To enhance the practicality of the predictive covert communication framework, several real-world factors can be integrated into the existing model. Firstly, environmental conditions such as weather patterns, terrain variations, and obstacles can significantly impact UAV dynamics and communication effectiveness. Incorporating environmental data into the Graph Koopman Autoencoder (GKAE) can be achieved by augmenting the node feature matrix with environmental parameters, allowing the model to learn how these factors influence UAV trajectories and communication strategies. For instance, using weather forecasts or real-time sensor data can help adjust the predicted paths of UAVs, thereby improving the accuracy of covert communication.
Secondly, sensor uncertainties must be addressed, as UAVs may not always have precise location data due to sensor noise or limitations. To account for this, the GKAE can be modified to include a probabilistic approach, where the predictions are treated as distributions rather than fixed points. This can be achieved by employing techniques such as Monte Carlo simulations or Bayesian inference to estimate the uncertainty in UAV positions, allowing ground nodes to adapt their communication strategies based on the confidence levels of the predictions.
Lastly, communication constraints such as bandwidth limitations, latency, and interference can be integrated into the framework. The GKAE can be designed to optimize the communication parameters dynamically, ensuring that the transmit power and modulation schemes are adjusted based on the predicted UAV locations and the current network conditions. This can involve real-time feedback loops where the central unit (CU) continuously monitors the communication environment and updates the ground nodes accordingly, ensuring that covert operations remain effective even under varying conditions.