>> Victor Bah: Hi. Welcome. I'm really glad that you're here, I'm really glad that
Kang is here. It's a pleasure to have him. He's a giant in the community. He's
done a lot of work for a long time. He I think graduated from Cornell, I forget
what year. Maybe I shouldn't say the year but -- I mean, he graduated from
Cornell, and he's been a professor at -- he's a professor at University of
Michigan, has graduated 61 PhDs.
>> Kang G. Shin: 63.
>> Victor Bah: 63 PhDs. And I think -- was Jennifer Laxalt your first PhD?
>> Kang G. Shin: No, Manny Krishna. You know Manny Krishna.
>> Victor Bah: Yeah, I know Manny Krishna. Manny Krishna was PhD and
Jennifer -- he was also Jennifer's advisory, who is here as a visiting professor
and gave actually gave a talk. And also here's another number for you, you
know. He has published 680 papers. So match that. Anyway, so of course he's
very prolific and since the time I've known him I can't believe how much energy
he has and how, you know, even to this day he's right up there with the leading
researchers in the most cutting edge fields. So it's a real pleasure to have him
here and I'm looking forward to hearing what you guys are doing in the cognitive
radio research. So Kang.
>> Kang G. Shin: All right. Thank you, Victor. I figure you have to discard what
Victor said at least by 50 percent. And what ever I have accomplished, they are
all students of mine, I'm just the ring leader, nothing else.
Any way, today I'm going to talk about the -- our recent activities on this cognitive
radio. We've been working on this maybe in last five, six years. And I listed my
current graduate students and post-docs and also the past students and
post-docs. By the way, this green color indicates the current, and the white color
indicates the past members of our group.
And by the way, I'll try to give you more of the overview instead of giving you
details of a mathematical derivations or anything as such because you can find
most of them from published papers. And also the list of all the things I'm not
going to be cover whole thing. I'll try to wrap things up within one hour. Probably
I'll spend most of the time on the sensing for white spaces as far as return of
prime usage. So we are talking about two different types of sensing. And also
the elaborate details of the sensing skills.
Over all goal of this project we are trying to make things more adaptive by
sensing environments, applications, and even the physical aspects and then use
this sense information to provide better quality service or more secure services,
whatever help you. We began with initially these wireless LAN adaptive quality
with service techniques, but recently I think we are focussing on these current
through radio technology.
We've been doing many different things, but we begin with sort of generic
architecture for current radio systems. Some of you may have seen these in
JSEC I think 2000 -- is it 2007 there was a special issue. So I may spend just a
few minutes on this without giving you details so you can find more. And then
the -- I'll talk about the -- how the sensor spectrum both in band sensing as well
as out of band sensing, essentially this adaptive sensing.
I'll explain what I mean by adaptive sensing. And also recently we've been
working on this secure cooperative sensing where we are dealing with the four
tier or cooperative sensors and transmitters. And also the -- we've been working
on the adaptation of resource usage or applications based on this sensed
information. If time allows I'll give you some sketch of what we've been doing.
And also we looked at these spatial reuse, not only time and frequency domain,
but also space using these multiple input, multiple output signal and multiband
[inaudible] with the antennas. And I'll try to conclude my talk with proof of a
concept implementation and experimentation. I like to emphasize this proof of a
content, not nothing real per se.
And probably you have seen these FCC web page. I saw [inaudible] office on
that same feature, so I'm not going to explain anything. And on the right hand
side, also, this is the -- this chart published by the Shared Spectrum Company as
part of an NSF report is widely cited.
Essentially what I'm trying to say is there has been explosive increase of wireless
applications and user population and also there will be lots of the types of
applications in emerging and requiring lots of more resources. And the spectrum
is not going to increase in proportion to the increase of this applications and the
user population. And that means that we may end up with the running out of the
spectrum and how are you going to cope with this? I like to make one comment
on this chart. Here I think this is somewhat misleading because almost all the
bands are utilized less than 25 percent and the people may say why bother when
this interesting spectrum is so terribly underutilized? Why do we worry about
sensing and managing all that because you'll get spectrum whenever you want
and whatever you want. This is very much like the Internet backbone when you
have less than five percent utilization of a link bandwidth or router bandwidth,
whatever, why do we have to worry about this curious, curious routing? There
are tons of papers there. And I remembered the one time at InfoComm there
was a panel against these quality of service saying that people working on LAN
problems, which may be true and I like to say this wireless case that isn't the
case. Even if it shows less than 25 percent utilization, first of all, this is more
simple and over small time period, and the -- we didn't certain [inaudible] the area
and over short time period some spectrum may be utilized hundred percent for
short time period. You may not be able to access or even if you access route is
too slow to be useful. Just like a CPS with the CL San Francisco in April, they're
all but 500 people, and I wasn't able to connect to the Internet. Or even if I
connect, I couldn't run anything, just times out.
So there will be a -- you know, a short term shortage when you like it or not, even
if you have very low average utilization.
Another important part is the wireless, the applications the users must cope with
are inherently unreliable and unpredictable, so but transmission environments ->>: [inaudible].
>> Kang G. Shin: Yes?
>>: Zero is still zero, right?
>> Victor Bah: Zero is ->>: Say some bands there that have absolutely nothing ->> Kang G. Shin: I think this is a zero ->>: Have like for example [inaudible].
>> Kang G. Shin: This slide, by the way, is a very shrunken version I get if you
expand they shouldn't be zero. There should be some nonzero small number.
But I guess if very close zero I guess that's very close to zero. Yes?
>>: [inaudible] like on your [inaudible] like if you [inaudible] spectrum allocation
as a provisioning problem, then economic provisioning would basically mean that
you provision for something that is close to the to the peak usage that the
average utilization is lower and you stop there, so that would mean that okay, it's
acceptable to have peaks close to hundred because that's basically, you know,
the economic they are provisioning for it and the average was always below that
but then just the fact that you are hitting hundred percent some of the time is not
-- does not really mean that there's scarcity.
>> Kang G. Shin: Yeah, sure. The question is the spectrum allocation with what
time granularity are you going to allocate spectrum. And also the -- at what level.
Okay? That's the question. If you want to lease a spectrum of this much for your
particular sort of a purpose, and you find the use, the leverage very low and can I
go back and say I want to lease only 50 percent of what I used to lease, usually
you don't do that. You do this over certain time period. So you got stuck.
I'm going to mention on that aspect later. Actually this opportunity usage will
facilitate that as well. Anyway, I don't claim anything on this. This just that the
sort of general argument for cognitive radios. It's not my own chart or anything.
In any case, the cognitive radio is presented as a flexible and adaptive means to
deal with this problem. In particular with the software to find the radios. Of
course I like to sort of [inaudible] on these CRs. CRs are focussing on mostly
flexibility and adaptability, not much of performance at this point. Although
people are beginning to be aware of this, like USRP. According to our
experiments we can get only about 200 some bytes per second bandwidth
because of a slow hardware and also slow network interface UBS. But USRP2 is
much faster and so are the RCB is much faster. I feel this is coming up. But for
the moment people are focussing on more flexibility than the performance.
Anyway, the key issue of this current radio or radius is to find some of the
available opportunities in multiple -- these spectrum bands as quickly and as
much as possible. When I say opportunities opportunity could be in time,
frequency or space domain. I think our most interesting work focused on time
and frequency domain, not space domain, although space domain is quite
interesting.
And another one since I think we are discovering these white spaces or spectrum
holes or whatever, and use those holes opportunistically whenever the legacy
users or primary users return you have to take their return very quickly and get
out of their way. And of course what I mean by as fast as possible, so far I think
at the 802.22 is the only -- the working standard draft, nothing standard yet,
specifies like two seconds. But anyway, these two must be satisfied, otherwise
this won't work.
Okay. As I said I'd like to talk about these generic architecture of the cognitive
radio networks, especially when we say aware networking. We are talking about
these environment awareness, especially radio environment, although most
people don't pay attention, but application awareness is very important as well,
depending on which application. Actually I was talking to someone, he was
working on sort of multimedia streaming, so multimedia streaming, based on
these application requirements you may want to allocate or manage a spectrum
differently. How do you do that? Well, spectrum management typically sits
around the MAC or physical layer, whereas applications are way above. And
how can I convey this? Essentially you have to use some form of the packet filter
like software cutting through all the different layers and conveying application
requirements to a spectrum manager and also depending on the availability or
spectrum condition, applicant may exercise elastic sort of the load demand,
therefore this spectrum condition information must be conveyed to application
through this particular interface.
Also you have to worry about mobility because depending on mobility you have a
different sort of a data rate, error rate or other problems you have to consider.
The adaptability, there are many different things you can do such as you can
adjust the sort of the application performance and related parameters or sort of
you can adjust the MAC protocol parameters or space case of the beam-forming.
The other one we are very familiar with the channel switching. I think you guys
worked on channel switching before, right? Also the channel switching can be
used to avoid or mitigate the effects of narrow banded jamming as well. If it's a
wideband jamming, it's not going to help, but if it's narrow band jamming, it can.
And also the security aspect. This is an important one. When you share
spectrum, obviously you are opening up your spectrum for others to access.
Even if use and encryption, decryption perhaps [inaudible] it is less secure than
not sharing your spectrum with others.
And also the -- all these discovery of a white space and also detection of
returnable primary users all that, if you have a malfunctioning radio device or
compromised device, this may not work. And how do you detect those -- the
malfunctioning or compromised devices and the filter that map when you try to
make decision, that's also important problem we've been looking at. As I said,
I'm going to talk about these generic architecture. There are four main
components. One is resource management. The other one is measurement
management, okay, sensing management. And the coordination. This is the
important part. Coordination within our group as well as between groups. By
within our group I mean when you opportunistically using particular spectrum
band with other -- the radios. And if you want to move it to another spectrum
band, you have to make sure all the members and move it through same target
band at the same time. That's a coordination. And also between groups. If
you're not careful all these secondary user groups may end up with moving to
same white space at the same time. In that case, a target wouldn't be good at
all. So how to coordinate these -- the migration of secondary user groups to
white spaces.
Obviously you want to do this without centralized coordinator. If you have a
centralized coordinator it's an easier problem.
And the last, not least, is the policy enforcement entity, which is very important
when you deal with the service providers different countries, a lot of [inaudible].
And I don't intend to go over all the details here, but these resource management
entity, essentially the -- what you do is you try to discover the white spaces or the
condition of a certain spectrum bands or channels. You want to sort of come up
with sort of the condition map of those bands and hopefully I think this map will
be maintained by individual the cognitive radio devices somewhere. And these
maps should be all same amongst all the members in the same group, otherwise
you may throw sort of the wrong conclusion on which spectrum band you want to
move it to.
So usually what we do is we're going to divide up the responsibility for sensing
different channels amongst all the members within the group and then you sort of
sense a subset of channels and then you disseminate these sensed information
to all the members within the group. So sense disseminate and then the -- all
others will listen and update the spectrum opportunity map. And of course
because of the lost messages or whatever, there could be some inconsistence in
the map, but nevertheless I think that the these this is the way we do and it works
pretty well by the way in general. And of course you may say we're going to
have a centralized place where we're going to put these spectrum opportunity
map and therefore we're going to access that. That has its own problems, too,
because what happens if they're centralized [inaudible]. The site is not
accessible. And I already mentioned the group coordination between the
members with the same group or between different groups. The typical state is
scan, listen and vacate. Vacate means whatever you agree to the discovery of a
return of a primary user then everybody must vacate the channel at the same
time.
Let me spend more time and give you details on the first part, spectrum
discovery using sensing. As I said, I want to focus on two different things. One
is out of band sensing, discovery of white interfaces outside the channel you are
currently occupying. That's out-of-band sensing.
The other sensing is you want to detect the return of a primary signal on the
channel you are currently utilizing. Remember the second part is for decks of a
primary user whereas the first part is to sort of provide the good quality with
service amongst the opportunity users. This spectrum sensing especially the
detection of a primary signal is done at physical layer using energy detection,
feature detection or matched filter. Even feature detection there are two different
things. All right. I'm going to point out by the way, I'm not a physical layer guy.
But we have to understand physical layer as much as we can so we can build a
MAC layer, the higher layer.
So special we are focussing on which detection mechanism to use and when and
also how often and also which sensors must cooperate with each other. The
selection of the cooperative sensors and also the when to and the which channel
to sense with the what detection scheme would be all MAC layer decisions.
>>: So is this a [inaudible] or is this [inaudible].
>> Kang G. Shin: This is -- oh, that's a in-band sensing. The out-of-band
sensing also related to, so which channels to send and when. This a more
generic thing. But I'm going to talk about the out-of-band sensing first in great
deal and in the in-band sensing later.
As you my imagine, though, by the way, we have here a simple case. Each radio
has only one not multiple antennas. You can have multiple antennas but we
found that like USR you have multiple, you know, channels. Interference a
serious issue. In any case, we tried to make problem very simply one way to
interface. So when you sense other channels outside the current channel, you
cannot send or receive data, and therefore sensing incurs overhead in terms of
what the network bandwidth and also in terms of energy consumption and
obviously I think the more frequency it sends, the more opportunities you're going
to detect, and therefore you may be able to utilize these opportunity for better
quality service amongst the secondary users. But that comes at the expense of
data bandwidth or energy that I mentioned. So obviously you have to make
some sort of optimization.
So two things we should consider for out-of-band sensing case. One is you may
want to detect as much spectrum opportunities or white spaces as possible and
therefore you can improve throughput or you can improve the utilization of a
spectrum.
Another one is when you detect sort of a returnable primary user, you want to
switch to this white space as quickly as possible as tow taking a long time to find
this white space. Since I think the 802.22 case, you have to vacate the channel
within two second. If you don't find the white space within two seconds, I think
that your communication will be done. You can't communicate anymore.
So we have to satisfy these two sort of at the requirements. So I'm going to
spend more time on detail on this. And in this certain case detecting these
incumbent return. Which of the energy or the feature detection we're going to
use? I'm going to talk about that, too.
All right. So let's get the maximal discovery of white spaces using pro active
sensing. By pro active sensing I mean you are trying to sense other channels
even if you don't have to move to -- you don't have to vacate the current channel.
Here I'm showing simple diagram. This blue sort of bar indicates periodic
sensing. There are three channels. Channel 1 sense the more frequently but
each time you spend just little time. Remember, the longer you observe, the
more accurate your detection of white space will be. And the more often you
sense, the more opportunities you're going to detect. And also I introduce sort of
the logical channel concept here. This logical channel is a collection of all these
opportunities of white spaces. You just put together and you view these as one
channel, logical channel that the secondary users can utilize. So see how it
goes.
Since you are sensing here this is -- oh, by the way, I'm assuming very simple
model here. The primary user encumbers the usage of a channel will be
modelled by simple on-off sort of the model. Although whenever primary user is
on this channel, then it is one occupied. Here for the moment I'm assuming that
these secondary users and primary users can occupy same channel at the same
time. Although you could, as long as it interferes by secondary users it's less
than these interference of temporal constrain, that's okay, but we are not
considering that here.
So as you can see, for this prop, even if there was white space since you didn't
sense just before this you don't detect this, whereas you detect all of this, here
again you don't detect this white space.
So as you can see, the more often you send, you won't be able to detect all these
-- the white space. And the channel 2 you have again here, and you are
mapping these the green box into the logical channel like this. So this much of
opportunities we've discovered that we can potentially utilize. And of course
while you are sensing you won't be able to utilize the white space or anything,
and therefore allowing the use of these detected opportunities.
Here what we want is we want to sort of determine optimal -- this sensing period.
As I said, the more often you do, you will find the more opportunities and
therefore utilizing more, but that will [inaudible] so we want to sort of capture the
tradeoff between the opportunity discovery and also the overhead associated
with this sensing.
One more thing that I like to point out, let's consider this 802.22, the digital TV
case. The television transmitters continue to transmit from say six a.m. to
midnight, and then from midnight to six a.m., no transmission. In other words,
the primary user will occupy this channel for long period and leave it idle for long
period as well. There is no point of sensing such channel very often.
On the other hand, if you deal with a similar network or some other stuff, this
on-off period pretty random. And depending on how fast these primary uses, the
usage of these channels change you may want to sample more often or more
infrequently. So I'm implying that this sampling period itself must be determined
adaptively, depending on usage pattern. Yes?
>>: [inaudible] do you talk about the interference spaces there?
>> Kang G. Shin: Interference space?
>>: Well, you have some of these opportunities are actually extending over into
-- or were they extended over into transmission spaces by whatever the original
owner was before the periodic sensing happens?
>> Kang G. Shin: Which one are you talking about?
>>: So just start with channel one up at the first green bar. For the first green
bar you have up there it ends with the actually owner transmits. But you wouldn't
actually know until your periodic sensing happened.
>> Kang G. Shin: That's correct. That's correct. So you are thinking this is also
opportunity as well because you don't sense, right? But actually it's not.
>>: Right. It's an opportunity to interfere.
>> Kang G. Shin: That's right. So the -- what I'm saying is this opportunity to
[inaudible] by the way, I'm not dealing with this detection of this change from zero
to one, it's in band sensing. I'm not covering that yet. I'll cover that in a minute.
So what you're saying is you detect this opportunity and you sense -- you don't
sense until this, right? And therefore you may view this whole period as sort of
the opportunity. But actually it's not, right, because of a return of this primary
signal. So you have to have in band sensing detecting the return of this primary
signal, and you will find out this is not available.
So I'll talk about that too.
>>: So you have a separate method of ->> Kang G. Shin: Well, the -- as I said, I present here the in-band sensing and
out of band sensing. I'm talking about out-of-band sensing right now.
Anyway, we did this optimization, and actually this is in our TMC I think 2008 May
issue. We sort of certify arrive this over [inaudible] maximum of these achievable
opportunity ratio, this AOR max. And according to our -- the detailed sort of the
evaluation, we can achieve a 98 percent. And also we detect the up to more
than 22 percent opportunities compared to non-optimal case.
I'd like to make one comment on this, by the way, here. The -- we have the
reference -- you've got to begin with the initial disciplining period, disciplining
interpol and then you perform this optimization based on sort of the additional
measurement or sensing result. And this optimization is not -- the convex
optimization is non-convex optimization. And therefore, depending on your initial
value, actually the solution will be different. Because you don't have a clear
optimal solution. That's one thing. That's the reason why we have different sort
of initial sampling period or sensing period.
And will, the other techniques are if you don't change these -- the sensing period,
whatever numbers you chose you're stuck with it throughout. That's the reason
why I showed that as well.
>>: [inaudible] about why you're using the sensing [inaudible].
>> Kang G. Shin: Actually see it detecting white space?
>>: Yeah.
>> Kang G. Shin: Well, the -- well, if you sense more often the primary, primary
users of the occupancy of a channel changes domestically, right ->>: [inaudible] microphones it didn't make a difference.
>> Kang G. Shin: That the correct. But the other -- like the several networks or
whatever [inaudible] quite different now. The TV case is not, as I said. Actually
we classify these channels in two different types. One is these long lasting you
know, the primary activities as opposed short lasting primary activities. So we
handle them differently. That's certainly a couple in details [inaudible] last year.
Okay. So for the -- this practical sensing you may say, well, I don't want to do
practical sensing. Because if I don't have to vacate the current channel it's
nothing but waste of the resources because I never move out, right?
Or if I move out very seldom, I don't gain much. You may -- I'm going to sense
the white spaces only when I have to vacate my existing, you know, channel.
That's reactive sensing. Only argument you can make against this reactive
sensing is since you have focused idea on availability of other channels, which
channel do you want to sense first? The worst case you may end up with sense
all the channels and find the white space on the last channel you sensed. Then
you got to wait very long. So that's no good. And also I argue with my students.
I have a better idea than that. What do you mean better idea? Well, we don't
have to do this periodical practice sensing, but we're going to do on demand or
react to a sensing. But we will remember which channel was available, which
channel was not available when we did this on demand sensing. And I'm going
to apply this Bayesian estimation technique or begin with a non-informative prior
and then if I do on demand sensing and I'm going to collect information on
availability or unavailability of a particular channel then I get one sample, right?
And I'm using this one sample. I'm going to calculate a posterior probability and
the use that as a prior probability for next stage. If I keep on doing that, perhaps
I can have figured idea on the availability or unavailability of these existing
channels. So next time when I have to vacate my current channel, I'm going to
sense according to the other -- these probability of availability of channel. And in
the end we did all this. We found that was pretty good by the way, pretty good.
Let me just go over these slides. This is reactive sensing. We are assuming
here magically we detect sort of a return of primary signal without any latency.
We are utilizing these channel 2 and the primary user came back, and then I
have to look for new home white space. So unfortunately I may try to check
channel 3 first. And then we find that channel 3 is not available, and therefore
we try channel 1 and channel 1 is available so we move and then utilize channel
1 like this. And then again, primary user with channel 1 returns so we have to
look [inaudible] I sense that channel 2 first and then channel 3 and this channel 3
is available so we occupy channel 3. So keep on doing that. Essentially the look
at the latency from the detection of the return of the primary user to the discovery
with new home in channel 3 at this latency. The longer this interpol, the worse
the performance of a secondary user is. And we want to shorten this. That's the
way I was talking about this Bayesian approach.
And we found out the optimal sensing sequence, in other words, there are say N
different channels other than the channel I'm currently using. And now the
primary user return to this channel and which channel should I sense first? I
want to sort of find this channel as quickly as possible, and obviously intuition
says I'm going to calculate sort of the probability of the channels available and
then out of all of this probability and which have the highest value I'm going to try
first. But that the sort of the obvious things.
When all the channels are homogenous, meaning that all channels come with
same capacity, that's an easier problem in terms of [inaudible]. So all the
channels just according to this value. However, if channel's heterogenous,
meaning that channels coming with different capacity, it's much harder problem
because, by the way, when you are on channel 1 and you need say two
mega-VPS and when you try to move it to another channel, you have to find
another channel of 2 mega-VPS or according to what you said, I'm going to have
a 1 mega-VPS from channel 1 and another 1 mega-VPS from channel 6 and I'm
going to put this together or achieve a 2 mega-VPS. That's possible, right?
That's possible. But there are other arguments of why that's possible. In that
case and I got to find that two white spaces are two channels, not one.
And if in the channel, channels have a different capacity, obviously that's much
more difficult problem. Yes?
>>: [inaudible] of channels like channel while being or used has no bearing on
what channel to ->> Kang G. Shin: Sure. Why are they dependent?
>>: They are dependent because actually C forces it to be dependent. If you've
got transmission going on one channel, then the channel adjacent to that is not
equal to the other channel.
>> Kang G. Shin: Okay. Okay. That.
>>: There's a little bit of that, too.
>> Kang G. Shin: You can handle that [inaudible] you know by separate
[inaudible] you can handle that. Furthermore, to simplify [inaudible] independent,
if you want to avoid these channels because of interference you can have that by
sort of the [inaudible]. And we found out this heterogenous case we can come
up with this suboptimal sequence that satisfies the necessary condition of
optimality.
Okay. Here is a simple sort of a model. By the way, one more thing that I'll point
out. The -- how many channels are there in the first place? Let's consider the
digital TV. TV channels come from 54 megahertz to I think it's 806 megahertz or
something all together. There are 68 channels, each of them 6 megahertz. So
there are quite a few. And if you try to search, say, 6, 7 other channels to find
the white space, regardless whether you [inaudible] or not, that's going to be very
expensive. Yes?
>>: [inaudible].
>> Kang G. Shin: What?
>>: I heard in New York City there are only two channels available.
>> Kang G. Shin: Whatever [inaudible] okay.
>>: That's the problem that we're dealing with. There are areas there's lots of
channels available but urban areas there are very few and actually the real
problem is that if there are not that many [inaudible] hardware and [inaudible]
urban areas [inaudible] rural areas. So it's not -- it's not sort of a technical issue
but it is definitely an issue for us.
>> Kang G. Shin: The thing though -- by the way, I feel that the [inaudible] these
TV channel case of 54 megahertz is a VHF and 806 is UHF. By the way, the
signal propagation property for this VHF and UHF channels are quite different.
You know, the high frequency channels all interfere less but signal propagate
much [inaudible] distance. And these low frequency channels the signal
propagate much farther if the interference range over much bigger.
So there are interesting the aspects especially the -- when you consider the
channel heterogeneity it's kind of interesting.
In any case, let's assume there are lots more channels and you don't want to
consider all these other channels to discover this white space, rather you want to
divide this channel to different groups. One is backup channel group, the other is
a candidate channel growth. This is a part of the [inaudible] about 22 standard
graph, by the way. I didn't create this one. And somehow you have to come up,
come up with the algorithm to put which channels in the backup channel, which
channel the candidate channels.
By the way, the candidate channel, because you don't sense, you sense only
channels in the backup channels. And when you choose these backup channels
you got to make sure that you don't have so many channels and therefore
sensing overhead will be low, but you will have -- must have enough channels to
discover enough channels for your need if you don't have enough, then you have
problems. So you got to do some optimization.
Okay. Let's look at these. I used to have animation, but somehow Apple
admission didn't work, so I eliminate it. So you have in-band channel sensing.
You are using particular channel, say if it's a TV band. Now primary user came
back and all of the TV stations on. Then obviously that channel won't be useful
for long time. What you do is you that case you will move that channel into the
candidate channels because it's not going to be useful. I'm not going to use it.
And then I have to find the channel that I can move it to from this backup
channel. So upon the vacation channel you have to ask -- you have to go to
these backup channel states, and you pick up enough channels for you to
maintain your sort of communication. And if for -- you can't find enough
channels, although there's a backup channel to satisfy your requirement what
can you do? You have to go and recoup some of these candidate channels and
promote them back to this backup channel.
Will, if some of the backup channels you send and find they are low group,
almost all the time occupied you demote those channels to the candidate
channels. So you have to have a transition like that. And as I said, the question
is how to form these backup channel list and also how to update these backup
channel list. That's an interesting question you have to address. One thing you
can do initial backup channel case, what you do is you can order all these
channels based on the probability of becoming idle. How to determine this initial
without and observation. You know, it's just like the TV band case. We know
which channel will be used over what time period. You can have some idea,
although you can update based on observations or sensing data, so you can
begin with something. Actually that alone is an interesting problem. Our
[inaudible] paper covers this in detail.
And of course the -- how the update the probability of a channel becoming idle or
not, I could mention that probability. I mentioned the Bayesian probability, the
estimation. Another one you can use would be a maximum likelihood. The
maximum likelihood, that estimation worked well only when I have enough sort of
the sense samples. If you don't have enough sense samples, that estimation's
not accurate. But Bayesian estimation is more robust, even if you don't have
enough samples.
So as I explained, this is reactive sensing using these Bayesian estimation that's
a reasonable weight to it.
This is the performance evaluation. The left-hand side, the [inaudible] dealing
with the optimal sensing sequence that I mentioned already and there are two
different types of delays. One is the type I delay. If you find the need of white
spaces during first round of a search, that we call type I delay. And the type II
delay is the delay associated with the discovery of a white space using more than
one round of search.
Actually this is the one requires sort of the backup channel list updates and all
that. And of course you have a combination of what? Right hand side is dealing
with a case of the backup channel list update and the -- if you combine these
backup channel list update, you can reduce these delay associated with the
discovery of the white space by 91 percent. It's pretty good.
>>: [inaudible].
>> Kang G. Shin: Yes?
>>: What is that test you've done? Are those like [inaudible] users or is it ->> Kang G. Shin: There's no real characteristic. That's the bad aspect.
Because the cognitive radio on this network is what secondary user will use.
They haven't been deployed yet. So what you do is I think more or less you have
to run simulation, or you derive a sort of mathematical formulation and the
[inaudible] basis simulation or NS-2 basis simulation it's a simulation result.
>>: There must be [inaudible] there must be some basis of input because it
seems the value of something like this is very much dependent on what the exact
characteristics are of [inaudible] users. As to what makes sense because it's all
seemingly data driven, right?
>> Kang G. Shin: Yeah. Well, yeah. I think we ran simulation here primarily on
[inaudible] television case. We have pretty good guess because TV you know
the [inaudible] stations will operate say 18 hours a day, 6 hours off. Some TV
stations will broadcast 24 hours a day, but quite a few don't. In that case, you
can have a very simple sort of the on-off model with a certain period. But if you
deal with the similar network stuff, I don't have statistics. Do you have any
statistic? I try to get ->>: [inaudible].
>> Kang G. Shin: Oh, yeah.
>>: [inaudible] measured it until [inaudible].
>> Kang G. Shin: I don't know. I was trying to get that information from this
wireless service providers, and they don't want to provide them. I couldn't get it.
I tried to persuade the [inaudible] company telecom. I didn't get it. And I tried to
talk to Sprint. I didn't get it.
>>: You should try [inaudible].
>> Kang G. Shin: Please do and share with us.
>>: I want to come back to [inaudible] point that it seems like channels are
dependent because TV channels like you said midnight between 6 like the
chances are if one is occupied, the other one's occupied, too, because it's just
that time of the day when TV channels transmit [inaudible] as well. I mean I
would guess there are times of the day when there's pressure on [inaudible]
pressure on the spectrum than other times. That creates dependence between
channel occupancy again.
>> Kang G. Shin: True, true, true. You know, if you have a measurement
probably I think that this will be a [inaudible] dependence, I think that the -- you
talk about the characterization of the channel utilization by primary users, TV
broadcasting station. The [inaudible] are visit during the daytime, you know, you
have more occupancy than nighttime, although I think because of the time
difference I don't know. And mostly you have more sort of the occupancy during
the date -- I'm sorry, during the week days as opposed to weekends although
people will use their cell phone during the weekends because of the price
structuring. So you may be able to derive this behavior as sort of a periodic
function. On a weekly basis or monthly basis or whatever. Except you know on
national holidays or whatever. But we haven't really done anything on the
characterization of this workload use pattern which is very important, though. We
should have, we should have otherwise [inaudible] I'm insisting on if we don't
have a list, we should have some benchmark just like a spec benchmark for CTP
giant. We all know that spec benchmark is not realistic. It's a [inaudible]
benchmark in my opinion. But since everybody using the same benchmark you
can compare with the apples against the apples.
But I don't claim anything about the representative [inaudible] of the traffic we
used for evaluation. Let's [inaudible] I'll try to finish up. I have a lot more, but I'll
try to finish up here the [inaudible] in other words, you want to detect sort of the
returning income. Could there be digital TV or, you know, small scale the
wireless microphones? And these 1122 standard specifies detectability
requirements like incumbent detection time should they be, you know, less than
or equal to two second. And also the listed section of [inaudible] probability
should be less than 10 percent. Why they said two second and less than 10
percent of a [inaudible] detection and a [inaudible] I don't know. I'm pretty sure
they have sort of a rationale behind these.
And also as I said, we want to sort of enhance the quality of service for
opportunistic users. For that we've got to minimize a sensing overhead. So
essentially we propose a two tiered -- but the sensing cooperative sensing
especially we wanted to have sort of a maximal cluster size measured in terms of
radius not number of sensors. Also, you got to pick the sensors in such a way
that these sensors will contribute sort of different pieces of information in
discovering the primary user. You don't want to record the sensors which will
provide the same information, redundant information.
And also the -- these I think we consider a bit here density, the current radius.
What we -- what's done here, the -- how to determine this sensing period and
how long you want to spend time on sensing. For example, energy detection you
spend typically one millisecond but the feature detection case depending on what
kind of feature you are detecting in the order of 10 millisecond or more. So the
question is do you want to use this feature detection every time or you want to
use this inexpensive energy detection multiple times. That's the question. And
[inaudible] depends on, depends on sort of the lowest signal level. If there's no
noise, then this energy detection is pretty good. But if noise power is higher than
certain level, this energy detection is totally useless.
So we want to answer questions when energy detection is better than feature
detection or when we have to use feature detection. That's what we worked on.
And we consider these IEEE 802.22 standard thing, the -- you know, 155
kilometers keep out radius and also the typical paces station with a radius
ranging 33 to 100 kilometers, although 33 kilometers typical.
Also we address all these signal to noise, all that, but anyway, the results are
here. If noise uncertainty is zero then the energy detection is pretty good here.
But if you increase noise uncertainty, the energy detection gets deteriorated.
Program, if you have the average receive the signal strength, the greater than
minus 115 DBM, then you should use a feature faction. You can show all of this.
Also, we derive that the three important parameters. One is average and receive
the signal strength threshold above which the energy detection is a [inaudible]
and also the average receive the signal strength above which energy detection is
feasible. Those are two parameters that we derive. Actually we are currently
working on several different things. One is, as I mentioned, the security aspects.
Malfunctions sensors compromise the sensors. And also the most [inaudible]
works assumes sort of a stationary transmitters and the stationary sensors.
These assumptions may not work when you deal with the small scale, the
wireless microphone for the transmitter. And the cognitive radios usually they
move, and therefore somewhat unrealistic. So we are considering mobile
transmitters and receivers.
We have a small result. This is the core net work shop. And I think we have
some -- yeah, we have the security related stuff at this [inaudible]. Assuming
these radios and transmitters are not moving, essentially what you are doing is
you are using this spatial temporal correlation because transmitters move,
receivers move, and then you are supposed to generate sort of the reference of
receive the signal strength and if there is a significant deviation you say, ah, this
guy's malfunctioning, and then you eliminate from sort of a [inaudible] making
process. That's the idea. It's no-brainer. It's very simple thing.
Actually I have a lot, but I'm going to skip all this and I'll finish off. Maybe I should
mention a little bit. Currently we have the mesh network testbed that built with
the [inaudible] the router and also the wavelength card and also the
Atheros-chipset-based, the network interface card deployed in our department
building. Now we have a 17 node version. And I think we had a pretty good time
measuring the link asymmetry, all that. That's how the Mobi-Com 2006 paper.
And also this Linux-based open software we did.
Currently we are building the new test bed at three different things. One is using
the robot. Actually we are moving access point a little bit. This is very similar to
the DARPA, this LANdroid. The access points are moving in such a way that you
can maintain connectivity or enhance a certain -- signal to noise ratio. And the
question is which direction you want to move, how much you want to move, and
then you take measurements to decide whether you made the right movement or
not. That's very similar to sort of the mobile robot, sort of a [inaudible] or
whatever. It's an interesting problem.
And the -- another one we have been playing with is USRP. USRP1 was pretty
bad, and the USRP2 we bought three, and we're in the process of building much
larger testbed. Perhaps we could have about 40 some. And I need about
quarter million or $300,000 for that. So I'm in the process of writing the
equipment [inaudible]. I hope I'll get it and also want to consider that MSR Asia's
at the RCB like sort of hardware. If it last Linux device driver, Victor, you may
remember, it has nothing to do with dislike and liking Windows, but now most of
the experiment's done on Linux, so we want to have that. See how it goes.
And these sample publications I have. And you can find most of the things on
the website right here.
>> Victor Bah: All right.
>> Kang G. Shin: I'm going to stop here.
>> Victor Bah: [inaudible] a lot of work. Are there any questions?
>> Kang G. Shin: As usual I missed the deadline by 13 minutes.
>> Victor Bah: Well, you started late as well.
>> Kang G. Shin: Is that right. Okay.
>> Victor Bah: All right. Thank you very much.
[applause].
>> Kang G. Shin: Thank you.