29 citations · 71 across the 48 of their papers we have counts for
10 papers · 1 filter
Test-Time Scalable AI-RAN: Inference Time Allocation for Cell-Free MIMO
Seonghoon Yoo, Sangwoo Park, Seok-Hwan Park +1
Artificial intelligence-enabled radio access networks (AI-RANs) are envisioned to consist of multiple AI-based modules, potentially developed independently by different vendors. In…
Reliable LLM-Based Edge-Cloud-Expert Cascades for Telecom Knowledge Systems
Qiushuo Hou, Sangwoo Park, Matteo Zecchin +4
Large language models (LLMs) are emerging as key enablers of automation in domains such as telecommunications, assisting with tasks including troubleshooting, standards interpretat…
Calibrating Wireless AI via Meta-Learned Context-Dependent Conformal Prediction
Seonghoon Yoo, Sangwoo Park, Petar Popovski +2
Modern software-defined networks, such as Open Radio Access Network (O-RAN) systems, rely on artificial intelligence (AI)-powered applications running on controllers interfaced wit…
Transferable Multi-Fidelity Bayesian Optimization for Radio Resource Management
Yunchuan Zhang, Sangwoo Park, Osvaldo Simeone
Radio resource allocation often calls for the optimization of black-box objective functions whose evaluation is expensive in real-world deployments. Conventional optimization metho…
Conformal Distributed Remote Inference in Sensor Networks Under Reliability and Communication Constraints
Meiyi Zhu, Matteo Zecchin, Sangwoo Park +4
This paper presents communication-constrained distributed conformal risk control (CD-CRC) framework, a novel decision-making framework for sensor networks under communication const…
Reliable Sub-Nyquist Spectrum Sensing via Conformal Risk Control
Hyojin Lee, Sangwoo Park, Osvaldo Simeone +2
Detecting occupied subbands is a key task for wireless applications such as unlicensed spectrum access. Recently, detection methods were proposed that extract per-subband features…
Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning
Jiechen Chen, Sangwoo Park, Petar Popovski +2
Neuromorphic computing leverages the sparsity of temporal data to reduce processing energy by activating a small subset of neurons and synapses at each time step. When deployed for…
Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +2
The dynamic scheduling of ultra-reliable and low-latency traffic (URLLC) in the uplink can significantly enhance the efficiency of coexisting services, such as enhanced mobile broa…
Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction
Kfir M. Cohen, Sangwoo Park, Osvaldo Simeone +1
AI tools can be useful to address model deficits in the design of communication systems. However, conventional learning-based AI algorithms yield poorly calibrated decisions, unabl…
Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition
Sangwoo Park, Osvaldo Simeone
An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel…