基于深度强化学习多目标低轨卫星切换OA

The high-speed movement of low earth orbit(LEO)satellites causes users to handover satellites frequently,which increases system overhead and signaling load.Meanwhile,the dynamic network topology and high user mobility further complicate mobility management.In view of the above,this paper proposes an AI-based satellite handover optimization strategy that integrates minimized handover frequency,satellite response time,and maximized system throughput.The deep reinforcement learning is used to realize intelligent decision.The method constructs a multidimensional reward function combining signal-to-noise ratio(SNR),service duration and response time.In addition,the long short-term memory(LSTM)network is introduced to enhance the capabilities of temporal modeling.Simulations show that,in the case of different user scales and scenarios,the proposed method outperforms existing approaches in improving throughput and reducing handover frequency and failure rate,which demonstrates its effectiveness in ensuring communication continuity and system stability.