Fifth-generation wireless technology, simply referred to as “5G,” is up to 100x faster than current 4G technologies. 5G wireless systems come with significant complexities that previous generations of cellular wireless did not experience, allowing lower latency for faster responsiveness, and the ability to connect more devices at once. To address these complexities, carriers are integrating artificial intelligence (AI) into their networks.
Defining Machine Learning and Deep Learning in 5G
Machine learning (ML) is a subset of Artificial Intelligence computer algorithms improved through experience rather than through programming. A fundamental 5G characteristic is predicting activity across the network and managing it. Since 5G transmits high volumes of data faster than previous networks, it is ideally suited to work with ML, which requires massive amounts of data to accurately predict activity. Machine learning in 5G is fast, accurate, and relatively seamless.
Deep learning uses many levels within ML, each providing a different interpretation of the data. The subsequent network of algorithms is known as artificial neural networks because it resembles the neural networks of the human brain.
Challenges for Today’s 5G Wireless Networks
Because 5G is so much more complicated than previous generations of wireless networks, machine learning is necessary to make networks work as to full potential. Current 5G systems use more power than predicted with lower actual data rates than estimated without making use of functions such as predictable user and channel estimation effects. The key to addressing these problems is replacing embedded algorithms that have been in place since the 2000s with deep learning designed for 5G and 6G.
Machine Learning Improves 5G Networks Data Traffic
5G networks operate at higher frequencies with very wide channels. They use highly complex antenna configurations which apply beamforming as well as other complicated connectivity systems. 5G networks use multiple-input-multiple-output (MIMO) antennas to handle much more data over the same data signal at the same time. MIMO allows far more data transmitted across the network without negatively impacting the transmission of other data.
Machine learning is the key to handling all that data without interruption and with lower power consumption. ML allows the 5G network to analyze data patterns and use learned models to transmit data more efficiently. Machine learning analyzes the results from transmitted and received baseband data and uses them to optimize wireless channel encoders. The optimizer is an artificial neural network (NN). Using a training algorithm, the NN creates a model of the channel and feeds the data into the ML algorithm. The more data the optimizer is fed, the better it can learn and produce more accurate results.
A 5G network, can therefore, not come close to achieving its full potential, without machine learning. 5G wireless networks must be predictive and proactive without the need for continuous programming of new algorithms. Integrating ML into 5G technology allows intelligent base stations to make decisions for themselves and create dynamically adaptable clusters based on learned data. This improves the efficiency, latency, and reliability of network applications.
How does Machine Learning Help 5G Networks and Vice Versa?
What does all this mean for the everyday data user? By using machine learning to improve network functions, 5G network operators are able to devote less time to managing networks. This allows for an increase in the development of Internet of Things (IoT) devices with more purposeful uses. Furthermore, users are able to connect to more IoT devices at once. IoT devices will become available to a variety of new fields, including business, manufacturing, healthcare, and transportation. Possible use cases include self-driving vehicles, time-critical industry automation, and remote healthcare.
On the flip side, the speed and low latency of 5G allows machine learning algorithms to make rapid decisions. Once a NN has been trained to perform a task or set of tasks, the analysis will become automatic, faster, and far less costly. Therefore, cloud-based services will speed up to approximate the speed of using a service locally. The data is then analyzed faster, allowing AI to develop according to a user’s needs.
With ultra-reliable low latency and data transmission up to 10 times faster than 4G, 5G networks powered by machine learning are poised to make the futuristic imaginings of the past a reality sooner rather than later.
