The Research On Efficient Intelligent Access Mechanism In UAV Ad Hoc Network

In recent years,the rapid development of Unmanned Aerial Vehicle(UAV)technology and the rise of multi-UAV collaborative applications have triggered the research of UAV Ad Hoc Network(UAVNET)in academia and industry.UAVNET has many characteristics that are different from traditional ad hoc networks,which makes it highly applicable in the military and civilian fields,but also brings many technical challenges,especially the MAC layer needs to implement efficient and intelligent channel access mechanisms.The MAC protocols of traditional ad hoc networks have their advantages and disadvantages,and they need to be targeted for improvement when applied to UAV ad hoc network scenarios.The rise of artificial intelligence in recent years has provided new ideas for solving various problems in the communication field.Inspired,we can try to optimize the MAC protocol in the UAVNET scenario with some intelligent means.This article is based on this idea to study the efficient intelligent access mechanism in UAV ad hoc networks.The first part studies the random competition mechanism in the UAVNET scenario.Research shows that competitive MAC protocols are more suitable for highly dynamic UAV ad hoc networks,and collision problems are the key factor affecting the performance of such protocols.To address this problem,we introduce the interference model of UAV ad hoc networks,and model the channel competition problem as a Markov decision process.Then we proposed a channel access mechanism based on reinforcement learning.In this mechanism,The UAVs are modeled as decision-making agents without priori information about the network(e.g.,the number of nodes,other agents’ strategies).Individual UAV agents learn their optimal strategy by using the historical sensory information including the number of collisions or successful transmissions.Finally,we verified the effectiveness of the proposed algorithm through simulation.Finally,we verified the effectiveness of the proposed back off algorithm through simulation experiments.The numerical results show that the intelligent back off mechanism proposed in this paper has good adaptability and can effectively improve the channel utilization and reduce the average back off delay of the nodes compared with the traditional back off algorithm in the traditional network.The second part studies the back off mechanism in the UAVNET scenario.In the reservation-based competition MAC protocol,the carrier sensing multiple access mechanism with collision avoidance is the most widely used access method.In this mode,the performance of the network mainly depends on the size of the competition window and the quality of the back off strategy.In view of this problem,we model the competition window adjustment problem as the MDP process.Based on the theory of reinforcement learning,we propose a reinforcement learning based back off algorithm.Finally,we verified the effectiveness of our proposed back off algorithm through simulation experiments.The numerical results show that compared with the traditional back off algorithm in the network,the intelligent back off mechanism proposed in this paper has good adaptability,which can effectively improve the channel utilization and reduce the average back off time of nodes.Delay.The third part studies the multi-packet receiving mechanism in the UAVNET scenario.The emergence of Multi Packet Reception(MPR)has changed the traditional channel conflict model and brought new ideas to the design of the MAC channel access mechanism of the ad hoc network.Based on this,we set up the corresponding access control model and power control model according to the multi-packet reception scenario of the UAV ad hoc network,and establish the optimization equation with the goal of maximizing the system throughput.Then we analyze the advantages and disadvantages of handshake-based MPR mechanism,and proposed a multi-packet receiving mechanism based on LSTM neural network predicting channel state information to optimize the distributed access control of MPR technology.Simulation results show that the proposed prediction-based MPR scheme can Effectively improve the throughput of UAVNET.