Machine Learning:The Panaceafor 5G Complexities

Abstract

It's not a myth that transition in next generation technology brings with it a set of exciting applications as well as challenges to the telecom ecosystem and in-turn paves way for new revenue streams. 5G enables ultra-high data rates, exceptional low latencies which enables the telecom operator for the facilitation of interesting parallels like IoT and Next-Gen Industrial enhancements like autonomous vehicles, connected mines, connected agriculture and mission critical communications by enhancing infrastructure, software and hardware components of the 5G system. As imminent new features of 5G like Multiple Input Multiple Output (MIMO), network slices, virtual network functions, indoor localization, Machine to Machine (M2M) capabilities are highly appreciated, it also opens new set of challenges like real time dynamic configurations, low latency handovers. These challenges can be addressed with the application of AI technologies to components at the crux of 5G system. Here in this paper, we discuss some of the major challenges such as data burst, improving performance, fault tolerance and traffic management with new components appended to the 5G system, required upgrades to existing technology and how Machine Learning (ML), Artificial Intelligence (AI), becomes the self-evident answer to these stumbling blocks.

On the challenges of optical disaggregated data center networking for ML/AI applications

“…The rise of popularity of Machine Learning (ML)/ Artificial Intelligence (AI) applications to boost performance at all levels [1], [2] has raised the need to make available computational capacities at different points of the whole network and communication infrastructure so as to be able to execute said applications, either in the form of small edge computational sites or larger data center sites. Indeed, ML/AI applications impose an intense usage of computational resources in the form of CPU/GPU power and memory, as well as large storage spaces, for the training of the algorithms, with a still notorious, although reduced, usage of computational resources during the inference phase [3], [4].…”