In the telecommunications industry, Network Planning and Optimisation serves as a foundational use case for Artificial Intelligence, acting as the “human-like intuition” required to manage increasingly complex digital environments. As networks transition to 5G and the Internet of Things (IoT), the “explosion” of endpoints makes AI-driven management a necessity rather than a luxury.
Within the larger context of AI use cases, this specific application focuses on transforming networks into autonomous, self-healing systems through several key functions:
1. Autonomous Traffic Management and Load Balancing
AI-led networks are designed to detect and isolate issues automatically, enabling autonomous traffic rerouting. By monitoring network nodes in real-time, AI algorithms identify when a specific element—such as a tower or server—is nearing capacity and distribute traffic to less congested nodes. This ensures intelligent load balancing across the infrastructure, which is particularly vital for managing the complex spectrum requirements of 5G networks.
2. Self-Organising Networks (SON)
A major advancement in this field is the enhancement of Self-Organising Networks (SON) through AI-driven analytics. This allows infrastructure to:
- Self-configure: Automatically set up new network elements.
- Self-optimise: Adjust parameters in real-time to maintain peak performance.
- Self-heal: Isolate faults and resolve them without manual intervention, reducing downtime and the need for human technicians.
3. Dynamic Resource Allocation and Forecasting
Unlike traditional static planning, AI utilizes traffic forecasting to predict future demands based on current events and usage patterns. By identifying peak usage times in advance, telecom providers can dynamically allocate resources like bandwidth and hardware, ensuring optimal user experience without over-provisioning or wasting energy.
4. Energy Efficiency and Sustainability
In the broader context of creating sustainable operations, AI contributes by optimising power consumption. It adjusts the energy usage of network elements based on real-time demand, ensuring that towers and data centres are not running at full capacity when traffic is low.
Integration with Other Key Use Cases
Network Planning and Optimisation does not exist in isolation; it is deeply interconnected with other strategic AI applications:
- Predictive Maintenance: While planning focuses on flow, predictive maintenance uses similar data from sensors on antennas and switches to forecast equipment failure before it disrupts the network.
- Network Slicing: Optimisation techniques are used to ensure that specific virtual “slices” of the network meet their unique requirements for speed, bandwidth, and security.
- Edge Computing: AI enhances edge computing by processing data closer to the source, reducing latency for critical applications like autonomous vehicles.
Analogy for Network Planning and Optimisation Imagine a city’s water system. In a traditional network, if a main pipe bursts or a sudden surge in demand occurs, engineers must manually turn valves and reroute water. AI-driven optimisation is like having a “living” pipe system: the pipes themselves can sense a drop in pressure, automatically expand their diameter to handle a surge, or instantly reroute water through side channels if they detect a leak, all while ensuring the pumps use the least amount of electricity possible.
Craig Miles.
Founder & Director at Yesway Communications | Wireless Technology, Training & Two-Way Radio Solutions | Advancing Inclusive & Global Education Through Innovation