When Parameter Tuning Makes Things Worse

One of the biggest misconceptions in RAN optimization is that changing a parameter always moves the network in the right direction. Sometimes it does. Sometimes… It creates a completely new problem.

Early in my career, I viewed parameter tuning as a way to fix specific issues. Adjust a handover threshold. Modify a timer. Update a mobility parameter. Improve a power setting. Simple. Or so it seemed. With experience, I realized something important: Every parameter in a mobile network exists within a much larger system. Changing one value rarely affects just one KPI. It often influences coverage, mobility, interference, capacity, and ultimately the customer experience. I’ve seen handover parameters adjusted to reduce Drop Call Rate… …only to increase unnecessary handovers and signaling load. I’ve seen coverage expanded by increasing transmission power… …only to generate more interference and reduce overall network capacity. I’ve seen aggressive load balancing improve congestion in one cluster… …while creating poor user experience in neighboring cells. The parameter itself wasn’t the problem. The lack of understanding about its network-wide impact was. That’s why today I rarely ask: “Which parameter should we change?” Instead, I ask: “What behavior are we trying to change?” The distinction is important.

When we focus on behavior instead of configuration, we naturally begin considering trade-offs, dependencies, and unintended consequences. This mindset also explains why automation has become so valuable. Technologies like SON—and now SMO with AI-driven optimization—don’t simply modify parameters. They evaluate network conditions, correlate multiple KPIs, apply predefined engineering logic, and continuously verify whether the expected outcome was actually achieved. That feedback loop is just as important as the optimization itself. But even the most advanced automation platforms depend on one thing: Good engineering decisions. Automation can execute changes at scale. It cannot replace a poorly designed optimization strategy. One lesson has stayed with me throughout my career. In RAN optimization, changing a parameter is easy. Understanding everything that change will trigger across the network… …that’s the real challenge. And that’s what separates optimization from trial and error.

What’s the most valuable lesson you’ve learned after a parameter change didn’t produce the expected result?

#RAN #RANOptimization #5G #SON #SMO #NetworkAutomation #Mobility #KPIs #Telecommunications #EngineeringLeadership