Abstract
In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors, which are then aggregated at each remote radio head (RRH) to suppress sensing noise. To realize efficient uplink feature aggregation, we allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique. Thereafter, these aggregated feature vectors are quantized and transmitted to a central processor (CP) for further aggregation and downstream inference tasks. Our aim in this work is to maximize the inference accuracy via a surrogate accuracy metric called discriminant gain, which measures the discernibility of different classes in the feature space. The key challenges lie on simultaneously suppressing the coupled sensing noise, AirComp distortion caused by hostile wireless channels, and the quantization error resulting from the limited capacity of fronthaul links. To address these challenges, this work proposes a joint transmit precoding, receive beamforming, and quantization error control scheme to enhance the inference accuracy. Extensive numerical experiments demonstrate the effectiveness and superiority of our proposed optimization algorithm compared to various baselines.
Abstract (translated)
在本文中，我们提出了一个基于协作边缘人工智能的云无线接入网络（Cloud-RAN）架构。具体来说，分布式设备捕获实时噪音污染的感知数据样本并提取出噪音局部特征向量，然后在每个远程无线接入节点（RRH）上进行聚合，以抑制感知噪声。为了实现高效的上行特征聚合，我们利用过顶计算（AirComp）技术，允许每个RRH同时接收来自所有设备的同一资源块的局部特征向量。然后，这些聚合的特征向量进行量化并传输到中央处理器（CP）进行进一步的聚合和下游推理任务。我们希望通过使用一种称为差分增益的代理准确度度量方法，通过提高模型的性能来最大化推理准确性。 关键挑战在于同时抑制耦合感知噪声、由敌对无线信道引起的AirComp失真以及由于前馈链路有限容量引起的量化误差。为了应对这些挑战，本文提出了一种协作传输预编码、接收波束形成和量化误差控制方案，以提高推理准确性。大量的数值实验证明了与各种基线相比，我们提出的优化算法的有效性和优越性。
URL
https://arxiv.org/abs/2404.06007
