How Machine Learning Will Boost 5G Network?

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Introduction

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) into their networks.

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 do Machine Learning and 5G Networks interact with one another?

How does this affect the average data user? With machine learning to improve network functions, 5G network operators can spend less time managing networks. This allows for increased development of Internet of Things (IoT) devices with more efficient use. Additionally, users can connect to multiple IoT devices at once. IoT devices will become available for various new areas, including commerce, manufacturing, healthcare, and transportation. Possible use cases include self-driving vehicles, time-critical industrial automation, and remote healthcare.

On the other hand, the speed and low latency of 5G allow machine learning algorithms to make quick decisions. Once an NN is trained to perform a task or function, the analysis becomes automatic, faster, and much less expensive. Therefore, cloud services will be accelerated to approach the speed of using the service locally. The data is then analyzed more quickly, allowing the AI to evolve according to the user's needs.

With data transfer rates up to 10 times faster than 4G and ultra-reliable low latency, 5G networks powered by machine learning are ready to quickly bring the future ideas of the past to life.

5G machine learning
Every research community has attempted to assess how machine learning would affect 5G in their field of study in light of the growing benefits and advancements of ML in wireless communication. This makes it very challenging to provide a concise summary of the application of AI/ML and its influence in 5G. As a result, we can categorise the works that apply ML to 5G into two categories: "generic ML classification," where we take into account all potential ML approaches from the literature, and "deep learning-based categorization," which only considers deep learning. Several leading publications think deep learning to be the most promising ML approach for the high complexity of 5G.

Main Goals and Components of 5G

Higher mobile data speeds propelled the development of 4G/LTE, but the 5G system faces harsher and more varied criteria, as shown in Table 1. The rollout of 5G technologies aims to provide extremely low connection latency and high throughput to enhance user experience (QoE). To meet these requirements, 5G focuses on three development axes to cope with new application areas: autonomous cars/driving, industrial automation/intelligent manufacturing (Industry 4.0), virtual reality, e-health, etc.
5G enablers directly contribute to network performance; However, an operational and efficient 5G network cannot be complete without artificial intelligence. For instance, 5G makes it possible to connect to several Internet of Things devices simultaneously and generates enormous amounts of data that need to be analysed using ML and AI. Internally integrating ML and AI allows wireless service providers to, for instance:

•  Identify dynamic changes and predict user distribution by analyzing historical data,
•  Forecasting peak traffic, resource usage, and application types; and optimizing and fine-tuning network parameters to expand capacity,
•  Eliminate gaps in coverage by measuring interference and using site-to-site distance information,
•  High degree of automation made possible by a distributed ML and AI architecture at the network's edge,
•  Application-based traffic control and aggregation across heterogeneous access networks,
•  Dynamic network partitioning to address different use cases with additional QoS requirements,
•  ML/AI-as-a-service offering to end users etc.

ML Applications in 5G Networks

Feature Extraction: Deep neural networks have the ability to automatically extract high-level information through layers of varying depths. This makes it possible to reduce costly hand-crafted feature design when handling heterogeneous and noisy mobile big data.

Leveraging Big Data: Unlike traditional ML tools, the performance of deep learning typically increases significantly with the size of the training data. Therefore, it can effectively use the vast amount of mobile data generated at high speed.

Unsupervised Learning: Deep learning efficiently processes un-/semi-labelled data, enabling unsupervised learning. This is important when handling large amounts of unlabeled data in mobile systems.

Multi-Task Learning: Transfer learning allows neural networks to adapt their hidden layer learning to a variety of tasks. This reduces the computational and memory requirements when multi-task learning in mobile systems.

Geometric Mobile Data Learning: There are specialized deep learning architectures for modeling geometric mobile data that revolutionize the analysis of geometric mobile data.

Conclusion

Machine learning (ML) technology is currently used in industry and academia due to its data-driven function to achieve high network performance. ML algorithms can be used across the network to predict potential problems without needing external resources. ML is a promising technique for detecting attacks in and beyond 5G networks.

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