Simulation of Collision Free Scheduling for Multi-Channel Communication Data in Low Delay Networks
With the rapid growth of information dissemination data and the widespread application of low-latency networks,the demand for conflict-free scheduling for low-latency networks has further increased.In order to address the issues of poor effectiveness and scheduling effectiveness of current communication scheduling algorithms,this paper proposes a conflict-free scheduling model based on the Actor-Critic algorithm in reinforcement learning.This model first analyzes the low-latency network architecture and monitors the data of multi-channel communication net-works in the state space.Then,based on the constrained Markov decision,conflict-free scheduling is performed,and the scheduling strategy is calculated based on the Bellman equation.Finally,the Actor-Critical algorithm in reinforce-ment learning is used to optimize the scheduling algorithm.The Actor is used to propose a scheduling plan,and Criti-cal is used to evaluate the scheduling situation,thereby achieving the effect of conflict-free scheduling.The experi-mental results show that the algorithm proposed in this article reduces the conflict rate by 8.72％,increases the throughput rate by 7.58％,reduces the probability of data loss and garbled code during transmission,and improves the transmission efficiency of communication data.
Conflict-free schedulingLow latency networkReinforcement learningMulti-channel communication data
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