We develop a novel graph-based trainable framework to maximize the weighted
sum energy efficiency (WSEE) for power allocation in wireless communication
networks. To address the non-convex nature of the problem, the proposed method
consists of modular structures inspired by a classical iterative suboptimal
approach and enhanced with learnable components. More precisely, we propose a
deep unfolding of the successive concave approximation (SCA) method. In our
unfolded SCA (USCA) framework, the originally preset parameters are now
learnable via graph convolutional neural networks (GCNs) that directly exploit
multi-user channel state information as the underlying graph adjacency matrix.
We show the permutation equivariance of the proposed architecture, which is a
desirable property for models applied to wireless network data. The USCA
framework is trained through a stochastic gradient descent approach using a
progressive training strategy. The unsupervised loss is carefully devised to
feature the monotonic property of the objective under maximum power
constraints. Comprehensive numerical results demonstrate its generalizability
across different network topologies of varying size, density, and channel
distribution. Thorough comparisons illustrate the improved performance and
robustness of USCA over state-of-the-art benchmarks.

我们开发了一种新颖的基于图的可训练框架，用于最大化无线通信网络中的功率分配的加权和能效（WSEE）；为解决问题的非凸性，我们提出了模块化结构并提供了基于图卷积神经网络（GCNs）的可学习组件，通过梯度下降法进行模型训练，它已经证明可以推广到不同网络拓扑结构，并且具有优越的性能和鲁棒性。