Unmanned aerial vehicle base station communication resource allocation strategy prediction method based on noise optimization

Abstract

The invention discloses an unmanned aerial vehicle base station communication resource allocation strategy prediction method based on noise optimization. The method comprises the following steps: 1, establishing a network model; 2, acquiring training data; 3, performing training iteration to obtain a network optimization parameter and a noise optimization parameter; 4, obtaining an optimized network model; 5, predicting an unmanned aerial vehicle base station communication resource dynamic allocation strategy. The method is simple in step and reasonable in design, the exploration space of the communication resource allocation strategy can be increased, the accuracy of the optimized network model is improved, and the method effectively adapts to the dynamic state of each user so as to predict and output the reasonable communication resource allocation strategy.

Description

technical field [0001] The invention belongs to the technical field of dynamic allocation of communication resources of unmanned aerial vehicle base stations, in particular to a method for predicting the allocation strategy of communication resources of unmanned aerial vehicle base stations based on noise optimization. Background technique [0002] In the future 5G / B5G mobile communication, cellular connection drones will be a crucial part. The UAV is equipped with a massive MIMO antenna array and a high-precision beamforming method, which can form a narrow beam with strong directivity and high gain, which can effectively overcome the practical problem of huge attenuation in the millimeter wave and above frequency bands while reducing neighbor interference. At the same time, with effective power control and bandwidth resource allocation strategies, the performance of the communication system can be improved under the condition of limited communication resources. Therefore, ...

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Problems solved by technology

[0003] At present, the dynamic allocation of UAV base station communication resources is generally through UAV trajectory optimization, DDPG algorithm, and DQN algorithm. UAV trajectory optimization cannot effectively combine the dynamics of each user and cannot achieve prediction; DDPG algorithm only The motion and bandwidth allocation of UAVs are controlled; the DQN algorithm realizes that UAVs adapt to the movement of ground users while maximizing channel capacity. However, since each UAV can only allocate rated sub-channels to each user, the bandwidth allocation flexibility to be optimized

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