  The state-of-the-art coding schemes for topological interference management
(TIM) problems are usually handcrafted for specific families of network
topologies, relying critically on experts' domain knowledge. This inevitably
restricts the potential wider applications to wireless communication systems,
due to the limited generalizability. This work makes the first attempt to
advocate a novel intelligent coding approach to mimic topological interference
alignment (IA) via local graph coloring algorithms, leveraging the new advances
of graph neural networks (GNNs) and reinforcement learning (RL). The proposed
LCG framework is then generalized to discover new IA coding schemes, including
one-to-one vector IA and subspace IA. The extensive experiments demonstrate the
excellent generalizability and transferability of the proposed approach, where
the parameterized GNNs trained by small size TIM instances are able to work
well on new unseen network topologies with larger size.
