UcnPowerNet: Residual learning based clustering and interference coordination in 5G user‐centric networks

Abstract

AbstractIn 5th Generation and beyond (5G&B) wireless networks, artificial intelligence can be explored to address problems with uncertain, time‐variant, and complex features. User‐centric network (UCN) can eliminate cell boundaries and reduce interference. And as for the intelligent resource management and interference coordination, the scalability of existing deep learning‐based works degrades greatly as the size of the network increases, which requires more layers to be stacked and causes gradient vanishing problem. In this article, a new deep learning‐based resource management model, UcnPowerNet, is proposed to approximate iterative algorithms for dynamic clustering and interference coordination in complicated UCN, where multiple residual blocks are concatenated with shortcut connections to asymptotically learn the mapping between input and output. Specifically, a cooperative weighted minimum mean square error (WMMSE) algorithm for UCN is carried out in the real domain to generate large near‐optimal training datasets; then we train UcnPowerNet to approximate the output of WMMSE algorithm by minimizing the loss function of mean square error. Moreover, convolutional layer and normalization layer are leveraged to decrease the number of weights and avoid gradients vanishing, respectively. Extensive experiments demonstrate the high approximation accuracy of UcnPowerNet with 94.90% sum‐rate relative to the conventional iterative algorithm while achieving more than 100 speed up.