Research on Data-Driven Intelligent Decision-Making Model System in Communication Engineering

With the increasing complexity of communication networks, how to efficiently manage network resources and make decisions has become a core research issue. Traditional decision-making methods often rely on rules or static models, which are difficult to cope with dynamically changing network environments. This paper proposes a data-driven intelligent decision-making model (DD-SDM), which combines deep learning, reinforcement learning and optimization algorithms to achieve multi-objective decision-making and resource optimization in communication networks. The DD-SDM model models the network state through a deep neural network, uses a reinforcement learning algorithm to optimize the decision-making process, and adjusts the allocation and use of network resources through an adaptive optimization mechanism. Experimental results show that DD-SDM is superior to traditional decision-making methods in terms of throughput, latency, and fault prediction accuracy. The model shows significant performance improvement in the simulation environment, with throughput increased by more than 15%, latency reduced by 12%, and significant improvements in system stability and fault prediction accuracy. This study provides new ideas and methods for intelligent decision-making and optimization of communication networks, and has broad application prospects.