Learning-based hybrid precoding has emerged as a promising solution for low-latency multi-user transmission in large-scale antenna systems. However, its practical deployment is often hindered by high training overhead, and poor generalization across varying system sizes. These limitations fundamentally stem from the fa...
Nai-Tian Zhang, Chen-Yang Yang· IEEE Open Journal of the Com...· 0 citations
Simulation results show that pre-trained XNPs achieve fast adaptation to new configurations with significantly fewer training samples and epochs than a graph neural network-based baseline, demonstrating that cross-system adaptability can be achieved by exploiting shared computational structure, rather than relying on l...
Graph neural networks (GNNs) have emerged as a promising approach to learning wireless policies efficiently by leveraging topology prior and incorporating relational inductive biases. However, when the optimal policy is not permutation equivariant (PE), conventional GNNs suffer from mismatched inductive biases, leading...
Baichuan Zhao, Chenyang Yang, Jianyu Zhao et al.· 0 citations
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