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5G wireless technology promises to dramatically improve the information, computer and communications industries by enabling countless new services with diverse requirements. The complex technologies on the horizon manage massive amounts of data. 5G wireless networks need machine learning (ML) to manage the volume of data with near light speed. More specifically, deep learning in 5G must be applied to reduce power consumption and improve performance.

What is Deep Learning?

Deep learning is a branch of artificial intelligence (AI) which mimics how the human brain processes information and creates patterns to aid in decision making. Deep learning is a subset of machine learning (ML) using artificial neural networks to perform unsupervised learning from unstructured or unlabeled data.

With the explosion of “big data” in every imaginable form from every industry, deep learning has evolved rapidly in recent years. Big data is drawn from sources such as social media, internet search engines, e-commerce platforms, and more. This data is easily accessible through cloud computing and other similar technologies. Still, it is mostly unstructured, and there is so much of it that it would take many years for humans to consume it and extract relevant information. AI systems are being developed to automate the unraveling of this vast information.

Machine learning uses a self-adaptive algorithm that improves on its ability to analyze and learn patterns with experience and additional data. Deep learning employs a hierarchical process of artificial neural networks to perform machine learning. Like a human brain, artificial neural networks contain neuron nodes that connect in a web-like pattern. This hierarchical function allows machines to process data in a nonlinear method, unlike traditional programs that are linear in their approach to analyzing data.

Deep Learning for 5G

Deep learning technologies have recently emerged as the most promising tool for handling complicated 5G wireless systems. An extraordinary amount of data is gathered when a 5G network and a user’s wireless devices communicate. Multiple variables come into play across the system, including user registration, calls, handover, IP assignment, data flow, and more. Deep learning is necessary to parse all this data across these variables. The data is then fed to the deep learning neural networks and used to improve the network’s ability to analyze the data and reach conclusions based on patterns. In this manner, deep learning impacts 5G wireless just as 5G wireless then further impacts deep learning.

Unlike previous generations of wireless technology, 5G makes use of new, high bandwidth frequencies to provide services, routing communications based on the speed needed for the service. This ultimately allows for improved and entirely new services that were previously only dreamed of. New techniques must be combined to make all this work. These include disruptive technologies, Internet-of-things (IOT), device-centric architecture, millimeter-wave (mmW) frequencies, Massive Multiple Input Multiple Output (MIMO), smarter devices for vehicle-to-vehicle (V2V) communication, and native support for machine-to-machine (M2M) communication.

Due to the number of highly interactive structures involved in the era of digital transformation, deep learning neural networks are imperative to meet the sophisticated requirements of 5G wireless systems.

Tools for Deep Learning on 5G Wireless Technology

The goal of 5G systems is to provide ultra-high throughput and ultra-low latency services to improve user experience. These underlying architectures for these new services are best implemented with deep learning methods. Latest generation hardware and software are required to support deep learning training and inference in complex settings. Tools that make deep learning on 5G wireless networks possible include:

  • Advanced parallel computing—Graphics processing units (GPUs) that incorporate thousands of cores speed up the learning process exponentially.
  • Distributed machine learning systems—Mobile network data is collected from a wide variety of sources and stored in multiple data centers. Distributed machine learning systems that support various interfaces are therefore required to allow deep learning training and evaluation across distributed servers.
  • Dedicated deep learning libraries—Dedicated libraries that allow programmers to access information others have already developed rather than building deep learning models from scratch simplify the process significantly.
  • Fast optimization algorithms—The objective functions that must be optimized through deep learning are quite complex, involving sums of extremely large numbers of functions. To improve the optimization process, newer algorithms that are more evolved than traditional ones allow neural network models to learn faster for mobile applications.
  • Fog computing—A set of techniques that allows applications and data storage to be deployed at the edge of networks, fog computing reduces communications overhead, offloads data traffic, reduces latency on the user end, and minimizes computational burdens on the server end.

Source: IEEE, Deep Learning in Mobile and Wireless Networking: A Survey

Because of the complex and diverse services expected to come about as 5G wireless technology advances over the next few years, deep learning will impact nearly every aspect of 5G wireless systems. High speed, low latency, and exceptional customer experience cannot be achieved without deep learning. An extreme amount of data from a variety of sources in numerous geographical locations must all come together to make these systems function. Deep learning algorithms are the key to bringing it all together and making the top 5G innovations predicted for tomorrow rapidly become innovations for today.

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