MT-GCNN: Multi-Task Learning with Gated Convolution for Multiple Transmitters Localization in Urban Scenarios

Abstract

With the advance of the Internet of things (IoT), localization is essential in varied services. In urban scenarios, multiple transmitters localization is faced with challenges such as nonline-of-sight (NLOS) propagation and limited deployment of sensors. To this end, this paper proposes the MT-GCNN (Multi-Task Gated Convolutional Neural Network), a novel multiple transmitters localization scheme based on deep multi-task learning, to learn the NLOS propagation features and achieve the localization. The multi-task learning network decomposes the problem into a coarse localization task and a fine correction task. In particular, the MT-GCNN uses an improved gated convolution module to extract features from sparse sensing data more effectively. In the training stage, a joint loss function is proposed to optimize the two branches of tasks. In the testing stage, the well-trained MT-GCNN model predicts the classified grids and corresponding biases jointly to improve the overall performance of localization. In the urban scenarios challenged by NLOS propagation and sparse deployment of sensors, numerical simulations demonstrate that the proposed MT-GCNN framework has more accurate and robust performance than other algorithms.

A Novel Low Complexity Two-Stage Tone Reservation Scheme for PAPR Reduction in OFDM Systems

“…Namely, efficient power management for wireless sensing transmission services has become an extremely important issue of Internet of Things (IoT) information delivery. If there is no proper power management and control, it will cause nonlinear amplification and distortion of the transmitted signal, especially in a multi-carrier environment [ 7 , 8 ]. Furthermore, due to OFDM technology possessing excellent spectral efficiency and the ability to resist inter-symbol interference caused by multi-path propagation [ 9 , 10 ], it has become one of the most mainstream wireless communication technology.…”

Signal Processing and Machine Learning for Smart Sensing Applications

“…Wang et al [ 19 ] proposed the MT-GCNN (Multi-Task Gated Convolutional Neural Network), a novel multiple transmitters localization scheme based on deep multi-task learning to learn the non-line-of-sight (NLOS) propagation features and achieve localization. The multi-task learning network decomposed the problem into a coarse localization task and a fine correction task.…”