Research On Anti-iamming Methods For Unmanned Aerial Vehicle Networks

In the modern informational battlefield,the unmanned aerial vehicle(UAV)has been widely used for its advantages such as no casualties,low cost,and good maneuverability.In order to complete more complex network tasks,the UAV group can independently construct various communication networks,however the open characteristic of wireless network makes it vulnerable to the j amming attacks.All types of jamming attacks pose severe threats to the communication quality of wireless networks.Therefore,the anti-jamming method of the UAV network has become one of the key research directions today.The existing research on anti-jamming methods of academic study mostly focuses on simple scenarios in wireless networks.However,the actual UAV network has the characteristics of high-speed movement,limited energy and variable channel,which poses new challenges to anti-jamming research.In order to solve the above problems,several UAV network models are proposed and the anti-jamming methods are studied to improve the payoff of UAV.A single-channel and multi-channel UAV network is proposed.This paper uses game theory to model the strategy adjustment between the UAV and jammer in UAV network as Stackelberg game.By analyzing and deriving the game equilibrium solution,the optimal transmission power of UAV against jammer is obtained.Under incomplete information,the Q-learning algorithm is used to derive the optimal anti-jamming strategy by iterative learning.Then a relay-assisted anti-jamming model based on pricing-purchase mechanism is proposed in the multi-channel UAV network and the optimal anti-jamming strategy of Stackelberg game is derived.The simulation results verify that the proposed anti-jamming method is superior to other anti-jamming methods in terms of payoff,and show the influence of distance and other parameters on anti-jamming performance.A point-to-point and distributed scenario is constructed for anti-jamming method research,the algorithms based on the Markov decision process and autonomous learning are proposed to obtain the optimal strategy to avoid being j ammed.Considering the UAV network is likely to become spectrum scarcity,introducing cognitive radio technology into the UAV network,all UAVs access the network as secondary users and apply the proposed algorithm to select channels intelligently to avoid being jammed.Under the proposed autonomous learning strate’gy,all UAVs have the ability to predict and evade jammers,and try to avoid collision between the used channel and other UAVs.The simulation results verify that the proposed algorithm can effectively improve the anti-jamming utility of UAV and the proposed cognitive strategy which can effectively improve the utilization of idle channels is better than the random access strategy.That is,each UAV prefers to select a channel with a large idle rate without conflict and avoid being jammed.