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