AI as a Key Growth Driver
In today’s rapidly evolving digital landscape, the world of telecommunications is more interconnected than ever. With the evolution from 5G to 6G, expansion of IoT devices, and proliferation of cloud technologies, the ways in which society communicate, work, and live is much more hyperconnected. This translates into more real-time data exchange, seamless connectivity, and constant access to information, as they all become necessities. This new era is reshaping industries, driving innovation, and creating unprecedented opportunities for global corporations.
As networks grow more intelligent and infrastructure becomes increasingly adaptable, the telecom sector plays a pivotal role in enabling this global ecosystem that connects people, devices, and data in ways previously unimaginable. The size and magnitude of the telecom industry has exponentially increased in the past year, and the market data provides proof. According to leading market research firm HG Insights, the size of the global telecommunications industry will reach over $57 billion by the end of 2025.
Artificial intelligence (AI) is a primary growth driver, which this technology creating new opportunities for innovation. As AI becomes more ubiquitous throughout society, the benefits are tremendous, including superior efficiency, improved accuracy, and stronger data-driven insights. In particular, AI optimizes organizational efficiency by automating repetitive tasks, allowing corporations to maintain which AI has transformed the telecom industry, particularly with the evolution from 5G to 6G, artificial intelligence radio access network (AI RAN), and network slicing. In this blog post, discover several ways AI has transformed the telecom industry, including the evolution from 5G to 6G, artificial intelligence radio access network (AI RAN), and network slicing.
Evolution from 5G to 6G
First, let’s examine the evolution from 5G to 6G. Next-generation use of this technology requires collaboration between service providers, software developers, and hardware manufacturers. Essentially, 6G will build on the foundations of 5G, while addressing its limitations.
The benefits of 6G will extend beyond the area of speed. First, 6G will be able to deliver much more rapid traffic than 5G, with the potential to reach several hundred gigabits per second (Gbps). In addition, 6G uses higher frequencies, including terahertz (THz) bands, to achieve these accelerated speeds. In terms of latency, 6G will also have much lower latency than 5G, with the goal of supporting one microsecond latency communications. AI and edge computing utilize 6G to embed AI into the network core to enable self-optimizing networks that adapt to changing conditions. Another key benefit is security, 6G providing enhanced security features, including the ability to embed advanced encryption and quantum-resistant algorithms into its infrastructure to ensure robust security. Finally, 6G will also have improved adaptability and programmability, simplified architecture design, improved energy performance, and the capability to support trillions of devices.
One use case for this evolution to 6G is ultra-reliable low-latency communication (URLLC) for mission-critical applications. This has profound implications in healthcare, autonomous vehicles, and industrial automation. Within healthcare, 6G enables real-time, ultra-low-latency communication between medical devices, wearable health monitors, and cloud-based AI systems. For instance, AI algorithms can detect and predict health anomalies in real time, with immediate response from healthcare professionals. This is particularly beneficial for patients in remote areas. By enabling high-resolution 3D imaging, tactile feedback (via haptic technology), and real-time collaboration across the globe, this advances telemedicine.
AI RAN
Artificial intelligence radio access network (AI RAN) is another trend that uses AI to accelerate the industry. As an evolution from ORAN, AI RAN uses artificial intelligence techniques to enhance and optimize the performance of Radio Access Networks (RAN), which are part of the mobile network responsible for connecting end devices (such as smartphones) to the core network through radio communication.
This integration of artificial intelligence (AI) into the Open Radio Access Network (ORAN) architecture adds an additional layer of intelligence to the network management and optimization capabilities. This is made possible by O-RAN’s open and modular design, particularly with the use of the RAN Intelligent Controller (RIC) to implement AI-based functions. Furthermore, AI RAN leverages machine learning (ML) and other AI technologies to improve various aspects of RAN.
The advantages of AI RAN are multifold. First, one key benefit is network optimization. With this capability, AI algorithms can analyze real-time data to optimize resource allocation, signal strength, and interference management, leading to better coverage and more efficient use of network resources. Next, by improving traffic management, AI predicts and manages network traffic, ensuring that resources are allocated dynamically based on demand, reducing congestion and improving user experience.
In addition, AI RAN enables networks to create self-organizing networks (SON). These networks can automatically configure, monitor, and optimize themselves with minimal human intervention. Therefore, networks can adapt to changing conditions and handle more complex tasks, like load balancing and fault management. In terms of predictive maintenance, networks also utilize AI to anticipate failures or faults before they occur, reducing downtime and improving reliability. Furthermore, AI optimizes power consumption and enables sustainability by adjusting network parameters based on traffic patterns and weather, which leads to greener, more sustainable networks.
One specific use case is dynamic radio resource management. AI is used to optimize the allocation of radio resources (including frequency, time slots, and power) in real-time based on traffic demand, network conditions, and user mobility. In a crowded urban area with fluctuating traffic, AI RAN analyzes data from connected devices, environmental factors, and network performance metrics to predict demand patterns. This then dynamically adjusts the allocation of network resources—including beamforming, spectrum allocation, and power levels—to ensure consistent coverage and high-quality user experience. This type of intelligent resource management improves network efficiency, reduces operational costs, and ensures higher-quality transmission feeds.
Network Slicing
Finally, AI RAN facilitates network slicing. By enabling intelligent resource allocation, dynamic slice adaptation based on real-time data, and predictive optimization, network slicing allows operators to create multiple virtual networks (slices) on a single physical infrastructure. This can then be tailored to specific use cases and service requirements, making AI-powered RAN a key component for effective network slicing implementation.
In addition, operators can create multiple, independent, virtual networks (known as “slices”) on a single physical network infrastructure. This translates into the ability to customize specific network performance features, including latency, bandwidth, and reliability to meet specific customer needs and use cases. Another benefit is that this occurs while these networks share the same underlying hardware, meaning that the network is divided into logical segments with specific capabilities based on the application.
A significant use case for networking slicing is network orchestration. Network orchestration is the automated management and coordination of various network elements, resources, and services to ensure efficient, flexible, and optimized operations across telecommunications networks. It involves the use of software tools, protocols, and technologies to manage complex networks, including both physical (hardware) and virtual (software-defined) components. With end-to-end network orchestration, AI is integrated and virtualized within orchestration platforms to ensure that slices are provisioned, managed, and terminated automatically, based on demand. This enables service agility, allowing service providers to rapidly launch new services and dynamically provision network resources.
ADLINK is Your Partner for Success
To keep up with this rapid growth of AI in telecom, ADLINK Technology has developed solutions to suit your needs. To summarize, this blog post explored several ways AI has transformed the telecom industry, including the evolution from 5G to 6G, AI RAN, and network slicing. Integrating AI and maximizing its benefits will be critical for business innovation and success. Selecting the ideal AI GPU server will be important for small businesses and global corporations alike to establish a robust server infrastructure with 6G and AI RAN. Learn more about ADLINK’s new AXE servers here, featuring best-in-class components and the newest technology. These telecom servers are ideal for both 6G and AI RAN applications, and can be customized to fit your project requirements. For additional information and to speak with a product expert, visit our website here now.