6 papers
FFR: Forward-Forward Learning for Regression
Xinyang Liu, Xuanyu Liang, Shiqi Ding +4
The Forward-Forward (FF) algorithm offers a computationally efficient and biologically plausible alternative to backpropagation (BP) by training neural networks through purely loca…
Scalable machine learning-based approaches for energy saving in densely deployed Open RAN
Xuanyu Liang, Ahmed Al-Tahmeesschi, Swarna Chetty +3
Densely deployed base stations are responsible for the majority of the energy consumed in Radio access network (RAN). While these deployments are crucial to deliver the required da…
Green O-RAN Operation: a Modern ML-Driven Network Energy Consumption Optimisation
Xuanyu Liang, Ahmed Al-Tahmeesschi, Swarna Chetty +1
The increasing energy demand of next-generation mobile networks, especially 6G, is becoming a major concern, particularly due to the high power usage of base station components RU,…
Energy Saving in 6G O-RAN Using DQN-based xApp
Qiao Wang, Swarna Chetty, Ahmed Al-Tahmeesschi +3
Open Radio Access Network (RAN) is a transformative paradigm that supports openness, interoperability, and intelligence, with the O-RAN architecture being the most recognized frame…
Enhancing Energy Efficiency in O-RAN Through Intelligent xApps Deployment
Xuanyu Liang, Ahmed Al-Tahmeesschi, Qiao Wang +3
The proliferation of 5G technology presents an unprecedented challenge in managing the energy consumption of densely deployed network infrastructures, particularly Base Stations (B…
Continuous Transfer Learning for UAV Communication-aware Trajectory Design
Chenrui Sun, Gianluca Fontanesi, Swarna Bindu Chetty +3
Deep Reinforcement Learning (DRL) emerges as a prime solution for Unmanned Aerial Vehicle (UAV) trajectory planning, offering proficiency in navigating high-dimensional spaces, ada…