okay
a could have not everyone
um my talking case about a transceiver optimization for multiuser remote a on a each channel
and in this work
we can see such scenarios
well base station at the multiple users exchange a pink and downlink channel uh uh uh i pink and only
information
where a to will eh
a we're a station
in such scenarios
the
given the user interfere with each other
and that we exploit the multiple antennas at the base station the station to in to use their interfering
and you our work we can see that the i'm don't for the protocol
uh here we were talking about using much what time that's to come have to interference
that zero focusing is a straight for the solution
we have uh
system with and user
the zero forcing to game need at least a two times and and ten us at the relay station to
separate a with and downlink signals
oh in pink still
and the here and now the interference free game called the signal alignment is propose is proposed to by this
paper papers
uh the basic idea is that
through proper a base station precoding the downlink signals are projected onto the same directions
a pink signals
then the really station only needs and and a two step rate
given the user to imposed the signal
and
in this paper we have also proposed a of balance this P
and they is also a interface for scheme and it can achieve a higher by jewish bidirectional sum rate than
both of the zero forcing on the signal i'm i
but so we want to ask
is this game is good enough and the can we achieve a higher by just close summary
all that these questions we want to first all four
a performance benchmark uh problems but map for that concerns sister
a a a our system or do
uh
base station and the relay stations uh is delay you keep the with and B and and are and that's
each of the and you users
is equipped be
thing going on a
uh here H B R is the channel matrix from base station to the relay station
H I R is that
channel what or from the i-th user or to the relay station
and uh the that will be G and that that will be a a are respectively the
base station transmitter and the receiver or which a matrix
that we is the really station to zero with me check
in the first phase pose the base station and all users transmit to the really station
and in the second phase
the really stations a cost uh or its received signal
to the base station use user
we assume that the channels in the two faces
uh we C program
uh you know we introduce the interference free on screen
after the two face transmission
uh a the base station and all the users will receive that
or what a pink signals and all the downlink signals
for the base station
it's a knows the downing signals
it can remove this signal by
self interference cancellation
and uh what it has to do is to separate of that
a pick signals of from a different user
therefore we have our first the into free screen
which means that a
different
the a pink signals from a the users we not interfere with each other at the base station receiver
and for each you there
or or a pink and downlink signals from other users at in to user interference
we should be there are therefore we have a corresponding to intervenes free screen
which means that a pink signal and
oh you know
oh each user we will not interfere don't
we were not need for other user
well all these interference free "'cause" that come be satisfied
we can calculate this is system
i pink read and the only three that
by this equation
here the P V P R and the P U are respectively the transmit power of the base station really
station and each user
uh the one or more to pretty factor is due to the two phase transmission
on of the people meeting on that in a minute it as the first the term is the really station
amplified noise
and the second term is the noise at the receiver a base station and you use user
and they say the bidirectional sum rate
uh
to at the maximum bidirectional sum rate can be achieved a
by talking to lay optimising the
street transceiver zero matrix on there these
can these days that base station power constraint screen and it is a is the really station power constraint
how one
this hmmm joint optimization problem is too difficult for us to
so they
therefore in this work
we we start to the alternating optimization
two all for or from a span to mark
here is our procedure
first that we was then initial is for each of the really station to you were and the base station
transmitter and uh
based there is C we C one
then you each that we optimize one of them by fixing the other two
after each side
we will check is the sum rate higher since that
a lot of the last as that
if yes to be go to know i mean it's the loop
and in the pouring
we will respect so with this three sample problem
hmmm
first we so the
we optimize might the relay station ten you work by fixing the base station to work
and the
we
well P mike that that we are to maximise the bidirectional sum rate
hi were these days that not convex summary the maximization problem
and we use the concept that would be to in this paper a to so to so that kind of
problem
the basic idea of the with to pull
it's clean here
the that we are maximising the summary i one plus had two
and the say the a two paul a region
and each are here corresponds to a with to pull
if we maximise the summary
uh uh with a P will be to pull we will achieve a boundary point
on a two on the triple be treated
if we can find the optimal more with me to paul we achieve
the optimal boundary point which will result maximum sum rate
and that
you are what system their with to paul is defined by these white or and
these can
so the original problem can be the by finding the maximum summary
with given
bits to poll
and then finding the optimal with to collect or
here
yeah know we use the bisection method or the to search the optimal but the bait
and then we have to solve this problem
yeah
and that so that problem we use the thing approach in this paper
and we use the bisection method that to search the maximum summary
which satisfies all these constraint
we can't be
some are eyes and to test a wider it satisfies all that constraint if you yes we try not a
one
you if know which files more one
and the full
for a tree
summary
we comes so this problem to see why there eight
satisfies is five these constraints here or not
if the minimize
mm no here is the lower than one we state
the people not rise is feasible
and
this problem after some mathematical derivation
can be
we write into this these form they say is the standard
code that collect constrained the code that the core problem
uh
which can be rewritten we which can become what had to be uh
semi-definite a problem with a rank one constraint
and so we start to the
why to use the semi-definite to jen to solve
to so wait
the procedure from here to here can be found in the journal paper a by provides a
and then we and the base station transmitter
oh when the really station
just he is fixed
the base station trans
the base station transmitter meter are only affects the town read
therefore we
maximise the that only grid here
a a it is also not comics summary max
maximization problem
we can use the with to pull here
original problem can be
can be so by
finding the maximum downlink three
uh with the people will be to people and since search open more with to pull or
and
uh a to find is the maximum
well to find the maximal maximum only agreed with that be it to pull we can of this problem
and we also use the bisection method is two
find is a mark or more D which satisfies all these constraint
and it these screen
um forms a second order cone people read in
there for each feed but it the problem can be reading into a second order cone problem the costs
a a late we optimize the base station receiver was based a C what only a pink read of the
of all we only might the having lead here
and the from the a three expression here we found that
each user ping
read is only a function
of the
i score along with a of this matrix
therefore this problem can be decoupled into
the and use up problem
each problem optimize one column of this matrix
and this problem can be easily be right in into the form of a
really racial maximization problem
that so
hmmm finally i want to talk about the convergence of the alternating optimization
uh a to me
in that summary increase ease
by iterations
so the alternating optimization will surely come work
but
since the original problem is not common
therefore of the come to the result depends on the initial value
hmmm
we cannot guarantee a uh
baltimore optimal out but
we can perform alternating optimization with mode all different initial is and is and choose the best one which can
words to the
i is the maximum
uh uh which which can to the high the summary
and the by doing so we can increase the probability to achieve optimal
so
here is the some some simulation results first i want to show the
a bidirectional sum rate was as uh
each region number
uh the in this speaker the release a number at a number is that has full the base station and
the number and the use number instead
a set that's two
the
in the power of each user is no that's one and the
and those of the base station the it's it has set has two
this three blue curve
uh the convergence performance of you
by using the you need to let was as the uh
and is this again uh
zero for the a game and the signal i gay
and the
to read curve
uh the convergence performance by using a me use initial value
the lower lower use these only one initial values and
a problem right curve use
use it time a random initial values and the choose is the best one
we can see that by using different in usual is the come were to the result out E
and a by using multiple initial values and the choose the best one we can increase the performance
and is a single galatians we found that
by using more and time take initial values
the
performance gain or were these right curve
is my to or four we can take the
right curve as a new optimal solution and sorrow as a bit performance but file
and in this curve
in this speaker are we compare as a sum rate performance of different at time zero
it's
uh okay as the base station the you base station and a number and the use a number as that
has to
uh the how uh
note chance or a set has is
uh you know a the bidirectional sum rate words as the relay station on a number
because see that the
traditional zero forcing to game
performs performs bad out when the at than and when the
and than a number at the relay station
is
is a low
and it out performs the signal i'm and
when the when when the relay station and the number is a lot
and are well form our proposed to balance this again outperformed performs ball
and as the
but curve here
is the alternating an eighteen optimisation
we can see that the performance gap between the
alternating optimization and a lot from our proposed the balance gain is quite small
therefore by four by proposing this performance but of we can see that our form proposed a scheme is the
new optimal solution
yeah i'd like to conclude my talk
uh uh this work we employed of alternating optimization
these to design a base station and a station transceiver us
you know a two we will a system
the in at to we aim at maximizing the bidirectional sum rate on the interference free tree
and we use a multiple initial values to perform alternating my station and select like the best one
by doing so we can treat the probability to achieve the global optimal solution
and the and the of the a the formants can be taken as a performance a by back for the
can them
and that we found that performance gap between the bit to our form a proposed the it that's again and
alternating optimization is small
it indicates that the balance this game is then the optimal solution
these uh some reference you use the in these talk
uh in this paper as you can find of the signal and scheme
and that they is our uh and of the scheme
and they
uh uh you this paper as you can send is uh
the concept of a it to pull
and they they say a in this it provides a lot paper you can fans them
techniques the a convex optimization
that's source and Q
sorry um
so you you mentioned a your that the is for to really a your systems
but made it is really thing with a so
just or and can you comment on
just just to about so interference "'cause" it seems like it's going to
um um
drastically in the to roots so you gonna achieve
uh yeah no i you insist can we only consider a single cell scenario
and is the intra sell you and the so so there is no inter self interference
and i was interest out interference a handled by
these
by these
no interference a constraint
the you
a three constraint
i is that the we will
we we don't have the rip we there will be no interest that interference
but uh and they
that's a
yeah
what do you to just three constraints
so this just a a a a uh a
the shows use six
sorry what we have been to students G constraint well don't to just
the max most the rate
oh yeah why don't use you can do as you have two reasons for was
for was it for the constraint
the first one we can well oh
because there's that if the
i so nice highly nap
the therefore for
by doing by doing so the sum the we will be
uh the same as to what you does the same we be to is don't to call you pick can
see that the just to maximise the sum rate
right
and the otherwise that the back can see didn't that can screen
and uh
we will achieve them
good mathematical result
because the
summary here are we'll be where simple
yeah