# A Mobile Network Planning Tool Based on Data Analytics

Authors: Jessica Moysen; Lorenza Giupponi; Josep Mangues-Bafalluy
Journal: Mobile Information Systems
 (2017)

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## Abstract

Planning future mobile networks entails multiple challenges due to the high complexity of the network to be managed. Beyond 4G and 5G networks are expected to be characterized by a high densification of nodes and heterogeneity of layers, applications, and Radio Access Technologies (RAT). In this context, a network planning tool capable of dealing with this complexity is highly convenient. The objective is to exploit the information produced by and already available in the network to properly deploy, configure, and optimise network nodes. This work presents such a smart network planning tool that exploits Machine Learning (ML) techniques. The proposed approach is able to predict the Quality of Service (QoS) experienced by the users based on the measurement history of the network. We select Physical Resource Block (PRB) per Megabit (Mb) as our main QoS indicator to optimise, since minimizing this metric allows offering the same service to users by consuming less resources, so, being more cost-effective. Two cases of study are considered in order to evaluate the performance of the proposed scheme, one to smartly plan the small cell deployment in a dense indoor scenario and a second one to timely face a detected fault in a macrocell network.


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## Body

## 1. Introduction

Nowadays, we are assisting to the definition of what 5G networks will look like. 3GPP has started multiple work items that will lead to the definition of a novel 5G radio and architecture [1]. The main vendors are publishing numerous white papers presenting their view on 5G networks and architectures. The EU commission has put in place an important 5G Infrastructure Public Private Partnership (5GPPP) program to fund research for 5G networks [2]. What is clear from all these converging visions is that network management in beyond 4G and future 5G networks has to face a whole new set of challenges, due to (1) ultradense deployments, heterogeneous nodes, networks, applications, and Radio Access Networks (RANs) and also heterogeneous spectrum access through novel technologies, such as Long Term Evolution Unlicensed (LTE-U) and License Assisted Access (LAA), all coexisting in the same setting, (2) the need to manage very dynamic networks where part of the nodes is controlled directly by the users (e.g., femtocells), energy saving policies generating a fluctuating number of nodes, active antennas, and so on, (3) the need to support 1000x traffic and 10x users and to improve energy efficiency, (4) the need to improve the experience of the users by enabling Gbps speeds and highly reduced latency, or (5) the need to manage new virtualised architectures.In this context, it is already widely recognized that the network needs to establish new procedures to become more intelligent, self-aware, and self-adaptive. An initial step in this direction has been introduced in 4G LTE networks, since Release 8, with the introduction of Self-Organising Network (SON). However, this vision needs to be further developed in 5G considering the huge complexity of these networks. As we already observed in [3], a huge amount of data is currently already generated in 4G networks during normal operations by control and management functions, and more data is expected to be gathered in 5G networks due to the densification process [4], heterogeneity in layers and technologies, the additional control and management complexity in Network Functions Virtualisation (NFV) and Software Defined Network (SDN) architectures, the increasing relevance of Machine to Machine (M2M) and Internet of Things (IoT) communications, the increasing variety of applications and services, each with distinct traffic patterns and QoS/Quality of Experience (QoE) requirements, and so on.The main objective of network management is then to make the network (1) more self-aware, by exploiting and analysing data already generated by the network (this is expected to drive network management from reactive to predictive) and (2) more self-adaptive by exploiting intelligent control decisions tools, offered by ML, based on learning and experience.In this paper, among many network management problems, we focus on smart network planning, which gives particular emphasis to the QoS offered to the users and the resources used by the operator to offer it. We believe that the use of smart network planning tools is crucial and inevitable for operators running multi-RAT, multivendor, multilayer networks, where an overwhelming number of parameters needs to be configured to optimise the network performance. The state of the art in network planning, in literature, and in the market offers a wide range of platforms systems and applications proposed by the research community and/or oriented to the industry. From an industry perspective, the market of commercial planning tools aims at providing a complete set of solutions to design and analyse networks [5–8]. For instance, in [9], the authors present an open-source network planning tool that includes different planning algorithms to analyse the network under different failures and energy efficiency schemes. However, these works in general focus on several configuration scenarios, RF coverage planning, network recovery test, traffic load analysis, and forecasting traffic, among others, and not directly on QoS offered to end-users and the resources that mobile network operators need to offer. Other works are more targeted to QoS estimation, but not in the area of network planning. The literature already offers different works targeting the problem of QoS prediction and verification, such as [10, 11]. In our preliminary work [12], we focus on complex multilayer heterogeneous networks where we predict QoS independently of the physical location of the UE, that is, based on data learned throughout the whole network. Preliminary results show that by abstracting from the physical position of the measurements we can provide high accuracies in the estimations of QoS in other arbitrary regions. Furthermore, the results presented in [13] show that data analysis achieves better performance in a reduced space rather than in the original one.This paper presents a smart network planning tool that works in two steps. First, it estimates the QoS at every point of the network based on user-collected measurements, which may have been taken at different time instants and anywhere in the heterogeneous network. We perform this estimation through supervised learning tools. This results in an appropriately tuned QoS prediction model, which is then integrated in the next step. Second, we adjust the network parameters in order to reach certain network objectives by evaluating different combinations and calculating the resulting QoS based on the model of step 1. Network objectives are set in terms of PRB per Mb. We focus on this specific QoS indicator because it combines information that is relevant to the operator (PRBs) and other that is relevant to the end-user (Mb of data) into a single metric. Therefore, the minimization of this indicator allows serving users with an improved spectral efficiency and offered QoS. More specifically, to carry out this optimisation we take advantage of GAs, which are stochastic search algorithms useful to implement learning and optimisation tasks [14], and so are adequate for our purpose.In order to evaluate the performance of the proposed scheme, we consider two use cases. The first one is in a densified indoor scenario, and the second one is in a more traditional macrocell scenario. In the first use case, we focus on how to plan a dense small cell deployment inspired in a typical 3GPP dual stripe scenario [15], where the parameters to adjust are the number of small cells and their position. In the second use case, we deal with self-healing aspects in macrocellular scenarios. We focus on readjusting the parameters of the surrounding cells to quickly solve an outage problem by automatically adjusting their antenna tilt parameters.We evaluate the performance of the proposed planning tool through a network simulation campaign carried out over the 3GPP compliant, full protocol stack ns-3 LENA module. We show that the proposed ML-based network planning, differently from other closed optimisation approaches, is extremely flexible in terms of application problem and scenario, and, in this sense, it is generic. Without loss of generality, particular attention has been focused on how to plan a dense 4G small cell deployment and how to quickly solve an outage problem. As can be seen, the approach is equally valid and shows good performance in both studied use cases, characterized by very different optimisation problems and scenarios. As for the RAT, we have focused on 4G and its evolution. The same technique can be applied also to 2G and 3G technologies.This paper is organised as follows. The general approach is described in Section2. It describes the ML-based network planning tool, its main design principles, and algorithms we use to build it. In Section 3, we discuss the specific design details and tuning of the QoS prediction model and we put all the pieces (i.e., prediction and optimisation) together. In Section 4, we present the details of the two use cases to which the tool is applied, the simulation platform, and the simulations results. Finally, Section 5 concludes the paper.


## 2. ML-Based Network Planning Tool Description

We propose designing a network planning tool, which works in two steps. First, we propose to model the QoS through the analysis of data extracted from the networks in the form of measurements. This phase requires to first prepare the data and then to analyse it. And second, we keep on adjusting the parameters and analysing the impact on QoS based on the previous model. In this way, the performance of the network is optimised to meet certain operator targets. Figure1 presents the different phases required by the proposed network planning.Figure 1
Architecture of the network planning tool.(  1) Data Preparation. This process aims at transforming data into a meaningful format for the estimation at hand. The target is to integrate and prepare large volumes of data over the network to provide a unified information base for analysis. To do this, we follow the Extract-Transform-Load (ETL) process, which is responsible for pulling data out of the source and placing it into a database. It involves 3 main steps: data extraction (E), whose objective is to collect the data from different sources; data transformation (T), which prepares the data for the purpose of querying and analysis; data loading (L), which loads the data into the main target, most of the cases into a flat file. This process plays an important role for the design and implementation of planning for future mobile networks. The objective is to create a data structure that is able to provide meaningful insights. Some examples of the kind of sources available in mobile networks are shown in Table 1 [3]. Here the data are classified based on the purpose for which they are generated in the network. The usage that is given nowadays in the network is also suggested in the last column. For the purpose of network planning, we plan to extract data reported by the UEs to the network in the form of UE measurements, in terms of received power, received quality, and offered QoS.Table 1
Relevant sources of information in mobile networks.
Source Information Usage Control info for short-term network operation Call/session setup, release, maintenance QoS, RRC, idle and connects mode mobility Discarded after usage  Control info for SON functions Info on Radio link failure, intercell interference, UE measurements, MDT measurements, radio resource status, cell load signalling, etc. Heuristic algorithms typically discard info after  Management information for long-term operation Fault configuration, accounting, performance and security management (FCAPS), Operations and Management, e.g., in OAM aggregated statistics per eNB, on network performances,# users, successful/failed HO, active bearers, information from active probing Mainly used for triggering engineer intervention  Customer Relationship Information Complaints about bad service quality, churn info Only used by customer serviceOnce the data has been collected, we prepare the data for storing, using the proper structure for the querying.(  2) Data Analysis. The objective of this process is to discover patterns in data that can lead to predictions about the future. This is done by finding this information/correlation among the radio measurements extracted from the network. We do this by applying ML techniques.(  3) Optimised Network Planning. The objective of this process is to find the configuration parameters for the optimised network planning based on the information extracted from the previous data analysis process. In the complex cellular context, we need to deal with several network characteristics that introduce high complexity, for example, the very large number of parameters, the strong cross-tier interference, fast fading, shadowing, and mobility of users. In order to deal with these issues and to guarantee an appropriate network planning, in this work, we propose to use GAs, which allow avoiding some of the problems of typical closed optimisation techniques (e.g., computational intractability) in this complex and dynamic scenario. More specifically, they work with chromosomes (i.e., a given combination of values for the parameters to be tuned in the network). For each of the chromosomes, they calculate its fitness score based on a given objective function (in our case, the QoS predicted by the model) [17]. Then, they select the chromosomes with the best fitness score and generate better child chromosomes by combining the selected ones and they keep on iterating until the objective function of the chromosomes generated reaches the performance target. In this way, GAs perform parallel search from a population of points, which represent the values of the different parameters to be tuned in the scenario and, jointly with other techniques, they have the ability to avoid local minima and use probabilistic search rules.


### 2.1. Modelling the QoS

We estimate the QoS at every point of the network based on measurements collected in different moments in time and from other regions of the heterogeneous network, that is, based on the measurement history of the network. To do this, we consider thedata preparation anddata analysis processes of the network planning tool. As mentioned previously, the objective of these 2 processes is to extract, prepare, and analyse the information already available in the network to provide insightful information from the analysis of it. In fact, in this kind of estimations, ML techniques can be very effective to make predictions based on observations. Therefore, we take advantage of ML techniques to create a model that allows estimating the QoS by learning the relation between PHY layer measurements and QoS measured at the UE. We propose using SL, since among many applications it offers tools for estimation and prediction of behaviours. In particular, we focus on a regression problem, since we want to analyse the relationship between a continuous variable (PRB per Mb) and the data extracted from the network in the form of UE measurements. Many regression techniques have been developed in the SL literature, and criteria to select the most appropriate method include aspects such as the kind of relation that exists between the input and the output or between the considered features, the complexity, the dimension of the dataset, the ability to separate the information from the noise, the training speed, the prediction speed, the accuracy in the prediction, and so on. We focus on regression models, and we select the most representative approaches. We then use ensemble methods to sub-sample the training samples, prioritizing criteria such as the low complexity and the high accuracy.We then build a dataset of user measurements, based on the same data contained in the Minimization of Drive Tests (MDT) database. The MDT is a standardized database used for different 3GPP use cases. The dataset contains training samples (rows) and features (columns) and is divided into2 sets, the training set to train the model and the test set to make sure that the predictions are correct. That training data develop a predictive model and evaluate the accuracy of the prediction, by inferring a function f(x), returning the predicted output y^. The input space is represented by an n-dimensional input vector x=(x(1),…,x(n))T∈Rn. Each dimension is an input variable. In addition, a training set involves m training samples ((x1,y1),…,(xm,ym)). Each sample consists of an input vector xi and a corresponding output yi of one data point i. Hence, xi(j) is the value of the input variable x(j) in training sample i, and the error is usually computed via yi-y^i or with the root mean square error.In addition to regression analysis, we exploit Unsupervised Learning (UL) techniques for dimensionality reduction to filter the information in the data that is actually of interest (thus reducing the computational complexity) while maintaining the prediction accuracy. We do this by feeding the data into an ensemble method consisting of Bagging/AdaBoost to manipulate the training samples. And after that, the SL techniques under evaluation are then applied. Details for each step are given in the following, and the whole process is depicted in Figure2.Figure 2
Modelling the QoS.(  1) Collecting the Data. The data we take into account comes from mobile networks, which generate data in the form of network measurements, control, and management information (Table 1). As we mentioned previously, we focus on MDT functionality, which enables operators to collect User Equipment (UE) measurements together with location information, if available, to be used for network management, while reducing operational costs. This feature has been introduced by 3GPP since Release 10; among the targets there are the standardization of solutions for coverage optimisation, mobility, capacity optimisation, parametrization of common channels, and QoS verification. In this context, the literature already offers different solutions for this feature. An example of that can be observed in, [18, 19]. Since operators are also interested in estimating QoS performance, in Release 11, the MDT functionality has been enhanced to properly dimension and plan the network by collecting measurements of throughput and connectivity issues [20]. Therefore, we collect for each UE (1) the Reference Signal Received Power (RSRP) and (2) the Reference Signal Received Quality (RSRQ) coming from the serving and neighbouring eNBs. The size of the input space is [l×n]. The number of rows is the number l of UE in the scenario, and the number of columns corresponds to the number of measurements n. The size of the output space is [l×1], which corresponds to the QoS performance in terms of the PRB per transmitted Mb. These measurements gathered at arbitrary points of the network throughout its lifetime are exploited to plan other arbitrary future deployments.(  2) Preprocessing the Data. In order to obtain a good performance during the evaluation, the input variables of the different measurements must be in a similar scale and range. So, a common practice is to normalize every variable between -1≤x(j)≤1 range and replace x(j) with x(j)-μ(j), over the difference between the maximum and minimum values of the input variables in the dataset, where μ(j) is the average of the input variable (j) in the dataset. The normalized data is then split into training and test set. We create a random partition from the l sets of input. This partition divides the observations into a training set of m samples and a test set p=l-m samples. We randomly select approximately p=(1/5)×l observations for the test set.(  3) Loading the Data. This process varies widely. As we mentioned before, depending on the operator requirements, the data can be updated or new data can be added in a historical form at regular intervals. For our propose, in this particular work, we maintain a history of all changes to the data loaded in the network.(  4) Reducing the High Dimensional Space. One of the problems that mobile operators have to face in this kind of networks is the huge amount of potential features we have as input. In our particular case, features would substantially increase as networks densify. Therefore, to deal with the huge amount of features, we propose applying regression techniques in a reduced space, rather than in the original one. The idea behind that comes from our previous work [12], in which we observed that using a high dimensional space did not result in the best performance. Therefore, we suggest applying the regression analysis in a reduced space. As a result, we take advantage of dimensionality reduction techniques to reduce the number of random variables under consideration. These methods can be divided into Feature Extraction (FE) and Feature Selection (FS) methods. Both methods seek to reduce the number of features in the dataset. FE methods do so by creating new combinations of features (e.g., Principal Component Analysis (PCA)), which project the data onto a lower dimensional subspace by identifying correlated features in the data distribution. They retain the c Principal Components (PCs) with greatest variance and discard all others to preserve maximum information and retain minimal redundancy [21]. Correlation-based FS methods include and exclude features present in the data without changing them. An example is Sparse Principal Component Analysis (SPCA), which extends the classic method of PCA for the reduction of dimensionality of data by adding sparsity constraints on the input features; that is, by adjusting a set of weights over the input features, it induces a matrix in which most of the elements are zero in the solution. In FS-SPCA the sparsity is used to select the f features that give the most useful information; as we increase the weight of SPCA, the number of features is reduced. That is, by adding sparsity constraints on the input features, we promote solutions in which only a small number of input features capture most of the variance. Some preliminary work on these features was presented in [13].(  5) Building the Machine Learning Models. We select some representative regression models, prioritizing criteria such as low complexity and high accuracy: (1) k-Nearest Neighbours (k-NN), (2) Neural Networks (NN), (3) Support Vector Machines (SVMs), and (4) Decision Tree (DT), and we analyse them by performing an empirical comparison of these algorithms, observing the impact on the prediction of the different kinds and amounts of UE measurements.(1)
k-NN can be used for classification and regression [22]. The k-NN method has the advantage of being easy to interpret and fast in training and parameter tuning is minimal.(2)
NN is a statistical learning model inspired by the structure of a human brain where the interconnected nodes represent the neurons to produce appropriate responses. NN support both classification and regression algorithms. NN methods require parameters or distribution models derived from the dataset, and in general they are susceptible to overfitting [23].(3)
SVMs can be used for classification and regression. The estimation accuracy of this method depends on a good setting of the regularization parameterC, ϵ, and the kernel parameters. This method in general shows high accuracy in the prediction, and it can also behave very well with nonlinear problems when using appropriate kernel methods [24].(4)
DT is a flow-chart model, which supports both classification and regression algorithms. Decision trees do not require any prior knowledge of the data, are robust, and work well on noisy data. However, they are dependent on the coverage of the training data, as is the case for many classifiers, and they are also susceptible to overfitting [25, 26].In order to enhance the performance of each learning algorithm described before, instead of using the same dataset to train, we can use multiple data sets by building an ensemble method. Ensemble methods are learning models that combine the opinions of multiple learners. This technique has been investigated in a huge variety of works [27, 28], where the most useful techniques have been found to be Bagging and AdaBoost [29]. Bagging manipulates the training examples to generate multiple hypothesis. It runs the learning algorithm niter times, each one with a different subset of training samples. AdaBoost works similarly, but it maintains a set of weights over the original training set and adjusts these weights by increasing the weight of samples that are misclassified and decreasing the weight of examples that are correctly classified [30].In summary, once the extracted data has been processed and loaded into a file, it is fed into a dimensionality reduction step, and subsequently into an ensemble step, which manipulates the training set by applying Bagging/AdaBoost techniques. The learning algorithm is then applied to produce a regression (see Algorithm1).Algorithm 1:Train regression algorithm.
Input: Input space of size [m×n](Xtrain), output space of size[m×1](Ytrain), Input space of size[p×n](Xtest), output space of size[p×1](Ytest), number of iterations (niter) Output:  model // Given the training set (Xtrain,Ytrain) Xtrain={x1,…,xm} Y train = { y 1 , … , y m } // Apply dimensional reduction step (if it is necessary) obtainc-dimensional input vector Apply regression analysis in a reduced space // Set up the data for Bagging/Adaboost for  k=1 to niter  do // Call regression algorithm model(k) ≔ regression algorithm, namely k-NN, NN, SVM, DT // Predict the average QoS QoSpredicted ≔ predict(model(k), Xtest) Evaluate performances against the actual value (Ytest) by NRMSE end for // Result of training base learning algorithm model≔ best(model) return (model)(  6) Evaluation of Accuracy. To evaluate the accuracy of the model, the performance of the learned function is measured on the test set. That is, we use a set of samples used to tune the regression algorithm. For each test value, we predict the average QoS and compare it with the actual value in terms of the Root Mean Squared Error (RMSE) of the prediction as follows RMSE=∑i=1p(yi-y^i)2/p, where p is the length of the test set, y^i indicates the predicted value, and yi is the testing value of one data point i. In order to compare the RMSE with different scales, the input and output variable values are normalized as follows: NRMSE = RMSE/(ymax⁡-ymin⁡), where ymax⁡ and ymin⁡ represent the max and min values in the output space Ytest of size [p×1], respectively.


### 2.2. Optimised Network Planning

As we mentioned before, the objective of this process is to close the loop by adjusting the parameters, and so the network performance, through a GA. This results in a GA organised into different phases, as depicted in Figure3.Figure 3
Optimised network planning.(  1) Create S Feasible Solutions. We create a set S={θ(1),…,θ(psize)} of feasible solutions (also called chromosomes or individuals), where psize is the starting population size. We denote by θ(s)=(θ1(s),…,θM(s)) the configuration parameters vector of an individual s, with θj(s) denoting the value for the parameter of eNB j, for instance, the transmitted power (θjtxp(s)), the antenna tilt (θjtilt(s)), or the action to switch ON or OFF (θjsc(s)) the jth small cell.(  2) Evaluate Network Deployment Performance. This function is responsible for evaluating the network deployment performance. The objective is to calculate the objective function (fitness) of each individual. Given a particular configuration parameter vector θ(s), this function is responsible for returning the average offered QoS, that is, the average network performance predicted based on the measurement history of the network. This function takes as input S feasible solutions and the model produced by the regression algorithm discussed in the previous section. That is, the output of Algorithm 1.The behaviour of this module is as follows. In each iteration, we collectXeval measurements in some arbitrary q points in the scenario. These measurements are obtained as a consequence of having configured the parameters of the scenario according to each θ∈S. Based on these measurements, the QoS in the points of interest is predicted by using the model generated in step 1 (seeevalNetPerformance function in Algorithm 2). Finally, for a given individual, the average of the predicted QoS at the q points is returned as an indicator of the performance of the system with this setup.Algorithm 2:Evaluate network deployment performance.
Input:  Configuration parameters vector (θ), the tuned model (model) Output: average QoS evalNetPerformance  ≔ function(θ, model){ // Call ns-3 network simulator evaluateθ(s)=(θ1(s),…,θM(s)) // Collect measurements atq points in the scenario Xeval={x1,…,xq} // Predict the average QoS Qo S predicted ≔  predict(model, Xeval) average QoS≔ mean(QoSpredicted) return (average QoS)As we mentioned before, our metric of interest is the PRB per transmitted Mb, since reducing it allows improving the QoS of the users while also improving the spectral efficiency of the operator. Therefore, the fitness function aims at finding the configuration of parameters for which the total PRB per transmitted Mb is minimized. The operator can target a desired value for the total PRB per transmitted Mb, and, based on this, the network planning tool can decide when the objective has been achieved and interrupt the operation. Therefore, as in any GA, we try to improve the tuning of parameters for each new generation through the processes described below (i.e., selection, crossover, and mutation). And our measure of improvement is the average predicted QoS values for the individuals belonging to each generation.(  3) Selection. We select the best fit individuals for reproduction based on their fitness, that is, this function generates a new population of individuals from the current population. This selection is known as elitist selection. Elitism copies the best e fittest candidates into the next generation.(  4) Crossover. This function forms a new individual by combining part of the genetic information from their parents. The idea behind crossover is that the new individual may be better than both parents if he takes the best characteristics from each of them. We use an arithmetic crossover, which creates new individuals (β) that are the weighted arithmetic mean of two parents. If θ(a) and θ(b) are the parents, the function returns as follows:(1)β=α×θa+1-α×θb,where α is a random weighting factor chosen before each crossover operation. That is, the arithmetic crossover operator combines two parent chromosome vectors to produce a new individual, where α is a random value between [0,1].(5) Mutation. This function randomly selects a parameter based on a uniform random value between a minimum and maximum value. It maintains the diversity in the value of the parameters for subsequent generations. That is, it avoids premature convergence on a local maximum or minimum. For that, we set to δ the probability of mutation in a feasible solution θ(s). If δ is too high, the convergence is slow or it never happens. Therefore, most of the times δ tends to be small. Finally, we replace the worst fit population with new individuals. The whole genetic algorithm is described in Algorithm 3.Algorithm 3:GA scheme.
Input:   Initial population (S), size ofS (psize), the tuned model (model), number of generations (g), rate of elitisme, rate of mutationδ Output: solution θ(∗) //Initialization for  i=1 to g  do // Return the value of average QoS describing the fitness of each individualθ∈S for all  θ∈S  do average QoS≔  evalNetPerformance(θ, model) end for // Elitism based selection select the beste solutions // Crossover number of crossovernc=(psize-e)/2 for  j=1 to nc  do randomly select two solutionsθ(a) and θ(b) generateθ(c) by arithmetic crossover to θ(a) and θ(b) end for // Mutation for  j=1 to nc  do mutate each parameter ofθ(j) under the rate δ and generate a new solution end for // The GA keeps on iterating until the new solution reaches the performance target. end for return the best solutionθ(∗)


## 2.1. Modelling the QoS

We estimate the QoS at every point of the network based on measurements collected in different moments in time and from other regions of the heterogeneous network, that is, based on the measurement history of the network. To do this, we consider thedata preparation anddata analysis processes of the network planning tool. As mentioned previously, the objective of these 2 processes is to extract, prepare, and analyse the information already available in the network to provide insightful information from the analysis of it. In fact, in this kind of estimations, ML techniques can be very effective to make predictions based on observations. Therefore, we take advantage of ML techniques to create a model that allows estimating the QoS by learning the relation between PHY layer measurements and QoS measured at the UE. We propose using SL, since among many applications it offers tools for estimation and prediction of behaviours. In particular, we focus on a regression problem, since we want to analyse the relationship between a continuous variable (PRB per Mb) and the data extracted from the network in the form of UE measurements. Many regression techniques have been developed in the SL literature, and criteria to select the most appropriate method include aspects such as the kind of relation that exists between the input and the output or between the considered features, the complexity, the dimension of the dataset, the ability to separate the information from the noise, the training speed, the prediction speed, the accuracy in the prediction, and so on. We focus on regression models, and we select the most representative approaches. We then use ensemble methods to sub-sample the training samples, prioritizing criteria such as the low complexity and the high accuracy.We then build a dataset of user measurements, based on the same data contained in the Minimization of Drive Tests (MDT) database. The MDT is a standardized database used for different 3GPP use cases. The dataset contains training samples (rows) and features (columns) and is divided into2 sets, the training set to train the model and the test set to make sure that the predictions are correct. That training data develop a predictive model and evaluate the accuracy of the prediction, by inferring a function f(x), returning the predicted output y^. The input space is represented by an n-dimensional input vector x=(x(1),…,x(n))T∈Rn. Each dimension is an input variable. In addition, a training set involves m training samples ((x1,y1),…,(xm,ym)). Each sample consists of an input vector xi and a corresponding output yi of one data point i. Hence, xi(j) is the value of the input variable x(j) in training sample i, and the error is usually computed via yi-y^i or with the root mean square error.In addition to regression analysis, we exploit Unsupervised Learning (UL) techniques for dimensionality reduction to filter the information in the data that is actually of interest (thus reducing the computational complexity) while maintaining the prediction accuracy. We do this by feeding the data into an ensemble method consisting of Bagging/AdaBoost to manipulate the training samples. And after that, the SL techniques under evaluation are then applied. Details for each step are given in the following, and the whole process is depicted in Figure2.Figure 2
Modelling the QoS.(  1) Collecting the Data. The data we take into account comes from mobile networks, which generate data in the form of network measurements, control, and management information (Table 1). As we mentioned previously, we focus on MDT functionality, which enables operators to collect User Equipment (UE) measurements together with location information, if available, to be used for network management, while reducing operational costs. This feature has been introduced by 3GPP since Release 10; among the targets there are the standardization of solutions for coverage optimisation, mobility, capacity optimisation, parametrization of common channels, and QoS verification. In this context, the literature already offers different solutions for this feature. An example of that can be observed in, [18, 19]. Since operators are also interested in estimating QoS performance, in Release 11, the MDT functionality has been enhanced to properly dimension and plan the network by collecting measurements of throughput and connectivity issues [20]. Therefore, we collect for each UE (1) the Reference Signal Received Power (RSRP) and (2) the Reference Signal Received Quality (RSRQ) coming from the serving and neighbouring eNBs. The size of the input space is [l×n]. The number of rows is the number l of UE in the scenario, and the number of columns corresponds to the number of measurements n. The size of the output space is [l×1], which corresponds to the QoS performance in terms of the PRB per transmitted Mb. These measurements gathered at arbitrary points of the network throughout its lifetime are exploited to plan other arbitrary future deployments.(  2) Preprocessing the Data. In order to obtain a good performance during the evaluation, the input variables of the different measurements must be in a similar scale and range. So, a common practice is to normalize every variable between -1≤x(j)≤1 range and replace x(j) with x(j)-μ(j), over the difference between the maximum and minimum values of the input variables in the dataset, where μ(j) is the average of the input variable (j) in the dataset. The normalized data is then split into training and test set. We create a random partition from the l sets of input. This partition divides the observations into a training set of m samples and a test set p=l-m samples. We randomly select approximately p=(1/5)×l observations for the test set.(  3) Loading the Data. This process varies widely. As we mentioned before, depending on the operator requirements, the data can be updated or new data can be added in a historical form at regular intervals. For our propose, in this particular work, we maintain a history of all changes to the data loaded in the network.(  4) Reducing the High Dimensional Space. One of the problems that mobile operators have to face in this kind of networks is the huge amount of potential features we have as input. In our particular case, features would substantially increase as networks densify. Therefore, to deal with the huge amount of features, we propose applying regression techniques in a reduced space, rather than in the original one. The idea behind that comes from our previous work [12], in which we observed that using a high dimensional space did not result in the best performance. Therefore, we suggest applying the regression analysis in a reduced space. As a result, we take advantage of dimensionality reduction techniques to reduce the number of random variables under consideration. These methods can be divided into Feature Extraction (FE) and Feature Selection (FS) methods. Both methods seek to reduce the number of features in the dataset. FE methods do so by creating new combinations of features (e.g., Principal Component Analysis (PCA)), which project the data onto a lower dimensional subspace by identifying correlated features in the data distribution. They retain the c Principal Components (PCs) with greatest variance and discard all others to preserve maximum information and retain minimal redundancy [21]. Correlation-based FS methods include and exclude features present in the data without changing them. An example is Sparse Principal Component Analysis (SPCA), which extends the classic method of PCA for the reduction of dimensionality of data by adding sparsity constraints on the input features; that is, by adjusting a set of weights over the input features, it induces a matrix in which most of the elements are zero in the solution. In FS-SPCA the sparsity is used to select the f features that give the most useful information; as we increase the weight of SPCA, the number of features is reduced. That is, by adding sparsity constraints on the input features, we promote solutions in which only a small number of input features capture most of the variance. Some preliminary work on these features was presented in [13].(  5) Building the Machine Learning Models. We select some representative regression models, prioritizing criteria such as low complexity and high accuracy: (1) k-Nearest Neighbours (k-NN), (2) Neural Networks (NN), (3) Support Vector Machines (SVMs), and (4) Decision Tree (DT), and we analyse them by performing an empirical comparison of these algorithms, observing the impact on the prediction of the different kinds and amounts of UE measurements.(1)
k-NN can be used for classification and regression [22]. The k-NN method has the advantage of being easy to interpret and fast in training and parameter tuning is minimal.(2)
NN is a statistical learning model inspired by the structure of a human brain where the interconnected nodes represent the neurons to produce appropriate responses. NN support both classification and regression algorithms. NN methods require parameters or distribution models derived from the dataset, and in general they are susceptible to overfitting [23].(3)
SVMs can be used for classification and regression. The estimation accuracy of this method depends on a good setting of the regularization parameterC, ϵ, and the kernel parameters. This method in general shows high accuracy in the prediction, and it can also behave very well with nonlinear problems when using appropriate kernel methods [24].(4)
DT is a flow-chart model, which supports both classification and regression algorithms. Decision trees do not require any prior knowledge of the data, are robust, and work well on noisy data. However, they are dependent on the coverage of the training data, as is the case for many classifiers, and they are also susceptible to overfitting [25, 26].In order to enhance the performance of each learning algorithm described before, instead of using the same dataset to train, we can use multiple data sets by building an ensemble method. Ensemble methods are learning models that combine the opinions of multiple learners. This technique has been investigated in a huge variety of works [27, 28], where the most useful techniques have been found to be Bagging and AdaBoost [29]. Bagging manipulates the training examples to generate multiple hypothesis. It runs the learning algorithm niter times, each one with a different subset of training samples. AdaBoost works similarly, but it maintains a set of weights over the original training set and adjusts these weights by increasing the weight of samples that are misclassified and decreasing the weight of examples that are correctly classified [30].In summary, once the extracted data has been processed and loaded into a file, it is fed into a dimensionality reduction step, and subsequently into an ensemble step, which manipulates the training set by applying Bagging/AdaBoost techniques. The learning algorithm is then applied to produce a regression (see Algorithm1).Algorithm 1:Train regression algorithm.
Input: Input space of size [m×n](Xtrain), output space of size[m×1](Ytrain), Input space of size[p×n](Xtest), output space of size[p×1](Ytest), number of iterations (niter) Output:  model // Given the training set (Xtrain,Ytrain) Xtrain={x1,…,xm} Y train = { y 1 , … , y m } // Apply dimensional reduction step (if it is necessary) obtainc-dimensional input vector Apply regression analysis in a reduced space // Set up the data for Bagging/Adaboost for  k=1 to niter  do // Call regression algorithm model(k) ≔ regression algorithm, namely k-NN, NN, SVM, DT // Predict the average QoS QoSpredicted ≔ predict(model(k), Xtest) Evaluate performances against the actual value (Ytest) by NRMSE end for // Result of training base learning algorithm model≔ best(model) return (model)(  6) Evaluation of Accuracy. To evaluate the accuracy of the model, the performance of the learned function is measured on the test set. That is, we use a set of samples used to tune the regression algorithm. For each test value, we predict the average QoS and compare it with the actual value in terms of the Root Mean Squared Error (RMSE) of the prediction as follows RMSE=∑i=1p(yi-y^i)2/p, where p is the length of the test set, y^i indicates the predicted value, and yi is the testing value of one data point i. In order to compare the RMSE with different scales, the input and output variable values are normalized as follows: NRMSE = RMSE/(ymax⁡-ymin⁡), where ymax⁡ and ymin⁡ represent the max and min values in the output space Ytest of size [p×1], respectively.


## 2.2. Optimised Network Planning

As we mentioned before, the objective of this process is to close the loop by adjusting the parameters, and so the network performance, through a GA. This results in a GA organised into different phases, as depicted in Figure3.Figure 3
Optimised network planning.(  1) Create S Feasible Solutions. We create a set S={θ(1),…,θ(psize)} of feasible solutions (also called chromosomes or individuals), where psize is the starting population size. We denote by θ(s)=(θ1(s),…,θM(s)) the configuration parameters vector of an individual s, with θj(s) denoting the value for the parameter of eNB j, for instance, the transmitted power (θjtxp(s)), the antenna tilt (θjtilt(s)), or the action to switch ON or OFF (θjsc(s)) the jth small cell.(  2) Evaluate Network Deployment Performance. This function is responsible for evaluating the network deployment performance. The objective is to calculate the objective function (fitness) of each individual. Given a particular configuration parameter vector θ(s), this function is responsible for returning the average offered QoS, that is, the average network performance predicted based on the measurement history of the network. This function takes as input S feasible solutions and the model produced by the regression algorithm discussed in the previous section. That is, the output of Algorithm 1.The behaviour of this module is as follows. In each iteration, we collectXeval measurements in some arbitrary q points in the scenario. These measurements are obtained as a consequence of having configured the parameters of the scenario according to each θ∈S. Based on these measurements, the QoS in the points of interest is predicted by using the model generated in step 1 (seeevalNetPerformance function in Algorithm 2). Finally, for a given individual, the average of the predicted QoS at the q points is returned as an indicator of the performance of the system with this setup.Algorithm 2:Evaluate network deployment performance.
Input:  Configuration parameters vector (θ), the tuned model (model) Output: average QoS evalNetPerformance  ≔ function(θ, model){ // Call ns-3 network simulator evaluateθ(s)=(θ1(s),…,θM(s)) // Collect measurements atq points in the scenario Xeval={x1,…,xq} // Predict the average QoS Qo S predicted ≔  predict(model, Xeval) average QoS≔ mean(QoSpredicted) return (average QoS)As we mentioned before, our metric of interest is the PRB per transmitted Mb, since reducing it allows improving the QoS of the users while also improving the spectral efficiency of the operator. Therefore, the fitness function aims at finding the configuration of parameters for which the total PRB per transmitted Mb is minimized. The operator can target a desired value for the total PRB per transmitted Mb, and, based on this, the network planning tool can decide when the objective has been achieved and interrupt the operation. Therefore, as in any GA, we try to improve the tuning of parameters for each new generation through the processes described below (i.e., selection, crossover, and mutation). And our measure of improvement is the average predicted QoS values for the individuals belonging to each generation.(  3) Selection. We select the best fit individuals for reproduction based on their fitness, that is, this function generates a new population of individuals from the current population. This selection is known as elitist selection. Elitism copies the best e fittest candidates into the next generation.(  4) Crossover. This function forms a new individual by combining part of the genetic information from their parents. The idea behind crossover is that the new individual may be better than both parents if he takes the best characteristics from each of them. We use an arithmetic crossover, which creates new individuals (β) that are the weighted arithmetic mean of two parents. If θ(a) and θ(b) are the parents, the function returns as follows:(1)β=α×θa+1-α×θb,where α is a random weighting factor chosen before each crossover operation. That is, the arithmetic crossover operator combines two parent chromosome vectors to produce a new individual, where α is a random value between [0,1].(5) Mutation. This function randomly selects a parameter based on a uniform random value between a minimum and maximum value. It maintains the diversity in the value of the parameters for subsequent generations. That is, it avoids premature convergence on a local maximum or minimum. For that, we set to δ the probability of mutation in a feasible solution θ(s). If δ is too high, the convergence is slow or it never happens. Therefore, most of the times δ tends to be small. Finally, we replace the worst fit population with new individuals. The whole genetic algorithm is described in Algorithm 3.Algorithm 3:GA scheme.
Input:   Initial population (S), size ofS (psize), the tuned model (model), number of generations (g), rate of elitisme, rate of mutationδ Output: solution θ(∗) //Initialization for  i=1 to g  do // Return the value of average QoS describing the fitness of each individualθ∈S for all  θ∈S  do average QoS≔  evalNetPerformance(θ, model) end for // Elitism based selection select the beste solutions // Crossover number of crossovernc=(psize-e)/2 for  j=1 to nc  do randomly select two solutionsθ(a) and θ(b) generateθ(c) by arithmetic crossover to θ(a) and θ(b) end for // Mutation for  j=1 to nc  do mutate each parameter ofθ(j) under the rate δ and generate a new solution end for // The GA keeps on iterating until the new solution reaches the performance target. end for return the best solutionθ(∗)


## 3. Design of the Network Planning Tool

This section presents a discussion on how to tune the model for the ultradense complex scenarios under consideration and then explains how this model is integrated into the global network planning tool.


### 3.1. Design and Evaluation of the QoS Modelling Component

This section compares the different options for each of the components that define our QoS model, namely, dimensionality reduction, ensemble methods, and regression methods. We consider different options for each of them: (1) FE-PCA and FS-SPCA for dimensionality reduction; (2) Bagging and AdaBoost as ensemble methods; and (3) k-NN, NN, SV, and DT as regression models, as anticipated in Section 2.1.Table2 summarizes the performance accuracy of each learning algorithm. Accuracy is measured as (1-NRMSE)×100. Based on this table, we discuss potential design decisions:Table 2
Overall model accuracy.
Approaches Regression model Bagging AdaBoost (1) FE-PCA (1.1)k-NN 90.33 % 91.98 % (1.2) NN 93.44 % 92.28 % (1.3) SVM 94 . 70 % 94.07 % (1.4) DT 92.84 % 93.60 %  (2) FS-SPCA (2.1)k-NN 89.69 % 90.88 % (2.2) NN 92.41 % 91.78 % (2.3) SVM 93.62 % 92.87 % (2.4) DT 91.22 % 92.08 %(1)
FS-SPCA is a very useful approach if we are interested in excluding features to retain minimal information redundancy. This can be observed in Figure4, which shows the NRMSE as a function of different number of features (f) selected by the SPCA, and where results reveal that for f=92 features we obtain the lowest NRMSE value. As a consequence, we select the 92 features that give us the most useful information. On the other hand, for the FE-PCA approach, we observed in Figures 5 and 6 that we can obtain the 70% of cumulative variance if we consider c=10 PCs. That is, with the first 10 PCs we already capture the main variability of the data. Therefore, on the one hand, FE-PCA would present less inputs to the SL step of the data processing chain at the cost of a prior processing of features. On the other hand, FS-SPCA would simplify the initial feature processing, since selected features are taken as they are, but the cost would be the higher needed storage. In terms of the overall accuracy of the prediction, by implementing the FS-SPCA approach, we can reduce the dimensionality of the data down tof features and still maintain almost the same accuracy with respect to FE-PCA approach. That is, if we consider the 92 features, we lose only 1% of accuracy with respect to FE-PCA (i.e., 92 inputs versus 10 inputs to the SL step). Therefore, it will depend on the specific network and operator to select whether computing or storage should be reduced and to decide whether the price paid in terms of accuracy is acceptable. (2)
When we build ensemble methods, SVM and NN regression models perform better when they are bagged than when they are boosted. This was expected, as Bagging combines many weak predictors (i.e., the predictor is only slightly correlated with the true prediction) to produce a strong predictor (i.e., the predictor is well-correlated with the true prediction). This works well for algorithms where by changing the training set the output changes. The opposite behaviour can be found ink-NN and DT regression models; that is, when these algorithms are boosted the models tend to provide better results than when they are bagged. That is, in order to improve the performance of AdaBoost, we use suboptimal values, for the number of the neighbours (k) for k-NN, and the number of trees (T) for DT; that is, we use values that are not that good, but at least better than random. Therefore, we make weak predictors by tuning the parameters to avoid the cases in which the regressors respond similarly [31]. This is not the case for SVM and NN. Since these learning algorithms do not have an input parameter that we can adjust to obtain a weak predictor without affecting the accuracy of the model, the probability that these algorithms provide better performance when they are boosted than when are bagged is lower. Some initial results about how a SVM can be used as a weak predictor can be found in [32]. Another option could be treating this kind of algorithm as a weak regressor by using fewer samples to train, as stated in [33]. (3)
By applying different regression models, and in particular when the SVM regression model is bagged, we improve by5% the overall accuracy of the prediction with respect to the k-NN model. More specifically, in terms of the NRMSE, k-NN exhibits an error of 10%, while SVM halves this value to 5%.Figure 4
NRMSE, a function of different number of features selected by the SPCA.Figure 5
Cumulative contribution of each PC to the original data’s variance.Figure 6
Variability of the data set as a function of thec=10 PCs.Results suggest that while all the regression models exhibit high accuracy, the bagged-SVM learning model is the one that better fits our needs and exhibits more accurate predictions. Therefore, in this work we focus on bagged-SVM to build the best model that fits the data.


### 3.2. Putting It Together: ML-Based Network Planning Tool

In summary, the proposed tool exploits the power of both components working together, namely, ML-based QoS modelling and GA-based network optimisation. The former is capable of extracting the most relevant information out of wealth of operational data measured in complex ultradense networks and make meaningful predictions for the operator and the end-user. This results in powerful network performance prediction models that are fed into the latter, which explores and finds the best combination of parameters for configuring the network elements, wherebest means the one that gives the best predicted QoS according to the learned model. The whole network planning process is depicted in Figure 7.Figure 7
Bagged-SVM network planning tool architecture.


## 3.1. Design and Evaluation of the QoS Modelling Component

This section compares the different options for each of the components that define our QoS model, namely, dimensionality reduction, ensemble methods, and regression methods. We consider different options for each of them: (1) FE-PCA and FS-SPCA for dimensionality reduction; (2) Bagging and AdaBoost as ensemble methods; and (3) k-NN, NN, SV, and DT as regression models, as anticipated in Section 2.1.Table2 summarizes the performance accuracy of each learning algorithm. Accuracy is measured as (1-NRMSE)×100. Based on this table, we discuss potential design decisions:Table 2
Overall model accuracy.
Approaches Regression model Bagging AdaBoost (1) FE-PCA (1.1)k-NN 90.33 % 91.98 % (1.2) NN 93.44 % 92.28 % (1.3) SVM 94 . 70 % 94.07 % (1.4) DT 92.84 % 93.60 %  (2) FS-SPCA (2.1)k-NN 89.69 % 90.88 % (2.2) NN 92.41 % 91.78 % (2.3) SVM 93.62 % 92.87 % (2.4) DT 91.22 % 92.08 %(1)
FS-SPCA is a very useful approach if we are interested in excluding features to retain minimal information redundancy. This can be observed in Figure4, which shows the NRMSE as a function of different number of features (f) selected by the SPCA, and where results reveal that for f=92 features we obtain the lowest NRMSE value. As a consequence, we select the 92 features that give us the most useful information. On the other hand, for the FE-PCA approach, we observed in Figures 5 and 6 that we can obtain the 70% of cumulative variance if we consider c=10 PCs. That is, with the first 10 PCs we already capture the main variability of the data. Therefore, on the one hand, FE-PCA would present less inputs to the SL step of the data processing chain at the cost of a prior processing of features. On the other hand, FS-SPCA would simplify the initial feature processing, since selected features are taken as they are, but the cost would be the higher needed storage. In terms of the overall accuracy of the prediction, by implementing the FS-SPCA approach, we can reduce the dimensionality of the data down tof features and still maintain almost the same accuracy with respect to FE-PCA approach. That is, if we consider the 92 features, we lose only 1% of accuracy with respect to FE-PCA (i.e., 92 inputs versus 10 inputs to the SL step). Therefore, it will depend on the specific network and operator to select whether computing or storage should be reduced and to decide whether the price paid in terms of accuracy is acceptable. (2)
When we build ensemble methods, SVM and NN regression models perform better when they are bagged than when they are boosted. This was expected, as Bagging combines many weak predictors (i.e., the predictor is only slightly correlated with the true prediction) to produce a strong predictor (i.e., the predictor is well-correlated with the true prediction). This works well for algorithms where by changing the training set the output changes. The opposite behaviour can be found ink-NN and DT regression models; that is, when these algorithms are boosted the models tend to provide better results than when they are bagged. That is, in order to improve the performance of AdaBoost, we use suboptimal values, for the number of the neighbours (k) for k-NN, and the number of trees (T) for DT; that is, we use values that are not that good, but at least better than random. Therefore, we make weak predictors by tuning the parameters to avoid the cases in which the regressors respond similarly [31]. This is not the case for SVM and NN. Since these learning algorithms do not have an input parameter that we can adjust to obtain a weak predictor without affecting the accuracy of the model, the probability that these algorithms provide better performance when they are boosted than when are bagged is lower. Some initial results about how a SVM can be used as a weak predictor can be found in [32]. Another option could be treating this kind of algorithm as a weak regressor by using fewer samples to train, as stated in [33]. (3)
By applying different regression models, and in particular when the SVM regression model is bagged, we improve by5% the overall accuracy of the prediction with respect to the k-NN model. More specifically, in terms of the NRMSE, k-NN exhibits an error of 10%, while SVM halves this value to 5%.Figure 4
NRMSE, a function of different number of features selected by the SPCA.Figure 5
Cumulative contribution of each PC to the original data’s variance.Figure 6
Variability of the data set as a function of thec=10 PCs.Results suggest that while all the regression models exhibit high accuracy, the bagged-SVM learning model is the one that better fits our needs and exhibits more accurate predictions. Therefore, in this work we focus on bagged-SVM to build the best model that fits the data.


## 3.2. Putting It Together: ML-Based Network Planning Tool

In summary, the proposed tool exploits the power of both components working together, namely, ML-based QoS modelling and GA-based network optimisation. The former is capable of extracting the most relevant information out of wealth of operational data measured in complex ultradense networks and make meaningful predictions for the operator and the end-user. This results in powerful network performance prediction models that are fed into the latter, which explores and finds the best combination of parameters for configuring the network elements, wherebest means the one that gives the best predicted QoS according to the learned model. The whole network planning process is depicted in Figure 7.Figure 7
Bagged-SVM network planning tool architecture.


## 4. Performance Evaluation of the Network Planning Tool

In order to evaluate the performance of the proposed scheme, we consider two cases of study.(A) Case Study # 1 : Deployment Planning in a Dense Small Cell Scenario. In this use case, we exploit the experience gained throughout the network to properly dimension and deploy (i.e., locate) the small cells in an indoor deployment. The goal is to improve the QoS offered to end-users while increasing the spectral efficiency by reducing the PRBs used per Mb transmitted.(B) Case Study # 2 : Self-Healing to Compensate for Faults. Cell Outage Compensation (COC) is applied to alleviate the outage caused by the loss of service from a faulty cell. For this use case, an adequate reaction is vital for the continuity of the service. As a result, vendor specific Cell Outage Detection (COD) schemes have also to be designed [34–36]. In this case of study we assume the outage has already been detected, and we focus on readjusting the network planning (antenna tilt) to solve the outage problem.The planning tool aims at guaranteeing that the network meets the operator’s needs. We proceed to design the appropriate planning tool by defining the following aspects:(1)
Data: we define the radio measurements that we extract and analyse from the network.(2)
Parameter: we define the network parameters that we aim to tune.(3)
Action: we define the possible actions to take in order to optimise network performance.(4)
Objective: we define the system level target.Table 3 presents this information for each use case under study.Table 3
Information relevant for the network planning tool.
Information Case study#1 Case study#2 Data RSRP and RSRQ coming from the serving and neighbouring cells RSRP and RSRQ coming from the serving and neighbouring cells  Parameter θ sc = ( θ s c 1 , … , θ s c N sc ), which is a binary vector that denotes if the small cells are switched ON or OFF θ tilt = ( θ til t 1 , … , θ til t M ), which is a vector that denotes the tilt value associated with each of the surrounding cells that are trying to fill the outage gap  Action Switch ON or OFF each small cell Adjusting the antenna tilt parameter  Objective Increasing the resource efficiency of our planning by dimensioning the deployment and locating LTE indoor small cells Readjusting the network planning to quickly solve an outage problemThe results of the use cases of application are described in the rest of the section. We first present the simulation scenario and then the simulation results for each use case.(A) Case of Study # 1 : Deployment of Indoor Small Cells. We aim at providing a network planning of a small cell indoor deployment to improve the QoS offered to end-users and to increase the resource efficiency (in PRB per Mb) of our planning.(  1) Simulation Scenario. The scenario that we set up consists of 1 Enhanced Node Base station (eNB), with 3 sectors. We need to plan the deployment of the small cell network defined as the standard dual stripe scenario based on 1 block of 2 buildings [37]. The building has 1 floor, with 20 apartments, which results in 40 apartments, as depicted in Figure 8. We consider that 1 small cell is located in each apartment and the planning will decide which one will be switched ON or OFF. The parameters used in the simulations and the learning parameters are given in Table 4.Table 4
Case of study#1: simulation parameters.
Parameter Value Propagation loss model HybridBuildings Shadow fading Log-normal, std =8 dB Scheduler Proportional Fair (PF) AMC model LteAmc::MiErrorModel Transport protocol User Datagram Protocol (UDP) Traffic model Constant bit rate Layer link protocol Radio Link Control (RLC) Mode Unacknowledged Mode (UM) Macro cell scenario Number of eNBs 1 site with3 cells eNB Tx power 46 dBm Small cell scenario Initial number of small cells (Nsc) 40 Small cell Tx power 23 dBm LTE Bandwidth 5 MHz Number of RBs 25 TTI 1 ms GA Type binary-valued Size ofS (psize) 100 Elitisme 2 Mutation changeδ 0.1 Bagged-SVM Number of iteration (γ) 1000 Epsilonϵ 0.1 Kernel Radial Basis Function (RBF) Simulation time 0.25 sFigure 8
Scenario.(  2) Simulation Results. The simulation starts with an initial deployment, where each apartment of the building has randomly deployed a small cell. The Signal to Interference and Noise Ratio (SINR) at each point of the scenario, obtained through this initial deployment, is depicted in Figure 9. The idea is to determine the most effective number and location of small cells by evaluating the performance of each individual θsc configuration. The configuration θsc is represented by a binary string, with dimension Nsc. Each element in the binary string represents if the jth small cell is ON or OFF. A value of 1 means that the power transmission of the jth small cell is set to 23 dBm, while a 0 means that the jth small cell is switched OFF. At each iteration (i.e., generation of the GA), the GAθsc process evaluates different strings, and when the evaluation is done, the GAθsc process provides a new configuration of small cells. Therefore, the proposed network planning tool takes advantage of the model generated through the data preparation and data analysis processes to estimate the QoS at any random point in the scenario given a certain θsc. Then, it learns online through theoptimised network planning tool the θsc(∗) best configuration to the small cell deployment by improving the configuration with each new generation (see Figure 10). This process is referred hereafter as GAθsc.Figure 9
Initial deployment, in which we consider one small cell located in each apartment.Figure 10
Final deployment, in which theGAθsc scheme finds the number of small cells to deploy.We evaluate the system performance of each deployment, represented by a binary string of dimensionNsc, on the ns-3 LENA module. When we get a new configuration from the GAθsc, we implement it in the system simulator and evaluate it again, until the network deployment reaches the network performance target set by the operator.Figures11 and 12 show the fitness of the best individuals found in each generation (Best) and the mean of the fitness values across the entire population (Mean), in terms of PRB/Mb and Average Throughput, respectively. Figure 11 depicts the time evolution of the average PRB per Mb in the scenario. We observe that as the generations proceed and the GAθsc evolves, the PRB/Mb decreases as it was expected, and so the efficiency of the planning increases because the average throughout increases and the PRBs consumed decrease. That is, at the 60th generation, the GAθsc reaches the best spectral efficiency for the planning. Figure 12 shows the evolution of the GAθsc scheme in terms of the average throughput in the whole network. We observe that for each new generation the GAθsc scheme finds the number and location of small cells that maximise the throughput. Figure 10 shows the resulting SINR map of the deployment. It can be seen that the SINR in the represented region of the network is also globally improved.Figure 11
Evolution of the average PRB per Mb.Figure 12
Evolution of the average throughput in the whole network.A comparison of different planning schemes is shown in Figure13. This figure shows the SINR performance for three deployments: (1) one where all the small cells are ON and there is a small cell in each apartment (tagged asinitial deployment), (2) the proposed GAθsc approach (tagged asfinal deployment), which results in 28 deployed small cells, and (3) a benchmark deployment based on a greedy algorithm. The greedy algorithm searches for the best deployment by testing a certain number msubset of string vector configurations, defining the number and position of switched ON nodes. For each configuration, the greedy algorithm computes the QoS and then it selects the configuration that provides the best QoS results. The argument msubset is discussed in [38]. It offers a tradeoff between a high number of configurations, which would result in high computational effort, and low number of configurations, which may result in local optima. We have tested different values against performances and we finally selected the msubset=10000. While the greedy search algorithm rarely outputs optimal solutions, it often provides reasonable suboptimal solutions [39]. This can be observed in Figure 13, where the number of small cells in the initial deployment is 40, in the greedy deployment it is 33, and when applying our GAθsc scheme it is 28. We observe in this figure that, in general, the GAθsc tends to work more effectively, since it provides improved QoS while deploying a reduced number of small cells. The reason for this is that the GA approach makes a much deeper estimation of the state of the environment through the regression analysis and counts on a more sophisticated combinatorial search scheme, based on the genetic approach, than the greedy scheme, which performs a limited search and may be trapped at local optima.Figure 13
CDF of the SINR (dB) in the building.(B) Case of Study # 2 : Self-Healing to Compensate for Faults. As we already mentioned, in this case of application, we focus on readjusting the network planning to quickly solve an outage problem by adjusting the antenna tilt parameter. To evaluate this, we generate a sector fault in a typical macrocellular deployment. That is, during a certain period of time, a sector is not able to offer service to its users. Therefore, the proposed network planning tool allows setting the antenna tilt for each compensating sector to automatically alleviate the outage caused by the loss of service from a faulty sector [40].(  1) Simulation Scenario. We consider a Long Term Evolution (LTE) cellular network composed of a set of M eNBs. The M eNBs form a regular hexagonal network layout with intersite distance D and provide coverage over the entire network. We assume that a sector in the scenario is down (see Figure 14). The parameters to tune are the antenna tilts of the cells neighbouring the affected sector. In particular, the surrounding M cells automatically and continuously adjust their antenna tilt until the coverage gap is filled. The vertical radiation pattern of a cell sector is obtained according to [41], where the gain in the horizontal plane is given by(2)Ghϕ=max⁡-12ϕHPBWh,SLLh.Here ϕ is the horizontal angle relative to the maximum gain direction, HPBWh is the half power beam-width for the horizontal plane, and SLLh is the side lobe level for the horizontal plane. Similarly, the gain in the vertical plane is given by(3)Gvρ=max⁡-12ρ-θtiltHPBWv,SLLv.All eNBs in the scenario have the same antenna model where ρ is the vertical angle relative to the maximum gain direction, θtilt is the tilt angle, HPBWv is the vertical half power beam-width, and SLLh is the vertical side lobe level. Finally, the two gain components are added by (4)Gϕ,ρ=max⁡Ghϕ+Gvρ,SLL0+G0,where SLL0 is an overall side lobe floor and G0 the antenna gain. We consider a cellular network whose system performance has been evaluated on the 3GPP-compliant ns-3 LENA module. The parameters used in the simulations and the learning parameters are given in Table 5. The macrocell scenario that we set up consists of 4 eNB with 3 sectors each, which results in 12 cells, as depicted in Figure 14.Table 5
Case of study#2: Simulation parameters.
Parameter Value Propagation loss model HybridBuildings Shadow fading Log-normal, std =8 dB Scheduler PF AMC model LteAmc::MiErrorModel Transport protocol UDP Traffic model Constant bit rate Layer link protocol-mode RLC-UM Macro cell scenario M MacroEnbSites 4 (each site has 3 cells) Number of cells 12 eNB Tx power 46 dBm LTE Intersite distanceD 500 m Bandwidth 5 MHz Number of RBs 25 Antenna parameters Horizontal angleϕ −180°≤ϕ≤180° HPBW Vertical10° : Horizontal 70° Antenna gainG0 18 dBi Vertical angleθ −90°≤ϕ≤90° SLL Vertical −18 dB : Horizontal −20 dB SLL0 −30 dB GA Type real-valued Tilt values (min–max) 0°–15° Size ofS (psize) 100 Elitisme 2 Mutation changeδ 0.1 Bagged-SVM Number of iteration (γ) 1000 Epsilonϵ 0.1 Kernel RBF Simulation time 0.25 sFigure 14
Scenario.Therefore, each feasible solution corresponds to a vector ofM=6 tilt values (i.e., each tilt value is associated with one of the surrounding cells that are trying to fill the outage gap).(  2) Simulation Results. We analyse performance results obtained through the network planning tool described in Section 2. Figures 15–17 show the fitness of the best individual found in each generation (Best) and the mean of the fitness values across the entire population (Mean). We observe that, for each generation, the population (i.e., the possible combinations of antenna tilts) tends to get better as generations proceed. Figure 15 depicts the time evolution of the PRB per offered Mb in the scenario during the evolution of the GAθtilt. We observe that as the GAθtilt evolves, the efficiency of the planning increases. That is, as the generations proceed, the GAθtilt finds the configuration parameter vector that minimizes the value of PRBs per transmitted Mb.Figure 15
Evolution of the average PRB per Mb.Figure 16
Evolution of the average throughput in the neighbouring cells.Figure 17
Evolution of the average throughput in the whole network.In order to analyse the overall impact of the outage and its evolution for each new generation, we depict the average throughput for neighbouring cells only (Figure16) and for the whole network (Figure 17).More specifically, Figure16 depicts the time evolution of the average throughput of the compensating sectors. That is, it shows the performance in terms of the throughput of the 6 neighbouring cells, which adjust their antenna tilt in order to compensate for the faulty sector. Figure 17 describes the average throughput of the whole network. From this figure, we observe that the GAθtilt scheme achieves at the 30-th generation 23 Mbps of average throughput in the whole network, while, in the neighbouring cells (Figure 16), the achieved throughput is only 15 Mbps, which is reasonable, due to the challenging service conditions in this area.Finally, theGAθtilt scheme is compared in Figure 18 with the self-organised Reinforcement Learning- (RL-) based approach for COC proposed in [42], where in order to design the self-healing solution (ACθtilt), the antenna tilt is adjusted. The considered RL approach is an Actor Critic algorithm, which is already proven to outperform different solutions for COC available in literature in [42]. Therefore, we consider this to be a good benchmark for comparison.Figure 18
CDF of the average SINR values in the faulty sector by two different COC approaches. TheGAθtilt based scheme is compared to a RL approach presented in [16].Figure18 depicts the CDF of the SINR of the network. We assume the user is out of outage when its SINR is above the threshold of −6 dB, as explained in [42]. Therefore, in this figure, we observe that ACθtilt is able to recover 95% of UE, while the GAθtilt is able to recover all the UE. We observe in this figure that, in general, the GAθtilt tends to work effectively, since it maintains the diversity in the values taken by the parameters during the generations and it makes a much deeper estimation of the state of the environment through the regression analysis approach than the ACθtilt scheme, which considers only the SINR and CQI feedback from the UE to determine the state of the environment and to estimate the general behaviour of the network.From these figures, we observe that using the planning tool to adjust the antenna tilt parameter, we are able to alleviate the outage caused by the loss of service from a faulty sector.


## 5. Concluding Remarks

In this paper, we have defined a methodology and built a tool for smart and efficient network planning that exploits and learns from the operational history reflected in the measurements gathered anywhere and at any time throughout the network. To build the QoS prediction model based on this historical data, we collect UE measurements according to 3GPP MDT functionality and we apply regression analysis techniques to estimate the Physical Resource Blocks (PRB) per transmitted Mb. This model is then used as objective function in a genetic algorithm (GAθ). By doing this, one can observe that each new iteration increases the efficiency of resource usage in the network while improving the throughput offered to the user.To demonstrate the flexibility of the proposed smart ML-based planning tool, in this paper, we have decided to apply it to two very different use cases and scenarios: (1) deployment of indoor small cells and (2) compensation of sector faults in a traditional macrocell deployment. For use case #1, results demonstrate the ability of the proposed scheme to deploy small cells in a network in such a way that the average throughput is increased while the PRBs consumed per Mb transmitted decrease, hence improving the spectral efficiency. Regarding use case #2, results demonstrate the ability of the proposed scheme to compensate 100% of outage users in the scenario and to offer them service. We have compared the performance of our approach in the context of the two proposed cases of study to state-of-the-art solutions based on a greedy algorithm for case 1 and reinforcement learning for case 2. Our scheme outperformed state-of-the-art solutions in both cases. We believe that the same technique can be successfully applied to many other planning problems of interest for operators.As a future work, we will analyse the planning of scenarios where a huge number of devices have to be provided with service. This is expected to benefit the performance of the estimation, since the data base will be enriched by many measurements, which is the basis of the intelligence of the approach.



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*Source: 6740585-2017-02-05.xml*