency of neighbor discovery.Then,simplify the algorithm,and the upper and lower boundary of the expected time are mathematically derived.Fully consider the ratio of radar and communication range(RCRR)in the integrated signal,carry out simulations.Numerical results denote that the performance criterion,such as time delay and discovery rate are improved compared with the classic complete random algorithm(CRA),and the efficiency is improved as RCRR increases.When the RCRR is 0.9,the time efficiency can be improved by about 60%.Finally,the proposed algorithm is extended to 3D scenes,and simulations verified the time effectiveness.3)Finally,aiming at the UAV mobile network,this thesis divides the neighbor discovery into two stages,the initial stage and the continuous stage.In continuous stage,the kalman filter(KF)model is introduced,the position of the UAV is predicted combining with the movement characteristics of the UAV,and a neighbor discovery algorithm based on prediction is proposed.Taking into account the prediction error and the topology changes,a two-stage switching mechanism is designed.Simulation shows that the KF neighbor discovery can improve the efficiency of neighbor discovery by nearly 86.7%,and overcome the long tail problem in traditional CRA.