Despite increased industry focus and investment in Wi-Fi solutions, our research show that Wi-Fi is still the dominant Quality of Experience (QoE) problem inside the home. For approx 2 out of every 3 homes, connecting the services and devices over the local Wi-Fi network is the biggest source of quality issues. So addressing these issues will make immediate returns.
Our data show that Wi-Fi issues can be grouped in three broad categories from what is the scarce resource
Coverage issues can sometimes be corrected by optimising location and position of the Wi-Fi Access Point, but in most cases issue resolution require stronger signal or multiple access points. Domos accurately identifies when this is the case and can guide the user through ordering and installation in the optimal location. Conversely, Domos also identifies when additional hardware will offer no benefit to QoE.
Congestion / Interference
Analogue radio noise sources are identified and either removed by the end user, or the access point and end user devices are configured to use a better channel or band. We support both in our general in-home optimisation algorithm.
The problem of congestion is more complicated, especially in multi-dwelling units. We have developed a separate optimisation algorithm that address the building as a whole, and consistently create 100%-200% more Wi-Fi bandwidth by freeing up channels when deployed in real buildings "in the wild". We do this by dynamic channel planning and optimising transmit power - basically teaching the routers to use their "inside voice".
Device Issues
When a single device is connected on a low rate it “hogs” the bandwidth causing an adverse effect for every other connected device. This means the end user devices are in fact the single most common source of Wi-Fi QoE issues experienced in the homes!
Addressing millions of different device models sounds like a daunting task, but fortunately our R&D was able to invent a generally effective way to address these issues - Rate Steering. The algorithm can identify each device type and model, and from that understand how each of them react and behave. From our experiments we have trained the algorithm to nudge or steer the selfish devices to civil behaviour.
This approach consistently increase Wi-Fi bandwidth by 100% - 150% when deployed “in the wild”, with any noticeable side effects.
White papers and research projects we are active in:
Area Optimisation
In-Home Optimisation
Rate Steering
Network Slicing / Software Defined Wireless Network
Fu5ion project
Wi5 / TNO project

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