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>> Jin Li: Hi. It's our great pleasure to have Professor Qian Zhang from Hong Kong University of Science
and Technology to come to Microsoft Research and give a talk on spectrum usage understanding and
dynamic spectrum sharing.
Professor Zhang joined Hong Kong UST in September 2005 as an associate professor. She's now the
co-director of Hong Kong [inaudible] Innovation Lab and Associate Director of the Digital Life Research of
Hong Kong UST. Before that she was with Microsoft Research Asia from July 1999, where she was the
research manager of Wireless and Networking Groups.
Dr. Zhang has won a number of prestigious awards including TR100from MIT Technology Review. She
received a bachelor from [inaudible] Young Research award from IEEE Communication Society in the year
2004. Received the best paper award in MMTC in 2005, best paper award from [inaudible] in 2006. Globe
Count 2007, ICDCS 2008 and ICC 2010.
She has been elected as IEEE Communication Society Distinguished Lecturer from January 2010 to
December 2011. Professor Zhang is also the editorial board member of a number of IEEE transactions,
and she was the chair of multimedia communication technical committee of IEEE Communication Society
from 2008 to 2010. And without further ado, let's hear what Professor Zhang has to talk about.
>> Qian Zhang: Thank you, Jin. First of all let me take this opportunity thank you Jin for the kind host. It's
really my great pleasure to come back to this campus after five years.
I still vividly remember I was here in 2005 before I left Microsoft, visiting different research groups and the
product team to do the product transfer. So it was a very nice experience for me.
Okay. So today I'm back to this place, and hopefully I'll deliver some of the recent study in my group. So
the title of this presentation will be Spectrum Usage Understanding Dynamic Spectrum Sharing. So this is
the very brief outline. So, first of all, share with you some of the background. And then we're talking about
the spectrum measurement, usage mining we conducted and after that, and after spectrum usage, we're
trying to track whether we can do some special interesting thing about spectrum sharing among the
multiple primary users. Finally will be the conclusion and some of the other directions under my group.
So I think most of the audience know pretty well about the background, which is that by year 2020 there will
be trillions of wireless devices. All those wireless devices, they want to talk, not necessarily want to talk to
one of them, but they need to talk in a wireless way, which cause a lot of challenge for the spectrum
efficiency in the way in which spectrum we can use and interference, how can we avoid it, how can we
achieve rather good performance, et cetera, et cetera.
And this also brings a lot of challenges to the network architecture design. So we're clearly seeing that for
the future wireless network it will need hundred to even a thousand times of increase in terms of the radio
density, in terms of the B Ray of the radio.
So all this actually motivates us to think about the better coordination way, I mean the way to coordinate
spectrum usage.
And so if we still stick with the current static allocation method, which is a lot of common use nowadays,
like USFCC, like Hong Kong [inaudible] and all those places, they use this fixed allocation for spectrum
usage. You run a TV service I give you a certain fixed band. This band will be used by you for this
particular location and the other people cannot use it.
If we use the static allocation method, we'll definitely see -- lack of spectrum for the future, our future
service wireless application. So most of you may see this graph many, many times when we're talking
about lack of spectrum. This is a typical graph that U.S. FCC actually distributes in terms of their allocation
map for different services.
And all these colorful things indicate that the spectrum we have has been almost allocated and if we're
talking about future service, 4 G, 5 G and all those fancy new services, we may not have enough spectrum.
And this one is the similar graph, has been disclosed by Hong Kong government. This is after they
announced spectrum allocation map for Hong Kong. Similar trend you can find that.
And this is more particularly looking at IMT service, and this has been announced that by 2010 -- 2020, and
if we're looking at spectrum requirement, this is a kind of prediction based on different usage demand,
whether there's usage demand is low or high, you will see that we will not have enough spectrum to serve
the IMT service.
>>: What's the source of that?
>> Qian Zhang: The source actually you can see from here. And a lot of people already did research,
because lack of spectrum so people are seriously looking at the usage of the spectrum. So you may
already see this graph again many times. This is the one distributed by DARPA and this is the one
distributed by shared spectrum which is a company doing a lot of management for the spectrum usage in
U.S.
And they give the similar conclusion in the way that in U.S. actually there's a lot of the spectrum has been
underutilized, because of many service may not be run over time, may not be run over the places. So
that's why the spectrum usage is not high. Okay.
So that's why with such a background, with such an observation, people starting talking about new
research paradigms, which is dynamic spectrum access. For dynamic spectrum access, besides the
traditional so-called primary user who is the license holder. And they can use the spectrum when they
want.
But because they're not use the spectrum all the place all the time, so this is why give the opportunity for
the so-called secondary user which is the intelligent user, they can use the spectrum opportunistically when
the spectrum is not being used by the primary user.
So this gives us an opportunity, and also in terms of the entire system goal, people talking about we want
to maximize the spectrum utilization, but minimize the interference to the primary user because they are the
license holder. So people are talking about large, about enabling technology which is connectivity radio.
Which has the capability to share the spectrum in an opportunistic manner.
So this is the general spectrum management framework. A lot of people have done a lot of research along
each direction. So the fundamental here is that we have a radio environment. So we want to understand
the radio environment in an accurate way. So as can help us doing the spectrum sensing. I need to know
where is the primary user, where the primary user will be back so I need to leave the spectrum to the
primary user. If I have a very good understanding about the radio environment, then this can help me to do
the spectrum decision.
This other spectrum now available, but it may not be available in the short period of time. So that's why I
cannot use that. Because once I use net, this is the opportunity. But just an opportunity for a short period
of time. So we need good understanding of the radio environment, with good sensing we can do good
decision.
Once we have a good decision we have a good way to share the spectrum and then this will again affect
the radio environment. So that's why in today's talk I want to particularly emphasize on two topics. One is
the understanding about the spectrum usage, which is the radio environment.
So we did some spectrum management and then some understanding about the spectrum usage. And up
to that I'll cover another topic which is by understanding about the spectrum usage, what can we do? So
we have spectrum sharing among the multiple primary users. This is another topic.
Okay. So the motivation of this work doing the spectrum management is very clear in the way that we see
shared spectrum did a lot of measurement here and there in U.S. What is the spectrum usage in China?
Particularly? And we only find out the conclusion people give out, while the spectrum is underutilized, the
spectrum is underutilized in China. The spectrum is underutilized in U.S., the spectrum is underutilized in
Europe. Is there any correlation we can find out among these spectrum usage?
If there is a certain correlation we can find out, then we can leverage those correlations, helping us to do
the spectrum prediction and this will again helping us to do the better decision in terms of spectrum sharing
and spectrum access.
So understanding the usage of the spectrum is not only to claim spectrum is underutilized but we want to
do a little bit further in the way how can we more efficiently access the spectrum?
Okay. So that's the purpose. This is the work we have been conducting. And this is the measurement we
conducted in China with the collaboration with the government in China. Actually the counter party of FCC
in U.S.
We conduct for -- we conduct for measurement. We conduct the measurement in four measurement sites.
Two places actually in the Guangdong region and the other two places in the suburban region. And the
measurement was released last year 2009, and the frequency band we measured actually starting from the
20 megahertz to 3G. And this has been published in last year's Mobicom. If you want to see detailed
measurement you can see from there.
This is the basic information related to the management equipment. So the four sites we use the same
spectrum analyzer, which is a rather strong spectrum analyzer. It can scan the frequency range from 20
megahertz to 3.6 gig. And the measurement resolution is one per like 200 K hertz. And then for each one,
the scanning or the frequency band, for each one, it takes 75 seconds. So we measure that with such a
resolution. Every second, 75 second.
So eventually we get those measurement points.
>>: Can you go back up?
>> Qian Zhang: Sure.
>>: Over seven days.
>>: Okay. Question. This measurement individual can take? Doesn't actually need FCC approval for
measurement alone, is that right?
>> Qian Zhang: Yes, you are right in this way. But then for this particular measurement, why we need to
collaborate with the government because we need a very strong attendance to support this type of
measurement. And those measurements, actually they have such equipment. That's why we collaborate
with them, share their resource.
>>: Is this ground level or is this above?
>> Qian Zhang: Actually, it is above. You can see this is a typical [inaudible] use.
And around all those spectrum band we did the measurement. Those are the typical services conducted in
China. Including CDMA, GSM 900, 1800 and broadcast TV band and also ISM band.
And this is a general graph to show you the very basic understanding about the management. So this is
the energy level overview for a certain location. This is a location three over seven days.
And by just a simple view, you can see that actually the energy level for all different services is not that
high. So generally tell you that the utilization is not high at all.
Then we want to see more detail about this, because this any energy level information. Then we're looking
at the real information which is useful for us, which is the channel state information.
So we want to converge the power level to the channel state information. So we want to indicate for a
particular channel if it is idle at a particular time, then that we will indicate a CSI value as 0. Otherwise, the
CSI value will be 1.
So now we do the conversion. We convert the energy level to CSI data. And the conversion we use
actually we just simply set a threshold. This is the similar method we use as shared spectrum use. So we
just set a threshold at 3G higher minimal value you can see for this particular period of time for this
channel.
So after that, you can get such information. So roughly you can get a similar idea. The spectrum is really
underutilized.
>>: When you say the minimum value seen in this channel, is that a .2 megahertz channel of bandwidth
basically it's a wider ->> Qian Zhang: No it's just a .2.
>>: If it's .2. Then even some of the frequencies. How come some of the frequencies is all blank? Some
fluctuation?
>> Qian Zhang: Because there's some channel actually basically there's no service.
>>: So the dark lines would be broadcast, cellular, CDMA and GSM?
>> Qian Zhang: Those places you can see those are GSM 900. And those services are GSM1800.
>>: To his question, I have the same question. So even the difference between the background was that
day and night should be higher than three-db. So even if the channel is blank, the minimum, the threshold
that 3 db you should get activity during the day.
>> Qian Zhang: No, if you set the minimal as the bench, as the basic benchmark, and as 3 db higher as
the case that there's some activity, and then in that case for most of the spectrum, because there are no
services running on top of it.
>>: Even if there's no service, there's [inaudible].
>> Qian Zhang: The noise level is not actually ->>: It's [inaudible].
>> Qian Zhang: It's not that significant. It's not higher than 3 db from our measurement estimate. So for
those first conversions we gather channel state information. But those state information is for particular
time slot.
But for the channel usage, what we care most about is the channel frequency duration. How long can the
channel keep idle in that way data if I know this information I can do more efficient spectrum access.
That's why we're in particular looking at the channel vacancy duration, we got the roll data in energy level
then converge to CI series, and further convert to get a channel duration, and then we need to check
whether the channel vacancy duration follow any distribution. So at the list. When they measure
theoretical whatever simulation study, if they want to understand, if they want to make assumption about
the channel usage, most of them believe this is, once they are, first of all, Markov channel. They basically
make such assumption, but we then seriously need to look whether those assumptions are true or not.
So that's why we checked the channel vacancy duration and we find out this channel vacancy duration
following explanation like distribution for all the channels we study. But they're not independently
distributed.
So we just do the simple approximation for all the measurement data we get. We find out the access
indicator channel vacancy nets and the wireless access KD, the probability, approximate probability.
And then we can find out, yeah, do we consider duration follow this exponential like distribution, but we can
find two more messages we want to deliver. First of all, it can be nearly exactly estimated. Second, the
channel state cannot be modeled as first order Markov model, which has been used widely for all different
papers as their basic assumption.
Why we can get this conclusion very simple, because we have the measurement data and we have the
approximation already. So if this channel state can be modeled as the first order Markov chain and we can
see that for this approximation explanation like distribution B and C the parameters should be the same.
But actually it's not the case in the measurement study.
And also if this is a first order Markov chain and then the current channel state will only depend on the
previous one. But actually this is also not the case for our measurement study. Okay. So this can give us
a simple conclusion. We cannot simply model our spectrum usage as a first order Markov chain. But how
can we model it efficiently, we don't have a perfect answer for that. Very sorry for this, because for different
channel we try different model. We find out you really cannot find a perfect model to fit all the channels.
>>: Can you go back one slide?
>> Qian Zhang: Yeah, sure.
>>: Maybe I'm not 100 percent what you mean by [inaudible] Markov chain. Because you can always
model anything as a [inaudible].
>> Qian Zhang: Uh-huh.
>>: Might be clearer than that. One could say however what shows to be is not -- is that at the same
channel? Meaning, if I'm modeling a particular channel, I'm modeling that channel and I'm going to find the
parameters for my Markov chain for that channel. But for this case, however that doesn't imply that the
model is very good, because it could be that in a channel I always [inaudible] 0s and then I have like
thousands of cases where that's the case. And that would give the first [inaudible] other channels where
actually I have more longer intervals than the second case is what happens. So...
>> Qian Zhang: No, what we do we have one week data even for a particular channel. And if you model it
as like a first order Markov chain and then actually for even for this particular channel with different days'
data you put in it, you can still not follow one single model. So that's still the case. It's not saying across all
the channels. It's just for this particular channel. Because of this seven-day information. You cannot use
one single model to feed it.
Okay. So besides giving kind of a negative conclusion we want to see some more like promising way. So
what can we do then? So we check the correlation. We want to see whether the spectrum usage have a
certain correlation. So in the way that whether for this particular time, if I know the channel stage, how
about the next certain period of time.
So this is a temporal correlation. And also for this particular channel, if I know the channel usage, how
about the channel next to it? So now will be the spectrum correlation.
And also for this particular channel, if I know its usage, how about the other place for the same channel?
So now it will be the spatial correlation. So we want to check whether the spectrum usage have any
correlation in terms of temporal spectrum and then spatial. We want to check the spectrum coefficient
which includes the likelihood of these two sequences.
So this is the kind of motivation for the work. And then we continue to check this study. So for the channel
stage series which we've already obtained just now, this is kind of a small level management. It's a
microscope management for the channel utilization. And we know that for a certain spectrum band actually
something like .2 meg and for some service, actually has a rather wide band, wide range of the spectrum
usage.
So we're looking at from the service point of view. So we defined our service congestion rate, which is
trying to examine across all the channels we see in the same service. For example, GSM 900. For
example, TV band. We want to check all the channels we see the same service for a particular time slot.
So that's why we detail checked the congestion, the service congestion rate for different service. We've
seen the service different spectrum band. And this is the information we can get. This is the one result we
get for GSM 900 upper link service. And you see that we measure -- we are trying to study the intra
service within this GSM 900. How about the spectrum correlation? Okay. For different frequencies
running in this GSM 900 service band is there any correlation in between?
So the conclusion we get, simple conclusion we get is there's a rather high correlation. So which means if
you measure certain spectrum band the usage you can more or less use that to help you to predict the
other frequency band, the spectrum usage in other frequency band.
So this is the motivation. We have the result. Then we say whether we can come out [inaudible] we can
come out a certain way to really do the spectrum prediction.
So the information we can get here is that for this different time series we have a lot of channel. And then
for each channel we have the channel state information. For that particular time slot the channel is busy or
the channel is idle.
Given that information, whether we can do some prediction, okay. So we find out we may be able to use
the kind of pattern to do the prediction. So what is a pattern? We consider a block as a pattern if H
appears many times. For example, this we can treat as a pattern. After one, if after one, then it will be a 0.
After 0, then it will be a 1. After 1, it will be a 1. So if we treat, this block as a pattern, because this pattern
appears many times, okay. So if this pattern appears, for example, 1,000 times and this pattern also is a
pattern, each appears also many times, for example, 990 times, then with these two patterns altogether we
can do some prediction.
If the blue pattern appears, more or less indicate the next time slot, the different channel will all be idle. So
we can do this. So this actually is idea we borrow from the database people. They have this time series
analyzers and this is a frequent pattern mining.
They're trying to use the frequently appeared pattern and so with this you can do the prediction. So this is
the idea we use. So that's why the spectrum prediction will include those three steps. The first step, find
out all those frequent patterns. And then you generate some prediction rules, and we call this as a pattern
association rule. Because you have those patterns, you find the relation of those patterns and then this
can help you do the prediction.
The next step will be simple. You just predict according to net rules. Okay. As I just mentioned, we
actually borrowed the idea from the data series, time series people, they do the research on this pattern
mining, frequent pattern mining.
But uniqueness or the challenge for this problem is that this frequent pattern mining actually works in two
dimensions. Normally when two people do the data series, time series, understanding, analyzers of the
data like the store market, they're normally one dimension information. But here we have both time and
spectrum event. That's why we call frequent pattern mining in two dimension.
So for this, actually there's no existing algorithm work. And in both two dimension, because this is a
dimension explosion problem. And the running time will be huge in the large scale data, for the large scale
data.
So that's why we need to think about the potential way to do the optimization. So the first optimization we
introduce is actually very simple idea. Say if this block is a pattern, it only becomes a pattern when all the
stop blocks are all patterns.
So with this, we can check that for a block X times Y, we need to check two X sub blocks. But this is still
huge if X or Y itself is large. Now we come out a second heuristic, kind of second optimization in the way
that a block is a pattern only when the pattern blocks are a pattern. For each block each parent block only
a four parent block.
So in this way we can come out a much more efficient optimization heuristic. So we apply this optimization
efforts and check all this pattern mining algorithm, and we can find out before optimization, while this
problem can quickly crash, but as optimization still running time is still long, my student try one day and he
gave up, has to figure out a new heuristic.
And this optimization 2 actually work. So this is the simple prediction result. And we can see that this is a
prediction we use applied TV one and GSM 900 upper link.
And the conclusion we can get is that prediction accuracy is extremely high when the training set is
sufficient. But the training set actually you only need to take like first three hours' data as the training set.
Then you can simply apply for the rest of the one week.
>>: Bear with me. Simpler prediction, like correlation? Linear correlation potential?
>> Qian Zhang: Yeah, we did have. So here is the comparison. So this is the prediction result we are
trying -- we're trying the same training and testing set. We compared. This is the occupancy of the
spectrum, and we compare our predicted, the frequency mining pattern mining 2 D prediction algorithm,
2-D first order Markov prediction.
>>: First, just using the one information, you're actually using many. So what single algorithm to exploit
the same number of datas they use to predict the next one is to just use linear correlation.
>> Qian Zhang: No, actually for this case actually we also use the first of three hours for training to train
the model, to train the Markov model and the parameter and then we use net to predict the next block.
>>: But based on the last date, right?
>> Qian Zhang: Yes.
>>: And you're predicting based on a number of states. So it doesn't seem ->> Qian Zhang: Yes in the case net we explore the spectrum dimension but no in terms of even for the
temporal one we actually take the similar information as input.
The only difference -- this does not take the information across multiple spectrum beds.
>>: First order Markov ->> Qian Zhang: It is the first for -- first Markov in terms of this particular spectrum bed.
>>: Okay.
>> Qian Zhang: Maybe we can discuss this off line.
>>: Yes.
>>: Question. For this prediction, data prediction patterns generated by basically the programs. I saw this
basically occurs in frequencies on the point to megahertz. So is it possible it's caused by the GSM band,
TV band they naturally have some kind of bandwidth, which is larger than this point? Which is causing this
prediction inaccuracy?
>> Qian Zhang: No, actually for GSM, it has a lot of spectrum sub end or across the same service. And for
different operators, they operate different sub band. For a particular operator you will not operate all the
spectrum bands. The government will actually allocate -- for example, we come out to spectrum sharing
patch. The government will did allocate certain bands to one operator, other sub bands to another
operator. That's why we have different operator running GSM service.
>>: Looking at the band, basically you're using it as an example, right? GSM, I don't actually expect
cognitive radio to operate in that band. It's quite a big band. TV is actually [inaudible] basically device will
operate, right?
>> Qian Zhang: Yes.
>>: So, for example, TV's correlation accuracy will be quite interesting seeing, for example, TV frequency
equals that band. First Markov prediction have only 75 basically, basically accuracy. Interested to
understand why it is of that. Is that because the traditional prediction missed energy in the slot, or basically
TV's frequency is shaped in such a way that it doesn't appear or something ->> Qian Zhang: I think the reason why for this previous work now fit pretty well because again as I just
mentioned we can now use a simple model to fix it and while our work can fit pretty well again because we
use the pattern, we find out the pattern to do the prediction. There's no particular model we're using
actually.
So I think that actually explains the difference.
>>: So if we can graphically display the patterns it may offer some additional information?
>> Qian Zhang: Sure. Sure.
And this actually is continued the measurement data and the data analyzes for different location, because
we measure in four locations. And we find out actually those are the same service in different location and
those are the prediction results. And for this previous slide, we check all this as the same service. And
then we check different services in different locations.
We want to see for the same location. We want to see whether there's a cross-service correlation. And,
for example, GSM900 and TV one has any correlation, the result is not promising in the way even using the
frequent pattern mining scheme you can not find too much correlation in between. This is mainly because
of the service nature.
So very brief summary about the measurement study. We conducted the spectrum measurement study for
four locations concurrently in China. And we checked the channel vacancy duration distribution, and the
conclusion we get is following [inaudible] distribution. But it shows first order Markov chain model cannot
estimate the real channel state perfectly.
And we continue conducting the study in terms of correlation, we find out the temporal spatial and the
special correlation really find, really exist. And the intra service spectrum correlation actually is quite high.
And we further propose a frequent pattern mining scheme and we get some interesting result in terms of
the prediction accuracy. So after we have the study about the channel condition, we want to see whether
we can base on those measurements find some interesting observations.
So this actually comes to the second piece of work, which is spectrum sharing among multiple primary
users. So as I just mentioned we did a measurement study and we're in particular looking at some
spectrum bands like CDMA, GSM900, GSM9000, 1800. Those spectrum bands are occupied by two major
service providers, wireless service provider in China, which is China Mobile and China Unicom. They run
the bands to run the service.
And this is what we call the collaboration coefficient. I will introduce what is the collaboration coefficient.
And for the frequency used in China Mobile and frequency used in China Unicom and the result we find out
the collaboration coefficient we defined it the portion of the time clock when the states of the two channels
are different. When China Mobile is busy, China Unicom is free. When China Unicom is busy, the China
Mobile frequency is idle. We define this as the collaboration coefficient in terms of one service provider you
are lack of spectrum but the other service provider actually they're free.
So this shows that the special coefficient is high for many frequency bands owned by those two service
providers. So the simple conclusion we can get is we have this unbalanced spectrum usage of those two
service providers. And now we literally give the question, how about we share among the service
providers?
When one service provider, you particularly are busy, why not just borrow, why is not just share this other
spectrum, that the other wireless service provider spectrum band. But now here is the question I am a
certain service provider. I own all my base stations. I don't want to share my infrastructure to other service
providers, because naturally they are my competitor.
So however we're not sharing the service infrastructure but just share the spectrum. We just share the
spectrum usage in terms of time portion. And those base stations, you're still -- if you're sort of the larger
user demand, you're short of spectrum resource, maybe you can come out with certain agreement with the
other service provider to temporarily rent or temporarily lease a certain portion of spectrum.
Okay. So this is the motivation. And then we're looking at is there a uniqueness or challenge for this type
of spectrum sharing. So, first of all, the base station, there belongs to the certain group. I cannot change
that. This is my infrastructure. I don't want to share with others. This is natural. So I don't want to
dynamically form any group. The group is there already. It belongs to different service provider.
And service provider, as I just mentioned, you normally are competitor with each other. So I want to
maintain a certain level of cooperation. I don't want to have a tight collaboration with you.
Because I don't want to give up my customer ownership. I don't want to give up my counter power for my
base station. And from the natural point from every service provider, you don't want to unconservatively
serve all your resource to other service provider.
Okay. So with those uniquenesses keep in mind we actually find out group bargaining solution is a perfect
model for this scenario. So in this case the base station you can still treat as individual. You form a group,
basically you form a group because belongs to the second service provider and then you collaborate with
the other group, under this group bargaining umbrella.
And base station in the same group you're trying to make efforts to improve your aggregate payoff.
Because from the service provider point of view I carry my own utility.
And so to carry out collaboration, the service provider you are backing each other at the very beginning of
each period. We said a lot of periods. So at each period -- at the beginning of each period you do the
bargaining. In terms of whether I can achieve agreement with the other and if I reach a certain agreement,
I will do collaboration. Otherwise I will do noncooperative way, which is the natural way we are running
today.
So this actually I will not go into the detail, but this simple umbrella, this simple problem can be solved with
a centralized way in the case net, a lot of information. All the information being shared among all the
service providers. Then I can treat this as a simple optimization problem.
Simple optimization problem I can solve it in polynomial with the existing convex technique. But the key is I
have to give up all the detailed information to the other service provider. I have to share all the information
in terms of my privacy, my business secrets, and this cannot work well to the large scale system.
So that's why we propose two distributed algorithms. One is a local bargaining algorithm. The other is a
Lagrangian decomposition method. So for the local bargaining method, actually it can work in a rather
scaleable way, and it can guarantee deconvergence process. I can guarantee this process can converge
in a rather fast way. I cannot give you performance guarantee. I cannot completely protect the privacy.
The Lagrangian decomposition method can work pretty well in a large scale system and it will protect all
your privacy information and also it can guarantee performance gap is optimally small compared with the
optimal centralized method.
But the bad thing it will have slow convergence speed. That's why we propose this two distributed scheme
in terms of different scenario you may decide which scheme to use. And this is the performance evaluation
we do some simple tasks in different topology.
Those are the one case, for example, those are the two service providers, the order blue base station
belongs to one service provider. All the red station belongs to another service provider. So they have
some contention relation as we indicate here. So here we compare four different schemes. The break
point scheme is we're not collaborating with other people. So this is a normal scheme we use today.
Everybody just run in a noncooperative way. And this is the central approach, CM, which give the optimal
solution in terms of the performance. But again scaleable. It's give out all the privacy information.
And this is the local, LB is the local bargaining scheme, and the LD is the Lagrangian decomposition
scheme. You can see now performance-wise difference and also the other property difference we have
already described before.
Okay. So those are the two typical cases we study. And finally I want to give a short conclusion. And also
introduce some of the other activities we're conducting.
So generally speaking, for this cognitive radio dynamic sharing paradigm, there's still a big gap between
having a flexible cognitive radio component building block and have a large scale deployment for the
cognitive radio networks. There's still a big gap in between.
So that's why recent years we can start and see some like spectrum cognitive radio system. For example,
the vector -- they're the other people in this group, in Microsoft Research, they have already conducted
some result on allowing this direction.
And also building and deploying cognitive radio networks is very complex task. So that's why there are a
lot of promising directions to go in terms of how to build from the component to the network and there are a
lot of policy and technology issues. They're intact together.
For example, like the service provider sharing technique we proposed. Whether we can really persuade
operators to really apply this kind of collaboration, are they willing to take those things? There are a lot of
policy technology-related issues. They're intact together.
And also the cognitive network technology is not only limited to the dynamic spectrum sharing, even with
dynamic sharing even with 4 G technology, 4 G standard. They're talking about the cognitive network
technologies. For example, you have different spectrum band how can you do aggregation, how can you
do the more efficient spectrum access even without primary use and second user concept. So all those
things are some promising direction to go.
So in our group, actually, align this three directions, spectrum policy, algorithm and also the test bed
prototype things, we are conducting some of the efforts. For example, for the algorithm part we are
studying for all different aspect in terms of how can you do more efficient sensing.
How can you do the prediction-based sensing and sharing, for example, because we have already some
prediction result. How can you do more efficient spectrum sharing and access? And maybe we can
introduce the corroborative concept into the cognitive radio networks. So all those efforts, all those
different directions we have studied paid some efforts on that.
And in terms of spectrum policy, we also have conducted several pieces of work. In terms of how can we
have a more efficient spectrum action way, and if we want to be truthful about also performance efficient
auction, how can we define the auction rule.
And also in the future dynamic spectrum market, how can we motivate our service provider? It's not only in
the technology motivating, naturally can find business model. If they can find they can earn money from
those dynamic spectrum access they will easily buy this concept.
Otherwise, you talk with operators, you have spectrum now being used, why not share with others, doesn't
make sense to them. And also incentive pricing, all those kind of things, are very interesting. So we're
doing something along this direction.
Finally, in terms of the test bed, we also pay some efforts in terms of how can we build cognitive radio test
bed so it's more efficiently evaluate all the algorithms we proposed. So we are spending some time in the
software defined radio test bed, this is USRP-based test bed.
So finally it's the short conclusion. I think along this direction cognitive radio, dynamic spectrum access
there's still a lot of work to do and it will eventually make significant impact to the future wireless network.
Fundamentally we need to have a better understanding about the spectrum usage before we're talking
about efficient spectrum access.
Also we need to think about both technology and economics point of view, because they are impacted with
each other. So that's why we need to kind of consider both when we think about new topics. And finally,
it's the real system deployment. It's not easy just -- it's very easy just talking about algorithm write papers,
but how can we persuade operators to really deploy the technology, how can we persuade equipment
providers, generally manufacturers that manufacture this capability, it's a lot of work. So that's why
collaborating with the industry is a definite must for the academic people. That's the end of my
presentation. Thank you.
>>: What sort of reaction are you getting from the service providers to this research?
>> Qian Zhang: Actually, we didn't talk directly to service providers, but we have a lot of collaboration with
the equipment provider, for example, Wahway [phonetic] Company in China, and they have very strong
interest in this technology. The reason is very simple. Because there's really lack of spectrum for the
future service, like 4G, 5G. And they have to take this cognitive-related idea.
As I mentioned not necessarily under primary user, second user category. Especially for the equipment
provider. They're kind of saying the dynamic spectrum access may be far to go, but the cognitive radio
idea is a must for the future technology.
>>: So your service provider, I would imagine one of the arguments you'd make it's not so much the
spectrum, which is that a lot of people have it? In other words, then how do you respond to that basic
argument? That we don't need any of this. Just give us more spectrum, take it away from people who
aren't using -- you've done your studies of spectrum usage. Some people aren't using it at all or not using it
very well. Just let's just reallocate it to the providers.
>> Qian Zhang: And giving you an example. Recently we discussed with Wahway, we said FCC in U.S.
has already said that TV band can be used as TV wide space for the other, for the other usage, right? So
suppose China or the other countries also pushed this direction? And then as an operator, it comes to the
equipment provider, say, well, give me a solution, which I can use the traditional 3G or GSM band for my
service, and meanwhile I also want to leverage the TV-wide space for my services.
They have not incentive in terms of talking about this. They say I will still use my original band but I also
want to leverage the actual TV-wide space band. But how can we provide service for that? Because from
service providers point of view, get used to the scenario net the band will be allocated to you and you can
have a certain sort of service guarantee for that band. But if we're talking about I will actually use TV-wide
space, no spectrum band, yeah, a lot of studies shows that the utilization is low. But still has.
So once the primary users are back to the service, how can you leave? How can you still, make your
service provider, certain good collaborative guarantee. So a lot of interesting discussion along this
direction.
>>: In this space, in this standardized something like three class of device, right? So basically the base
station actually require you to not only sense, also have a database and have basically GPS device. So
basically you have to rely upon basically history and the collective information to make decisions so we can
use the spectrum. And record, basically those lower class device which doesn't have GPS that will
basically require you to lower down your basically your transmission.
I think that's basically the U.S. basically in an immediate sense. Primary user will have basically more high
power to use it, basically, channel. So even some of the channels here showing 50 percent channel
utilization, that basically -- I mean if this is indeed occupancy level of that channel, if you allow secondary
system to utilize channel, that interrupts the primary user interface, because this copy here may not be all
that basically well if they're [inaudible] a lot of issues involved in the basic thing. You may not be able to
sense basically the allocations.
So I'm wondering how this dynamic sensing versus just basically try to be absolutely sure using it for time
basically for information. For example, your early spectrum graph, there are some channels you don't
sense any energy at all during the entire seven-day period. Then using those channels, it's almost
basically shows [inaudible] I mean you're not afraid of interfering with anything.
Do we really want to use really sophisticated technology?
>> Qian Zhang: So actually this is a very complicated thing from again combined policy and technology.
FCC really U.S. part really push actually including Europe pushed a concept as the radio map database.
So I will just mention the radio map database. Everybody just go to the radio map database to check the
usage of the primary user.
So in that way we'll have a better understanding about the channel. So, first of all, how to build this radio
map. So there's a lot of complicated issues. You need to do the sensing. You need to do the cooperative
sensing so as to avoid the hidden terminal issue. And also helping you build a more accurate radial map.
The other thing is as I just mentioned for those spectrum bands you want to borrow, you want to reuse,
even though their utilization is very low but there's still some primary users on going. So that's why when
you're an operator trying to deploy service on top of those opportunistic channel, you have to have the
capability, for example, your base station, you originally deployed with a certain band. Now the primary
come back, you have to release the spectrum. You do spectrum borrowing, you can borrow spectrum from
the other neighboring base station, but if you borrow from that there will be like kind of borrowing
propagation. How can you solve that? All those things bring some new topics for the service provider.
Originally I get used how to do the planning, how can I plan my base station in a certain region I want to
deploy service. But now you give them opportunity, which is not a prominent 100 percent opportunity for
them. This actually brings a lot of new issues for them.
Those are the issues in the industry -- I mean, in the wireless service provider and service equipment
provider, they're seriously looking at those issues now. So really seriously they're looking at those issues
from the deployment point of view, rather than the paper kind of work.
>>: So I like the -- I mentioned I like the dynamic spectrum a lot for a different application. So here's my
question. Looks to me -- when you look at cooperation between service providers and you are saying
service, I think what's good is that that's a very important market which has a lot of demand. If you optimize
anything, that's a great thing.
But at the same time, my impression is that the distribution, if I have two cell phone providers, the
distribution both in terms of time and geographical location, of those two operations are essentially the
same. And the spectrum is free. Do I saturate? So it's like most of the time they have to impose there, but
that doesn't matter. The only place it matters is when I'm saturating the capacity.
And when I'm at capacity at a certain geographic region, my competitor is also at capacity at that same
region. If I can borrow from neighboring cells, I can borrow from my own cells in the neighboring, so what
exactly -- can you actually do anything? I guess that's the question.
>> Qian Zhang: Yes. That's actually the question for us when we first see the measurement result we get,
we're interested in seeing why those results can really exist in a way that we think about GSM as such a
crowded service already. Why China Mobile and China Unicom have such a distribution in terms of their
spectrum usage. We actually are trying to check for the service provider, but it's very pity -- kind of
confidential information they cannot distribute and disclose to us.
But I think why we're still interested and talking along that direction, I think first of all we really get those
measurement data, I think it's nice to disclose it. Second, even this apply for GSM900, there's still such an
opportunity exists for different spectrum bands. Running among different service providers. Then it's still
again naturally to share the resource, but again service provider, they're competitors. They don't want to
give you too much information. But just in such a way how to do the resource change.
>> Jin Li: Let's thank Professor Zhang for the excellent talk.
>> Qian Zhang: Thank you.