5 citations · 9 across the 10 of their papers we have counts for
12 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,…
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…
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…