Edge-Oriented Adaptive Multi-Task Network for Modulation and Signal Type Classification

Modulation and signal classification are two highly correlated core tasks in wireless communications and are the core foundation of intelligent spectrum management in Future Internet and 6G networks. Although their objectives differ, the two tasks often share a substantial amount of underlying information in the feature space. However, focusing solely on their commonalities while neglecting their intrinsic differences may lead to suboptimal model performance. Therefore, by taking into account both the correlation and inherent differences between the two tasks, we propose TAMTNet, a task-adaptive multi-task network for edge deployment in Future Internet. TAMTNet introduces Extremely Efficient Spatial Pyramid (EESP) into the shared layer to efficiently extract multi-scale temporal information. In addition, a multi-gate mixture-of-experts (MMoE) mechanism is employed after the shared layer to enhance the modeling capability of task-specific features. Furthermore, to address the difficulty of deploying deep models on resource-constrained edge devices, a joint lightweight framework combining quantization-aware training and knowledge distillation is proposed, which significantly reduces model complexity while maintaining performance. Extensive experiments conducted on the simulation and real-world over-the-air transmission datasets demonstrate that the TAMTNet model achieves excellent performance on both modulation and signal classification tasks across a wide range of signal-to-noise ratios and radio transmit gain conditions. Meanwhile, the low-bitwidth lightweight models are able to maintain classification performance comparable to the full-precision model while significantly reducing model storage and computational complexity.