Research on Communication Resource Allocation Strategy Optimization Based on Deep Learning

As wireless communication networks become increasingly complex and communication equipment proliferates at a rapid pace, the demand for communication is correspondingly rising. In order to enhance the overall performance of the network, it is essential to utilise the available resources in an optimal manner, develop efficient algorithms, and implement effective policies. This work proposes a communication resource allocation strategy optimisation model based on deep learning, with the objective of enhancing the overall performance of multi-cell and multi-user wireless communication networks. Initially, the model collates and preprocesses a substantial quantity of historical communication data, encompassing the communication requirements of users, network traffic, channel state information, and interference levels. Subsequently, the data is fed into a deep neural network, and the model is trained by a backpropagation algorithm, which continuously adjusts the network weights and biases to minimise the loss function and ultimately optimise the resource allocation strategy. The trained model is capable of generating the optimal resource allocation scheme in real time, according to the prevailing network status and user requirements. This enables effective reduction of channel interference and maximisation of the efficiency and stability of signal transmission. The results of the experimental analysis demonstrate that the deep learning model not only markedly enhances the communication quality of the network, but also markedly improves the efficiency of resource utilisation, particularly in scenarios involving high user density and complex interference environments.