Interference Management Strategy For Multi-beam LEO Satellite Uplink

Satellite communication have been widely studied due to the advantages of the wide coverage of communication services,few geographical restrictions and high reliability of transmission.And satellite communication systems are an important part of the 6G(the Sixth Generation Mobile Communication System)communication system in the future.Among them,LEO(Low Earth Orbit)satellite communication is the main development direction of satellite.LEO satellites can support personal handheld terminals and comprehensive intelligent services because of low orbital altitude and low path loss.LEO satellite communication system is a typical system with resources.Channels and powers are limited resources on board.Efficiently using limited resources can improve quality of service,which has significant implications with the development of LEO satellite communications.For the contradiction between co-channel interference and resource utilization in LEO satellite wireless communications,this paper conducts research on power control and resource allocation in uplinks.The main research contents are as follows:(1)Due to the long distance between satellites and the earth,uplink users need considerable energy to achieve satellite communication.Therefore,the energy efficiency is very important for user terminals.In addition,multi-beam LEO satellite networks may cause co-channel interference between beams because of full frequency reuse.Moreover,power competition among users will easily occur if the users begin to privately increase the transmission power.Power competition limits capacity and reduces energy efficiency.To solve these problems,this paper proposes a power control strategy balancing capacity and energy efficiency.A dual-objective optimization model is established based on the maximum transmission power constraint and the minimum signal to interference plus noise ratio(SINR)constraint.The model is solved by the Lagrange algorithm.The algorithm can adjust the optimization degree of the energy efficiency and capacity by preference factors.The simulation experiment results show that,while the user minimum transmission rate increases,the proposed power control strategy has better interference suppression than the capacity-optimized power control strategy.(2)Due to the limited resources on board and the non-uniform distribution of traffic,the resource utilization is low in LEO satellite systems.In addition,the rapid movement of LEO satellites makes networks complex and changeable.Traditional resource allocation strategies are difficult to adapt networks that change frequently.To solve above problems,this paper proposes a resource allocation strategy based on deep reinforcement learning.The strategy establishes a power and channel joint allocation model,which optimizes the weighted sum of frequency efficiency,energy efficiency and blocking probability.Resources of channel and power are allocated to new users based on state information including channel allocation,new user requests,and transmitted services.In the reward decision mechanism,the maximum reward is obtained by maximizing the increment of the optimization objective.However,in the optimization process,the decision will focus on the optimal allocation for current users and ignore the quality of service for subsequent new users.To avoid the situation,current service beams will be state integrated with hightraffic beams.States of beams are refactored to maximize long-term benefits and improve system performance.Simulation experiments show that resource allocation algorithm based on deep reinforcement learning can effectively improve resource utilization and reduce blocking probability.