Lightning Talk: Uplink Interference Optimizer, How to Optimize a Cellular Network...- Oscar Gonzalez

Key Takeaways

I'm a data scientist working at Ericson mainly in optimization of cellular networks that well that's what I'm about to explain and the use case uh is just the appli interference Optimizer that is just how to optimize a solell network with a sing in a single shot with graph Nar networks uh mainly I'm going to focus in the Appling interference problems so because you could have many different optimizers and that's the problem that we are going to try to optimize so the first thing that I should explain is what is a c network and it's just one type of telecomunications network in fact it's the one that we are all using right now because it's the one that where mobile phones are using and it's just that the Telecommunications network is divided into cells and what is a cell a cell is just a geographical area that could be bigger smaller can have one or many antennas and that the users that are inside this cell are using the antennas of the cell for receiving and transmitting information that could be phone calls uploading dloading files or well any type of information that the users are uh want to receive or transmit also each cell of the cell network has certain parameters that you can modify for changing the behavior of the cell and in fact that's the parameters that we are going to use for trying to optimize the cellular network uh the one thing that we have to take into account is that if we modify those parameters we are going to affect our neighbor cell only the one hope neighbor cell but if we change the parameters we change the behavior of our cell and the neighbor and well first of all what is the objective that we are trying to achieve here and is okay we want to optimize the the network because we have identified some cells that we are going to call them isue cells and are just cells that have a certain problem it could be an interference problem or any other type of problem and the previous solution was to U deploy a reinforcement learning algorithm to try to solve that so the approach was okay um we have a simulator that we have built that tries to represent fairly the behavior of the cellular network and then we have the real Network the problem is that the simulator was not accurate enough uh so we have to train in the simulator and then also train in the real network but since we have to collect data for many days to do the optimization U we end up doing this iteration with the reinforcement algorithm over the real Network for several weeks and we end up degrading the network of the client so well the client was not very happy with that um so the solution we are proposing here is okay we are going to change all of that uh we are going to do all the optimization offline and for that we try to build a really accurate model that represents the reality and then optimize the parameters with respect to that model so uh you can see in the slide in fact we find a performance indicator that is correlated with the problem that we're trying to solve then we have a model that is going to be a graph nura Network for also modeling the relation with the neighbors and as inputs of that model we are going to have some fixed variables selected by radio experts that are going to be the kpis and then the parameters that we will change once we have trained the model we start the second step that is okay with any type of algorithm it could be an iterative algorithm genetic algorithm or whatever we change the parameters to try to improve the performance indicator that is going to predict the GNN and well that's the framework that I'm going to explain so now for explaining the specific problem or the specific optimization that we are going to do is the Appling interference and what is the Appling interference the apping interference is the interference that occurs when the users try to transmit something to the network it could be uploading a file or a phone code and um we have some interference of the neighbor when does this happens when we have a neighbor that has a lot of users in the border of the cell so our users are transmitting information to the antennas but also the usage of the neoral transmitting information to their antennas but also to our antennas so they cause the noise the performance indicator that we are going to use for trying to solve this is the SR the SR is just the Rel between the signal and the noise the higher the SR the better the signal with respect to the noise the lower the SR the worse and the parameters that we are going to try to change are the B Zer and the alpha that are two power control parameters if we increase them then our users are going to transmit with more power so less noise in our cell the bad thing is that if we increase the power control parameters then our neighbor is experiencing the opposite is experiencing more more noise because we are transmitting with more power so we have to balance those two in the optimization then we will use as I mentioned before the graph neur Network as a well model we are calling this digital twin even it is not really a digital twin because it's going to model really accurate the reality and it's going to model really accurate the reality mainly because of two things one is that we have a huge amount of data because I mean all of us with our model phones are really generating data in any moment and we can collect this data in the cellular network and the second uh thing is that we don't have Pro the same problems as other use cases in graph networks and is we only have to use one layer of a graph Network because when you when you have to use many layers and you stack them you end up in problems as they over smoothing and your model is not accurate enough but here we only care about the one hope neighbors so we don't have the problem so with a one layer unn and then a multier perception is enough for constructing a really accurate model and in fact what we have here is a model that has less than 1% of error and well I repeat the as the inputs we are going to use the kpis the parameters and also in the modeling the relation between the different cells we are going to use geographical information and the number of users that are uh coming and going forward from one use from one to another and then we end up in the well in the optimization framework uh in the optimization framework in the left part you can see uh that we have detected some issue cells the roles for detecting the issue cells are defined for radio experts but mainly is that they have the performance indicator in a really low and the issue cells have some parameters that we will optimize the only parameters that we will change is the isue cells however this also could be expanded to also change the neighbor parameters to try to optimize and then with the graph Nar Network that we have trained with the data of many different networks we are going to predict the sinr with the coverent parameters and here it comes the changing of the parameters of the network and for that as I mentioned before we can use any type of algorithm at the beginning we use an iterative algorithm the problem with that is that since the graph n network um when you change some parameter it affects your neighbor you have to do many Loops in datation so it's better a genetic algorithm or even a reinforcement algorithm and use the model as the environment instead of the reality and then we keep changing the parameters to optimize the SR uh well until we met the final condition that is that our Sr is optimized and as I said is the if the model is accurate enough these results are well can be applied to the reality what we have done for all of this is collect a week of data try to optimize the parameters for that week of data and since the behavior doesn't really change too much in an specific cell during the time if you optimize or find the parameters that optimize this last week are going to be the parameters that optimize the next week or even the next month uh well so that's the use case and now I Havey to answer any questions well the variable and everything that is used especially the gpis are um selected by the radio experts that we have in our teams um I think they divide it into um different kpis or variables that we can use and we start to try is is one of the most critical Parts because if we like select the gr variables the model is is going to imper a huge drop in performance uh but I can I mean specifically to no no yeah the parameters not is part of the kpis yeah is true that that the the the thing that is that there are fixed variables is not really really true but uh I mean it's an approximation that at least for the case of the app interference works really well yeah that's I think is our next well part of the Journey action because we have other um projects that are about planning and well we will try to combine them but we are not yet here it's not that the simulation is as slow is that at least by her coding or trying to come up with equations that mod the reality um we have tried that for many many years but we don't like find uh the simulator that is accurate enough mainly because the equations maybe are clear but then the movement of the users through your network is not that clear or at least you cannot simulate that really well so um it depends of what Optimizer you have but in this case of the AppLink is just an Optimizer that the um clients tend to run in a critical point so they tend to run this when they have really bad performance in some celles maybe one month and a half two months or at least that's the idea there are other use cases where the uh the time of the iteration is quite shorter that's um the problem with that is that for now we are using aggregated daily data so we can do that cannot do that but it's one of the other things in the road map uh the problem is that we have different parts in Ericson so we are going we collect the data and we try to optimize but then the part that is uh like coding um the core of the network is another part of ericon so I would hope that in the future at least these two parts start to collaborate so we can do that but for now what we have is the core so in real time we have really I mean there's control of the appli interference and other problems but these are a really simplified control and then we have this department and it's like okay we collect the data we don't do it in real time but the algorithms that we can give to you are more accurate okay if that's all I I like I I think we can go all to lunch thank you very much and