5 papers
LLM-Based Emulation of the Radio Resource Control Layer: Towards AI-Native RAN Protocols
Ziming Liu, Bryan Liu, Alvaro Valcarce +1
Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic.…
From Simulation to Practice: Generalizable Deep Reinforcement Learning for Cellular Schedulers
Petteri Kela, Bryan Liu, Alvaro Valcarce
Efficient radio packet scheduling remains one of the most challenging tasks in cellular networks, and while heuristic methods exist, practical deep learning-based schedulers that a…
Time-Series JEPA for Predictive Remote Control under Capacity-Limited Networks
Abanoub M. Girgis, Alvaro Valcarce, Mehdi Bennis
In remote control systems, transmitting large data volumes (e.g., images, video frames) from wireless sensors to remote controllers is challenging when uplink capacity is limited (…
A Lossless Compression Technique for the Downlink Control Information Message
Bryan Liu, Alvaro Valcarce, K. Pavan Srinath
Improving the reliability and spectral efficiency of wireless systems is a key goal in wireless systems. However, most efforts have been devoted to improving data channel capacity,…
Optimizing Wireless Discontinuous Reception via MAC Signaling Learning
Adriano Pastore, Adrián AgustÃn de Dios, Ãlvaro Valcarce
We present a Reinforcement Learning (RL) approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network…