The Asia Pacific (APAC) market is expected to generate demand for billions of home networking devices over the next few years as economies in the region heat up, societies become more urban, and demand for always-on services is stimulated by the young demographic segments of the market.
We had an opportunity to connect with Jon Walkenhorst, CTO of Technicolor’s Connected Home business unit, about ways NSPs can meet this demand with new strategies and cost-effective solutions that deliver quality, intelligent Wi-Fi connectivity.
Walkenhorst: If we look at the Asia-Pacific region, we see two things happening. We see an emerging market looking to expand wireless access to their consumers as the region increases its influence on the world economy.
At the same time, there are economically developed countries in the region that have heavily invested in broadband connectivity to the home, but are wrestling with the fact that the user experience is increasingly determined by the quality of their Wi-Fi experience.
We also have urbanization occurring which is contributing to high density population centers across the region – and growing demand for multi-dwelling units (MDUs) as people move into apartment buildings.
And then we have this idea that demographics are playing a major role. We have a huge population of millennials -- and the generation following the millennials -- who have only ever understood this idea that connectivity is wireless, and they want wireless everywhere and they want content everywhere.
This is creating opportunities – and challenges – for network service providers (NSPs) in the Asia-Pacific region. They have a very unique challenge because of this incredible density. They need to figure out ways to provide connectivity without either spending large amounts of capital or dealing with large numbers of unhappy customers, so they’re looking for wireless strategies and solutions that meet those minimum demands.
Walkenhorst: NSPs should not just focus on getting more bandwidth to homes, but also on being more intelligent about how they manage wireless access to the bandwidth when consumers are in their homes.
For instance, you can have a lot of radio access points in a home, but still not have a lot of throughput.
This is because throughput is not always advertised as part of the radio signal from the access point. We need to find a way to send some type of additional messaging to the end devices so they choose the right radio to connect to.
It turns out that there are two issues that can be easily confused...and which can conflict with each other to affect performance and access if they are not managed correctly.
On one hand, you have the issue of access. If your main wireless access point is in the living room, and you want Wi-Fi connectivity in the bedroom on the other side of your home, you might not get a strong enough signal to get access to the internet. You might think about getting a Wi-Fi extender.
On the other hand, if you live in an apartment building in which every unit has a Wi-Fi access point, the reason the wireless experience is so poor may be because of interference...not the strength of signal. In fact, interference occurs when too many signals are too strong.
Walkenhorst: Let’s start by talking about the concept of RRM -- or radio resource management. Technicolor has been in the business of selling wireless devices for more than a decade. In the last two and a half or three years, we have taken that knowledge and built a number of algorithms that will allow us to not only know what’s connected to the network but also understand the performance characteristics of the devices connected to the different access points on the home network.
We know, for instance, that if a device is a printer and it is trying to connect while you’re trying to talk on the phone, it will affect the quality of the voice connection. A lower performing radio will not likely affect the experience of the printer.
We would like to automatically move the printer off to a different radio that is independent of the rest of the devices in your home that need the fastest throughput.
But there is another problem. Let’s imagine you have a party in your house, and everybody is in the kitchen with their devices trying to get access to the Internet. The radio signal every device is trying to access is loud or strong (you have four bars), but throughput is poor because every device is trying to use the same wireless access point.
Using this RRM with other technologies that make it possible to move devices around between the radios, we can help the service provider build a network that is optimized for not only the devices but for the throughput. This means that if too many devices in the kitchen are trying to access the Internet, RRM would start routing devices to a weaker radio signal elsewhere in the home or even next door that has a different connection to the Internet that would, in fact, offer better throughput because it is not being utilized.
These kinds of intelligent capabilities are included in technologies we have developed and are evolving with Technicolor’s Wi-Fi Doctor and Wi-Fi Conductor family of products.
In an urbanized area, where you live in an MDU – such as an apartment building – we can use those same algorithms to determine and build a holistic view of all the radios in that building – not just in a single apartment.
Being able to treat all radios with the same algorithm allows us to effectively and efficiently turn up radios, turn down radios, turn off radios that aren’t needed, and allow you to connect to the network in the most efficient and fastest way possible.
Walkenhorst: Starting with Telstra, our customer in Australia, we have worked quite extensively on fine-tuning the Wi-Fi Doctor algorithm, and we’re in the process of testing the Wi-Fi Conductor algorithms so that we are able to deploy that at scale.
We have introduced this concept of a Wi-Fi Experience Index, in which we’re able to determine the quality of that signal, and by determining the quality, we can help the service provider determine who is likely to call the service desk and who isn’t.
By looking at the Wi-Fi Doctor algorithms we can determine which customers, when they call, just need an additional access point, or extender. We can also take it one step further, we can look at the network independent of the customer making phone calls, and anticipate problems before they manifest themselves and be proactive.
We can say, “Oh, this person’s wireless home network is ready to fail.” We can look at the drop-outs as the customer moves around and say, “Why don’t we send them an intelligent extender ahead of time,” improving their quality of service before they know that a problem is going to manifest itself.
That’s an example of going beyond using our algorithms to identity and respond to problems, to proactively get ahead of the problem in the first place.