Why is Machine Learning Important for 5G Wireless Systems?

Why is machine learning important for 5G wireless systems?

The fifth generation wireless technology, simply called "5G", is 100 times faster than the current 4G technology. 5G wireless systems come with significant complexity not experienced by previous generations of mobile wireless networks, allowing for lower latency, faster response, and the ability to connect multiple devices simultaneously. To deal with these complexities, carriers integrate artificial intelligence (AI).

Defining machine learning in 5G

A subset of artificial intelligence computer algorithms known as machine learning (ML) are enhanced by experience rather than programming. The prediction of network activity and its management are key aspects of 5G. Since ML requires enormous amounts of data to accurately predict activities, 5G is perfect for ML work because it sends large volumes of data faster than prior networks. In 5G, machine learning is quick, precise, and almost seamless.

Challenges for today's 5G wireless networks

Because 5G is much more complicated than previous generations of wireless networks, machine learning is essential for networks to operate at their full potential. Current 5G systems use more energy than predicted, with lower actual data rates than estimated, without taking advantage of features such as predictable user and channel estimation effects. The key to solving these problems is to replace the embedded algorithms that have been in place since 2000 with deep learning designed for 5G.

Machine learning improves the data traffic of 5G networks

5G networks operate at higher frequencies with extensive channels. They use highly complex antenna configurations, beamforming, and other complicated connection systems. 5G networks use multiple-input-multiple-output (MIMO) antennas to simultaneously process much more data over the same data signal. MIMO allows much more data to be transmitted over the network without negatively impacting other data transmission. Machine learning is the key to processing all this data without interruption and with lower power consumption. ML enables the 5G network to analyze data patterns and use learned models to transfer data more efficiently. Machine learning examines the outcomes of baseband data that is transmitted and received and uses them to improve wireless channel encoders. It uses an artificial neural network as the optimizer (NN). It builds a channel model using the NN training technique and sends the data into the ML algorithm. The optimizer can learn and deliver more accurate results as it is given more data. Therefore, without machine learning, the 5G network cannot perform to its full potential. Without the need to constantly design new algorithms, 5G wireless networks must be proactive and predictive. Intelligent base stations may now make their own judgments and build dynamically adaptive clusters based on learnt data thanks to the incorporation of ML into 5G technology. This enhances the dependability, efficiency, and latency of network applications.

How machine learning is impacting 5G wireless technology

Machine learning (ML), to put it simply, is a subset of artificial intelligence that develops statistical models and algorithms to carry out certain tasks without explicit instructions, relying instead on patterns and inference. ML algorithms develop mathematical models based on sample data, or training data, to make predictions or judgments without being explicitly programmed for the purpose. When compared to today's fragile and manually-designed systems, learned signal processing algorithms can power the next generation of wireless devices with considerable reductions in power consumption and gains in density, throughput, and accuracy.

How can we use machine learning for 5G?

A fully functional and efficient 5G network cannot be complete without artificial intelligence. Integrating ML and AI at the network edge can be achieved with 5G networks. Massive amounts of data must be processed using ML and AI due to the simultaneous connections to many IoT devices made available by 5G.

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

• A high level of automation thanks to a distributed ML and AI architecture at the edge of the network.
• Application-based traffic control across access networks.
• Dynamic network partitioning addresses different scenarios with different Quality of Service (QoS) requirements.
• Introducing 5G enhancements using machine learning.

At this year's Mobile World Congress Barcelona, we showcased some of our latest 5G technology innovations across various disciplines. One common theme in many of our demos is the use of machine learning techniques to improve the overall 5G system. Our demos show how AI can benefit from the power and efficiency of 5G operating in various use cases in the sub-7GHz and mmWave bands. Check out our demos that use machine learning to improve:

• Massive MIMO channel state feedback for increased user throughput and system capacity.
• Mobile mmWave beam prediction for higher capacity and longer battery life.
• Positioning accuracy thanks to a combination of 5G measurements, GNSS, multipath profiles, sensor inputs.
• Network planning for efficient mmWave coverage expansion with different types of nodes.
• AI-powered RF sensing for indoor positioning today with Wi-Fi, 5G in the future.

Potentials and limitations of machine learning for 5G communications

As the 5G network becomes increasingly complex and new uses emerge, such as autonomous cars, industrial automation, virtual reality, e-health, and more, ML will be critical to making the 5G vision a reality. As with any new technology, there is significant potential to be achieved and limitations to be overcome.

ML potentials for 5G communications include:

• Enhanced Mobile Broadband (eMBB): Enables new applications with higher data rate requirements in a uniform coverage area. Examples ultra-high definition video streaming and virtual reality.
• Massive machine-type communications (MMTC): The scalable need for connectivity to increase the number of wireless devices with effective transmission of small amounts of data across wide coverage areas is a major feature of 5G communications services. This kind of traffic will be produced by applications like body-area networks, smart homes, IoT, and drone delivery. mMTC needs to be able to accommodate novel and unanticipated uses.
• Ultra-Reliable Low Latency Communications (URLLC): Connected healthcare, remote surgery, critical applications, autonomous driving, vehicle-to-vehicle (V2V) communications, high-speed train connectivity, and smart industrial applications will prioritize reliability, low latency, and mobility over data rates.

ML limitations for 5G communications include:

• Data: High-quality data is essential for ML applications, and the type of data (labeled or unlabeled) is critical in deciding what kind of learning to use. The quality of ML depends on the data it is fed.
• No Free Lunch Theorem: The no-free-lunch principle This theorem states that all ML algorithms will be equally effective at inferring unobserved data if all distributions that could be produced by the data are averaged. This indicates that the objective of ML is to determine what kind of distribution is relevant for a particular 5G application and which ML method performs best on certain data, not to develop the best learning algorithm possible.
• Hyperparameter selection: Hyperparameters are values set in ML algorithms before training begins. These values must be chosen carefully as they affect any updated parameters from the learning results.
• Interpretability versus accuracy: From a stakeholder perspective, complex interactions between independent variables can be challenging to understand and may not always make business sense. Therefore, a trade-off must be made between data interpretation and complete accuracy.
• Privacy and security: ML algorithms can be exposed to adversarial attacks, such as modifying the input sample to force the model to classify it in a different category than its true class.

Conclusion

Machine learning is changing how the next generation of 5G systems is designed. Using machine learning to solve 5G wireless connectivity problems has shown better performance than the traditional 4G communication system design method.