  Flexible duplex networks allow users to dynamically employ uplink and
downlink channels without static time scheduling, thereby utilizing the network
resources efficiently. This work investigates the sum-rate maximization of
flexible duplex networks. In particular, we consider a network with
pairwise-fixed communication links. Corresponding combinatorial optimization is
a non-deterministic polynomial (NP)-hard without a closed-form solution. In
this respect, the existing heuristics entail high computational complexity,
raising a scalability issue in large networks. Motivated by the recent success
of Graph Neural Networks (GNNs) in solving NP-hard wireless resource management
problems, we propose a novel GNN architecture, named Flex-Net, to jointly
optimize the communication direction and transmission power. The proposed GNN
produces near-optimal performance meanwhile maintaining a low computational
complexity compared to the most commonly used techniques. Furthermore, our
numerical results shed light on the advantages of using GNNs in terms of sample
complexity, scalability, and generalization capability.

Networking and Internet Architecture;Machine Learning;Signal Processing