Unsupervised Power Allocation Based on Combination of Edge Aggregated Graph Attention Network with Deep Unfolded WMMSE

To address the challenge of optimizing power distribution for transmitter-receiver pairs within ad hoc wireless networks, deep learning techniques have been employed to navigate the complexities of evolving network topologies and variable channel characteristics. This approach has necessitated the use of extensive training data sets due to challenges in interpretability. Moreover, there's an anticipation for the deep learning model to unearth more generalized and distinct features. In response to these challenges, we introduce the Unsupervised Power Allocation based on Attentive Graph Representation (UPAR) method. UPAR combines an Edge Aggregated Graph Attention Regression Network (EGRN) with Deep Unfolded Weighted Minimum Mean Square Error (DU-WMMSE) within an unsupervised learning framework. Initially, EGRN leverages an attention mechanism and Gate Recurrent Units (GRU) to amalgamate features from adjacent nodes and their connecting edges, enhancing the identifiability and generalization of the graph's features. Subsequently, DU-WMMSE applies unsupervised learning to refine these graph features through regression training, transforming WMMSE iterations into efficient, interpretable layers within the neural network. This process also introduces adjustable parameters within the network, employing both feed forward and back propagation phases during end-to-end training. This method significantly narrows the solution space for the objective function in an unsupervised manner, thereby accelerating the training process and enhancing the efficiency of the entire deep learning model. Extensive comparative and ablation studies confirm the UPAR method's superior performance and adaptability across a variety of scenarios.