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