Energy-Efficient Power Allocation in Wireless Networks using Graph Neural Networks

We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. The framework is designed to unfold a classical iterative algorithm, namely the successive concave approximation (SCA), which suboptimally solves this NP-hard problem. Our unfolded SCA (USCA) framework consists of modular structures, which are enhanced with learnable components parameterized using graph convolutional neural networks (GCNs). Its layers are progressively trained through a stochastic gradient descent approach driven by unsupervised loss and regularization terms. Numerical experiments demonstrate that USCA outperforms state-of-the-art benchmarks across different network topologies of varying size and density.