PhD Thesis Pubic Defense – Machine Learning-based Random Access Techniques for Massive Connectivity
We invite you to attend the PhD thesis defense of Muhammad Awais Jadoon, PhD candidate in the Signal Theory and Communications program at UPC and former Research Assistant at CTTC.
His work focuses on machine learning-based random access strategies to enable massive connectivity in future Internet of Things and 6G wireless networks.
- Doctoral candidate: Muhammad Awais Jadoon
- Thesis title: Machine Learning-based Random Access Techniques for Massive Connectivity
- Date & Time: Thursday, July 16, 2026 – 11:30 (CET)
- Location: CTTC Auditorium (Building B6)
- Directors: Monica Navarro, Adriano Pastore
- Tutor: Antonio Pascual Iserte
Abstract
Massive machine-type communication (mMTC) is a key building block for future wireless networks, where millions of sensors, machines, vehicles, meters, and smart devices are expected to communicate automatically with little or no human involvement. These devices usually send small pieces of information, but they do so unpredictably, and many of them may try to access the wireless network at the same time during events such as alarms, industrial faults, traffic incidents, or environmental changes. This creates an important challenge: how can a network support massive numbers of low-power devices while avoiding congestion, packet collisions, long delays, and unfair access to shared wireless resources? Traditional access methods often rely on fixed rules or heavy coordination, which may not adapt well when the number of active devices and traffic conditions change. This thesis explores the use of reinforcement learning, especially multi-agent reinforcement learning, to make random access more intelligent and adaptive. In this approach, devices learn from simple feedback from the network and gradually improve their decisions about when to transmit and when to wait. The goal is to develop scalable and efficient access strategies that reduce unnecessary transmissions, improve network performance, and provide fairer opportunities for devices to communicate. This work contributes toward smarter connectivity solutions for massive Internet of Things applications and future 6G networks.
Muhammad Awais Jadoon is a Research Engineer at InterDigital and a PhD candidate in the Department of Signal Theory and Communications at UPC. His doctoral research focuses on machine learning-based access techniques for massive connectivity. Previously, he was a Research Assistant at CTTC, where he participated in the Marie Skłodowska-Curie ITN WindMill programme as an Early-Stage Researcher. As part of his research training, he completed research secondments at the Swiss Data Science Center and Nokia Bell Labs. He holds MSc in Electrical Engineering from the University of Ulsan, South Korea, and BSc from the University of Engineering and Technology, Peshawar.