Research On User Level Resource Allocation Algorithm In Multi-beam Satellite Communication System With Full Frequency Reuse

As the space network in the space-air-ground integrated network,satellite communication systems can provide seamless coverage for the ground and plays an essential role in maritime communication,emergency communication,and other fields.In recent years,with the rapid deployment of satellite networks,space orbit resources are limited,and the frequency resources available for satellite communication are increasingly insufficient.The application of multi-beam antennas enables satellite systems to realize networking in the way of full frequency multiplexing,effectively improving the utilization rate of frequency resources.However,the introduction of a large number of spot beams and the co-frequency interference caused by frequency multiplexing bring severe challenges to the resource management of the system and seriously affect the service quality of users in the system.In order to ensure the service quality of users in the multi-beam satellite network system with full frequency reuse,it is necessary to deepen the granularity of system resource optimization to the user level,so that the communication resources of each user can be more reasonably allocated.This paper will study the user level resource allocation technology for traditional multi-beam satellite system with fixed beam and beam-hopping satellite system respectively.By jointly allocating time slots,frequency,and power resources,users’ latency performance and the service quality of the satellite system are optimized.Firstly,in the traditional multi-beam satellite system with full frequency multiplexing,the user level resource allocation problem for joint uplink and downlink optimization in end-to-end communication scenarios is studied.This problem is described as a mixed integer dynamic programming problem.To solve the problem,this paper proposes a twostage algorithm to minimize the total time spent by all paired users to complete data transmission in the system.In the first stage,the Deep Reinforcement Learning(DRL)algorithm is used to make decisions on the paired user scheduling and uplink and downlink sub-channel resource allocation.After the user’s sub-channel assignment is fixed,the Successive Convex Approximation(SCA)algorithm is applied for the power allocation of the downlink in the second stage.This step can obtain the optimal solution which maximizes the total end-to-end communication rate of the system.Through the continuous execution of this two-stage algorithm,the system benefit is maximized.It can be seen from the simulation results that the proposed joint paired user scheduling and uplink and downlink resource allocation algorithm can effectively reduce the time spent by all users to complete data transmission.Secondly,in the beam-hopping satellite system with full frequency multiplexing,a beam-user level resource scheduling algorithm based on dual PPO(Proximal Policy Optimization)networks is proposed for the forward link communication scenario where user data requests continue to arrive.Considering the traffic demand of the ground cell and the packet delay of users in the cell,the beam-level resource allocation network and user-level resource allocation network are combined to generate appropriate resource allocation strategies,including beam scheduling,beam power allocation and user resource block allocation.In the case of the continuous arrival of data requests from users in the system,the total latency of arrived data packets and the consumption of power resources are minimized under the limitation of the system packet loss rate.