Joint User Association and Power Allocation in Heterogeneous Ultra Dense Network via Semi-Supervised Representation Learning

Heterogeneous Ultra-Dense Network (HUDN) is one of the vital networking architectures due to its ability to enable higher connectivity density and ultra-high data rates. However, efficiently managing the wireless resource of HUDNs to reduce the wireless interference faces challenges. In this paper, we tackle this challenge by jointly optimizing user association and power control. The joint user association and power control problem is a typical non-convex problem that is hard and time-consuming to solve by traditional optimization techniques. This paper proposes a novel idea for resolving this question: the optimal user association and Base Station (BS) transmit power can be represented by some network parameters of interest, such as the channel information, the precoding matrices, etc. Then, we solve this problem by transforming it into an optimal representation function learning problem. We model the HUDNs as a heterogeneous graph and train a Graph Neural Network (GNN) to approach this representation function by using semi-supervised learning (SSL), in which the loss function is composed of the unsupervised part that helps the GNN approach the optimal representation function and the supervised part that utilizes the previous experience to reduce useless exploration in the initial phase. Besides, we use the entropy regularization to guarantee the effectiveness of exploration in the configuration space. To embrace both the generalization of the learning algorithm and higher performance of HUDNs, we separate the learning process into two parts, the generalization-representation learning (GRL) part, and the specialization-representation learning (SRL) part. In the GRL part, the GNN learns a representation with a tremendous generalized ability to suit any scenario with different user distributions, which processes offline. Based on the learned GRL representation, the SRL finely turn the parameters of GNN on-line to further improving the performance for quasi-static user distribution. Simulation results demonstrate that the proposed GRL-based solution has higher computational efficiency than the traditional optimization algorithm. Besides, the results also show that the performance of SRL outperforms the GRL.