Multimodal Communications for 5G and Beyond

- Overview

5G networks are designed to be heterogeneous and converged, combining licensed and unlicensed wireless technologies. They can use a variety of spectrum resources, from sub-3 GHz to 100 GHz and beyond, and can operate in both lower bands and mmWave. 5G networks are also virtualized and software-driven, and they use cloud technologies. 

5G networks can support multimodal communication, which combines 5G with other technologies like artificial intelligence (AI) and natural language processing (NLP). This combination can enable new capabilities, such as:

• Fast uploads, downloads, and streaming: 5G's high speed and low latency can support the fast upload, download, and streaming of short videos.
• Real-time interactivity: 5G can ensure real-time interactivity.
• Rapid response speeds: 5G can enable rapid response speeds for digital marketing and smart digital agriculture e-commerce platforms.

5G is expected to improve connectivity in underserved areas and cities. It can also enhance digital experiences through machine-learning-enabled automation. By 2029, Ericsson estimates that there will be nearly 40 billion connected IoT devices in the world, and 5G will be a platform to connect them all.

- Leveraging Machine Learning and Artificial Intelligence for 5G

The heterogeneous nature of future wireless networks, consisting of multiple access networks, frequency bands and cells (all with overlapping coverage areas), creates network planning and deployment challenges for wireless operators. 

Machine learning (ML) and artificial intelligence (AI) can help wireless operators overcome these challenges by analyzing geographic information, engineering parameters and historical data:

• Predict peak traffic, resource utilization and application types
• Optimize and fine-tune network parameters to achieve capacity expansion
• Eliminate coverage holes by measuring interference and using inter-site distance information

5G can be a key enabler in bringing ML and AI to the edge of the network. The integration of ML and AI with 5G multi-access edge computing (MEC) enables wireless operators to deliver:

• Decentralized ML and AI architecture at the network edge enables high levels of automation
• Application-based traffic steering and aggregation across heterogeneous access networks
• Dynamic network slicing addresses various use cases with different QoS requirements
• Providing ML/AI as a service to end users

- AI and ML Evolve Alongside Wireless Cellular Networks

The Third Generation Partnership Project (3GPP) has been working hard to develop specifications to further integrate AI/ML into 5G and Beyond networks. The great work continues to be done as wireless cellular networks prepare to take advantage of some of the latest advances in AI and ML. 

When it comes to traditional applications of AI and ML in wireless cellular networks, efforts tend to focus on a few key areas where intelligent classification and regression are useful, including:

• Enhance performance through traffic forecasting and management, where ML models analyze traffic patterns to predict demand surges and adjust network resources accordingly. It then automates resource allocation by dynamically allocating bandwidth and other network resources where they are needed most, optimizing the performance of high-demand applications such as streaming media, gaming and virtual reality. 
• Improve security through anomaly detection, where AI can monitor network traffic in real time to detect and respond to unusual patterns that may indicate security threats, such as DDoS attacks or unauthorized access attempts. In addition, AI and ML can enhance security protocols, including developing more secure biometric authentication methods and detecting vulnerabilities in network infrastructure.
• AI can help with network slicing, as this network feature allows operators to create multiple virtual networks with different characteristics on a single physical infrastructure. This is critical to supporting a variety of applications, from IoT devices with lower data requirements to high-bandwidth applications such as 4K video streaming, which have specific requirements for latency, speed and reliability. 
• Enhanced user experience. Even without network slicing, artificial intelligence can be used to analyze network conditions and user behavior to dynamically adjust quality of service (QoS) settings to ensure optimal service levels for various applications and services. Artificial intelligence is used together with predictive analytics to evaluate data on user behavior and device performance, helping to predict user needs and adjust services accordingly to enhance user experience.