henrikeh

I don’t know about this cable specifically, but it can be done by transferring more power to the optical signal.

Erbium-doped fiber amplifiers work by utilizing a nonlinear optical effect where energy is transferred from a pump laser to the signal. This is in principle possible in any optical (glass) fiber, but by doping with exotic elements, the amplification characteristics can be optimized. Erbium is suitable for the conventional communication wavelengths.

For reference I have a PhD in information theory and signal processing for fiber channels.

> I used to work at a hedge fund known for its diagnosis process, a logical process of clearly defining the suboptimal outcome that occurred, agreeing on how it should have gone and what had happened, and if it's indeed wrong to have gone this way, what to do about it.

Do you know of some publicly available material on this? I’d love to hear about their approach.

We used constellation shaping and a rate adaptive code to tailor tailor the bitrate of each channel. It varied between something along 64-QAM and 256-QAM depending on the SNR in the channel.

Post processing times were not too bad. It ran on a standard desktop computer and gave an estimate of the data rate in about a minute (can’t remember exactly). Of course, compared to actual transmission that is terrible slow, but that was only due to the implementation and need of this experiment.

Thanks :-)

It is a while since I have been into optical signal processing, but I will ask my colleague who is much more well-versed.

I should clarify that we did measure every channel (polarization, wavelength and fiber core) individually. It would not be fair if we just measured one and multiplied ;)

(And yes, that took forever. A shout out to A. A. Jørgensen and D. Kong for their endurance in that.)

I can’t answer for the chip aspect (which is the truly novel part of this research), but many of the signal processing and coding techniques are being deployed in new optical transmission systems. Constellation shaping and rate adaptive coding were two techniques we used in this paper to ensure that individual channels were as ideally utilized as possible.

Thanks! I’ve reached out to my colleague who worked on the chip side of this project.

The two other comments gave very good general answers, but I happen to have worked on this specific project, so I can give some very specific details (as far as my memory goes.)

Lab testing of this scale of transmission involves a bit of “educated simplification”. We had some hundreds of wavelength channels, 37 fiber cores and two polarizations to fill with data. That is not realistic to actually do within our budget, so instead e split the system into components where there is no interference. For example, if there is different data on all neighboring cores compared to the core-under-test, then we dare to assume that the interference is random, without considering neighbors’ neighbor etc.

This reduces our perspective to a single channel under test with known data and then at least one other channel which is just there as “noise” for the other channels. The goal is to make the channel-under-test have a realistic “background noise” from neighboring interference. This secondary signal is sometimes a time-delayed version, sometimes a completely independent (but real) data signal.

This left us with a single signal of 32 GBd (giga symbols / s). This is doable on high-performance signal generators and samplers.