Skip to main content

Fed up with spotty Wi-Fi at home? Read this

In a previous blog, I raised the touchy topic of how hard it is to get great in-home Wi-Fi. It’s a real problem for operators; the excellent ultra-broadband service you bring to the home is often undone by poor connectivity inside the home, causing unnecessary costs and customer dissatisfaction.

The topic also resonated with readers – with the main comment thread being: “Sooo, let’s fix this already…” And fix it we can. But first, to reiterate, there are three areas that need consideration:

  • Coverage
  • Performance
  • User experience 

Improving coverage requires a mesh network. Not just some sort of tree structure, but one where all the elements can speak to each other. The main gateway providing the connection to the access network is, of course, the principle node, but all nodes (or beacons, as we like to call them) in the in-home network should be able to communicate without having to pass through the gateway. This allows the network to become far more flexible, using intelligence to make decisions about rerouting. Which leads us to the next imperative: performance

Nokia Wi-FI

In telecoms networks, we take for granted tools that constantly monitor and measure the state of the network to predict and prevent, or detect and repair, issues in near real-time. Why not apply these self-healing principles to the in-home Wi-Fi network? Adding intelligence could enable dynamic path selection so the shortest path between beacons is always taken. Similarly, if a link fails or suffers from interference, embedded intelligence would select the best alternative path and reroute traffic before users notice anything. If beacons are monitoring their own backbone connections, they can also automatically choose between Gigabit Ethernet or Wi-Fi as appropriate.

Another idea from the telco network, especially important when delivering video, is adjusting performance depending on the connected device. So how about a machine learning algorithm for our Wi-Fi network that distinguishes an immobile 4K TV from a wandering smartphone and optimizes the connection for each?

Intelligence and machine learning can significantly improve coverage and performance of in-home Wi-Fi, which obviously goes a long way to creating happy customers. Intelligence can also improve the user experience when it comes to installing and configuring the network, which is a headache-inducing weak point of many of today’s Wi-Fi solutions. If intelligent gateways and beacons can all talk to each other, they can self-organize at start-up far more quickly than with manual intervention.

Nokia Wi-FI

We have, of course, been working hard on these challenges at Nokia and invite you to visit our dedicated Nokia Wi-Fi solution webpage to see how you can already create a perfect broadband experience in every corner of the home. The important thing is that in-home Wi-Fi doesn’t have to be a weak point in your ultra-broadband service. Get it right and it’s an opportunity for increased customer loyalty and lower costs.

Share your thoughts on this topic by joining the Twitter discussion with @nokianetworks using #WiFi

Laszlo Gyalog

About Laszlo Gyalog

Within Nokia’s Fixed Networks Division, Laszlo leads the Broadband Devices marketing, focusing on how to extend a broadband offer into the home with meshed Wi-Fi, and how to fully optimize the Wi-Fi performance with advanced analytics. Outside business hours, Laszlo enjoys toying around with anything technology related (he is an engineer after all), photography and going for long walks with his wife and their dog.

Tweet me @Laszlo_G

Article tags