Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels
This work addresses link scheduling for wireless networks, but it is incremental as it compares existing supervision methods rather than introducing a new approach.
The paper tackles the problem of binary power control in wireless interference networks by training a power control policy using graph representation learning, showing that different supervision levels (supervised, unsupervised, self-supervised) impact performance metrics like throughput and sample efficiency.
We consider the problem of binary power control, or link scheduling, in wireless interference networks, where the power control policy is trained using graph representation learning. We leverage the interference graph of the wireless network as an underlying topology for a graph neural network (GNN) backbone, which converts the channel matrix to a set of node embeddings for all transmitter-receiver pairs. We show how the node embeddings can be trained in several ways, including via supervised, unsupervised, and self-supervised learning, and we compare the impact of different supervision levels on the performance of these methods in terms of the system-level throughput, convergence behavior, sample efficiency, and generalization capability.