CCSL Research Hub

Explore tools, datasets, and supplementary materials that support our ongoing research. This section includes software frameworks, hardware references, publications, and open-source resources developed or used by the CCSL team. Our research spans multiple cutting-edge domains, bridging theory and practical applications to address emerging challenges in communication, healthcare, and computing. We focus on three main areas:

Wireless Communication

digital-twin-for-wireless-communication-ccsl

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
sensing-and-localization-ccsl

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
near-field-communication-ccsl

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
AI-for-wireless-communication-ccsl

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.

Read More

Body Area Network

Overview of wearable sensing technologies for comprehensive human health monitoring.

BioLink: Internet of Body via HBC for Healthcare

Human Body Communication (HBC) is an innovative technology that uses the human body itself as a transmission medium for data exchange. This approach enables secure, energy-efficient, and reliable communication between smart health sensors, wearable devices, and medical implants in and around the body. Unlike traditional wireless methods that rely on radio frequency signals prone to interference and privacy risks, HBC offers low power consumption, reduced signal loss, and enhanced data privacy by transmitting signals through direct skin or body tissue contact. This project focuses on advancing
Close-up view of a microfabricated sensor chip used in advanced biomedical and communication systems.

Ultra-Low-Power HBC Transceiver ICs

The CCSL advances next-generation Human Body Communication (HBC) through innovative ASIC implementations that enable ultra-low power, secure, and efficient data transmission using the human body as a communication medium. Our research focuses on silicon-proven solutions that bridge the gap between theoretical HBC concepts and practical wearable/implantable applications. We develop mixed-signal transceivers that support multiple communication modes, adaptive signal processing, and AI-enhanced performance optimization. The lab's ASIC implementations demonstrate >100x energy efficiency

AgriLink: Internet of Plants for Smart Agriculture

Dense vegetation severely attenuates 2.4–5 GHz RF links (additional foliage loss of order 1–3 dB/m, and often >30 dB across canopies), forcing higher transmit power or more relays in plant/greenhouse settings. In contrast, Plant-Body Communication (PBC) utilizes signals within the stem, where ionic pathways and distributed capacitances form a guided, low-radiation medium, thereby reducing path loss and environmental variability. We target two practical outcomes: 1. quantify and model the intra-plant channel across coupling modes (galvanic vs. capacitive), species (herbaceous Dieffenbachia vs
Wearable health monitoring system with edge analytics for real-time diagnostics, emergency alerts, and secure cloud reporting.

On-Body HBC Networking

To advance innovations in next-generation Internet of Bodies (IoB) networks, there is a growing need for miniature, ultra-low-power devices capable of continuous and reliable operation. This need has led to a surge of interest in Human Body Communication (HBC), where nodes exchange messages directly through the human body as a secure and energy-efficient medium. With evidence showing that HBC can achieve up to 100× lower power consumption compared to conventional RF technologies, our research focuses on developing novel communication and networking techniques that facilitate connectivity over

Read More

AI Accelerator

EdgeCIM: A Hardware-Software Co-Design for CIM-Based Acceleration of Small Language Models

The deployment of language models is rapidly shifting from datacenters to edge devices such as laptops, smartphones, and embedded platforms, driven by the demand for interactive, low-latency, and privacy-preserving applications. In this context, Small Language Models (SLMs) have emerged as practical candidates, yet their inference reveals inefficiencies in conventional accelerators. While GPUs and NPUs process the GEMM-heavy prefill stage efficiently, they remain underutilized during the GEMV-dominated decoding phase, resulting in limited throughput and excessive energy consumption at the edge

Efficient AI Across Edge, Near-Edge, and Cloud

Modern applications like smart cameras, self-driving cars, and VR devices rely on powerful AI models. Running these models quickly and efficiently across phones, edge devices, and cloud servers is a tough challenge. Our work develops two frameworks to make this possible: DONNA finds the best way to split and run AI models across different types of devices, from traditional CPUs and GPUs to new Compute-In-Memory (CIM) accelerators, so they use less energy while staying fast. HiDist takes the idea further by looking at the whole system: edge devices near the user, stronger near-edge servers, and