Signal Detection for IoT Networks with Unknown Channel Models: A Knowledge-Driven Approach

This paper considers a signal detection problem for uplink transmission in Internet-of-Things (IoT) networks. Due to the non-idealities of IoT devices such as amplifier's non-linearity, the exact end-to-end channel model as well as the accurate channel state information (CSI) is not available at the receiver (i.e., base station), which precludes the possibility of using classical model-based signal detection methods. Deep learning (DL) techniques have been recognized recently as an effective tool to deal with this challenge. However, for IoT scenarios, devices typically transmit data using short packets with few pilot symbols. The amount of training data is insufficient to train a detector for each device individually. In order to combat the data scarcity barrier and enable few-shot learning, this paper proposes to aggregate pilot symbols from different devices in an intelligent manner to train a universal signal detector which is applicable to all possible channel conditions. To be specific, a knowledge-driven signal detector architecture is devised following the modular design methodology for classical communication system receivers. Under this framework, three neural networks (NN), termed as channel feature extractor, signal feature extractor, and signal classifier, respectively, are employed to form decision statistics and make estimates on the transmitted symbols. Furthermore, borrowing the ideas in domain adaptation, a novel component termed as link discriminator is integrated into the architecture to improve the generalization capability of the detector. Simulation results demonstrate that the proposed knowledge-driven detector outperforms the existing solutions in the sense that it enjoys higher detection accuracy and can be well trained with less training data.