Lightweight Foundation Model for Wireless Time Series Downstream Tasks on Edge Devices

While machine learning is widely used to optimize wireless networks, training a separate model for each task in communication and localization is becoming increasingly unsustainable due to the significant costs associated with training and deployment. Foundation models offer a more scalable alternative by enabling a single model to be adapted across multiple tasks through fine-tuning with limited samples. However, current foundation models mostly rely on large-scale Transformer architectures, resulting in computationally intensive models unsuitable for deployment on typical edge devices. This paper presents a lightweight foundation model based on simple Multi-Layer-Perceptron (MLP) encoders that independently process input patches. Our model supports 4 types of downstream tasks (long-range technology recognition, short-range technology recognition, modulation recognition and line-of-sight-detection) from multiple input types (IQ and CIR) and different sampling rates. We show that, unlike Transformers, which can exhibit performance drops as downstream tasks are added, our MLP model maintains robust generalization performance, achieving over 97% accurate fine-tuning results for previously unseen data classes. These results are achieved despite having only 21K trainable parameters, allowing an inference time of 0.33 ms on common edge devices, making the model suitable for constrained real-time deployments.

Foundation Model for Wireless Technology Recognition Using IQ Timeseries

Wireless Technology Recognition (WTR) is essential in modern communication systems, enabling efficient spectrum management and the seamless coexistence of diverse technologies. In real-world conditions, WTR solutions should be able to handle signals from various resources with different sampling rates, capturing devices, and frequency bands. However, traditional WTR methods, which rely on energy detection, Convolutional Neural Network (CNN) models, or Deep Learning (DL), lack the robustness and adaptability required to generalize across unseen environments, different sampling devices, and previously unencountered signal classes. In this work, we introduce a Transformer-based foundation model for WTR, trained in an unsupervised manner on large-scale, unlabeled wireless signal datasets. Foundation models are designed to learn general-purpose representations that transfer effectively across tasks and domains, allowing generalization towards new technologies and WTR sampling devices. Our approach leverages input patching for computational efficiency and incorporates a two-stage training pipeline: unsupervised pre-training followed by lightweight fine-tuning. This enables the model to generalize to new wireless technologies and environments using only a small number of labeled samples. Experimental results demonstrate that our model achieves superior accuracy across varying sampling rates and frequency bands while maintaining low computational complexity, supporting the vision of a reusable wireless foundation model adaptable to new technologies with minimal retraining.

A Comprehensive Overview on UWB Radar: Applications, Standards, Signal Processing Techniques, Datasets, Radio Chips, Trends and Future Research Directions

Due to their large bandwidth, relatively low cost, and robust performance, UWB radio chips can be used for a wide variety of applications, including localization, communication, and radar. This article offers an exhaustive survey of recent progress in UWB radar technology. The goal of this survey is to provide a comprehensive view of the technical fundamentals and emerging trends in UWB radar. Our analysis is categorized into multiple parts. Firstly, we explore the fundamental concepts of UWB radar technology from a technology and standardization point of view. Secondly, we examine the most relevant UWB applications and use cases, such as device-free localization, activity recognition, presence detection, and vital sign monitoring, discussing each time the bandwidth requirements, processing techniques, algorithms, latest developments, relevant example papers, and trends. Next, we steer readers toward relevant datasets and available radio chipsets. Finally, we discuss ongoing challenges and potential future research avenues. As such, this overview paper is designed to be a cornerstone reference for researchers charting the course of UWB radar technology over the last decade.