Telecom networks have undergone significant transformation in recent years, driven by the growing demand for faster and more reliable connectivity and the need to support the proliferation of next-generation technologies. Traditional physical networks are increasingly being replaced by virtualised architectures, enabled by advances in automation, artificial intelligence (AI) and machine learning (ML). The addition of these next-gen technologies is enabling more flexible, scalable and efficient network management, thereby reducing costs for operators and ensuring faster service launches.
Virtualised architectures, such as those enabled by software-defined networking (SDN) and network functions virtualisation (NFV), decouple network functions from the hardware they run on. This separation allows for more dynamic and adaptable network management, as functions can be scaled up or down without physical hardware changes. Meanwhile, automation is playing a crucial role by streamlining network operations and reducing manual intervention. Automated systems can handle routine tasks such as provisioning, monitoring and troubleshooting with minimal human input, leading to faster response times and reduced operational costs. AI and ML further enhance network management by providing advanced analytics and predictive capabilities. AI and ML algorithms can analyse vast amounts of data to identify patterns, detect anomalies and make real-time decisions, allowing for the proactive management of network performance and issues. The integration of virtualisation, AI and ML represents a significant shift towards building a more flexible, cost-effective and scalable network architectures. Collectively, these technologies are poised to enhance network performance and facilitate faster deployment of new services.
A look at the key trends shaping the landscape of telecom networks today…
New network service generations enhance speed and reliability while increasing complexity
Telecom networks are increasingly evolving to support the transition from 4G to 5G services and are now gearing up to support the launch of 6G. Both 5G and 6G represent significant departures from legacy networks, prioritising low latency and enhanced wireless broadband capabilities over previous generations. The shift from 4G to 5G brought significant improvements in speed, low latency and support for a massive number of connected devices, enabling advancements in internet of things (IoT), autonomous vehicles and smart cities. A key application of 5G technology is private networks, which provide enterprises with dedicated high speed wireless communication. These networks offer exclusive access to authorised users and devices, giving companies full control over network security and accessibility. Private networks ensure faster speeds, better coverage and low latency, as data traffic is not shared with public networks.
With the rise of data-intensive applications such as augmented reality, virtual reality and AI-driven technologies, the need for even faster and more responsive networks is driving the launch of 6G, which promises speeds up to 100 times faster than 5G and the introduction of more sophisticated use cases such as ultra-reliable low-latency communications and massive machine-type communications.
However, supporting emerging technologies such as 5G and future 6G introduces new demands and challenges for network management. For instance, with 5G, networks must handle considerably increased data rates and support a higher density of connected devices. This involves upgrading infrastructure to accommodate greater bandwidth and ensuring that network elements can handle higher data volumes without performance degradation. Additionally, 5G introduces new spectrum bands, including millimetre waves, which require advanced management strategies to mitigate issues such as signal attenuation and interference. The complexity of managing 6G networks will involve integrating advanced technologies such as terahertz frequencies and new network paradigms, including network slicing and advanced AI-driven automation. These innovations will require managing intricate network topologies with numerous virtualised and physical components while ensuring consistent performance and reliability of services.
Increasing adoption of SDN and NFV to improve network agility and scalability
SDN and NFV are driving innovation in telecom networks by decoupling network operations from physical infrastructure, making networks more efficient and agile. SDN leverages software-based controllers to manage and direct network traffic, allowing operators to create and control virtual networks through software. One key application is software-defined wide area network, which uses a centralised control function to intelligently route traffic, improving performance, enhancing user experience and reducing IT costs. Meanwhile, NFV virtualises traditional network functions like load balancers and firewalls, running them as software-based virtual functions. This approach optimises resources, reduces congestion, and lowers operating and capital expenses by using shared infrastructure instead of dedicated hardware.
The adoption of SDN and NFV brings key advantages, such as faster time-to-market for new services, scalable operations and lower energy consumption. These technologies enable customisable network solutions for enterprises, support applications such as virtual private networks and cloud connectivity, and allow operators to create network slices for IoT use cases, ensuring critical applications receive the necessary bandwidth and low latency. SDN and NFV also allow the dynamic allocation of resources based on real-time demand, further enhancing cost efficiency and optimising network performance.
The growth of SDN and NFV reflects a broader shift towards decoupling software from hardware to create agile, cost-effective networks. However, challenges such as interoperability, lack of standardisation and vendor lock-in pose obstacles to wider adoption. Operators also need to align their operational and business systems with these technologies to fully realise their benefits. Technological challenges include immature standards, latency variations and security risks, with SDN’s centralised control plane being a potential single point of failure. Despite these hurdles, SDN and NFV offer the potential for higher returns and improved service quality compared to traditional networks and their uptake is expected to increase considerably in the next few years. The global SDN market is growing at a considerable pace, projected to rise from $26.8 billion in 2023 to $145.2 billion by 2032, with a CAGR of 20.9 per cent. The NFV market, valued at $27.2 billion in 2023, is expected to reach $134.4 billion by 2032, with a CAGR of 18.9 per cent.
Innovative RAN architectures helping drive down network costs
Telecom companies are adopting new approaches to radio access networks (RAN) to reduce the need for additional physical assets like towers and antennas, thereby lowering capital and operational costs, speeding up network service deployment and fostering vendor competition. Categorised as “xRAN,” these new approaches include open RAN (ORAN), centralised RAN (CRAN) and virtualised RAN (VRAN). ORAN helps disaggregate hardware and software components, reducing reliance on single vendors and lowering network-related costs. This modular approach allows operators to select from a broader range of suppliers, fostering a more competitive market environment and potentially leading to cost reductions in network deployment and maintenance. Although ORAN is still in the early stages of widespread commercialisation, its adoption is anticipated to grow significantly. Industry projections suggest that ORAN revenues will account for more than 10 per cent of the overall RAN market by 2025. This growth is expected to be fuelled by advancements in technologies such as AI, ML and big data analytics. These technologies enhance ORAN’s capabilities by enabling intelligent algorithms for real-time traffic analysis, anomaly detection and predictive maintenance, which optimise network performance without the need for additional hardware.
Meanwhile, CRAN centralises network functions across multiple mobile sites, allowing shared use of equipment. This centralisation simplifies network management and reduces costs associated with maintaining separate infrastructure at each site. CRAN also facilitates improved resource allocation and service delivery, making it an effective solution for urban and densely populated areas. VRAN further complements these approaches by virtualising network functions, which decouples network hardware from software. This separation supports greater scalability and flexibility, enabling operators to adjust resources dynamically in response to demand. VRAN’s ability to run multiple virtual instances on a single physical server helps in reducing capital expenditures and operational costs.
Network automation enabling enhanced operational efficiencies
As operators seek to address the growing complexities in network design and management, network automation is rapidly gaining traction. The increasing demand for higher data speeds, capacity and coverage has led to more intricate networks spanning various devices, locations and environments. Traditional manual management methods are proving inadequate for handling the scale and dynamic nature of technologies such as IoT. This shift necessitates network automation to ensure efficient, seamless operations.
Network automation employs software and technology to streamline the management, provisioning, configuration and operation of network devices and services. By automating these tasks, organisations reduce reliance on manual intervention for device provisioning, configuration changes, software updates, network monitoring and troubleshooting. This results in enhanced operational efficiency, fewer human errors, faster response times and improved network reliability.
Traditionally, network automation has been facilitated by SDN, which separates the control plane from the data plane. This separation enables centralised network management, simplifying administration and reducing manual configuration needs. SDN also supports the rapid provisioning and deployment of network services, abstracting network functionality from hardware and allowing for more flexible management. Today, network automation is being significantly advanced through the integration of AI and ML. AI and ML algorithms analyse extensive data to forecast network issues, optimise resource allocation and enhance overall performance. These AI-powered solutions facilitate proactive maintenance, reducing downtime and improving resource utilisation. They dynamically adjust routing, bandwidth and load balancing based on real-time traffic patterns, thus boosting user experience and operational efficiency. AI also strengthens network security by detecting anomalies, preventing unauthorised access through sophisticated intrusion detection systems, and refining authentication processes.
The global network automation market, valued at $3,615 million in 2022, is projected to grow at a CAGR of 21.9 per cent from 2023 to 2033. In India, telecom operators like Reliance Jio, Bharti Airtel and Vodafone Idea are increasingly adopting AI-driven solutions to boost network performance and customer experience.
Achieving lower latency through edge computing
Driven by the rising demand for low-latency processing, real-time data analytics and connected devices, the market for edge computing is witnessing considerable growth. Edge computing brings computation closer to data sources, thereby reducing latency, bandwidth usage and transmission costs. It also minimises the reliance on distant data centres, enhancing security by keeping sensitive information local. Many firms across industries such as retail, manufacturing, healthcare and transportation are adopting edge solutions for faster data analysis, predictive maintenance and improved customer experiences. For instance, physical clothing stores are using edge computing solutions to run certain applications that need extremely low latency, such as mixed reality mirrors in changing rooms, smart shelves at counters and automated checkout options. Meanwhile, several manufacturing firms have begun using edge computing solutions to store data closer to its source in the production chain, eliminating the need to send it to distant cloud servers for analysis. This enables quicker analysis and process adjustments on the factory floor, while also enhancing predictive maintenance capabilities. In the healthcare sector, edge computing is being used to deploy data, analytics and processing power where it is needed most – in hospitals, operating rooms, or patients’ homes.
Despite its advantages, edge computing introduces new cybersecurity risks and integration challenges into telecom networks. Data processed outside corporate firewalls is more susceptible to attacks, including risks of physical tampering in uncontrolled environments. The expanded attack surface increases the chance of security breaches, such as distributed denial-of-service attacks. While challenges remain, an increasing number of firms are recognising the potential of edge computing to drive innovation, improve efficiency and enhance customer experiences. The growth of 5G networks and the upcoming launch of 6G services will further accelerate its adoption by enabling high speed, low-latency applications.
Sustainable energy management strategies gaining traction
The ever-growing demand for reliable telecom connectivity has increased the energy footprint of telecom infrastructure. The expansion of telecom towers, data centres and 5G networks requires significant power, adding to the industry’s ecological footprint. For instance, data centres account for about 5 per cent of global greenhouse gas emissions, primarily from server and cooling systems, while telecom towers rely heavily on diesel generators, contributing to around 1 per cent of India’s carbon dioxide emissions. As 5G and IoT devices proliferate, energy consumption will rise further due to the need for more small cells and multiple-input multiple-output antennas in 5G networks, along with challenges posed by the disposal of IoT devices. AI also contributes to energy use, particularly during the intensive training processes for AI models.
To address these challenges, telecom firms are adopting sustainable energy strategies. These include using solar energy and renewable solutions to power telecom towers, deploying energy-efficient cooling systems in data centres and leveraging virtualisation to optimise energy use in cloud computing. These measures, however, entail significant initial costs, presenting a considerable financial burden in an already capital-intensive industry. The Indian telecom industry has also taken steps such as using diesel-free sites and sharing towers to reduce energy use. However, increased government support, through tax incentives or subsidies, is essential to encourage the broader adoption of renewable energy and reduce the sector’s overall energy footprint.
Future prospects and challenges
Telecom networks have undergone significant evolution in recent years to address the growing demand for faster and more reliable connectivity. Each new generation of network technology has introduced major enhancements in speed, capacity and functionality. Today, virtualisation, automation, AI and ML are at the forefront of these advancements, driving the creation of more efficient and adaptable networks capable of meeting complex and dynamic needs.
However, these innovations come with their own challenges. Since virtualised components often operate independently and require sophisticated coordination, increasing virtualisation can create difficulties in maintaining visibility and control across a dispersed network infrastructure. Meanwhile, the integration of AI and ML adds further complexities, as these systems require constant tuning and validation to ensure they perform accurately and effectively. The management of legacy networks has also become increasingly complex, and integrating new technologies with existing infrastructure poses considerable difficulties. Going forward, the anticipated roll-out of 6G technology, which promises to deliver unprecedented speeds and capabilities, will introduce even greater complexities in network management.
Nonetheless, these challenges can be optimised through the continued integration of AI-driven automation, advanced ML algorithms and sophisticated virtualisation techniques, paving the way for more intelligent, self-managing networks. As the demand for ultra-high-speed connectivity and reliability continues to grow, the telecom industry’s focus will increasingly shift towards managing complexity, optimising legacy systems, adopting new technologies and lowering the energy footprint of telecom networks.
