<h3>Harnessing AI-driven traffic forecasts to reduce network energy consumption by 50% without impacting user experience</h3> <p>Improving the energy efficiency of mobile networks is critical, both from an operational expenditure standpoint (given the rising electricity prices) and from an environmental perspective (industries have ambitious goals to reduce their CO2 footprint in the face of the ...

<p>In the 5G era, capital expenditure (CAPEX) and operating expenditure (OPEX) are dramatically increasing. Driven by exponentially increasing traffic demands and with mobile networks pressured to become more energy efficient, changes need to be made to ensure networks adapt to these challenges whilst retaining high quality of service.</p> <p><strong>Enter the O-RAN Universe</strong></p> <p>In th...

<p>One must experience being locked in a trance watching the hypnotically slow updates of a progress bar to truly know the pain of long-running compute jobs. Luckily for most of us in the machine learning space, there is a simple way to reduce this headache: distributed training on multiple GPUs. While incurring a higher capital expenditure, due to the extra hardware required, faster experiment i...

<p>5G infrastructures will consume significantly more power than the 4G systems they replace. This is in part due to the inexorable growth of user data supported by the much greater capacity available through 5G technologies, but also due to the larger number of base stations deployed to support this capacity. As a result, over 90% of leading mobile network operators have expressed concerns about...

<p>Decomposition is a novel technology for service-level traffic stream quantification, which complements and enhances current traffic classification solutions that are heavily based on Deep Packet Inspection (DPI).</p> <p>Examining network traffic through DPI can be expensive, as it requires high-performance computing platforms or dedicated hardware to process individual packets at ever increasi...