  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. Rational user association and power control schedule in
HUDN can reduce wireless interference. This paper proposes a novel idea for
resolving the joint user association and power control problem: the optimal
user association and Base Station transmit power can be represented by channel
information. Then, we solve this problem by formulating an optimal
representation function. 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, 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. We separate the learning process into two parts,
the generalization-representation learning (GRL) part and the
specialization-representation learning (SRL) part, which train the GNN for
learning representation for generalized scenario quasi-static user distribution
scenario, respectively. Simulation results demonstrate that the proposed
GRL-based solution has higher computational efficiency than the traditional
optimization algorithm, and the performance of SRL outperforms the GRL.
