Mobile Network Planning/Optimization: Automation Versus Uncertainty
By Dr. Konstantinos Stavropoulos, Product Management
“Uncertainty is our only certainty.” Many mobile network engineers – including me, back in my consulting days – liked to use this line. The line still rings true today, especially when it comes to network planning and optimization.
Indeed, there are multiple sources of uncertainty to cope with. Here are just a few examples:
- A multi-technology network should perform optimally – but complexity (including 5G) may affect performance.
- A network operator needs to deploy more sites/cells – but budget availability is not a given.
- Engineers try to create a great network plan – but planning permissions may delay or derail rollout.
- The network is built to support specific use cases – but their nature or customer demand may be inaccurate.
Uncertainty is even captured in today’s news headlines, such as those about potential power blackouts in Europe causing mobile network outages. So, how can mobile network operators address uncertainty? How can engineers plan and optimize networks to be as profitable as possible, despite uncertainty? Automation can help.
Automation: addressing revenue challenges
In the old days… We typically view the past through rose-tinted glasses. But when I look back at my consulting days, targeting a use case (i.e. a single service: voice) was definitely simpler. Today, mobile gaming has emerged as a key revenue generating/maintaining use case. There is also interest in knowing how mobile networks would handle other exciting 5G use cases, such as drone package delivery.
When relying on manual network planning and optimization, the need to consider several use cases (and their customer demand) is a significant challenge for network operators. With software-driven automation, this simply means running a software program more times, even concurrently, using different inputs. For example, the potential customer demand for a use case could be based on the existing population and relevant service adoption, or on more comprehensive – including geolocated (e.g. crowdsourced) – data. Importantly, various assumptions and data combinations could easily be used as inputs to independent software runs.
Automation: addressing cost challenges
Cost is a multifaceted term, and this is true in mobile networks too. Let’s take energy use, a “hot” industry topic as operators are trying to create more sustainable, greener networks. To minimize energy-related costs, a network plan should be as energy-efficient as possible. In other words, the number of sites/cells to deploy should be as low as possible. This is effectively the question I was asked in my consulting days – “How many sites/cells does this plan include?” But site/cell number minimization is not an easy task. A simplified example follows:
A mobile network operator could deploy up to one hundred sites in a given period. Due to resource limitations, it may only be possible to deploy eighty or ninety sites. Should the operator’s engineering team create a one-hundred-site plan, and then identify the best eighty and ninety sites? Or should the team create three separate plans, independent from each other? These questions do not necessarily lead to the same outcome.
Automated planning and optimization can incorporate many different cost-related options, even if their likelihood or timing is unclear. For example, the decommissioning of legacy technology (i.e. less energy-efficient) sites/cells will happen sooner or later. Site/cell-specific power limits may also be required for cost savings. Other cost-related inputs, such as locations where equipment is likely to be stolen or vandalized (which should therefore be avoided for site deployment), may be accurate today, but not in the future.
Automation: responding to change
Imagine this scenario: The nominal network plan has been ready for weeks. The engineering team worked hard and did a great job, meeting the network operator’s tight timelines. In fact, a few site locations are almost ready for equipment deployment. Suddenly, unexpected news breaks: the operator has agreed to share site infrastructure with a competitor. Time to rework the plan…
Life is full of surprises. (For the record, in my consulting days I also had the “pleasure” of reworking a plan following a site-sharing agreement.) Such unforeseen developments put engineers under renewed pressure to deliver, as network deployment speed is crucial. In effect, a new rollout plan is needed, based on a new set of available site options. Can we rely on automation here, too?
Yes, we can. In the above scenario, the new site location, relevant antenna height and (if applicable) antenna sharing options would be essential inputs. Depending on the sharing agreement, the site-specific cost of using the competitor’s infrastructure would also be key in determining the most profitable plan. By incorporating such diverse inputs – along with coverage and capacity targets, and traffic forecasts – which are fundamental for realistic network designs, automated planning and optimization can help engineers respond to change more effectively and efficiently.
In summary, automation can help mobile network operators address uncertainty in network planning and optimization. Automation can lead to better (and ‘right-the-first-time’) network plans, increased network revenue, reduced infrastructure cost, and faster response to change. More importantly, the benefits of automated planning and optimization have already been confirmed in live networks. Automation works, and that’s a certainty.
WATCH the related webinar which covers the topic of this blog – ‘Automation Versus Uncertainty: Best mobile network planning/optimization practices’, to hear specific examples of how automation has helped address planning/optimization uncertainty and more.
The webinar is based on 5G and pre-5G planning/optimization projects, and on insights from network engineering teams worldwide. Watch now for an interesting discussion, which includes Q&A session.