How Artificial Intelligence Improves 5G Wireless Capabilities

Here's What We Will Cover:

  1. What is Artificial Intelligence?
  2. What is 5G?
  3. What Role will Artificial Intelligence Play on 5G Networks?
  4. Why is 5G Relevant to the Field of AI?
  5. How Does 5G Help AI?
  6. How Machine Learning is Impacting 5G Wireless Technology
  7. How Can We Leverage Machine Learning and AI for 5G?
  8. Why is Machine Learning Important for 5G Wireless Systems?
  9. Potentials and Limitations of Machine Learning for 5G Communications
  10. Top 5G Innovations on the Horizon

What is Artificial Intelligence?

Artificial Intelligence and 5G Wireless Capabilities

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, usually computer systems. AI programs focus on three main cognitive skills: learning (acquiring data and creating rules for sorting that data), reasoning (choosing the right data to achieve the desired outcome), and self-correction (fine-tuning the data sorting for the most accurate results). The data sorting rules are known as algorithms, which offer step-by-step instructions for how to achieve an outcome.

What is 5G?

All US cellular carriers have now launched some form of 5G. What is 5G? 5G simply stands for fifth-generation cellular wireless. Its standards were first set in late 2017. There are three basic types of 5G service: low-band, mid-band, and high-band. They’re all incompatible right now, and they all perform differently. Even though all the US carriers “have” 5G right now, it will be another couple of years before we see significant changes from it. By comparison, 4G first rolled out in 2010, and it was 2012/2013 before major apps that required 4G to work became popular. However, Ericsson, a leading provider of Information and Communication Technology (ICT) for service providers, estimates that by 2024, 40% of the world will be connected by 5G.

The “G” in 5G simply stands for “generation.” 1G was analog cellular service. 2G technologies were the first generation of digital cellular technologies. 3G technologies improved speeds from 200kbps to several megabits per second. 4G technologies are currently offering hundreds of Mbps and even up to gigabit-level speeds. 5G offers several new aspects: bigger channels to offer faster speeds, lower latency for higher responsiveness, and the ability to connect more devices at once.

The Evolution of 5G

Image Source: Towards Data Science

What Role will Artificial Intelligence Play on 5G Networks?

The Role of artificial intelligence on 5G networks

There are many complexities inherent in adopting 5G networks, and one way the industry is addressing those complexities is by integrating artificial intelligence into networks. When Ericsson surveyed decision-makers from 132 worldwide cellular companies, over 50% said they expected to integrate AI into their 5G networks by the end of 2020. The primary focus of AI integration is reducing capital expenditures, optimizing network performance, and building new revenue streams. 55% of decision-makers stated that AI is already being used to improve customer service and enhance customer experience by improving network quality and offering personalized services. 70% believe that using AI in network planning is the best method for recouping the investments made on switching networks to 5G. 64% of survey respondents will focus their AI efforts on network performance management. Other areas where cellular decision-makers intend to focus AI investments include managing SLAs, product life cycles, networks, and revenue.

There are challenges associated with integrating AI into 5G networks, of course. Effective mechanisms for collecting, structuring, and analyzing the enormous volumes of data amassed by AI must be developed. For that reason, early AI adopters who find solutions to these challenges will emerge as the clear frontrunners as 5G networks become connected.

Why is 5G Relevant to the Field of AI?

While our smartphones have gotten increasingly smaller, the core algorithms that run them have not evolved since the 1990s. Therefore, 5G systems consume far more power than desired and achieve lower data rates than expected. Replacing traditional wireless algorithms with deep learning AI will dramatically reduce power consumption and improve performance. This approach will be fundamentally more significant than focusing AI primarily on network management and scheduling.

Further, bandwidth used by current cellular networks operates on the radio spectrum. The electromagnetic waves in the frequency range of the radio spectrum are called radio waves. Radio waves are widely used in telecommunication, along with numerous other modern technologies. National laws strictly regulate interference between users of different radio waves, and the International Telecommunication Union (ITU) oversees the coordination of these laws. There is concern that the growing use of wireless technologies will overcrowd the airwaves our devices use to communicate with one another. One proposed method for resolving this issue is to develop communication devices that don’t broadcast on the same frequency every time. AI algorithms would then be used to find available frequencies by enabling intelligent awareness of RF activity that was not previously feasible.

5G and the field of AI

While 5G is up to 20 times faster than 4G, it offers more than just faster speeds. Due to its low latency, 5G speeds will allow developers to create applications that take full advantage of improved response times, including near real-time video transmission for sporting events or security purposes. Additionally, 5G connectivity will allow more access to real-time data from various solutions. 5G leverages Internet of Things (IoT) sensors that last for years, requiring far less power for operation. This could allow remote detection of farming irrigation levels and equipment condition changes in factories. Doctors could securely access patient data more easily. All these opportunities will require the use of AI to make them functional.

How Does 5G Help AI?

Edge computing is the concept of processing and analyzing data in servers closer to the applications they serve. While it is growing in popularity and opening new markets for telecom providers, among other industries, many have argued that introducing “connected” products, such as coffee cups and pill dispensers, did not cause the market to spike as expected. Recent AI technology advancements, however, have begun to revolutionize industries and the amount of value all this connectivity can provide to consumers by combining big data, IoT, and AI.

5G accelerates this revolution because the 5G network architecture easily supports AI processing. The 5G network architecture will change the future of artificial intelligence. 5G will enhance the speed and integration of other technologies, while AI will allow machines and systems to function with intelligence levels similar to that of humans. In a nutshell, 5G speeds up the services on the cloud while AI analyzes and learns from the same data faster.

How Machine Learning is Impacting 5G Wireless Technology

What is Machine Learning?

Simply put, machine learning (ML) is a subset of AI that creates algorithms and statistical models to perform a specific task without using explicit instructions, relying instead on patterns and inference. ML algorithms build mathematical models based on sample data, called training data, to make predictions or decisions without being programmed specifically for that task. Learned signal processing algorithms can empower the next generation of wireless systems with significant reductions in power consumption and improvements in density, throughput, and accuracy when compared to the brittle and manually designed systems of today.

Deep learning is a subset of machine learning in which the algorithms used have many levels that each provide 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. Neural networks that learn how to communicate effectively, even under harsh impairments, are fast becoming a reality.

How Can We Leverage Machine Learning and AI for 5G?

machine learning and AI for 5gA fully operative and efficient 5G network cannot be complete without AI. ML and AI integration into the network edge can be achieved through the use of 5G networks. 5G enables simultaneous connections to multiple IoT devices, generating massive amounts of data that must be processed using ML and AI.

When ML and AI are integrated with 5G multi-access edge computing (MEC), wireless providers can offer:

  • High automation levels from the distributed ML and AI architecture at the network edge
  • Application-based traffic steering across access networks
  • Dynamic network slicing to address different scenarios with varying quality of service (QoS) requirements

Why is Machine Learning Important for 5G Wireless Systems?

Existing 4G networks use Internet Protocol (IP) broadband connectivity to transmit, which offers poor efficiency. ML and AI allow 5G networks to be predictive and proactive, which is essential for 5G networks to become functional. By integrating ML into 5G technology, intelligent base stations will be able to make decisions for themselves, and mobile devices will be able to create dynamically adaptable clusters based on learned data. This will improve the efficiency, latency, and reliability of network applications.

Potentials and Limitations of Machine Learning for 5G Communications

As the 5G network becomes increasingly complex and novel uses such as autonomous cars, industrial automation, virtual reality, e-health, and others emerge, ML will become essential in making the 5G vision a reality. As with any new technology, there are both significant potentials to be achieved and limitations to be overcome.

Potentials of ML for 5G communications include:

  • Enhanced mobile broadband (eMBB): Allows for new applications with higher data rate demands over a uniform coverage area. Examples include ultra-high-definition video streaming and virtual reality.
  • Massive machine-type communications (mMTC): A key characteristic of 5G communication services is the scalable connectivity demand for expanding the number of wireless devices with efficient transmission of small amounts of data over extended coverage areas. Applications like body-area networks, smart homes, IoT, and drone delivery will generate this type of traffic. mMTC must be able to support new and as yet unforeseen uses.
  • Ultra-reliable low-latency communications (URLLC): Connected healthcare, remote surgery, mission-critical applications, autonomous driving, vehicle-to-vehicle (V2V) communications, high-speed train connectivity, and smart industry applications will prioritize reliability, low latency, and mobility over data rates.


Limitations of ML for 5G communications include:

  • Data: High-quality data is essential to ML applications, and the type of data (labeled or unlabeled) is a crucial factor when deciding which type of learning to use. ML is only as good as the data it receives.
  • No Free Lunch theorem: This theorem states that if all possible data-generated distributions are averaged, every ML algorithm will have the same performance when inferring unobserved data. This means that the goal of ML is not to seek the absolute best learning algorithm, but to understand what kind of distribution is relevant to a specific 5G application and which ML algorithm has the best performance on that particular data.
  • Hyperparameters selection: Hyperparameters are values set in ML algorithms before training begins. These values must be selected carefully because they influence the eventual parameters that are updated from the learning outcomes.
  • Interpretability vs accuracy: From a stakeholder standpoint, the complex interactions between independent variables can be difficult to understand and might not always make business sense. Therefore, a tradeoff must be made between interpreting data and complete accuracy.
  • Privacy and security: ML algorithms might be subject to adversarial attacks, such as modification of an input sample to force a model to classify it in a different category from its genuine class.

Top 5G Innovations on the Horizon

With all this potential for using ML and AI to integrate with 5G networks, industries are already working toward innovating with 5G. Some of the top innovations on the horizon include:

  • Sports: 5G will provide advanced viewing features such as 3D viewing and various perspective views of a live game.
  • Wireless virtual reality (VR): With 5G, users will be able to enjoy VR content anywhere at any time.
  • Augmented reality (AR): 5G will provide realistic AR services such as virtual zoos.
  • Live performances: 5G will provide extremely high-quality live performances from wireless devices.
  • Game streaming: Games will be processed on the cloud via 5G and streamed while allowing input from others.
  • Online sing-alongs: Many people will be able to sing along online together using 5G capabilities.
  • Self-driving cars: This technology will require computing power that can only be achieved through 5G networks and AI as 3D maps of cities are uploaded to vehicles in real-time, traffic is updated, and software updates are pushed through.
  • Wireless home: Some of the earliest 5G devices will include wireless hotspots for the entire home.
  • Low-power scanners such as certain farm equipment, ATMs, medical equipment, and remote control heavy machinery: These items don’t need a constant connection and will, therefore, be able to work on the same battery for 10 years while still periodically sending data. Technicians with specialized skills will be able to operate machinery from anywhere in the world.
  • Public safety and infrastructure: Cities and other municipalities will be able to operate more efficiently using 5G networks. Utility companies will be able to track usage remotely, sensors can notify public works departments when drains flood or streetlights burn out, and municipalities will be able to quickly and cheaply install surveillance cameras.
  • Healthcare: Telemedicine, remote recovery, physical therapy via AR, precision surgery, and even remote surgery are all possibilities. Hospitals will be able to create sensor networks to monitor patients, physicians will be able to prescribe smart pills to track compliance, and insurers will be able to monitor subscribers to determine appropriate treatments and processes.

    Top 5G Innovations on the Horizon infographic
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