Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semisupervised algorithms. However, semisupervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength or channel state information in wireless sensor networks to localize users in indoor/outdoor environments. In this letter, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reducing data-collection costs while achieving acceptable accuracy

Vehicular intelligence at the edge : a decentralized federated learning approach for technology recognition

In the evolving landscape of vehicular networks the need for robust scalable and decentralized learning mechanisms is paramount. This paper introduces a novel Decentralized Federated Learning (DFL) framework for wireless technology recognition in vehicular networks essential for intelligently allocating spectrum resources in multi-Radio Access Technology (multi-RAT) scenarios. In contrast with centralized learning at the base station level our approach leverages Roadside Units (RSUs) for model training and aggregation eliminating central server dependency and enhancing resilience to single points of failure. Each vehicle trains a Convolutional Neural Network (CNN) for wireless technology recognition using the Fourier transform of In-phase and Quadrature (IQ) samples collected from a specific combination of technologies. The proposed frame-work is comprised of two steps. First Centralized Federated Learning (CFL) is employed at the RSU level to create an aggregated model considering the users' connectivity status. Second DFL is utilized to establish a global model at each RSU by sharing models with neighboring RSUs. This approach not only preserves data privacy and security but also optimizes learning by leveraging local computations and minimizing the need for extensive data transmission. Our experimental analysis validates the viability of this approach in providing a scalable and resilient solution for technology recognition in vehicular networks. Our results indicate that DFL surpasses its centralized counterpart by 30% in sparse deployments with low connectivity rates