Why simulated networks are the key to realizing the promise of 5G

The arrival of 5G promised a new era of seamless connectivity and unprecedented speed. Yet, more than five years since the first deployments, availability and utilization remain limited. Despite this, the wider tech industry is steadfast in its belief that 5G is essential to meeting the demands of modern consumers and businesses. In developed economies, flawless streaming, gaming, and always-on connectivity are now expected as standard. Meanwhile, industries such as autonomous vehicles, mining, and logistics increasingly depend on 5G to power automation, requiring near-perfect network stability and minimal latency. The stakes have never been higher.

For CSPs, the challenge lies in optimizing networks that are not only more intricate but also resource-intensive to maintain. This means the issue isn’t just meeting growing demand - it’s doing so efficiently and sustainably. Advanced 5G technologies, such as massive MIMO antennas, enable multiple data streams to travel in parallel, revolutionizing connectivity. However, they’re also fundamentally increasing the complexity of managing interference between base stations. Optimizing such networks requires configuring thousands of radio frequency (RF) parameters—a monumental task, even for the most sophisticated engineering teams.

Traditionally, CSPs have relied on manual drive testing, where engineers physically travel across network areas to identify weak spots and make adjustments. These methods, however, are slow and expensive. Recognizing these limitations, CSPs are now turning to intelligent, automated solutions to remain competitive.

Complex challenges demand intelligent, simulated network solutions

This the purpose of the RAN! Reinforcing Autonomous Networks Catalyst—a collaboration of leading telecom players designed to transform 5G optimization through AI and digital twin technology. Led by industry heavyweights such as Huawei, China Mobile, and Telkomsel, the project uses simulated reality of communication networks (SRCON) to combine high-fidelity digital twin simulation, multi-modal parameter optimization, and intelligent beam management algorithms to tackle the challenges of modern network optimization.

Fundamentally, SRCON creates a virtual replica—or digital twin—of the network, accurately simulating how signals behave in real time. SRCON is capable of dynamically adjusting network parameters, using techniques like reinforcement learning to optimize 5G beam directions and traffic distribution. This allows CSPs to test and tweak configurations in a digital environment, bypassing the need for traditional drive testing. SRCON’s architecture also includes PCI MOD90 simulation optimization, which minimizes interference between base stations through advanced modelling techniques.

A critical component of SRCON’s success is its use of TM Forum assets, which provided an established foundation for the project’s architecture and methodologies. Resources like the Digital Twin for Decision Intelligence (DT4DI) Reference Architecture (IG1310A) and the Autonomous Networks Guiding Principles (IG1229) were pivotal in designing SRCON’s framework. By adhering to these proven guidelines, the team ensured their approach was aligned with global best practices, making it robust and replicable. The project also leveraged TM Forum’s Autonomous Networks Evaluation Methodology (IG1252) to measure and validate the system’s performance during development and deployment.

Digital twins enable a scalable 5G future

The results speak volumes. Trials in Hong Kong and Indonesia demonstrated a 79% reduction in network interference, a 15% boost in user speeds, and a dramatic 50% reduction in gaming lag. By automating recovery processes, OSSera's solution has significantly decreased downtime during network failures, reducing resolution times from an hour to just five minutes. The project’s architecture is also designed for scalability. Its AI-driven algorithms, guided by TM Forum’s standards, can be deployed as standardized software, running on cloud-based or local systems with minimal customization. This adaptability makes it ideal not only for telecommunications but also for industries ranging from smart cities to logistics hubs.

This initiative to integrate AI and digital twins reflects a broader shift within the telecommunications industry toward automation. Implementing such solutions at scale requires significant upfront investment and a willingness to overhaul traditional workflows. Moreover, while AI-based systems are powerful, they rely heavily on high-quality data inputs. Despite these challenges, it’s increasingly clear that intelligent automation is not a luxury - it’s a necessity.