Unmanned aerial vehicle CSMA access method based on adaptive adjustment strategy

Abstract

The invention discloses an unmanned aerial vehicle CSMA access method based on an adaptive adjustment strategy, and the method comprises the steps that an unmanned aerial vehicle agent initializes theaccess state and access strategy of the unmanned aerial vehicle agent before communication connection is built; when a data packet needs to be transmitted, busy and idle conditions of each channel are monitored in the unmanned aerial vehicle ad hoc network; a channel with the highest access success rate is selected from the available channels; the unmanned aerial vehicle samples the current access strategy according to the random strategy distribution and executes the access to the unmanned aerial vehicle ad hoc network; the unmanned aerial vehicle obtains transmission feedback after executing the access strategy, corresponding strategy return is obtained, meanwhile the access state is transferred; the unmanned aerial vehicle updates the access strategy according to the state transition and the obtained instant return; after a period of iterative learning, each unmanned aerial vehicle agent learns an access strategy most suitable for the current unmanned aerial vehicle ad hoc networkenvironment; compared with a traditional access mechanism, the method has the advantages that the low collision rate and high throughput can be realized, and the fairness of each node to channel use is not influenced.

Description

technical field [0001] The invention relates to the field of unmanned aerial vehicle ad hoc networks, in particular to a method for accessing unmanned aerial vehicle CSMA based on an adaptive adjustment strategy. Background technique [0002] UAVs have the advantages of diverse uses, strong flexibility, and autonomous control, and have received extensive attention and development in military and civilian fields. Compared with a single UAV system, the cooperative application of multiple UAV systems has the advantages of stronger survivability, higher scalability, and faster completion of tasks, but it also brings many technical challenges. For collaborative communication between UAVs, how to design an efficient multi-UAV communication network that meets the needs of future development has become an urgent problem to be solved. [0003] Most current research on UAV networks is based on central control scenarios, in which UAVs rely on ground control centers or satellites for c...

AI Technical Summary

Problems solved by technology

[0007] The invention provides a CSMA access method for unmanned aerial vehicles based on an adaptive adjustment strategy, the purpose of which is to solve the technical problems of high access collision rate and poor adaptability in the prior art, and the method can achieve a low collision rate , high throughput and does not affect the fairness of channel usage by drones

Reinforcement Learning (RL) in machine learning is an effective way to solve this kind of stochastic optimization problems. Its solution is based on the Markov decision process (MDP). After an action, you can get a certain reward, and then adjust the follow-up decision according to the reward

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