Multiagent RL and Scalability Challenges for Random Access in MTC
Machine-type communication (MTC) paradigm in wireless communication will help connect millions of devices to perform different tasks without human involvement. There are numerous use cases of MTC, such as a factory automation, fleet management, smart homes and smart metering, e-health and smart logistics, etc. Mostly, the devices called machine-type devices (MTDs) in the MTC network…
Muhammad Jadoon
My experience as a Marie Curie ESR
I am a Marie Curie PhD fellow and part of ITN WINDMILL as an early stage researcher (ESR). We are working on the integration of machine learning algorithms in wireless communications networks. I am often contacted by students about my experience as an ESR and about Marie Curie fellowship, and the most commonly asked question…
Slotted ALOHA with Reinforcement Learning
In this article we will discuss the potential of reinforcement learning (RL) to learn a backoff control policy for slotted ALOHA-type random access. We will use deep reinforcement learning (DRL) to learn a policy for multi user random access system. Slotted ALOHA Background Slotted ALOHA (sALOHA) protocol [1] for random access in wireless networks has…