ESSENCEAbstract
This project investigates how self-supervised learning can be used in wireless communication systems to improve adaptability, efficiency, robustness, and scalability in future networks.
Deep neural networks are already widely used in wireless applications. However, recent advances in other research areas show that foundation models—models that are pre-trained on large and diverse datasets and later adapted to specific tasks—can offer better generalization across different conditions. In wireless communication systems, such models must be compact and efficient to operate under practical constraints.
The project focuses on channel state information, which describes the current conditions of the wireless channel. The central research question is how to design compact foundation models based on channel state information that can adapt to changing environments and system requirements.
To address this, the project studies self-supervised learning on unlabeled channel state information in order to extract features that are useful across multiple tasks, such as radio resource management and interference mitigation. These features are designed to generalize across frequency bands and deployment scenarios. They can then be fine-tuned locally using only a small amount of labeled data, enabling real-time adaptation directly on the device.
Key challenges include balancing model complexity and efficiency, dealing with noisy or delayed channel state information, and designing learning objectives that reflect the dynamics of wireless communication systems. The project will result in compact and efficient learning models, as well as a benchmarking framework to support the systematic evaluation of self-supervised learning methods in wireless communications.
Research Questions and Objectives
Developing a large-scale foundation model for wireless communication systems is currently limited by computational requirements and the availability of sufficiently large real-world datasets. Instead, this project focuses on fundamental self-supervised learning methods for wireless communications, with the goal of extracting versatile features from channel state information that support key functions at the medium access control and physical layers.
The research objective is to develop robust and efficient self-supervised learning methods that produce pre-trained features based on channel state information. These features should be transferable with minimal fine-tuning across:
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Frequency bands, ranging from below six gigahertz to millimeter-wave frequencies and beyond, covering a wide range of propagation conditions.
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Transmission environments, including indoor and outdoor deployments, as well as rural, urban, and other heterogeneous scenarios.
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Downstream tasks, such as multiple-antenna signal processing, radio resource management, and interference mitigation.
The overall goal is to establish foundational self-supervised learning approaches for scalable wireless systems and to develop compact, efficient pre-trained models. These models will be fine-tuned locally at network nodes to adapt to specific system settings, environmental conditions, and task requirements.
Based on this goal, the project addresses the following central research questions:
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How can self-supervised learning objectives be designed to capture the spatial, temporal, and frequency-dependent dynamics of wireless systems?
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How can model complexity and computational efficiency be balanced to enable real-time, on-device adaptation across diverse environments and tasks?
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How can noisy, quantized, sparse, or delayed channel state information be handled in a reliable and robust manner?
While the project focuses on wireless communication systems, these questions are also relevant to other application domains that require learning methods that are transferable, efficient, and robust.
Expected Results and Novelty
This project develops fundamental self-supervised learning methods for wireless communication systems, with a focus on extracting robust and versatile features from channel state information. These features are designed to generalize across different frequency bands, deployment scenarios, and system tasks, with the aim of improving the adaptability, efficiency, robustness, and scalability of next-generation wireless networks.
Models trained using large datasets will be adapted for use on edge and resource-constrained devices through model compression techniques. They will then be fine-tuned using only a small number of labeled examples to meet specific system requirements. Knowledge learned in one frequency band or deployment environment will be transferable to others, reducing the amount of new labeled data required when adapting to new conditions.
An important outcome of the project will be adaptive self-supervised learning methods that support real-time online learning. This allows models to continuously update themselves as new data becomes available, without requiring complete retraining. The project advances self-supervised learning for wireless systems by designing learning objectives that reflect the complex and dynamic behavior of wireless channels, which vary across space, time, and frequency.
A further innovation lies in developing methods that can reliably handle noisy, quantized, sparse, or delayed channel state information, ensuring that models remain robust under realistic operating conditions. While the primary focus is on wireless communications, the resulting methods are expected to be relevant to other application areas that require efficient and transferable learning techniques.
Because self-supervised learning is still emerging in wireless communication research, the project will also develop a benchmarking framework to enable systematic evaluation of self-supervised learning methods across different wireless tasks. This framework will assess performance in terms of generalization, robustness, efficiency, and adaptability. Although not a central objective, robustness against adversarial attacks may be explored in related supervised student projects.
