7 papers
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,…
An Explainable AI Framework for Dynamic Resource Management in Vehicular Network Slicing
Haochen Sun, Yifan Liu, Ahmed Al-Tahmeesschi +4
Effective resource management and network slicing are essential to meet the diverse service demands of vehicular networks, including Enhanced Mobile Broadband (eMBB) and Ultra-Reli…
Maximising Energy Efficiency in Large-Scale Open RAN: Hybrid xApps and Digital Twin Integration
Ahmed Al-Tahmeesschi, Yi Chu, Gurdeep Singh +4
The growing demand for high-speed, ultra-reliable, and low-latency communications in 5G and beyond networks has significantly driven up power consumption, particularly within the R…
Exploring O-RAN Compression Techniques in Decentralized Distributed MIMO Systems: Reducing Fronthaul Load
Mostafa Rahmani, Junbo Zhao, Vida Ranjbar +4
This paper explores the application of uplink fronthaul compression techniques within Open RAN (O-RAN) to mitigate fronthaul load in decentralized distributed MIMO (DD-MIMO) system…
Enhancing Open RAN Digital Twin Through Power Consumption Measurement
Ahmed Al-Tahmeesschi, Yi Chu, Josh Shackleton +4
The increasing demand for high-speed, ultra-reliable and low-latency communications in 5G and beyond networks has led to a significant increase in power consumption, particularly w…