Rick, what the paper describes is otherwise known as MIMO. Each AP transmits a signal on the same frequency channel. As long as the receivers can decorrelate the propagation paths from the different APs, they can reconstruct the desired signal.
In traditional MIMO, each transmitter sends multiple beams in different directions, and each receiver would combine the bit streams from all of the propagation paths. In this DIDO, it looks like each receiver is only interested in one of the propagation paths, rather than aggregating the signals from all of the paths. The net effect is the same, though.
These are clever techniques that APPEAR to violate Shannon's limit, but in fact they don't. They depend on decorrelated propagation paths, much as you would have if you used multiple separate cables in parallel. If the signals paths become more correlated, you will lose that spectral efficiency. For example, bring the APs physically very close together compared with the distance to the receivers. That sort of thing makes it difficult to decorrelate the different propagation paths.
I'm puzzled by this technology - but very much appreciate the need for strong signals in our local areas and the frustrations so many of us experience with local "dead" areas. The photograph shows a single "head" broadcasting to 8 iPhones yet the text mentions that there is a centimeter sized zone at the cell phone (which would imply that positioning of the phone is critical). Late in the article, there is mention that 350 transmitters could cover San Francisco. Are there regional transmitters and then local repeaters? Do the transmitters synthesize a small region of interest for each phone? At what speed can the transmitter maintain the connection with a moving mobile phone?
Sounds pretty ambitious for a startup consisting of just eight full-time engineers. The fact that it is supposed to support different latencies and accomplish so much in so little time is a testament to Perlman's entrepreneurial spirit.
@DrQuine: The way I understand it, the user devcie can move wherever the user wants and the system tracks it--but this capability has yet to be proven at mass scale.
The radio heads can be placed somewhat randomly and don't have t create traditional radio cell coverage areas since they apparently work by using overlap and interference, but here my understanding gets a bit fuzzy.
I'll ask Steve to jump on and answer your question.
@Rick, this is true. Generally these days you have to scale up to about fifty people before you can sell out to Facebook for $16B or so...
I'm not sure exactly where this technilogy will fit. Cellular as we know it today is one place, but there is also the possibility that there could be something new growing from this. High speed in the local link is only one part of the equation. What kind of backhaul infrastructure would be necessary to support this? Is this really cellular technology (i.e. highly mobile) or potentially the basis for a (finally competitive!) new ISP technology?
Well...it appears from the white paper that the data center does channel estimation (with the test signal) on all the AP and receiver paths. Say there are N AP's and M receivers. So the data center is looking at complexity of O(N*M) in channel estimation.
After doing the channel estimation, the data center looks at the necessary "clean" waveform that it wants to get to each receiver, inverts the N*M matrix of impulse responses, convolves that with the desired waveform vector, and the result is the transmission vector.
When the transmission vector passes through all those channels, it is distorted by that N*M set of channels, just as expected from tha channel estimation. After distortion, the original desired signals all arrive at the receivers. Slick.
A couple of things I'd be interested in learning more about. First, the O(N*M) complexity problem, is there a way around that? Second, you'd probably want to update your channel models quick enough to stay within the coherence time. But that will eat away at your data rate, unless they're estimating on the fly (which is possible).
I'm a skeptic with "we broke Shannon's Law" schemes, having seen a few (hey, spiral modulation anyone?), but this looks pretty slick, on the face of it.
What are the engineering and design challenges in creating successful IoT devices? These devices are usually small, resource-constrained electronics designed to sense, collect, send, and/or interpret data. Some of the devices need to be smart enough to act upon data in real time, 24/7. Specifically the guests will discuss sensors, security, and lessons from IoT deployments.