Machine Learning Explained

When the 5G network is fully rolled out and established, based on the fairly incredible amounts of information which can be transmitted on the network, the sheer amount of data the network administrators will have access to is undoubtedly overwhelming and daunting. The analytics of such data from providers has long been dealt with via automated systems; however, these have their limits and since the nature and size of the beast has changed evolution is required. Many networks are now turning to advanced machine learning, and so in this article we will touch on it, and explain exactly what it is and how useful it can be if deployed efficiently and equitably.

What Is Machine Learning

Machine learning is an entire specialised field in itself, but, in short, is the utilisation of artificial intelligence in a system which gives it the ability to learn and develop or improve automatically, without being explicitly programmed to do so. On the face of it, a very complex algorithm to observe data and real-life examples and look for patterns it has experienced previously and then automatically act upon it. This reaction may be a specific action towards the data subject, or an action which affects the environment utilised by the data subject. All is done autonomously, without human interaction.

How Would This Help 5G?

Machine learning can be used on a 5G network almost exactly as described above. One of its main uses is to take transmitted and received baseband data and use the results to optimise wireless channel encoders. This is an important application. Channel encoding, whether wireless or not, is used to eliminate redundant binary digits from the digitised signal. In a singular instance the optimisation wouldn’t be notable. On an enormous infrastructure, however, the optimisation would be significant and essential in maintaining both a reliable and robust network.

Machine learning is a fast-developing field and we’re looking forward to seeing it evolve along with the evolution of 5G, amongst other things.

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