The global telecommunications landscape is currently navigating a pivotal shift toward total network autonomy, moving beyond traditional human-led operations to embrace systems that can self-heal and self-optimize without constant manual oversight. This evolution is largely defined by the TM Forum’s maturity model, which classifies network automation across six distinct levels, starting from manual configurations at Level 0 and culminating in the “holy grail” of Level 5, where the network operates entirely on its own. While many top-tier operators have successfully reached Level 4, where high-level automation handles most tasks under human supervision, the jump to Level 5 remains the final and most difficult frontier. To cross this threshold, German radio network optimization specialist brown-iposs has entered into a strategic partnership with the Baltic telecommunications provider BITÉ Group. This collaboration aims to dismantle the technical and psychological barriers preventing full autonomy by implementing a system that prioritizes validation, transparency, and business-driven engineering.
The necessity for this transition is particularly evident within the BITÉ Group’s operations across Lithuania and Latvia, where the telecommunications market is undergoing a period of intense transformation. Driven by a surge in fixed-wireless access and general digital consumption, data traffic in the region is expanding at an annual rate of over 25%. To accommodate this demand, BITÉ has embarked on a massive infrastructure modernization program, which involves upgrading or modifying at least one base station every single day across its network of approximately 3,000 mast sites. Managing such a dynamic environment through manual performance reviews or traditional engineering workflows has become physically and economically impractical. The modern 5G rollout requires a level of agility that historical data analysis cannot provide, creating a strategic imperative for BITÉ to adopt a real-time, autonomous management framework that aligns technical adjustments with high-level business goals.
Bridging the Confidence Gap Through Algorithmic Transparency
The most significant obstacle to achieving Level 5 automation is not a lack of processing power or data, but rather a “confidence gap” that exists between human engineers and autonomous systems. Moving to Level 5 requires the complete removal of the “human in the loop,” a prospect that often causes hesitation among technical teams who fear losing control over critical infrastructure. Many existing automation tools operate as “black boxes,” providing outputs and making changes without explaining the underlying logic or the data points used to reach a conclusion. To overcome this, brown-iposs utilizes its CARAT (Classification and Root-Cause Analyzing Tool) solution, which adheres to a “white-box” philosophy. This approach ensures that every step of the decision-making process is visible, allowing engineers to inspect the logic, the input parameters, and the predicted outcomes of every automated action before it is fully committed to the live network.
By integrating previously siloed data streams—ranging from specific user behavior and service usage to granular radio performance metrics—the CARAT system constructs a comprehensive, holistic view of the network environment. This level of transparency is essential for the validation phase, where human experts monitor AI-driven decisions in real-time to ensure they align with established engineering standards and business priorities. As the system consistently proves its reliability and accuracy, the need for manual oversight naturally diminishes, allowing the operator to transition from supervised automation to the fully autonomous state required for Level 5 status. This gradual building of trust transforms the relationship between the engineer and the machine, moving away from skepticism and toward a partnership where the AI acts as a reliable extension of the engineering team’s strategic intent.
Implementing Practical Use Cases for Network Resilience
The collaboration has already demonstrated the practical utility of Level 5 automation through several live use cases that directly enhance network stability and performance. One of the primary applications is intelligent compensation for outages, which addresses the inherent vulnerabilities of hardware-dependent networks. In a traditional setup, the failure of a base station results in a coverage hole that persists until a physical repair is made. However, under the CARAT-driven model, the network automatically detects the outage and instructs neighboring sites to adjust their antenna tilts and power levels to fill the gap. This proactive response ensures that the end-user experience remains consistent even during hardware failures or scheduled maintenance, shifting the operational paradigm from a reactive “break-fix” cycle to a self-healing environment that maintains service continuity without human intervention.
Furthermore, the partnership focuses on continuous quality optimization by identifying and mitigating complex radio issues such as interference and “over-shooters.” An over-shooter occurs when a cell’s signal propagates far beyond its intended coverage area, causing significant interference in distant sectors and degrading the overall capacity of the network. The autonomous system continuously monitors user experience metrics and correlates them with radio conditions to detect these anomalies. Once an issue is identified, the AI recalibrates antenna parameters to retract the signal to its proper boundaries, thereby cleaning up the radio environment. This automated tuning leads to higher data throughput and a more stable connection for the customer, accomplishing in minutes what would typically take a team of engineers days or weeks to diagnose and resolve through manual drive tests and data analysis.
Redefining Engineering Culture and Economic Sustainability
A core tenet of the brown-iposs methodology is a rigorous engineering philosophy that balances technical capability with actual business value, often asking if an optimization “should” be done rather than just if it “can” be done. Because changes in a Radio Access Network (RAN) can have cascading effects on neighboring cells, the partners employ a sophisticated modeling phase to validate potential impacts before implementation. This ensures that every automated adjustment is specifically targeted toward improving measurable key performance indicators, such as the signal-to-interference-plus-noise ratio (SINR). This scientific rigor, which draws inspiration from complex fields like astrophysics, ensures that the network remains stable even as it undergoes constant, minute adjustments. This level of precision prevents the system from making unnecessary changes that could lead to unforeseen service disruptions.
This shift toward full autonomy has also triggered a significant cultural transformation within BITÉ’s engineering department, a phenomenon often described as the “washing machine effect.” By automating the repetitive, high-volume tasks of data collection and basic parameter tuning, the technology clears away the “noise” that typically consumes an engineer’s day. This allows the staff to transition into higher-value roles focused on strategic problem-solving and long-term network planning. With a unified and trusted data stream provided by the automation platform, internal debates regarding data accuracy have largely disappeared, replaced by discussions on how to proactively improve performance. This transition turns the engineering team into a driver of network quality and capital efficiency, rather than a reactive group tasked with constant fire-fighting and manual troubleshooting.
Achieving Long-Term Scalability and Operational Efficiency
The transition to Level 5 RAN automation provided a clear path toward sustainable economic growth by optimizing both capital and operational expenditures. By maximizing the performance of existing hardware through AI-driven optimization, BITÉ was able to extend the lifecycle of its current assets and delay the need for expensive new site builds. Furthermore, the ability to remotely validate the work performed by third-party contractors ensured that physical installations matched the digital designs without requiring costly follow-up site visits. Energy management also saw significant improvements, as the autonomous system could power down specific network layers during low-traffic periods while neighbors automatically adjusted to maintain coverage. These efficiencies demonstrated that high-level automation is not just a technical milestone, but a critical tool for maintaining profitability in a high-growth market.
As the project moved forward, the focus shifted toward expanding these capabilities across the broader Baltic region and integrating more advanced 5G features. The roadmap included the implementation of refined beamforming patterns and predictive reconfiguration, where the network anticipates traffic spikes before they occur, such as during large public events or seasonal migrations. By establishing a foundation of data unification and algorithmic transparency, the partnership between brown-iposs and BITÉ Group provided a functional blueprint for the global industry. The success of this initiative proved that even agile, mid-sized operators could lead the way in network innovation. The move toward a fully self-healing, autonomous network was ultimately realized through a combination of technical precision and a commitment to empowering human expertise through superior machine intelligence.
