Wireless Communication

The CCSL advances next-generation wireless communication through a synergistic combination of theory, system design, and artificial intelligence. Our research explores digital twin frameworks for both indoor and outdoor wireless scenarios, enabling real-time system monitoring, optimization, and predictive analysis. We leverage multimodal sensing to enhance situational awareness and resilience, while reconfigurable intelligent surface (RIS)-aided localization pushes the boundaries of positioning accuracy. In integrated sensing and communication (ISAC), the lab investigates fundamental theory and develops novel multiplexing techniques to balance communication and sensing performance. A key research thrust focuses on the integration of artificial intelligence (AI), across wireless applications, from resource allocation to network optimization. We also address near-field communication challenges such as concept validation, codebook design, and channel estimation. Furthermore, CCSL is actively engaged in the design and evaluation of AI-driven radio access networks (AI-RAN) and open RAN (O-RAN), laying the groundwork for intelligent, adaptive, and interoperable future networks.

Research Projects

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Digital Twin For Wireless Communication

Digital Twin For Wireless Communication Digital Twin (DT) in wireless communication refers to a high-fidelity virtual replica of a physical communication environment that enables real-time monitoring, analysis, and optimization of wireless systems. By bridging the physical and digital domains, DTs provide significant advantages such as predictive system behavior, performance enhancement, rapid prototyping, and reduced testing costs. At CCSL, we are developing DT frameworks across various scenarios, including both indoor and outdoor wireless environments, with a particular focus on real-time DT

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Sensing and Localization

Sensing and Localization Sensing, localization, and integrated sensing and communication (ISAC) are emerging paradigms in wireless communication that extend networks beyond connectivity to include environment awareness and precise positioning. These capabilities enable advantages such as improved situational awareness, enhanced security, and efficient spectrum utilization. Projects

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Near-Field Communication

Near-Field Communication Near-field wireless communication refers to the regime where the transmitter and receiver operate at distances comparable to the electromagnetic wavelength, making conventional far-field assumptions invalid. This opens opportunities for high-capacity, spatially selective communication and precise sensing due to the richer spatial degrees of freedom. Projects

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AI for Wireless Communication

AI for Wireless Communication AI for Radio Access Networks (AI-RAN) and Open RAN (O-RAN) represent key enablers for next-generation wireless systems, introducing intelligence, adaptability, and openness into network design and operation. AI-RAN, with its machine learning capability, and O-RAN, with its open and interoperable architecture, foster flexibility, vendor diversity, and innovation in RAN deployment. We’re currently updating this section. More content and visuals will be added soon.

Meet the Team

Abdulkadir Celik

Ahmed Nasser Ismail

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Bumin Kagan Yildirim

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Zhandos Zhakipov

Discover More Research Areas in the CCSL Group

Overview of wearable sensing technologies for comprehensive human health monitoring.

Body Area Network

Body Area Network Our work explores new frontiers where the human body and nature itself become powerful channels for communication. We are driven by the vision that signals can be harnessed and transformed into reliable pathways for exchanging information. By combining innovative circuit design, ultra-low-power integrated systems, and bio-inspired networking, we seek to enable technologies that go far beyond traditional wireless communication. Our research spans from the Internet of Body, where multiple biosensors can communicate seamlessly through the human body, to the Internet of Plants

Applied Artificial Intelligence

Applied Artificial Intelligence At the Applied Artificial Intelligence group, we develop multidisciplinary AI-driven technologies for smart cities, transportation, agriculture, and healthcare. Our research integrates sensing, communication, computation, and autonomous decision-making to address practical real-world challenges. We leverage advanced AI technologies, including machine learning, computer vision, and large language models, as key enablers for our solutions. In smart health, we are developing LiDAR-based platforms for human activity recognition and rehabilitation, enabling accurate