Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels

• URL: http://arxiv.org/abs/2110.01722v1
• Date: Mon, 4 Oct 2021 21:22:12 GMT
• Title: Wireless Link Scheduling via Graph Representation Learning: A Comparative Study of Different Supervision Levels
• Authors: Navid Naderializadeh
• Abstract summary: 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 show how the node embeddings can be trained in several ways, including via supervised, unsupervised, and self-supervised learning.
• Score: 4.264192013842096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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