Many of the next generation of satellites will be equipped with numerous degrees of freedom in power and bandwidth allocation capabilities, making manual resource allocation impractical. Therefore, it is desirable to automate the operation of these highly flexible satellites. This paper presents a novel approach based on Deep Reinforcement Learning to allocate power in multibeam satellite systems. The proposed architecture represents the problem as continuous state and action spaces. We make use of the Proximal Policy Optimization algorithm to optimize the allocation policy for minimum unmet system demand and power consumption. Finally, the performance of the algorithm is analyzed through simulations of a multibeam satellite system. The analysis shows promising results for Deep Reinforcement Learning to be used as a dynamic resource allocation algorithm.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep Reinforcement Learning for Continuous Power Allocation in Flexible High Throughput Satellites




    Publication date :

    2019-06-01


    Size :

    376882 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English