A Leo Satellite Channel Allocation Method Based on Reinforcement Learning
A satellite channel and allocation method technology, applied in the field of LEO satellite applications, can solve problems such as time correlation, and achieve the effect of accelerating algorithm convergence
Active Publication Date: 2022-03-25
GUILIN UNIV OF ELECTRONIC TECH
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[0004] The present invention provides a LEO satellite channel allocation method based on reinforcement learning, which can realize cross-beam scheduling of LEO satellite channel resources, thereby adapting to business differences between beams, and uses Q-Learning algorithm to learn optimal channel allocation strategy and perform dynamic channel allocation. Allocation, to solve the time correlation problem in the system channel allocation process
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[0039] The present invention proposes a method for allocating LEO satellite channels based on reinforcement learning, the process of which is as follows Figure 4 As shown, the specific steps are as follows:
[0040] (1) Initialize the relevant parameters of the LEO satellite system, reset the number of pre-allocated channels to 0, and reset the beam set X, system channel set Y, and user set U according to the specific parameters of the system;
[0041] (2) The system pre-allocates a fixed number of channel resources to each beam, which is set to 10 in this embodiment;
[0042] (3) Within the time interval T of each service request, the system allocates resources to the user once. If the pre-allocated channel resources can meet the user's needs, the system will reclaim the surplus channel resources to the resource pool; if the pre-allocated channel cannot meet the user's needs Demand, the resource pool schedules channel resources, and trains the optimal allocation strategy th...
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The invention discloses a method for allocating LEO satellite channel resources based on reinforcement learning, which includes the following steps: (1) a centralized resource pool on the satellite pre-allocates channel resources to each beam cell; (2) if the pre-allocated resources are surplus, the It recycles to the resource pool. If the pre-allocated resources cannot meet the user's needs, the channel resources will be dynamically allocated; (3) The system uses the Q-Learning algorithm for training to find the optimal allocation strategy. Channel scheduling; (4) The system enters the next service request time interval and allocates channels by combining fixed channel pre-allocation and dynamic channel scheduling. The present invention manages channel resources through a centralized resource pool to adapt to business differences between beams, and uses reinforcement learning to solve the time correlation problem in channel allocation, thereby realizing efficient channel resource allocation in the LEO satellite system.
Description
technical field [0001] The invention relates to the technical field of LEO satellite application, in particular to a reinforcement learning-based LEO satellite channel allocation method. Background technique [0002] Among various types of satellites, low-orbit satellites have the characteristics of small path loss, short communication delay, and flexible orbital positions. Through low-orbit satellite constellations, seamless coverage of global regions can be achieved. However, resources such as spectrum, power, and time slots available for satellite systems are extremely scarce and valuable, and it is an urgent problem to allocate satellite network resources reasonably and efficiently. [0003] Due to the dynamic changes in the coverage area caused by satellite movement and the non-uniform distribution of ground users, the traffic load is changing all the time, and the on-board resources of low-orbit satellites have been solidified when they leave the factory. The tradition...
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IPC IPC(8): H04B7/185
CPCH04B7/18539H04B7/18543
Inventor 郑飞皮昭周陬仇洪冰
Owner GUILIN UNIV OF ELECTRONIC TECH