Smart cities. Augmented reality. Net neutrality. The Internet of Things. These are just some of the buzzwords in telecommunications right now. They all indicate a technology-driven shift in the industry, one that has fundamentally changed the successful telecom business model.
As telecom companies seek to cut costs and keep customers, they must also take advantage of emerging technologies to power innovation.Some of these innovations are incredibly flashy; smart cities and autonomous vehicles, once the stuff of science fiction, continue to capture the imagination. Other innovations are less visible, but can have great impact. Device testing is one domain where three key trends are driving value for telecom companies.
DUT Will Go OTA
The advent of 5G technology has already redefined the device testing landscape: Cutting-edge telecom companies have shifted from turnkey testing to a system-level approach, more likely powered by software than by the conventional bench-top instruments.
And as 5G technologies and standards evolve, they’ll continue to drive new testing methodologies, most notably a shift from attaching a device under test (DUT) to a cable, to using over the air (OTA) instead.
What’s driving this broad shift to OTA? The miniaturization techniques, such as systems-in-packaging (SiP) that come with 5G devices.
A single module in a 5G device might not only contain a transceiver and antenna, but also integrate a low-noise amplifier (LNA) and power amplifier. There’s simply no way to test such a module by plugging in a cable.
OTA testing is also necessary for multi-element antenna arrays, which are increasingly becoming the preferred method for mobile communication. These arrays help prevent path loss at mmWave frequencies. And for sub 6-GHz frequencies, they boost capacity.
It’s likely that arrays will continue to get more compact over time. Furthermore, 5G devices will also come to employ many more antennae than even the devices in production today.
While OTA still has its limitations, the technique addresses some key issues introduced by the demands of 5G. Most importantly, it offers a means to handle the complex interactions and dependencies among components like power amplifiers, wireless transceivers, and antenna modules.
Tighter Relationships Between Field Technicians and NOC’s
The past decade has already seen the development of closer relationships between the network operations center (NOC) and the testing laboratory. Indeed, NOC integration tools are often already part of a telecom’s testing arsenal. In the coming years, this relationship will extend to include field technicians.
Attempts to solve network problems via the NOC often lead to unexpected consequences. Such attempts are among the top causes of operations outages–and increased network operations costs. The best remedy for this issue, robust laboratory-based testing, helps eliminate network problems before they occur.
In the past, it was very difficult to gain synergy between these simulations and real-time operations control. Today, thanks to automation, telecom tests like protocol simulation and device load testing can be run very quickly to help diagnose and solve problems on a live network.
Meanwhile, hardware failures are often best addressed in the field. And the well-documented explosion of IoT-enabled devices means that there are now far more connected devices in the field than ever before.
This is one of the factors driving ever-closer relationships between the NOC and field technicians, who must be able to test, diagnose, and solve problems on site, in real time. Another is the adoption of two key emerging technologies:
- Cloud computing: A close connection between field technicians and the NOC requires fast, seamless data sharing. Technicians must be able to download test sequences from the NOC and also upload their results to diagnose an issue. Cloud computing has made it much easier for NOC’s to maintain databases of test sequences that field technicians can access from virtually anywhere.
- Automation: Modern networks contain a multitude of protocols and protocol networks, making for an incredibly complicated testing environment. The communication of test sequences from the NOC to field technicians can be automated, further streamlining the process. Results can also be returned from the field in a predetermined form so that the data can immediately be used alongside automated NOC monitoring.
A Move to Predictive Maintenance
Device testing has conventionally been treated as a step that happens before a new release, for instance during prototyping, or when there’s a problem, such as during an outage. But ongoing testing provides the data necessary for predictive maintenance, a strategy that can prevent outages and reduce maintenance expenses.
Powered by algorithms and machine learning, predictive maintenance uses data to generate insights about how and when equipment needs service by comparing baseline data (the normal operating parameters) with real-time performance data. Thus predictive maintenance requires ongoing testing and monitoring of devices and equipment, but it also offers several key benefits:
- Reduced downtime: Performance issues can be identified early, before they cause a failure. This contributes to fewer equipment breakdowns, and therefore less frequent network outages.
- Decreased maintenance costs: Equipment is ordinarily serviced at regular intervals to prevent breakdown, whether it actually needs attention or not. Predictive maintenance ensures that equipment is serviced only when necessary, eliminating the expense of unnecessary maintenance and parts replacement.
- Maximized depreciation: Predictive maintenance helps to extend the lifespan of equipment because potential problems can be caught early, before they cause major failures.
- Improved customer satisfaction: Reducing network outages and equipment downtime helps keep customers satisfied. Increased satisfaction and customer lifetime value (CLV) are both indirect benefits of predictive maintenance.
Although predictive maintenance is already widely used in the manufacturing sector, it’s relatively new to telecom. The industry is already using predictive analytics for better customer service and root cause analysis. Predictive maintenance is a natural next step, and it can be implemented from the infrastructure level (that is, cell towers, generators, data centers, etc) all the way down to the consumer level (set-boxes in customers’ homes).