Over-the-air computation (AirComp) is a promising technology converging communication and computation over wireless networks, which can be particularly effective in model training, inference, and more emerging edge intelligence applications. AirComp relies on uncoded transmission of individual signals, which are added naturally over the multiple access channel thanks to the superposition property of the wireless medium. Despite significantly improved communication efficiency, how to accommodate AirComp in the existing and future digital communication networks, that are based on discrete modulation schemes, remains a challenge. This paper proposes a massive digital AirComp (MD-AirComp) scheme, that leverages an unsourced massive access protocol, to enhance compatibility with both current and next-generation wireless networks. MD-AirComp utilizes vector quantization to reduce the uplink communication overhead, and employs shared quantization and modulation codebooks. At the receiver, we propose a near-optimal approximate message passing-based algorithm to compute the model aggregation results from the superposed sequences, which relies on estimating the number of devices transmitting each code sequence, rather than trying to decode the messages of individual transmitters. We apply MD-AirComp to federated edge learning (FEEL), and show that it significantly accelerates FEEL convergence compared to state-of-the-art while using the same amount of communication resources.

中文翻译：

---

用于通信高效联合边缘学习的大规模数字无线计算


无线计算 （AirComp） 是一项很有前途的技术，它通过无线网络融合通信和计算，在模型训练、推理和更多新兴的边缘智能应用中特别有效。AirComp 依赖于单个信号的未编码传输，由于无线介质的叠加特性，这些信号会自然地添加到多接入信道上。尽管通信效率显著提高，但如何在现有和未来基于离散调制方案的数字通信网络中容纳 AirComp 仍然是一个挑战。本文提出了一种大规模数字 AirComp （MD-AirComp） 方案，该方案利用无源的大规模访问协议来增强与当前和下一代无线网络的兼容性。MD-AirComp 利用矢量量化来减少上行链路通信开销，并采用共享量化和调制码簿。在接收者处，我们提出了一种近乎最优的基于近似消息传递的算法来计算叠加序列的模型聚合结果，该算法依赖于估计传输每个代码序列的设备数量，而不是尝试解码单个发送者的消息。我们将 MD-AirComp 应用于联合边缘学习 （FEEL），并表明与最先进的相比，在使用相同数量的通信资源的情况下，它显着加速了 FEEL 收敛。