>> Ranveer Chandra: It is my pleasure to introduce Aakanksha Chowdhery a PhD student from Stanford
who’s interviewing for a post [indiscernible] position here at MSR. She’s a, Aakanksha works in an
interesting space on DSL and disk fiber at the lowlier of the network stack. She was an intern with us
last year and she’s won, did some work on dynamic spectrum access. That work was very valuable to
some of the, some of the responses to the FCC’s and BRM we filed for Microsoft. Aakanksha’s also won
numerous awards. She was a silver medalist from IIT Delhi in the WD Department. That means she was
the person with the highest GPA. She won; she’s the first woman to win the Marconi Young Scholar
Award, right Aakanksha, that’s a prestigious award. So she’s won some great awards and today she’ll
tell us about some of the exciting work she’s been doing as part of her thesis.
>> Aakanksha Chowdhery: Thank you Ranveer for the kind introduction. It’s always great to come back
here even though it’s rainy weather, you know, ha, ha. So I’m very excited to talk about my research
and tell you all about how we can squeeze multi hundred megabits per second to gigabits per second
out of copper which is real economical. This work is in collaboration with my advisor, John Cioffi at
Stanford and my colleagues Hao and Haleema. Hao has already graduated.
So let me begin with a big picture overview. What we see in this picture is a home network. But instead
of having a single PC connected to internet what we see is a numerous set of devices ranging from your
servers, X-Box, phones, lap tops to here we have IPTV. There are three trends that are apparent in the
space. What is apparent is that there’s an explosion in terms of the number of connected devices. In
fact we expect by 2020 there will be fifty billion connected devices as per Cisco projections which are
rather conservative. But what that means is that’s five times the population of the world. So on an
average you would have roughly five to ten connected devices per home. So that’s a lot of devices to
support on each home.
>>: [inaudible] for home devices, right, it’s…
>> Aakanksha Chowdhery: It’s for outside, yeah.
>>: So these are mostly cows wearing sensors?
>> Aakanksha Chowdhery: Yes.
>>: [inaudible]
[laughter]
>> Aakanksha Chowdhery: Cows wearing sensors, okay, ha, ha. That’s a nice analogy.
>>: [inaudible]
>> Aakanksha Chowdhery: What’s more interesting in this space is that these connected devices are not
connecting only by a cellular connection as you just mentioned. They’re increasingly offloading to the
Wi-Fi or the femto cell, or the small cell inside the home because the spectrum is shared and you no
longer can support all of it on your cell or spectrum. So increasing the topology or the architecture we
see is you have a Wi-Fi modem or a femto cell modem, or some kind of small cell and then a wired
connection to get to this modem which provides the internet access. Then thirdly there is an increasing
demand for streaming video. By streaming video I mean there’s an explosion in terms of IPTV
subscribers, there’s You Tube videos increasingly being watched, and a lot of video streaming or video
conferencing is happening in the space.
So to summarize, we do not have enough spectrum in wireless which is why I was working with Ranveer
last summer, and its shared medium. So to support these devices inside the home we want to be able
to get this wired connection somehow at high speed. This is what my talk is going to focus about. How
do we get this wired connection which use to be in the range of one to ten megabits per second to multi
hundred to gigabits per second, to support all these devices?
So to give you a big picture of overview of what I mean by this wired connection, what are the options?
So this picture actually shows you the number of subscribers and how they’re growing in worldwide
wireline broadband access market. You have three options, you have fiber and in news you’re
increasingly hear Google’s fiber. You have cable where cable you usually hear Concourse doing that. In
DSL you increasingly hear, you hear AT&T working in the space. What I want you to take away from this
graph is that, so worldwide DSL is the largest fraction, it’s sixty-seven percent of the fraction totals.
More interestingly, and the second largest is cable and the reason this is increasing is because in
developing countries the penetration is increasing. But very interestingly even when you’re increasing
the fiber connections you are not running it all the way to your home. In fact you are running all the
way to your curb, or your building, and then you’re using the existing telephone cable or the TV that the
cables connections to provide access.
Why is that the case? Economics drives it so it’s simply economical per prescriber to do cable or DSL
than to do fiber. The joke there goes, sorry, go ahead.
>>: You said [indiscernible] what you mean is there is copper everywhere already.
>> Aakanksha Chowdhery: Exactly.
>>: Okay.
>> Aakanksha Chowdhery: You’re running, laying down fiber all the way to your watch costs additional
money while historically DSL was the telephone line connections that were laid a hundred years ago.
Then cable started out as TV that was laid again decades ago. So now that we know what our options
are in wireline broadband access, where do we go from here? What kind of speeds can we support on
that?
>>: [inaudible] getting expensive. So do you know about the difference in prices like per meter
between copper and fiber? I was thinking like for new departments like new neighborhoods coming up,
what is like, are we still going to be like lining of copper or are we going to put in fiber?
>> Aakanksha Chowdhery: Actually that’s a great question and the answer lays that metal as a copper is
a valuable metal. So actually the price the glass might be cheaper, it’s deploying fiber as in building the
network is where the value of the cost goes up. Per meter I think copper will be costlier. The fiber is
roughly the same cost and new deployments I would maybe prefer…
>>: [inaudible] interesting [indiscernible] Delhi which is like in developing countries where labor is
cheap extracting copper and replacing it with fiber actually is a lead gain because you can extract the
copper and sell it…
>>: [inaudible]
>> Aakanksha Chowdhery: True.
>>: And that was very cheap. It was cheap to make that switch.
>> Aakanksha Chowdhery: Okay, no, but what you are roughly getting out is at the end of the day
someone has to pay. Why would you pay for gigabits per second if you’re only going to use hundred
megabits per second? So that’s where the value ends up being so. The topology that has been
supported is run fiber all the way up to say MSR building and then run copper all the way up to your
homes because that’s an existing infrastructure. All of you don’t want to pay hundred dollars per month
to get hundred gigabits per second to your office.
>>: Maybe.
>> Aakanksha Chowdhery: Maybe, ha, ha.
[laughter]
Okay, Victor your bill is going up, ha, ha.
>> Victor: Well, no, no…
>> Aakanksha Chowdhery: Ha, ha.
>> Victor: I’ve been paying too much.
[laugher]
>> Aakanksha Chowdhery: Ha, ha.
>>: I think the story is that there’s already, I mean there’s countries we have already the copper line
laid…
>>: Yeah.
>>: And I believe that’s the majority of the four hundred million…
>>: It’s a mute point. This is just statistics [indiscernible] that’s okay.
>> Aakanksha Chowdhery: Okay, so let’s actually do some technical work here. So we were talking
about ten megabits per second as what an average customer gets today, maybe five. With yours you
can actually go up to forty, fifty megabits per second. But I’m talking hundred megabits per second or
gigabits per second. Why is that not possible today and how can we get there?
So what this picture is showing, so this is your home network where you will have a modem. This is a
copper line which will run all the way to a DSL access node. This DSL access node was deployed by a
service provider and this is connected to the core network. Increasingly this DSL access node might be
at your curb, or close to your home, and a fiber might be deployed between service provider’s central
office and the access node. Sorry, wrong way.
What’s interesting here is that these copper lines are share; they share the same cable or binders. So
what this picture is showing is that this is the wire that’s laid inside the network and these wires are
shared inside the same cable. Why is that interesting? It’s interesting because it introduces noises and
interference that bring down the speed. So there are two major problems that hamper the
performance in DSL networks. One is intermittent noises and these noises come from in-home devices.
So this is the noise injected at the home end. The other’s from copper impairment you often have
bridge taps or other kind of impairments which already exist because these networks were laid down
much earlier and you don’t necessarily know what exists out there. This is radio interference from AM
radio of other radio bands that exist in the same spectrum which is really one to, veer to one megahertz,
it’s [indiscernible] to eight megahertz.
This is the problem that I was talking about where multiple copper lines share the same binder. So this
will be a binder and when they share the same binder they’ll crosstalk into each other. Crosstalk is
similar to your interference in wireless. Imagine two antennas placed right next to each other and then
leaking signals into each other. That’s the kind of interference we’re talking about.
So how can we handle this interference? Can we do something smart about it? So traditionally the
service providers only configured this network so they built this modem, they had this line, they will
provide you the modem, you connect to it and you get the speed. So they had designed the modem for
worst case scenario. But now we can actually manage it in the cloud and that’s what I’m going to talk
about. This is where my work comes in and my advisors work come in. I’m going to emphasize what my
contributions are in the space.
So that’s what I call a Dynamic Spectrum Management approach. So Dynamic Spectrum Management is
really a more broader term than just managing the spectrum. I’m going to start with an overview of
what I mean by Dynamic Spectrum Management in the first part of the talk. Then I’m going to focus on
a specific problem that exists in Next-generation DSLs called Vector DSL Technology which is an exciting
space and upcoming space. Then I’m going to talk about certain extension to cable access networks that
we are working on right now and then other research projects I’ve worked on. So this going to be how
I’m going to structure my talk.
So let’s start with Dynamic Spectrum Management. So this was the network that you’re already familiar
with. We’re going to use the Spectrum Management Center. The Spectrum Management Center is
simply some device in the cloud which is collecting data from the core network about what kind of
performance is happening here. So imagine you have a, or somewhere which is collecting all this data.
It’s very similar to software define networks for those of you familiar with those. What is really
happening is that on a data plane you’re collecting the channel and noise conditions via something
called an SNMP interface. The kind of things you collect are channel or noise values, data rates, retrains,
error rates are a few examples. Then what you want to configure is you want to somehow re-profile
these lines or basically configure the lines so that as a service provider you can make more revenue and
get better network performance to your subscribers. The kind of things you will change and I’m going to
talk about this in details is power, codes, margins, speeds and then use some kind of sophisticated signal
processing in the next-generation technology.
So the point that I want you to take away here is that these are really software defined access networks
if I may use the term. So instead of software defined networks we are really using access networks and
then somehow configuring them in software instead of using the worst case design where the modem
was pre-configured for a worst case scenario. Being able to use all this data and change the algorithms
on the fly and make the entire network adaptive is where this gets really interesting.
So what can we do in the controlled plane? So let’s go back to the fundamentals. This is the Shannon
Capacity Formulation where this is what we can get out of our channel. This is the capacity that we can
get, this is the bandwidth and the capacities [indiscernible] proportioned to the signal and noise ratio.
So what we can change is one, we can change the coding. So we can change the power levels. We can
change the receiver structures and how we detect, whether we detect symbol by symbol or sequences.
That’s the physicalier aspect. We can change the number of dimensions. This is another interesting
space. We can increase the bandwidth over which we are receiving the signal. We can use more
antennas so if I start detecting multiple copper lines at the same time instead of detecting a single line
I’m using spatial dimensions just like using multiple antennas in wireless space. Then this is the noise
aspect and this is whatever we can’t cancel.
So in the control plane we are somehow trying to configure these values change this by using signal
processing. This noise I’m going to basically get rid of it when I can. So in a single user what I’m showing
here is this channel actually varies with frequency. So usually we are use to single-carrier systems where
the channel is pretty flat. In this case the channel antenna weights with frequency. So what this is
showing the bottom curve, the bottom black curve is showing is nice over channel gain or if you may
think of it is a [indiscernible] channel gain. The optimum thing to do if you have a bandwidth of W here
is to water fill. Just to remind you what water filling means is that when your channel is better
[indiscernible] channel is worst you want to fill more energy. When your channel is worst or
[indiscernible] channel gain is better you want to fill less energy. The best distribution of how you
should load energy or power in the space is up to a single water level. So that’s the water filling solution
and this was proposed much earlier.
The way you can view this is that you’re basically chopping up your entire spectrum into small spectra,
into small chunks and on each you are using optimal energy to get the capacity and you’re basically
adding them together. So that’s the Shannon Capacity Formulation.
>>: [inaudible] quick question for clarification. So in what times are they thinking of adapting the
system here for packet, or few seconds, minutes?
>> Aakanksha Chowdhery: They’re going to change on the scale of hours. We’re configuring the
network. This is not wireless.
>>: Okay.
>> Aakanksha Chowdhery: Yeah.
>>: And this, then this…
>> Aakanksha Chowdhery: So, so this…
>>: This meaning noise…
>> Aakanksha Chowdhery: No, no, okay so let me, let me clarify your question. So as a network service
provider I’m going to configure the level of hours, as a modem I’m going to adapt at the level of
minutes. So twenty seconds is a training period for a DSL modem. So what ends up happening is that if I
see a very large noise then I do not want to adapt to it, if I do not adapt to it all have to retrain. By
retraining I mean I break the connection and then I restart the connection. So I would be basically doing
it at that level. I basically do not want to retrain if I can maintain the connection. Because retraining
means that if you’re watching a football match the football match will freeze right there, the connection
will break, and then it will restart. Okay, so, so to clarify your question I’m going to, let me get to the
point that I’m going to make out of this slide. So the reason I was talking about these multi-carrier
systems is that each modem can use this multi-carrier transmission or this adaptive bit-loading to adapt
to the noise that it sees here.
So that’s the first aspect. We have multiple crosstalking users. What gets interesting here is that for a
single user you would simply water filling on lines that were channel gain. When you add a second user
your noise now depends on the interference that you see from the second user. As a result you need to
fill power on top of this interference now. What we have found and this is the solution that we
proposed was something called was multi-level water filling where basically you end up decomposing
the entire spectrum into multiple bands were in each band you’re doing water filling. The users
emphasize one band over another based on whether that’s good or bad for the network. So that
basically ends up happening.
I’m going to explain this in much more detail. This was just an overview. So to collect the two points
that I made…
>>: [inaudible] clarify. Are all users using the entire frequency band or you are also [inaudible]?
>> Aakanksha Chowdhery: That’s a great question. So when you say multi-carrier with adaptive bit
loading I can choose how much power to put on each tone or sub, each chunk. So I can say that I want
to use the entire band but I can put power differently on each tone.
>>: [inaudible] the modem does that?
>> Aakanksha Chowdhery: Yes, that is, this is the configuration technique that it’s using which is called
water filling. So I want to somehow tell the modem what kind of water filling to use how much power to
use to configure this technique. Actually when I showed this water filling this is actually the simplest
thing that they can do. For those of you familiar with the areas called Levin Compello. It’s a greedy
scheme to do bit loading and is already implemented in modems today.
>>: So when you talked about retraining is that, is training the process of setting these power levels or is
it…
>> Aakanksha Chowdhery: Yes.
>>: Okay.
>> Aakanksha Chowdhery: Yes, but you can change powers on the fly. You can change what’s on-the-fly
using something called S bit swapping, so training is basically when I’m doing the training sequence to
detect when the channel and the noise is set up.
>>: So when, so [inaudible] end of each sub-carrier you have [indiscernible] is that how [inaudible]?
>> Aakanksha Chowdhery: Yes, yes exactly. So the reason I used OFDM with adaptive bit-loading
because in wireless systems OFDM is equal bit loading, at least traditionally. But here on each, I can
choose the number of bits and they can go all the way from two to fifteen bits, that’s how it’s configured
in these systems.
>>: A bit of jumping ahead though but let me ask…
>> Aakanksha Chowdhery: Yeah.
>>: So why is this…
>> Aakanksha Chowdhery: Let me complete this slide and then…
>>: Okay, what I was going to ask this last comment was made.
>> Aakanksha Chowdhery: Okay, go ahead.
>>: The last comment was on the adaptive bit-loading why does it when we receive most or more of
this in the wireless domain?
>> Aakanksha Chowdhery: Why don’t we see more of this in the…
>>: Yeah.
>> Aakanksha Chowdhery: Because the most gains that you get out of wireless are really in the coding
space. So if you use two bits worth of six bits you’re not going to get as much gain as you get out of
putting a strong code on multiple tones. So they can do this and there are some gains but the, because
the fading happens, the variation happens at such time scales that putting strong codes or putting a lot
of redundancy is where you get the most gains in wireless systems. Does that help?
>>: Yeah it does. I was just…
>>: [inaudible]
>> Aakanksha Chowdhery: How wide is this?
>>: Getting a sense of how, what your theory models are too.
>> Aakanksha Chowdhery: Okay, no, so really when you talk about, this is not really fading systems.
When I talk about problems here, this is in-home interference or radio interference which crops…
>>: [inaudible]
>> Aakanksha Chowdhery: Up once in awhile. So we talk about all these probabilities but we’re really
trying to not get the modem to retrain to often on subscriber to complain.
>>: So how wide [inaudible]?
>> Aakanksha Chowdhery: Its four point two five kilohertz.
>>: Thank you for that.
>> Aakanksha Chowdhery: Yeah, so we are talking about, so in the largest, the VDSL which goes all the
way up to seventeen megahertz you can have all the way up to four thousand ninety-six sub-carriers and
an ADSL one today you can have, ADSL one had one twenty-eight and ADSL two plus at two fifty-six subcarriers. So we really have very large values of N here. So I don’t want to run some complex
optimization scheme which I’ll get to.
So this is a slide which I really like. So let me take a moment here. So this is the network which you
already had and this was connected to the core network. I already introduced you to the spectrum
management center which is collecting the data and it’s going to control it. At level one I can choose the
codes. So I can control, I can control each line on a line by line basis so I have the data for a single line. I
can choose how to provision the coding scheme on that line, how to provision the rates on that line so I
can get rid of these copper impairments, this in-home interference, and this radio interference. How I’m
going to do that in short will be somehow use a coding scheme and a provisioning scheme that will
make the line stable. So don’t run it faster than it can support. Why is that a challenge? That’s a
challenge because when you did not have noise status sticks you had no idea what the line was doing.
So you were just shooting an arrow in the dark. And…
>>: This is happening at what level like, is it happening, how many homes are you thinking of, how many
devices per home?
>> Aakanksha Chowdhery: Millions…
>>: And this is happening on the SMC?
>> Aakanksha Chowdhery: Yes.
>>: Okay…
>> Aakanksha Chowdhery: So, so…
>>: So it’s sort of…
>> Aakanksha Chowdhery: So the algorithms are, so the algorithms that I am proposing are more
generic and generalized. In terms of actual implementation my, this is happening at my advisor’s
startup. So they are actually working with millions of lines.
>>: What, what, but so do you ever do decisions across DSLAMs?
>> Aakanksha Chowdhery: Yes.
>>: Oh, so what do you do across DSLAMs? Is there optimization to be done across different DSLAMs or
this is just DSLAM level optimization?
>> Aakanksha Chowdhery: So depends on how much information you can share. Can we do that later?
Because I’ll not get to the slide, I’ll do that at the end of the slide I promise.
>>: Okay binary answer.
>> Aakanksha Chowdhery: Yes you can do it.
>>: Okay.
>> Aakanksha Chowdhery: You have to get permissions of data you can share.
>>: I see but technically there’s value to actually optimizing across DSLAM.
>>: This was my thesis.
[laughter]
>> Aakanksha Chowdhery: Ha, ha, okay, let me get this slide and I’ll get to it.
>>: [inaudible]
>> Aakanksha Chowdhery: No that’s great; I like questions that’s great. So DSM level two is really the
idea of what I was explaining in the multi-level water filling. We are basically configuring the energy on
multiple lines to get rid of this crosstalk. Now can I get rid of this crosstalk? Not really, what I can do is I
can manage the crosstalk. I can say that I’m going to load less power in certain bands and hence
introduce less interference into others. Then vectoring is where we actually cancel the crosstalk and
noise using signal processing. There’s another technique called bonding which you’ll see in news which
is basically creating more bandwidth by putting multiple lines together.
So what you need to remember out of this slide is that now with these three levels, three increasing
levels of complexity you can actually get a lot more bandwidth out of these systems. The next slide I’m
going to show how much more but just to give the take away points we are really shooting for higher
speeds, improved stability. Because stability means that you will not call AT&T to say hey my DSL line is
not working. Longer loops, if I’m a service provider I want to provide the same rate for longer length of
loops. Then reduced costs because I don’t want to invest or upgrade my networks if I can choose to do
that.
So here this is a graph which summarizes roughly the kind of rates you can get. So let’s look at what this
is showing. This is the DSL Loop Length from customer premises, so this is your home, up to the
deployment boundary which can be up to access node which is at the service provider. These are the
data rates. So notice they are going from zero to one fifty megabits per second. I haven’t plotted the
gigabits per second which are possible with DSM level three plus bonding.
So let’s focus on how we get towards this direction. So anytime I’m deploying fiber to the curb I’m going
to basically move on the curve here so I’m going to increase the data rates. Very simple principle you’re
reducing the distance you’re bringing down attenuation. This is the space where we are working. This is
when we are using the data. So from now no DSM which is the blue curve at five hundred meters, when
I go to DSM level one or two I start at forty megabits per second. I’ve gone all the way up to fifty-five to
sixty megabits per second at thirty-seven point five percent gain. The DSM level three we’ve gone all
the way up to one twenty-five megabits per second and that’s really two hundred percent gain. So this
is really the interesting space which most Talco’s are entering now.
Now I can get to your question. You can answer [indiscernible] question.
>>: What is a phantom bonding you mentioned [inaudible] on this slide?
>> Aakanksha Chowdhery: Bonding, I’m not going to talk about, basically a club multiple DSL lines
together and use a passive optical network kind of architecture.
>>: Multiple DS line together and still basically stay within [inaudible]?
>> Aakanksha Chowdhery: Yeah. I can talk about it. Let me get to this question…
>>: Is this a simulation or is all of this the real [indiscernible]?
>> Aakanksha Chowdhery: So, that’s a great question. This is real, this is starting to get deployed so
DSM Level one. So, I mean these results are from simulations but this is already getting commercialized
and vectoring is something that has been the most demonstrated in labs. So that’s…
>>: Which [indiscernible] which curve [inaudible]? They’re all…
>>: They’re all…
>> Aakanksha Chowdhery: They’re all simulations but I’m just telling you that what has been
commercialized and what’s not been commercialized. So we’re getting here, we’re going to get here
but this is the stuff we are in right now. Vectoring we are starting to move and I’m going to show you
what this space looks like, your question.
>>: My question was like just sound like what, so you talk about a bunch of like interference and
impairments. I was just wondering when you do optimizations across DSLAMs what is the value of that,
like…
>> Aakanksha Chowdhery: So that…
>>: What’s the technique do you…
>> Aakanksha Chowdhery: The key value that I want to get as service provider, let’s say I’m AT&T and
you are another service provider and we are competing. We want to get the maximum value out of our
networks. So we want to basically provision maximum number of subscribers at whatever rates they’re
paying for, right. How do you do that? You basically want to bring down the interference that these
guys are creating to me and I want to creating to them. So that’s where the key value lays. Other than
that if there are impairments I want to somehow tell these users to not blast power, because the
coupling between multiple lines happens really at the power level. If I’m going to blast power on my line
it’s a cocktail party problem.
>>: Those are two different DSLAM they interfere or?
>> Aakanksha Chowdhery: No, no, so okay. So I confused your question so in a single DSLAM…
>>: Right.
>> Aakanksha Chowdhery: So at any time the lines that will interfere are the ones that are on the same
cable. They can terminate a different DSLAMs belonging to different service providers, because DSLAM
is owned by a service provider. It is the box that I carry AT&T.
>>: But how is this getting, I think really now we just [indiscernible] like the optimizations are happening
we already at a DSLAM level or you’re actually there’s some optimization that’s happening? Like do you
take like [indiscernible] in your DSLAM? Do you take other DSLAMs into account or not?
>> Aakanksha Chowdhery: I can, statistically I can. What I’m getting is that if I want to do that at a
spectrum management center I information’s from both. In commercial deployment…
>>: Did I hear you right? Did you say that different lines can go to different DSLAM [inaudible]?
>> Aakanksha Chowdhery: Yes.
>>: And that’s why you need to, so basically…
>> Aakanksha Chowdhery: If there’s…
>>: If you have different pieces of information in some sense right. I have like portions…
>> Aakanksha Chowdhery: No this is not very common, because in U.S. you don’t have unbundling but
in UK you have unbundling; so in a single cable I can lease lines to multiple service providers and each
one can take their box and say this is what I’m doing with it and we can actually see some of those. So
yes every line can have different pieces of information and that’s where the big fights happen in
standards where, hey this guys going to blast its power why should I not be allowed to blast power?
>>: Yeah.
>> Aakanksha Chowdhery: So any other questions at this point, because this was my overview of DSM
and I’m now going to emphasize one particular problem? No, okay. So I’m going to…
>>: [inaudible] question, question about the phantom bonding.
>> Aakanksha Chowdhery: I can answer further.
>>: He was asking…
>>: I was asking what is the phantom bonding?
>> Aakanksha Chowdhery: So what is happening in phantom bonding is you basically pick multiple lines
and you’re delivering data to your users, right.
>>: Okay.
>> Aakanksha Chowdhery: So you somehow want to use these two lines together to deliver data in
time. So you basically use a different architecture.
>>: So my understanding is for the DSM Level three is like interference alignment?
>> Aakanksha Chowdhery: Yes, yes, yes.
>>: So this is basically, I mean basically it would cancel out [inaudible].
>> Aakanksha Chowdhery: Exactly.
>>: Just trying to understand what…
>> Aakanksha Chowdhery: So bonding is really happening at higher layer. We’re basically putting
packets together from multiple things. So it’s like, so in…
>>: [inaudible]
>> Aakanksha Chowdhery: I think the simplest way to explain that is, okay, just imagine that I have two
Wi-Fi modems, you and your neighbor and they decide to cooperate. Then I’m going to send packets to
both the Wi-Fi modems and then I can stream the packets to you. So they’re basically happening at
packet level.
>>: I mean you need the two modems to communicate between them…
>> Aakanksha Chowdhery: So you’re…
>>: So basically how [inaudible] happened, right.
>> Aakanksha Chowdhery: Yes, you need some, some sort, some way to somehow, yes.
>>: So you need to basically assume there’s a back channel for the…
>> Aakanksha Chowdhery: Exactly.
>>: [inaudible]
>> Aakanksha Chowdhery: Exactly but this is starting to happen.
>>: Oh really…
>> Aakanksha Chowdhery: There are comp, there are startups looking at it. Basically they’re putting
packet by packet information together because there are a lot of open Wi-Fi’s so we’re trying to look at
that. Again what is not clear is how much value we can get out of it in real systems where Wi-Fi’s are
not open yet. So that’s…
>>: [inaudible]
>> Aakanksha Chowdhery: That’s a space which people are starting to enter and the gains have been
demonstrated in simulations. Any other questions at this point?
>>: [inaudible]
>> Aakanksha Chowdhery: Okay, so at DSM Level one, basically my work has been on developing a
tiered rate adaptation approach and showing using noise statistics to improve stability and what I in
terms of impact I really took it to UK NICC standards, that’s their telecommunication body there and it’s
now commercialized. So I actually participate in the standardization process and talked to a lot of
companies in that space. DSM Level two we proposed, so their existed optimization algorithms with
those of you familiar with the space we came up with a practical approximation called multi-level waterfilling that is robust to changes that happen in noise levels. So coming back to [indiscernible] question
when noises change at levels of seconds now you can actually bit swap instead of imposing certain…
>>: [inaudible]
>> Aakanksha Chowdhery: Conditions…
>>: [inaudible]
>> Aakanksha Chowdhery: Hi, thank you, I’m really sorry…
>>: We also get confused…
[laughter]
>>: [inaudible]
>> Aakanksha Chowdhery: Ha, ha, okay…
>>: Sometimes I wish I was [indiscernible]…
[laughter]
>> Aakanksha Chowdhery: Ha, ha, I’m really sorry.
[laughter]
>>: He’s actually watching…
>>: A target moment, yes.
[laughter]
>>: He’s actually very sick but he’s watching…
>> Aakanksha Chowdhery: Okay, okay, so here I led the standardization in U.S. ATIS which is American
Telecommunications standards called Coastline Group and then UK NICC Group. Then DSM Level three
for practical deployments of vectoring we proposed novel DSM algorithms and here the theoretical
bounds were also not enlisted so that was also something we were, we have derived.
So this is where we are going to spend the rest of the talk biding the last part. So this is the exciting part
next. So what is the difference between Legacy systems and Vectored DSLs? So looking back this is a
cartoon of modem two DSLAM, Port one. This is the line card and this is modem two to Port two. What
we were doing in earlier DSLAMs was that I was decoding the signal at a single user level. So in
information theory it makes sense this is interference channel and the speeds I was getting was
between one to twenty-five megabits per second of course depending on the length of the line. With
Vectored DSLs I’m really using a common receiver. So instead of decoding signal at each port I now have
some mechanism to decode signals together. So you’re using sophisticated signal processing and the
information theory makes sense this is really looking like a multiple-access channel and you can start
with speeds of multi hundred megabits per second. In wireless domains this is really like putting two
antennas together and adding upstream direction you are decoding both the signals together.
>>: [inaudible] only for the uplink?
>> Aakanksha Chowdhery: No this is also for the downlink. I’m going to focus on upstream because
that’s where I have most done simulation results.
>>: Right.
>> Aakanksha Chowdhery: But downlink it also works and there are very interesting results there to.
>>: It’s obviously a different approach, right, because you can’t…
>> Aakanksha Chowdhery: You can do pre-coding…
>>: Okay, okay.
>> Aakanksha Chowdhery: Yes, so you would not be coding but you’re pre-coding. There are certain
limitations to that but we can talk about it. So this is a nice slide that I wanted to show us to why is
vectoring exciting and why are we excited about it now? So, notice that the dates here are all late two
thousand twelve so lot of Talco’s at this point are starting to move toward vectoring because they see
that out of Legacy networks they can squeeze a lot of speed now. So AT&T has announced a fourteen
billion investment into Project VIP which was in November and they’re moving into different cities. You
hear Google Fiber all the time but AT&T is getting into the space big time and trying to get the maximum
amount out of their networks. Deutsche TeleKom which is in Europe has committed to the fiber to the
curb and vectoring. They are increasingly upgrading their lines, something like twenty-five million lines
are being upgraded. Then HUAWEI which is really a DSLAM manufacturer is also spending a lot of
energy to moving towards vectoring.
So just to give you an idea I’ve just picked some news pieces to say that at this point in time a lot of
companies are excited and putting a lot of money into the space to actually see how much they can get
in real networks. Okay, so what are the problem they would face when they go and actually deploy this
in real networks? This is where things get really interesting. So here this was my DSLAM. This is my
earlier DSLAM, here I was doing single line, single user by user decoding. Here I have upgraded my lines
and I’ve basically put a common receiver to decode signals. When I’m deploying can I upgrade fifty to
two hundred lines in one go? That never really happens. AT&T doesn’t decide that I’m going to upgrade
two hundred lines at two a.m. at night. That never happens so incremental upgrade of subscribers just
to make this deployment possible it’s important that I should be able to incrementally upgrade the
number of lines that are vectored.
In unbundling you will often share lines between Legacy and this next generation because different
service providers move to different technologies at different points in time. In the UK you have these
different service providers which lease lines out of the same binder. Then the most important part is
that in terms of a service provider I will only upgrade my lines if I have the economic incentive. So if I
have a cable or a binder which consists of multiple lines the maximum gains I get are when the lines are
really short. So those are the lines which I’m going to upgrade. So you might be limited in signal
processing or economic incentive.
So when I, basically you need to get that, when I go and deploy vectored DSLs they’ll often exist with the
Legacy DSLs I want to get maximum value out of what I’m upgrading at the same time keeping the
customers that I already have happy. Why is that a challenge? So the data-rate gains can be very
limited if I use the existing state-of-the-art solutions based on what I already know. So we understand
interference channels. We know how to handle this. We know how to handle for example multiple
access channels and information theory. When I maximize the data-rates of these two systems
independently because I just upgraded by DSLAM here but I have no way to know what I should do to
configure powers here. I’m going to have this crosstalk that is happening from these vectored systems
in to non-vectored systems and non-vectored systems into vectored systems. Both of them can take
away the gains that I get out of my vectoring technology, the new technology that I just upgraded.
I’m going to call this Mixed-binder Iterative Water-filling and we can go into details later. But the
[indiscernible] here is based on the multi-level water-filling solution that I was already doing. They are
only aware of the interference that they see and these people have no idea what is going in the rest of
the network. They are just cancelling interference that they are seeing. So it’s the inter-domain
interference which becomes the challenge. Can we come up with solutions which are practical enough
to take that into account?
>>: Is this problem temporary to like all those…
>> Aakanksha Chowdhery: No.
>>: It’s not temporary.
>> Aakanksha Chowdhery: It’s not temporary. In fact one of the very interesting papers that recently
came out from Talco’s was that often you only want to upgrade parts of your system. So you want to
put a DSLAM and then another DSLAM with limited single processing. So tier’s of your system. So they
basically want to do partial vectoring is what I’m getting at. So it looks temporary because I’m talking
about incremental upgrade but often they want to do partial vectoring because these are business
decisions which are made by companies. So if I have fifty million subscribers and fifty-two hundred lines
I only vector what ten lines if that’s where the most value lays.
>>: [inaudible]
>> Aakanksha Chowdhery: Because vectoring subscribers are also suppose to pay more. If I’m going to
get a hundred megabits per second I’m going to pay more for it as opposed to ten megabits per second.
So I’m only going to upgrade what I have the economic incentive for. Any other questions?
So this is a flavor of what exists out there today and why that’s a problem. I’m using a very simple
example. So here we have two lines which are vectored and one line which is non-vectored. These two
lines are using, they’re three hundred meters long and this is twelve hundred meters long. This is
showing the crosstalk that is happing between the two users. What this part is going to show you is the
data rate that we are getting on these vectored lines which are of the order of hundreds of megabits per
second and the data rate that we’re getting on non-vectored lines which are of the order of six megabits
or ten megabits per second.
So what we observe here is that as we upgrade this line to hundreds of megabits per second, on the
Legacy line we have lost the data rate. It’s one third of what existed out there which I’m sure if your
neighbor upgraded you’ll not be happy with, and then, so or all the other way to look at it is that to, if I
wanted to upgrade my technology and maintain six megabits per second here then I’m not really getting
any gains out of the new technology. So that’s not fun as a service provider.
The real problem here is that you’re not able to reduce the crosstalk in the mix binder scenarios. Now
that’s an easy thing to say but we don’t even know the theoretically optimum solutions in the space.
So…
>>: May I ask you a…
>> Aakanksha Chowdhery: Sure.
>>: Kind of question.
>> Aakanksha Chowdhery: Yes.
>>: Why should we believe your simulations?
>> Aakanksha Chowdhery: Why should we believe my simulations?
>>: Yeah, because we were, we are to [indiscernible] build…
>> Aakanksha Chowdhery: Build per hybrids and such.
>>: So you agree with us, right…
>> Aakanksha Chowdhery: Yes.
>>: So you kind of know how we work. So you’re presenting to us a lot of results with lots of simulation
in it and I’m actually very appreciative and [indiscernible] think that’s great but what are the indust,
what I also don’t understand the scale maybe is not possible to [indiscernible]. How do go about
convincing folks that invest in this technology because what I’ve done is actually [indiscernible]. Can you
give us some background of what terms of simulation you’re doing and how…
>> Aakanksha Chowdhery: Okay…
>>: What their using and why…
>> Aakanksha Chowdhery: The tools that they’re using that actual channel data. So often we take the
data from actual channels or the models of the channels and then we are simulating based on the
techniques that we are seeing in the transmitter and receiver signal processing site. So in that sense
they’re completely believable but of course in real environments you see a lot more impairments and
you counter that question, I mean of course no one will invest in this. So I don’t want to say this on
video but the point is that this is actually being deployed on the other side of the network, ha, ha. Yeah,
it is being tried out so we’re developing the theoretical tools on Stanford and then this is being tried out
on the other end.
>>: You talk about the technique on the [inaudible] channel that you have [inaudible]…
>> Aakanksha Chowdhery: Exactly.
>>: When an important question like [indiscernible] asked is a kind of…
>> Aakanksha Chowdhery: Okay.
>>: RC unit different interference in following, seeing I mean…
>> Aakanksha Chowdhery: Okay.
>>: For the household appliance, radio, and…
>> Aakanksha Chowdhery: Okay, so, at this point…
>>: Different timeline, right.
>> Aakanksha Chowdhery: Yes, so the best way to answer that question is the reason why we have
these three different levels of DSM. So the first level of DSM is really handling this interference by
talking about what kind of things go up and down. The crosstalk is a persistent problem. So here you’re
trying to cancel it. So the timeline issue is really handled to maintain stability.
>>: Yeah.
>> Aakanksha Chowdhery: And the crosstalk issue is really to get rid of the interference…
>>: Here is this but crosstalk channels, I mean in reality…
>> Aakanksha Chowdhery: Yeah.
>>: How stable that crosstalk channel is…
>> Aakanksha Chowdhery: Oh, okay.
>>: So in wireless interference alignment…
>> Aakanksha Chowdhery: Yes.
>>: Right, I mean basically you need to measure this…
>> Aakanksha Chowdhery: Okay I can answer that question very simply. How often do you turn on your
DSL modem or turn it off?
>>: If it’s turned on and basically if it stayed on is the crosstalk matrix red to be stable?
>> Aakanksha Chowdhery: Yes.
>>: I mean…
>> Aakanksha Chowdhery: Yes.
>>: [indiscernible] the wind blows, I mean…
>> Aakanksha Chowdhery: Yes, of course…
>>: Basically…
>>: It’s not just on it’s also how much when you’re using it to deliver traffic, right? So it’s…
>> Aakanksha Chowdhery: That, that’s exactly the question where that should be the next level of DSL
where they should actually turn it off when it’s not being used, ha, ha. That’s being…
>>: [inaudible] all this interference is that how it works, right.
>> Aakanksha Chowdhery: Yes.
>>: Okay, wow.
>>: Okay, that’s [indiscernible].
>>: That sucks, okay.
[laughter]
>> Aakanksha Chowdhery: [indiscernible] I like wireless, ha, ha. That’s a great question. That’s where
people always get to me. It’s like here they should turn it off. I mean there is a low-power mode but
more companies out there actually use it so far. They’re trying to get there because energy
consumption is becoming important. So that’s really the next dimension, ha, ha. But right now we’re
talking speeds and not energy. That’s like, okay let’s, let’s…
>>: [inaudible] a problem here crosstalk doesn’t really matter…
>>: [inaudible], ha, ha…
[laughter]
>> Aakanksha Chowdhery: No, no but, but vectoring all the things that I’m saying these are adaptive
receivers. So they are basically going to run with an adaptive loop which is your LMS filter and they’re
just going to adapt to that matrix. Now if things change at a scale of wireless the gains will start going
away. But as long as they don’t change at that level you are still in good shape. Now why should we
believe it? So, I mean there was a demo that, the startup that I’m talking about actually showed this is
working stuff now. So my job was to take it to standards. It’s like convince people that this is a viable
solution, ha, ha.
>>: Apparently they’re convinced and so they [inaudible].
>> Aakanksha Chowdhery: Yes.
>>: So the stuff that’s in the standards is your stuff, is that right?
>> Aakanksha Chowdhery: Yes, the stand…
>>: Directly with you, yeah.
>> Aakanksha Chowdhery: Yes.
>>: Well you and Stanford, yeah.
>> Aakanksha Chowdhery: Yeah.
>>: That’s great.
>> Aakanksha Chowdhery: Okay, so and the key area we are using in all these was basically not to load
more power than is necessary at any given time. So we are at forty minutes right now and I wanted to
cover two approaches. One was the optimization-based framework where we are actually developing
theoretical solutions and then the heuristics-based approach as to why the practical approximations
make sense. So, I want to get some sense of time as to…
>> Ranveer Chandra: [inaudible] twenty minutes.
>> Aakanksha Chowdhery: Twenty minutes, okay. Okay so in the optimization based framework I’m just
going to give you heuristic perspective and not go into every single detail. But, I think most of you will
get because I showed pictures earlier. So we all talked about this is an adaptive bit-loading system. So
there is power on each tone and there is a data-rate associated with each vectored user. There’s a datarate associated with each non-vectored user.
So one of the simplest formulations of modeling the problem is to come up with a weighted sum-rate
maximization which Jin would be very familiar with, but what we’re really doing is we’re trading off the
rate that we can successfully decode at non-vectored lines versus vectored lines. So there’s a trade-off
as I increase the rate of the vectored lines I am going to not be able to decode more at rate for nonvectored lines. So there’s this boundary which we call achievable rate region which we can get by using
a weighted sum-rate maximization formulation. How do we move on different points in this boundary?
We’re going to put different weight vector for the rates that go to non-vectored lines and the vectored
lines.
So this is the mathematical formulation and let me parse this for you. What we’re really doing is we’re
maximizing the sum, the weighted sum of rates of vectored lines. So this is the rate of vectored lines
and the weighted sum of non-vectored lines with respect to two constraints and very simple constraint.
One is coming from total power on all tones so my modem cannot transfer at more than a third of the
amount of power. This is a PSDMASK constraint which is basically saying that on every tone FCC limits
how much power I can put. So what can I get out of these? So my optimization variable here will be
power.
Now why am I interested in this problem and what am I really doing? So what we understood was this
part of the problem was understood alone. This part of the problem was understood alone, putting the
optimization of these two together and then coming up with practical approximation is really where
things get interesting.
>>: Question.
>> Aakanksha Chowdhery: Yes.
>>: For these formulations I notice you maximize the total weight of the basically the value of the rate.
>> Aakanksha Chowdhery: You can, you can…
>>: [inaudible] talk about the rate utility [indiscernible].
>> Aakanksha Chowdhery: We can talk about rate utility but here what, what…
>>: [inaudible] rate utility means…
>> Aakanksha Chowdhery: Yeah, yeah, I know…
>>: You take the violation of, I mean of the utility value of the rate…
>> Aakanksha Chowdhery: Yes.
>>: May not be the meaning…
>> Aakanksha Chowdhery: Exactly.
>>: Is that right, I mean…
>> Aakanksha Chowdhery: Exactly.
>>: What [inaudible] versus ten megabits…
>> Aakanksha Chowdhery: Exactly.
>>: It’s not ten times.
>> Aakanksha Chowdhery: Yeah, you can talk about rate utility but I just chose to show the simple
formulation. You can easily put a function of rate up there. But as you know that when you put utility
then water filling is no longer the optimal solution anymore, right?
>>: I think in reality, I mean rate utility probably make more sense…
>> Aakanksha Chowdhery: No, what ends up happening is that with weights, so when you put for
example log utilities and even in wireless systems it ends up being this weighted utility in the smaller
time signals. Because you just change the weights that’s all that is happening. You basically have one
over R bar, right? Here rates are not changing as much. So, did that make sense?
>>: Okay.
>> Aakanksha Chowdhery: Yeah, okay. So we’re going to use some tools from convex optimization
theory to answer this question to solve this problem. So the primal problem if you look at just this
problem and I’m not, I’m, I probably will not have enough time to convince you that this is not convex so
we’ll take that offline. I’m going to solve the dual problem because the problem is coupled on all tones.
What, all I’m doing is simplifying the problem and then I’m going to use a tool called Dual
Decomposition and I’ll solve problem on each tone and come back and solve this problem basically solve
the optimize, the power constraint aspect.
What I’m going to, what we use as a result from optimization theory where when the number of tones
are large enough solving the dual problem is a good enough approximation for solving the primal
problem. So here is the overview and we’ll not go through all the details but this is the dual problem
that we’re solving. This is the totals of problems so the way this formulation works is that I start here,
solve the problem on each tone and then go back here and solve the problem to satisfy the power
constraints. These Lagrange multipliers get configured there.
Okay, so let’s see how much we want to explain.
>>: Can I ask a question?
>> Aakanksha Chowdhery: Yes.
>>: [inaudible] go back.
>> Aakanksha Chowdhery: Yes.
>>: So what [inaudible] constraint. So what package are you using to use the [indiscernible] method to
solve these dual? What’s the software package that you use for that?
>> Aakanksha Chowdhery: Its [indiscernible] I entered it by hand.
>>: Oh, okay.
>> Aakanksha Chowdhery: Yeah, I…
>>: So I was wondering why don’t you circle the built in…
>> Aakanksha Chowdhery: Why don’t I not use the built in?
>>: Yeah.
>> Aakanksha Chowdhery: Because there’s an inner loop here that totals the problem too.
>>: There’s a what, sorry?
>> Aakanksha Chowdhery: There’s an inner loop, you can use the subgradient or ellipsoid if there’s only
a single problem, right. There’s an inner loop…
>>: Ah, so this is the, okay, I see.
>> Aakanksha Chowdhery: So you have to be able to solve the inner problem and then the outer
problem, right. If this was the only problem that you were solving then, that was great. So if this
problem were directly convex then that all sounds great. I’m going to show you can come up with a
successive convex approximation but just to check the convergence properties I did not use them, you
can. I just wanted to make sure that things are converging and everything else. So I’ve implemented
most of the stuff by myself. It’s not that hard, ha, ha.
So the only two things that I’m going to do in a few minutes is that I’m going to convince you that the
non-vectored users all they were doing was they had this signal to interference noise rations, right, on
which they’re data-rate depended. So your data-rate on any chunk or sub-carrier depended only on the
signal to interference noise rations. For the vectored users we are using some interesting sophisticated
signal processing, something called an MMSE or minimum mean square decision feedback equalizer. It
is implementing successive decoding from information theory but what is really happening is that if I am
the first user to be decoded I see all the noise, and if I’m the last user to be decoded I see the least
amount of noise because all the other users crosstalk is no longer seen in my case. That’s how it
translates into my formulas that I used for data-rates. So there is a notion of who gets decoded first and
who gets decoded last.
I’m going to just skip this block because the key infusion is just how things are getting decoded. So the
signal to interference noise ration based, is based on, its [indiscernible] proportional to your own
transmit power. But in terms of the powers of other users it really depends only on the users which are
not decoded yet. For the non-vectored users it depends on this noise-whitening matrix which shows up
in this noise-whitening filter. So two things to note here, one you’re only seeing interference from users
which are not decoding yet and then this noise-whitening filter is actually able to correlate the noise
from all the non-vectored users and get rid of it. That makes things very interesting.
So coming back to your original problem when I take these expressions and want to solve for my power
spectrum on each tone I have two options, one is either I can go for an exhaustive search of powers
because I know the PSD mask limits, or I can go for a successive convex approximation approach. But
both of them by now looking at the problem are very complex that they cannot be implemented on a
fifty to two hundred lines binder.
>>: How many levels of power do you have…
>> Aakanksha Chowdhery: How many levels of power, bits? We are talking about bits…
>>: Yes.
>> Aakanksha Chowdhery: So, so…
>>: [inaudible]…
>> Aakanksha Chowdhery: Because this is a discreet bit loading…
>>: You can tune the power to whatever…
>> Aakanksha Chowdhery: You can.
>>: Or is it for very discreet…
>> Aakanksha Chowdhery: Very discreet but loyal.
>>: How many are those bits?
>> Aakanksha Chowdhery: You can load bits…
>>: [inaudible]
>> Aakanksha Chowdhery: Between zero and fifteen.
>>: Fifty?
>> Aakanksha Chowdhery: One-five…
>>: Fifteen.
>> Aakanksha Chowdhery: Fifteen, because that’s how they were going for binary.
>>: And that’s how modems [inaudible] or whatever.
>> Aakanksha Chowdhery: Yeah.
>>: Great.
>> Aakanksha Chowdhery: On each tone I can load fifteen bits. But most modems will not have loaded
fifteen on all tones because power constraint will have already been exceeded by then. So, okay, so
what I’m showing now is how much you can get out of the theoretical optimum because that’s where
the interesting space lays. So Revco was already what you’re familiar with and this simulation scenario
two users and the third non-vectored line. These were two vectored lines they X access with the datarate of vectored users and the Y access was the data-rate of non-vectored users. With the black line
what I’m able to see is that if I choose hundred megabits per second for my vectored users I’m getting
back my six megabits per second for the non-vectored line. That’s almost one seventy percent gain or
other way to interpret it is that I’m keeping my existing customer at what it was. Alternatively if I chose
to keep it at six megabits per second I’m actually getting the gain of the new technology all the way
through.
So this is really great because now you can actually get the gains off of grading these systems. I’m
showing you these three line simple examples because I wanted to explain the practical approximation
but we have tested this for twenty-five line or fifty line simulations also. Why does this work? This is
where things actually, we can get to practical approximations. So we’re really using the channel
structure to our advantage. Here notice that these are short lines and these are long lines. Long lines
mean more attenuation so the colors actually correspond to what other colors here. We are showing
channel magnitude versus frequency all the way up through thirty megahertz. This is short lines channel
gain; this is long lines channel gain. So this is more at higher frequencies.
This is the interesting part. This is the crosstalk from short user to the long user. Notice that it is greater
that the direct channel gain off the long user. So only in this space you can actually get any useful
transmission, while for the crosstalk channel from long to short lines its way below.
>>: I have to ask the same question again is this a simulation or is it…
>> Aakanksha Chowdhery: Through channels. The channels you can, in the real channels it would just
be a distribution over what I’m showing. So the crosstalk from short vectored lines can dominate the
long non-vectored lines. So there’s value to actually doing the spectrum optimization just by looking at
the channel structure. Now what is happening in mixed multi-level water-filling which was the state-ofthe-art solution versus MixOSB?
So this is the power spectra at the point where there was hundred megabits per second on the vectored
line. The non-vectored line with the state-of-the-art was getting two megabits per second and the,
MixOSB the optimum solution was getting six megabits per second. Notice here you have three bands,
the red and blue curves correspond to the two vectored lines and the black curve corresponds to the
non-vectored line. When these two overlap you’re interfering with each other. When you’re interfering
with each other basically the non-vectored line is seeing a lot of power from these vectored lines and it
just backs off and does not load power in these frequencies. On the other hand in this window we see
that the, it’s almost gone to [indiscernible] transmission but it’s not seeing power from the vectored
lines. It’s not seeing as much interference so it is able to load power.
So effectively you’ve just exploited the channel structure and figured out which bands to prefer versus
which bands to not prefer, so that whole idea of multi-level water filling starts to emerge again. This is
where we’re again going to use the concept of multi-level water filling except that we’re going to
generalize it to this mixed-binder scenario. There is a difference because I’m going to water fill on
equivalent channel gains for my short vectored lines. So these are not the same channel gains, this one
over SNR is now just coming out of that whole box that I had for [indiscernible] decoding.
So I start with this water filling distribution on my equivalent channel gains. Then if I’m the short
vectored user I would be given some indication that this band is preferred and this band is not
preferred. There is a way to do that with the existing standard. Then I move bits out of my nonpreferred band to my preferred band. I only stop and I hit the PSDMASK so you basically specify which
bands are preferred and which are not preferred. So this is a variation of what an existing water filling
solution. The way we indicate whether we are using multi-level water filling versus single-level water
filling is using another palometer that exists in the standard called target margin versus max margin.
Okay…
>>: [inaudible] I see a lot of parallels within this work and some [indiscernible] for this [indiscernible]
spaces which is you had the situations of networks which are all different lengths…
>> Aakanksha Chowdhery: Okay.
>>: That you’re trying to work so, you know the longer that network…
>> Aakanksha Chowdhery: Okay.
>>: Gets sabotaged by the shorter length…
>> Aakanksha Chowdhery: Yeah, this is the same near far problem, yeah.
>>: It sits right next to, your answers are a very serious problem and almost has a bigger problem to
that can bring down this whole space if not solved because you completely lose the advantage of a long
range network…
>> Aakanksha Chowdhery: Okay.
>>: If you’re already being sabotaged by the short range network.
>> Aakanksha Chowdhery: I’m really excited to talk about…
>>: So I don’t know how…
>> Aakanksha Chowdhery: There will be some…
>>: There’s a map there but I think you’re, there’s a bunch of…
>> Aakanksha Chowdhery: I’m happy to talk more but I think that the questions time variation at which
things happen would be…
>>: Yeah, so the question then at that level we’re asking there…
>> Aakanksha Chowdhery: Yeah.
>>: You need to have much finer grade control and so…
>> Aakanksha Chowdhery: Happy to talk further…
>>: [inaudible] map so quickly here so, interesting.
>> Aakanksha Chowdhery: Okay, happy to talk further on that. These cut-off frequencies they are
actually determined using a search algorithm, a bisection search algorithm at the SMC. So let me first go
through the gains and then we’ll go to the implementation why this is simple and practical to
implement.
So we had seen the black curve earlier and we’d seen the red curve earlier. So the red curve was the
state-of-the-art solution and the black curve was the theoretical optimum for the same scenario, two
vectored lines, three hundred meters, and one non-vectored line, twelve hundred meters. The green
and the blue curves now are what we get out of this practical approximation. So the green curve is
obtained when we have optimized our cut-off frequency. Again we’ve optimized which band to prefer.
The blue curve when we have chosen just a [indiscernible] assumption for whatever bands to prefer. In
both cases you see that when I provision my vectored line at hundred megabits per second you’re
getting as much as one sixty-one percent gain with this new algorithm which is very simple. At six
megabits per second you are basically actually getting some gain out of upgrading to new technology.
Now, implementation because that’s what I was pitching earlier, so the spectrum management center
has to somehow convey these cut-off frequencies to the modem and it has to tell them that this is a
multi-level water filling solution. The way it conveys the cut-off frequencies is by using the PSDMASK
and depressing it slightly in different bands. This is how we got through the standards bodies. But that’s
the only palometer it’s conveying to the modems. Then the modems do their water filling and then
change the water filling distribution just like I described. But the fun thing here is that they can adapt.
So they see higher noise in the higher frequencies and they can water fill back to the lower frequencies.
Instead of coming up with a centralized solution which says okay this power spectra and this is an actual
problem in UK networks they were like, we want to impose this power spectra on anything that’s lower
than this land. So like that’s not a doable solution. Okay, so with two important points here are that
now you have a very low complexity solution as far as the modems go, you don’t have to change
anything there. It’s a near distributed, the reason I say near distributed is because you do have to give
some information to the modems based on statistics you collect but not a lot of information. The
modems are able to adapt.
Now this slide was basically meant to convey mathematically some of the complexity. So you had mixed
multi-level water filling which was sub-optimal but very simple. Let me parse the parameters for one
second. So N is the number of frequency tones that can go all the way to four thousand ninety-six. K is
the number of lines and K one is the number of vectored lines. These can be of the order of twenty-five
to fifty. So the big term here is N log N in terms of the number of frequencies.
So mix multi-level water filling was very simple. MixOSB which is theoretically optimum is exponential in
the number of users and then linear in the number of tones. Where we get most of the gains are by
changing this term to this but I already explained the infusion of why this is really simple to implement.
We basically got it through standards, so.
This is what the summary slide of what the work that I have done in proposing these energy or how do
you control the power levels in next generation DSLs. I basically led the standardization of this to the
COAST-NAI report which got published last year. I was actually, so, and the kind of data it gains you’ve
got and are in the range of fifty to one sixty percent.
Okay, so this was the research summary so far of what we have done in the space of DSLs and next
generation DSLs. We’re exploring some of these ideas also extend to coaxial cable networks and I was
hoping to spend five minutes on that and that would be the end of my talk. Is that good?
>> Ranveer Chandra: [inaudible]
>> Aakanksha Chowdhery: Okay, or if people want me to stop here I’ll just give a reset summary and we
can do this in question and answer session to, your call.
>> Ranveer Chandra: [inaudible]
>> Aakanksha Chowdhery: Okay, so coaxial cables that’s the other medium that we talked about for
wire line access. This is really a wave guide. So historically this was deployed because TV networks were
deployed and it is heavily deployed in North America but if you look at everywhere else the penetration
of DSLs is higher. But what’s interesting is that you’re basically going to send signal between the center
conductor and the outer shield so this is really a wave guide or sort of same.
Where cable networks are going now is that they have these headend distributions and then these drop
cables going all the way up to each home. So you have the signal starting here and then going all the
way here. So this is your cable network and this will be shared between multiple homes. So if you have
a cable provider telling you that it’ll provide gigabits per second speeds it will be shared with all your
neighbors. How many neighbors it can be shared with? It can be all the way up to five hundred to two
thousand.
So what’s interesting in this space is that they are increasingly moving towards something called hybrid
fiber coaxial system because they want to bring down this fifty to, five hundred to two thousand to fifty
to two hundred. Just like DSLs they’re basically trying to move fiber closer to you so that the coaxial
cable is smaller length. Then your rates are higher so they can get higher rates for smaller number of
users. Currently it’s lying in the range of thirty-eight megabits per second downstream and twentyseven megabits per second upstream. This was DOCSIS one point X but they’re going all the way to ten
to two gigabits per second with DOCSIS three point one which is something that you might have read in
the news because that’s an up and coming standard for cable networks.
Now what is interesting here is that they’re moving in two directions. One they are going from multicarrier systems with better coding and they’re expanding the spectrum that they can operate in. So as
[indiscernible] that analog space not only white spaces became hard but cable networks are also doing
work to use this for data. So existing plans for example they had this forty-two megahertz that they
were using now they’re trying to decide between mid-split, high-split, and top-split which basically are
different configurations of spectrum. But what is interesting is that they right now are operating in the
speed of hundred megabits to three hundred megabits per second and they basically want to get here.
So which one they decide depends on certain logistics of what amplifiers to replace and what to do.
Where we are active here is that basically this is a shared system in time dimension. What we are
exploring is whether you can get more out of doing multi-user techniques. Can you super impose signals
and do better? That gets really interesting because now you can share things between multiple users
and given that now it’s becoming a multi-carrier system you can do this on different bands. They have
fairly flat bands with attenuation changes for different bands but it’s not a lot of variation adjust in
frequencies.
So the key point to take away here is that it’s an interesting space which is moving toward gigabits per
second. But more is probably possible by using certain interesting techniques and we are looking at how
we can use the shared medium by using other dimensions, so. This is an ongoing project and I cannot
say more.
So this was my work in coaxial cable networks and then I have also done some work in multi-cell
wireless networks on similar ideas of limited, basically using these mixtures of interference and multiple
access channels. I can talk more about it. Then I had one project in home power line communications
for those who are interested where I was using the cyclo-stationarity in power lines to propose an
Opportunistic CSMA protocol. That was a fun project to learn all about power lines and CSMA at the
same time.
This was the work I did at Microsoft Research with Technology Policy Group. This was basically to
investigate how much opportunities are there for unlicensed secondary usage in thirty megahertz to six
gigahertz spectrum using the data from Microsoft Spectrum Observatory at Technology Policy Group.
This [indiscernible] tells me has gone into an MPR, to reply for an MPR [indiscernible] so. That’s, that
impact and that’s the summary of the various research projects that I’ve worked at.
That will be the end of my talk.
>> Ranveer Chandra: Thank you.
[applause]
Any further questions?
>>: Question, I mean you’ve done a lot of work on this vector DSL [inaudible].
>> Aakanksha Chowdhery: Yes.
>>: I assume you also basically tried to work on wireless interference…
>> Aakanksha Chowdhery: Yes, yes.
>>: [inaudible]
>> Aakanksha Chowdhery: Yeah, so I have papers on multi-cell wireless.
>>: How do you compare these two steps wireless interference versus DSL [indiscernible] crosstalk?
What’s similar? What’s different?
>> Aakanksha Chowdhery: Can you kind of elaborate your question. I mean are you trying…
>>: So…
>> Aakanksha Chowdhery: To give me an idea…
>>: [inaudible]
>> Aakanksha Chowdhery: Of what the Lagrange looks like or what’s…
>>: No, basically the first slide basically in the two channels…
>> Aakanksha Chowdhery: Okay.
>>: How the two, what are the similarity and difference…
>> Aakanksha Chowdhery: Okay.
>>: In this two space? The channel characteristics, I mean the stability in estimation of [indiscernible]
crosstalk matrix…
>> Aakanksha Chowdhery: Okay.
>>: I mean…
>> Aakanksha Chowdhery: I think, so you’re asking a very interesting question and I think as you
probably know whenever you’re comparing wireless versus wire line, wire line tends to be more
stationary channel compared to wireless where things are…
>>: [inaudible]
>> Aakanksha Chowdhery: Changing at much faster rate. As a result whatever channel estimation you
are doing here things are not that great. I’m not going to say they’re A grade but here by the time you
get any information you’re not going to be able to use it, right.
>>: Did you measure or did you see measurement work on the…
>> Aakanksha Chowdhery: Okay, wireless side?
>>: Yes.
>> Aakanksha Chowdhery: No.
>>: Wireless side…
>> Aakanksha Chowdhery: I’ve seen…
>>: [inaudible]
>> Aakanksha Chowdhery: I’ve seen measurements on DSLs not on wireless.
>>: Okay.
>> Aakanksha Chowdhery: Not on wireless aspects. I’ve only seen channel models on models for...
>>: The reason I ask this is in basically in term of practically…
>> Aakanksha Chowdhery: Yeah, yeah, yeah…
>>: [inaudible], right?
>> Aakanksha Chowdhery: Yeah.
>>: The channel characteristics…
>> Aakanksha Chowdhery: So the practical implementations for multi-cell work are happening at
[indiscernible]. I talked to those folks but I haven’t seen the measurements myself.
>>: We have [inaudible] to wireless [inaudible]…
>> Aakanksha Chowdhery: Okay, yeah, I’m not an expert in that area…
>>: [inaudible]
>> Aakanksha Chowdhery: I just was taking all my work and seeing if it applies and what kinds of gains
are possible in simulations.
>>: This basically until the last work you talk about basically energy based on allocation. How, is it
possible basically to basically project a zero vector onto those unlicensed…
>> Aakanksha Chowdhery: I was…
>>: [inaudible]
>> Aakanksha Chowdhery: I was thinking about that so you’re thinking in terms of interference
alignment. One of the key requirements of interference alignment is that the number of dimensions has
to be larger, right.
>>: Yeah.
>> Aakanksha Chowdhery: In this case we’re transmitting on all the dimensions that we’re actually
using. So…
>>: [inaudible]
>> Aakanksha Chowdhery: We have three dimensions we’re using all three. If I had one extra wire
which is possible because if you, so this is differential so have a twisted pair so you are using the
difference of the two signals and the two lines. If you basically make it common vectoring then what,
you use the binder as the reference point and use all the other wires from there. Then you have this
additional antenna and then you can start talking about the kind of things you’re talking. But here if you
use all the transferred dimensions then you’re not going to be able to do it. Then you have to meet all
these constraints of the papers that came out as to whether I can actually use those transfer dimensions
or not.
>>: What did you win the Marconi Award for?
>> Aakanksha Chowdhery: For work on vectored and non-vectored DSLs because it was taken all the
way to standards. So it was really end to end work in terms of impact.
>>: [inaudible]
>> Aakanksha Chowdhery: Thank you.
[applause]