Marie Line

Alberi Morel

Illyyne Saffar
email: illyyne.saffar@nokia.com

Deep Kamal

Singh

Cesar Viho
email: cesar.viho@irisa.fr

Multi-task Deep Learning based Environment and Mobility Detection for User Behavior Modeling

Keywords: User Behavior, Preferences, Context of use, Deep Learning, Multi-task learning, Environment, Mobility

Cognition of user behavior can be seen as an efficient tool for automation of future mobile networks. As a matter of fact, it amplifies the intelligence of autonomic networks in a sense that the network is more aware of the operational context. However, predicting the context of mobile users is a prerequisite for inferring the user behavior. This paper deals with the user behaviour modeling. The model includes the prediction of two main features related to mobile user context: the environment and the mobility. Practically, the question is how and where the mobile user consumes the mobile services. We investigate Deep Learning based methods for simultaneously detecting the environment and the mobility state. We empirically evaluate the effectiveness of the proposed methods using real-time radio data, which has been massively gathered from multiple diversified situations of mobile users.

I. INTRODUCTION

Future 5G mobile networks will support a large range of new services or applications, like Enhanced Mobile Broadband (eMBB), Massive Machine Type Communication (mMTC) and Ultra Reliable Low Latency Communications (URLLC). To meet the customer demands of cost-efficient services and high satisfaction, cognition of mobile user behavior is considered as a good candidate for building intelligent and suitable 5G autonomic networking systems. As a matter of fact, this knowledge feeds the network intelligence in the sense that the system becomes more aware of the operating context. The idea is to use cognition of mobile user behavior to enhance the self-adaptation capabilities of 5G networks. This will help 5G networks to face the variable consuming habits of users that impact the network conditions. This shall be achieved while individually meeting the demands or needs of customers, thus focusing on user satisfaction.

However, the awareness of the use context of mobile users is a prerequisite for inferring the mobile user behavior. It depicts all mobile user situations during which they consume mobile services or use varied applications. In [START_REF] Husnjak | Identification and Prediction of User Behavior Depending on the Context of the Use of Smart Mobile Devices[END_REF] the use context is defined as "any information that can be used to characterize the situation of a person that is considered relevant to the interaction between a user and an application, including the user and applications themselves".

The potential value of this instance lies in its possibilities to predict noticeable trends in user behaviors/habits related to the different contexts/situations of the mobile user. As a consequence, the user behavior that defines the way a user consumes the given requested service can be seen as an abstraction of the use context.

Today, assessing the use context remains a difficult task. It is particularly problematic when the objective is to detect the personal usages or preferences automatically without requiring constant user interaction through personalized and refined questions [START_REF] Kaasinen | User Acceptance of Mobile Services -Value, Ease of Use, Trust and Ease of Adoption[END_REF]. Consequently, the reliable detection of mobile user context attributes considering multiple user-situations in a non-intrusive way, i.e., without human intervention, is an open issue that still needs to be fully addressed. Such information can help many operators to configure or optimize their networks efficiently while minimizing management and operation cost. Thus, the key idea is to automatically infer the mobile user context using Machine Learning techniques. This can be done using Deep Learning techniques that can learn in an automatic way, in real-time, and benefit from the value added by the massive data. Currently, such approaches are popular and are regarded as promising solutions. This is due to their ability to deal with complex problems and to characterize the inherent relationships between the inputs and outputs of a system without human involvement [START_REF] Wang | Machine Learning for Networking: Workflow, Advances and Opportunities[END_REF].

This paper, presents a preliminary study of user behavior modeling when considering the user behavior as an abstraction of the diverse usage situation experienced by mobile users. We propose to model the user behavior with the Quality-of-Experience-influencing features related to mobile user context: the environment and the mobility. Quality-of-Experience (QoE) is a metric that measures the mobile user's satisfaction depending on his or her experience of the service. The user's environment and mobility are important factors since they have a big influence on QoE. For example, a user who is indoor would experience a very different service quality as compared to those who are outdoor, all else being equal. Actually, the environment and the mobility form the conditions in which a mobile user consumes the requested services/applications. In practice, in order to estimate the environment or mobility, we have to answer the following questions: how and where a mobile user consumes the mobile services? Besides, these questions have to be answered at the same time (simultaneously) to detect and then predict the user behavior. Thus, to achieve it, we investigate the association of both Indoor/Outdoor Detection (IOD) and Mobility State Estimation (MSE). IOD refers to the detection of the mobile users' environments, that is to infer whether the user is Indoor or Outdoor [START_REF] Saffar | Machine Learning with partially labeled Data for Indoor Outdoor Detection[END_REF]. MSE refers to the estimation whether a given user moves with low, medium or high speed. This is also specified as a requirement by LTE/5G specifications [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF].

We investigate a multi-task based deep learning solution to simultaneously detect a user's environment and mobility state. This leads us to following contributions. We provide an analyses of mobile user behaviour and use context. We perform mobility state detection, which is further developed to achieve simultaneous multi-output (IOD/MSE) classification. The empirical evaluation based on real-time and highly representative 4G radio data, consisting of 3GPP signals measured by phone devices, shows the effectiveness of our approach. This data includes ground truth information and the whole dataset has been massively gathered from many diverse mobility situations and many environment types. The results also prove that the simultaneous detection of the environment as well as the mobility state estimation can be achieved with high accuracy.

The rest of this paper is organized as follows. Section II introduces the prior related work; Section III defines the user behavior and presents a discussion in terms of service usage; Section IV studies the user behavior modeling focusing on the simultaneous detection of usage environment and mobility state.

II. RELATED WORK

Only a few works in literature have addressed IOD and MSE using Machine Learning techniques.

The work in [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF] addressed IOD using a set of signals coming from mobile phone sensors (radio signals, cell signal strength, light intensity, battery temperature ...). They proposed an IOD solution based on ML algorithms and more precisely a semi-supervised ML approach. Their solution, implemented on different android devices, shows a 92.33% of accuracy. It provides the highest detection performance in comparison to existing methods such as a supervised classifier. In [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF], authors considered IOD from the network side as a classification issue. Once the indoor or outdoor location is detected, this new information further helps with other signals to localize the mobile user. For the IOD classification task, they used the radio signals RSRP and RSRQ signals and tested many algorithms: Support Vector Machine (SVM), logistic regression and random forest. SVM was the solution retained since it performed best. These papers show the interest of using ML approaches for IOD. Thus, this motivates us to investigate similar solutions.

Concerning mobility detection, some works already exist in literature. The work in [START_REF] Ben Hadj | Optimization of Radio Measurements Exploitation in Wireless Mobile Networks[END_REF] uses radio signal measurements collected under different mobility situations (low, medium, high speed). The signal attenuation is automatically estimated. This, in turn, helps to efficiently classify the mobile user In [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF], the authors propose a method for estimating the UE mobility, that relies on UE history information about the UE cell sojourn time. Using such information, the eNB classifies the speed to one of the three mobility classes defined in the 3GPP standards. Both methods for mobility estimation, [START_REF] Haddad | Mobility state estimation in LTE[END_REF] and [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF], have shown good results. Both [START_REF] Haddad | Mobility state estimation in LTE[END_REF] and [START_REF] Ben Hadj | Optimization of Radio Measurements Exploitation in Wireless Mobile Networks[END_REF] confirm that the user mobility is strongly correlated to a user's environment. In [START_REF] Michaelis | Balancing high-load scenarios with next cell predictions and mobility pattern recognition[END_REF], authors use the knowledge extracted from simple features such as the cell ID and the sojourn time, in previously visited cells, to predict the user mobility and his future location. An interesting work has been presented in [START_REF] Ge | User mobility evaluation for 5G small cell networks based on individual mobility model[END_REF], where authors evaluate the user mobility in 5G networks according his behavior, his preferences and his tendencies.

To the best of our knowledge, no study has been conducted on the association of IOD and MSE using Deep Learning techniques. To efficiently estimate the user environment and mobility state at the same time, our proposed deep learning solution will use a dataset composed of radio signals as well as the data linked to power, quality and mobility.

III. USER BEHAVIOR: MODEL AND ANALYSIS

The context of use is defined in [ISO 13407:1999] standard as the characteristics of the users, tasks and the environment in which the system is used. This standard highlights the requirements to know not only the context of use, but also information on user (demographics), system and applications because the context affects the service/application usage situation as well as the user and the used technology. Therefore, the user behavior shall be the direct reflection of all the usage situations of mobile services.

We propose to model the user behavior by abstracting diverse usage situations experienced in the delivery zones of mobile services. For this, the model is based on the QoEinfluencing factors of user contextual information that links the The use context includes the environment, with more refined information like indoor/outdoor location (instead of only the exact user coordinates), device speed and orientation, the social context, i.e., people around the user and the type of access medium. The applications used on mobile devices are diverse. They include communication, web, social network applications, multimedia streaming, TV channels applications and also mobile apps [START_REF] Siris | Mobile Quality of Experience: Recent Advances and Challenges, Workshop on Information Quality and Quality of Service for Pervasive Computing[END_REF]. The user behavior depicted in Figure 1 is then modeled as a multi-attribute entity. Each attribute is directly linked to the service usage and is defined as follows:

• Environment (indoor, outdoor ...),

• Mobility (low, high ...),

• Connectivity (3G, 4G, 5G, ...),

• Social state (alone or in a group) • Application (conversational, video, ...) • Content (news, sport, action movies, ...) Contrary to traditional TV users who just watch scheduled programs, mobile service users are free to choose the content they want, at any point in time and space. During a day, a user can be in different situations, such as walking outdoor, in a car, at the work office, in a mall, in a café or at home. As a matter of fact, mobile users' preferences for certain applications or contents is linked with the usage situations [START_REF] Ickin | Factors influencing quality of experience of commonly used mobile applications[END_REF]. Statistical studies show that mobile phones are mostly used in a building for internet service (80%) and for a call (70 %) [START_REF]HetNet Forum[END_REF]. This can be explained by the fact that the different use contexts pose their own limitations, which in turn impact the potential application usages. In [START_REF] Siris | Mobile Quality of Experience: Recent Advances and Challenges, Workshop on Information Quality and Quality of Service for Pervasive Computing[END_REF], [START_REF] Soikkeli | Diversity and End User Context in Smartphone Usage Sessions, Next Generation Mobile Applications[END_REF], [START_REF] Kaasinen | User Acceptance of Mobile Services -Value, Ease of Use, Trust and Ease of Adoption[END_REF], the most commonly mentioned physical environments of application usage are again indoors (waiting halls or lounges, work, home and cafes), but also include vehicles, such as public transportation and private cars. In motion, audio is the preferred media, whereas, during stationary reception, text and video are the most pleasant media. For information assimilation, the café environment is preferred, while the bus or car environment is not that preferred. This is explained by the calm and the pleasant atmosphere of a café which is suitable for focusing on viewing. In the bus context, people may focus their attention to, e.g., watching mobile TV, but the complicated task on the move results generally in an unpleasant experience of entertainment.

Consequently, the mobile devices are mostly used in indoor or in-car context. The environment and mobility are therefore important factors for describing the mobile user behavior when he is experiencing a service. In the following, we investigate the prediction of the user behavior when connecting to cellular networks and experiencing an application. The prediction will focus only on two QoE-influencing features, the environment and the mobility state.

IV. USE CASE ANALYSIS: ENVIRONMENT AND MOBILITY

DETECTION

In this section, we study the simultaneous detection of user environment (Indoor, Outdoor) and mobility state (Low, Medium, High) using a deep learning approach.

A. Why Multi-Task Deep Learning?

In machine learning, we use the concept of Multi-Task Learning (MTL), when a single model is used to solve a series of related tasks (by task we mean: regression, prediction, etc.). The idea of MTL is inspired from human learning activities. As humans, we often use the same basics that provide us with the necessary skills to learn several tasks and master more complex techniques. For example, we use the algorithmic and the theoretic knowledge, which serves as a base acquired at school, to further learn different programming language.

Generally, to guarantee good performances while training a Multi-Task model, we should ensure that there is a correlation between the different tasks. Indeed, correlation between tasks means that some relation exists among the tasks and capturing such information can result in better performance, as compared to learning each task independently [START_REF] Li | Self-Paced Multi-Task Learning[END_REF] [START_REF] Xu | A Survey on Multioutput Learning[END_REF].

Works in machine learning treat IOD and MSE tasks independently. However, in practice, analyzing the user behavior requires simultaneously processing both the tasks since they both impact the user behavior. The idea of this paper is to predict the user behavior by detecting the environment and the mobility state at the same time. This requires to simultaneously perform MSE and IOD using an approach based on multitask deep learning (DL-MTL). Actually, DL-MTL for the user behavior context can provide a better generic model rather than studying every specific behavior separately. When for a given single task the features are not sufficient, or very noisy to the point that the model can not quantify its relevance, then DL-MTL can help solving this issue. DL-MTL can do that by using additional features intended for other tasks to clarify and provide additional information and evidence for its relevance or irrelevance [START_REF] Zhang | A survey on multi-task learning[END_REF], [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF].

B. Dataset Description

To use Deep Learning, it is useful to have massive input data. Furthermore, deep learning is used for the problems where modeling relationships between large number of parameters is not tractable. This is the case in our problem because we simultaneously infer both the attributes of environment and mobility. The model has to extract, at same time, the complexity of the variety of situations imposed by all the available combinations generated by the two attributes. Actually there are two ways to collect data (Figure 2) :

-Drive test mode: this mode is widely used to collect data.

It is a mode in which a vehicle or people equipped with sensors move in specific places to collect data. However, this controlled mode imposes limits on capturing the reality. Such data collection campaigns are run for limited hours per day during short periods (couple of weeks) and at some specific places. For user behavior prediction with DL-MTL approach, using data collected in drive test mode will not deliver a generic model, since, we will miss big sides of users' behavior, for example the case of his night life, etc. -Crowdsourcing mode: Recently surfaced with big firms like Google and Netflix, the crowdsourcing mode allows to gather data more cheaply and in large quantities, more than ever before. But most importantly, it allows to better reflect the users' behaviors by capturing a huge diversity of their experiences. In this mode, the collected data consists of signals measured by the mobile phones. This data is gathered and controlled by the network of a tenant (e.g. service provider or application provider) in order to mine personal information. To be able to do this, first users should agree and should themselves install the necessary software.

In our case, we propose to collect data for the training phase using a crowdsourcing mode controlled by the operator. This mode allows us to gather data inside the network and corresponding to UE-specific radio measurements and additional data required to do the labelling and then to train the model in supervised mode (Figure 3). This, in turn, improves the machine learning performance because with this data collection mode, we get more highly representative data.

For our study, we are using a dataset that has been collected in a crowdsourced mode. With this campaign of data measurement, we try to build a dataset as close as possible to the complexity and the variety of several locations in France visited by mobile user moving in real world. Figure 4 shows the places where our dataset has been collected. Every red point corresponds to a visited place (and implicitly the roads linking two visited points). The collection duration corresponds to 16 months with an average 24h/7 with an average of 1 measurement per 15 seconds while the mobile phone session is active and 1 measurement per 2 minutes otherwise. For 16 months of massive collection the measurements are related to many different and diversified environments like mountain, beach, forest, companies, cafes, streets, bars, parks, restaurants, lakes, etc. This includes many cities and places like countryside, villages, small cities, metropolis, and different countries, but for this paper we are only studying the data collected in France. Thanks to this long data collection period, the data has been collected in every type of weather such as rainy, sunny, snow, etc.

C. Environment and Mobility Detection system: architecture and implementation

We aim to compare the system with two independent classification tasks versus the one with two simultaneous tasks. The system performing simultaneous tasks uses similar input signals, whereas, the system with independent tasks uses separate input signals for each task. There exist many methods in literature for MTL with deep learning: parameters sharing, feature sharing,... [START_REF] Ruder | An overview of multi-task learning in deep neural networks[END_REF], [START_REF] Zhang | A survey on multi-task learning[END_REF]. We are interested in the most used approach which is a classical method of MTL consisting in sharing hard parameters. MTL with sharing of hard parameters consists on sharing the first hidden layers between the tasks. Then some specific layers are specified for every task. Using this method reduces often the overfitting risk. Thus, we propose the MTL system as described in Figure 5. It is composed of three main parts :

• Input: A first input layer fed with shared features for both IOD and MSE tasks treated simultaneously. These inputs consists in of 7 main signals. Recall that we focus Outdoor) and MSE (3 Classes: Low, Medium, High). DL-MTL is implemented with both Scikitlearn and Keras in python. We tune Neural Network hyper-parameters using a GridSearch algorithm (which simply is an exhaustive search through a manually specified subset of the hyper-parameters for Deep Learning). The DL-MTL module is then a feed forward neural network composed of an input layer, an output layer, 5 fully connected hidden layers. We added also 2 dropout layers to regularize and minimize the over-fitting.

The compared system which implements independent tasks is explained as follows. IOD task is done using the input features and a supervised Deep Learning algorithm described in [START_REF] Saffar | Machine Learning with partially labeled Data for Indoor Outdoor Detection[END_REF]. MSE task uses a part of features described above and use a similar Deep Learning algorithm.

D. Results and discussion

The training is done using the labeled part of our dataset.

For training, we used (70%) of the labeled data and we used the (30%) remaining for the model performance evaluation. DL-MTL shows good performance in terms of accuracy when trained on our crowdsourced dataset: 97.21% for IOD and 93.18% for MSE. However, the accuracy alone can not quantify the overall performance even if faced with the issue of unbalanced data. Indeed, preliminary statistics of our data show that the data proportion between indoor and outdoor classes is unbalanced. For the IOD task, as shown in the figure 6, we have 75% Indoor instances vs. 25% Outdoor instances. For the MSE task, we have 90% Low vs. 5% Medium vs. High 5% instances.

This issue of unbalanced data is not related to the data collection constraints, but it is rather due to the real human activity. Effectively, users are more static and indoors rather than in mobility situation or outdoors. Moreover, users prefer to use their mobiles in specific situations. Thus, we also use F1-score metric in addition to the accuracy for performance evaluation. F1-score is one of the most used metric in case of unbalanced data classes. F1-score metric by definition is the weighted average of Precision and Recall according to the following relation: II, the performance gain for MSE task is approx. 10%. Note that this performance improvement was obtained without changing any other parameter like input features, data etc. The correlation between the environment and the mobility has been beneficial for both the tasks. With such structure they have shared data inputs that brought to each others meaningful additional information (for example a user can't be indoor with medium or high speed). Moreover, the extra output contributes further in fine tuning the neural network parameters during training. That is why DL-MTL is showing better performance.

F 1 -score =
For our work, we consider that 5% of error is tolerable that is to say an F1-score of 95%. This is inspired from network dimensioning requirement. Indeed, while mobile networks dimensioning, an error up to 5% is qualified as an admissible error rate. The first task delivers performance above the fixed threshold, however the MSE is still under 95% of F1-score. Considering this, the IOD task performance achieves a good performance, but MSE still needs to be improved.

The confusion matrix for MSE illustrated in Figure 7 shows that the model is classifying well the Low mobility class (with more than 90% of "true positives"), but relatively less well for the Medium and High mobility classes (with less than 75% of "true positives"). This is explained by the fact that the dataset is unbalanced as shown in Figure 6 that depicts the mobility state distribution per environment. This challenge is inherent to the user behavior: people are mostly in static condition than in high speed. Moreover, as soon as the user speed increases, the network quality deteriorates and consequently the user switches to 3G or even to GSM (sometimes). These two phenomena result in less data collected in these scenarios. For future work, we intend to investigate further to solve the issue due to the lack of balance between the mobility classes by adding other relevant features for training. The objective will be to help DL-MTL to detect the Medium and the High

V. CONCLUSION

The use context has a great importance for tracking user's behavior or preferences. The personalization shall be done for multiple contexts of use and thus, address multiple users and usage situations.

Thus, in this paper, we focused on modeling user behaviour and the use context. Two important elements of user behaviour and use context were considered: environment (indoor or outdoor) and mobility (low, medium or high). These tasks named as indoor/Outdoor detection (IOD) and Mobility State Estimation (MSE) were performed using deep learning. We used a real massive data-set consisting of input signals obtained from the mobile network. In terms of contributions, we provided a discussion on mobile user behavior and use context. Then, a deep learning and data based approach was used for mobility state estimation, which was finally developed into a module that simultaneously performed the IOD and MSE tasks. Notably, this module (called DL-MTL) showed better performance as compared to another approach which performed these tasks independently. In future, we plan to focus on improving the results of MSE task. We will also focus on tackling the issues of unbalanced data and validating our model considering many more varieties of user profiles.

Fig. 1 .

 1 Fig. 1. User Behavior Model

Fig. 2 .

 2 Fig. 2. Data collection modes

Fig. 3 .Fig. 4 .

 34 Fig. 3. Data collection scheme for training and serving phases

Fig. 5 .

 5 Fig. 5. Multi-Task Learning using Deep Learning architecture. Note that there are several hidden layers and not just one as it may initially appear.

Fig. 6 .

 6 Fig. 6. Mobility State distribution per Environment

Fig. 7 .

 7 Fig. 7. Confusion Matrix for the MSE Task -Multi-Task Learning case

  ApplicationType VoIP, live or non live service, Web access, VoD with short/full movies, on-line gaming Content Action movie, interview, video-conference or video call, image, synthesis

		Environment	Indoor (home, office, café) / Outdoor (incar, suburban, urban)
	Context	Mobility, speed Access medium, radio interface	Walking, driving, standing, sitting, Wired, wireless, 3G/4G/5G, WLAN
		Social	Alone, with a person, with a group

TABLE I QUALITY

 I -OF-EXPERIENCE-INFLUENCING FACTORS OF CONTEXTUAL INFORMATION usage situation and the application. The contextual information is summarised in Table I. The contextual information is classified into two main categories of QoE influencing factors: use context and application.

  2. P recision.Recall P recision + Recallwhere Precision is the number of correct positive results divided by the number of all positive results returned by the classifier, and Recall is the number of correct positive results divided by the number of all relevant samples.

		F1-score with DL F1-score with DL-MTL
	IOD	92.81% [4]	97.19%
	MSE	78.30%	87.69%

  tasks simultaneously can optimize the user behavior model in general. As shown in TableII, IOD and MSE have better performance with a DL-MTL architecture. As seen in Table

	TABLE II
	ENVIRONMENT AND MOBILITY SUPERVISED CLASSIFICATION
	PERFORMANCE: F1-SCORE VS. DL ARCHITECTURE & DL-MTL

ARCHITECTURE

Resolving the


Illyyne Saffar
email: illyyne.saffar@nokia.com

Marie Line

Alberi Morel

Deep Kamal

Singh

Cesar Viho
email: cesar.viho@irisa.fr

Semi-supervised Deep Learning-based Methods for Indoor Outdoor Detection

Keywords: Environment Classification, Deep Learning, Indoor Outdoor Detection, Semi-Supervised Learning, 3GPP radio measurement, crowdsourcing, real user activity

Detecting whether a mobile user is indoor or outdoor is an important issue which significantly impacts user behavior contextualization and mobile network resource management. Indoor Outdoor Detection (IOD) can be performed within mobile networks or in user terminals. Implementing IOD in mobile networks is interesting for operators as it is less costly, easier to deploy, and more energy efficient with centralized computing. This paper investigates hybrid/semi-supervised Deep Learningbased methods for detecting the environment of an active mobile phone user. They are based on both labeled and unlabeled large real radio data obtained from inside the network and from 3GPP signal measurements. We empirically evaluate the effectiveness of the semi-supervised learning methods using new real-time radio data, with partial ground truth information, gathered massively from multiple typical and diversified locations (indoor and outdoor) of mobile users. We also present an analysis of such schemes as compared to the existing supervised classification methods including SVM and Deep Learning.

I. INTRODUCTION

5G is the next evolution of mobile networks for accommodating the ever-growing user-demands, services and applications, guaranteeing a better Quality of Experience [START_REF] Pirinen | A brief overview of 5G research activities[END_REF]. The improvement is possible thanks to additional cognition from information on user behavior, obtained through user behavior contextualization. The idea is to inject cognition learned from the consuming habits of individuals and communities as well as their behaviors into mobile 5G networks. The additional knowledge will help them grow smarter and be more efficient when faced with the increasing complexity of network management combined with numerous new applications and their heterogeneous needs. As a first step, in this paper we focus on detecting the environment of a mobile user connected to a cellular network. More precisely, to infer whether the mobile user is indoor or outdoor. The Indoor Outdoor Detection (IOD) is a cornerstone of the user behavior contextualization, which in turn can be used for learning user behavior, adapting the mobile network resources, etc. [START_REF] Kaasinen | User acceptance of mobile services: Value, ease of use, trust and ease of adoption[END_REF] [START_REF] Siris | Mobile quality of experience: Recent advances and challenges[END_REF].

Machine Learning (ML) is one of the key technologies to be used for user behavior contextualization, which in our case is detecting the user environment (either indoor or outdoor). Actually, ML extracts information from data to look for patterns, and then uses them as predictor functions when analyzing future data. Building such a representative knowledge requires a highly representative and diversified dataset. However, most of IOD works, showing excellent performances, are based on datasets collected in a "drive-test" mode, which is unfortunately limited to specific environments. This often makes it difficult to be generalized to a real user behavior. For this reason, to design an efficient IOD tool which is able to capture the real behavior of mobile users, the user data shall be collected within mobile networks using a crowdsourcing approach [START_REF] Marina | Impact of indoor-outdoor context on crowdsourcing based mobile coverage analysis[END_REF], [START_REF] Cainey | Modelling download throughput of LTE networks[END_REF]. This approach consists in gathering real and large network measurement data, which is derived within network or is sent to network by multiple mobile phones (or other connected devices) using standardized procedures. Indeed, due to their small size and popularity, mobile devices allow users to access wireless networks anywhere and anytime, while doing various activities in all kinds of environments. Additionally, it allows for a continual and fine-grained spatio-temporal monitoring and analysis. Thus, ML algorithms trained on datasets collected in crowdsourcing mode allow to learn very diverse real-world environments.

In literature most of IOD works mainly use the received signal power as input for the IOD model [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF], [START_REF] Mekki | HTTP adaptive streaming with indoors-outdoors detection in mobile networks[END_REF]. Actually, this signal is highly correlated to user environments. However, only using it for IOD is not sufficient to guarantee IOD's good performance. Alone, it is not sufficient especially while facing ambiguous measurement points in mobile environments or in ambiguous user locations relative to eNB (evolved Node B). Hence, there is a need to vertically expand the dataset used to solve the IOD issue by adding other signals. Therefore, we propose to use new input signals which are related to the quality, the UE location and the user mobility. As a consequence, in this paper IOD uses Reference Signal Received Power (RSRP ), Channel Quality Indicator (CQI), Timing Advance (T A) and Cell Id. They represent 3GPP signals or indicators. RSRP and CQI are measured by UE and sent to eNB via standardized protocols. T A and Cell Id are derived inside the network when the user is connected. Note that IOD done in the network should consider the constraint of minimal human intervention.

For this, we propose to study semi-supervised Deep Learning-based methods for training automatic IOD classifiers. These methods are a mix of supervised and unsupervised approaches which can learn from partially labeled dataset. Such dataset reduces human intervention to the minimum possible. Indeed, the labeled data, used for ML training, is either tagged manually or automatically. Manual data tagging can be expensive and complex and even unfeasible for certain mobile operators if they have to tag all the collected data. We investigate therefore three semi-supervised approaches that 1) learn from both labeled and unlabeled data and 2) make use of information on received power, quality, distance and mobility. The promise of semi-supervised learning is that we can get our ML algorithm to learn from "unlabeled" data, which in turn is easier to obtain. A single unlabeled example may be less informative than a single labeled example. Nevertheless, we can get tons of such less informative examples, by collecting huge crowdsourced unlabeled signals. Now, if our algorithms can exploit this unlabeled data effectively, then it will enable us to learn more possible environment types related to the user behavior. That way the data will closely reflect the users' habits. Coming back to ML, today, deep learning surpasses all classical ML algorithms [START_REF] Zhang | Deep Learning in Mobile and Wireless Networking: A Survey[END_REF], [START_REF] Lecun | Deep learning[END_REF]. As said in [9]:"The more data we have, the wider the Neuronal Network is, the better the performances are."

The rest of paper is organized as follows. Section II presents the related works on IOD and mobility estimation. Section III analyses the input features (signals) selected for the dataset. Section IV, studies the impact of the mobility and distance information on IOD performance. Sections V and VI describes and compares three semi-supervised approaches: Cluster-thenlabel, Co-Training and Self-Training methods. Section VII evaluates the performance of Self-Training, shown to be the best, versus the volume of both labeled and unlabeled data. Finally, we conclude this paper with a brief discussion of open challenges, with a view to future research directions.

II. RELATED WORK

Works in literature have addressed IOD much more from mobile point of view than from the network or infrastructure point of view. In [START_REF] Zhou | Iodetector: A generic service for indoor outdoor detection[END_REF] authors propose to use a threshold of a signals set collected from some phone sensors related to: radio signals, cell signal strength, light intensity as well as the magnetic sensor to infer whether the mobile user is indoor or outdoor. Like [START_REF] Zhou | Iodetector: A generic service for indoor outdoor detection[END_REF], [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF] also addressed IOD using the same set of sensors plus the sound intensity, battery temperature and the proximity sensor. The investigated IOD solution is based on ML algorithms and more precisely a semi-supervised ML approach. Their solution, implemented on different android devices shows a 92.33% of accuracy and provides the highest detection performance in comparison with existing methods including supervised classifier. This solution shows the interest of using semi-supervised ML approaches for IOD. Thus, this motivates us to try similar solutions on the network side.

In [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF], authors have considered IOD at network side as a classification issue. Once the indoor or outdoor location is detected, it helps with other signals to localize the mobile user by estimating its longitude and latitude in a most possible accurate ways. For the IOD classification task, they used RSRP and RSRQ signals and tested many algorithms: Support Vector Machine (SVM), logistic regression and random forest. SVM was the solution retained since it performed best.

In [START_REF] Ben Hadj | Optimization of Radio Measurements Exploitation in Wireless Mobile Networks[END_REF] authors optimize the use of radio measurements in wireless networks. Literally, they use radio signal measurements collected in different situations of mobility with varying speed (low, medium, high). They dynamically estimate the signal attenuation. This in turn helps them to efficiently classify the mobile user environment (pedestrian, incar, nonmoving) and it finally improves the handover process. This confirms that the user mobility is strongly correlated to his environment. Nevertheless, this proposition is still at an early stage and it has not been thoroughly developed yet.

Many works in literature have addressed the user mobility estimation. In [START_REF] Haddad | Mobility state estimation in LTE[END_REF], authors use RSRP measurement according to the speed dependent time variations of shadowing to compute the UE speed. They propose two methods: either based on a spectral analysis or based on a time-based spectrum spreading. For both methods the variation is compared to a reference curve or a look-up table (database) and according the difference analysis the UE speed is computed. In [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF], authors propose a method for estimating the UE mobility, that relies on UE history information about the UE cell sojourn time. Then neighbouring eNBs exchange among them the learned network topology as well as the UE sojourn time history. Using such information, the eNB classifies the speed to one of the three mobility classes defined by 3GPP. Both methods for mobility estimation in [START_REF] Haddad | Mobility state estimation in LTE[END_REF] and [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF] have shown good results, however they estimate the speed of UEs in some specific use cases. In addition, they are complex to setup.

For us, the issue is not to study the mobility itself, but rather to exploit a simplified mobility indicator in order to improve the IOD performance. For this reason, we employ the standardized 3GPP procedure of mobility estimation. Actually, this low complexity approach is advantageously simple. But, according to literature, the 3GPP procedure is not precise enough if one requires an accurate mobile speed estimation [START_REF] Herculea | Straight: Stochastic geometry and user history based mobility estimation[END_REF]. However, in our study, we aim to evaluate whether the mobility indicator, as an additional input, can bring enough rich information to improve the performance of IOD system.

In [START_REF]User Equipment (UE) procedures in idle mode[END_REF], [START_REF]Radio Resource Control (RRC); Protocol specification[END_REF], the user mobility is estimated and classified to one of the three categories (Normal, Medium, High). This estimation using standard 3GPP procedures is done as follows:

• 1-Compute the number of handovers or cell re-selections (denoted by N CR) during a sliding time window (denoted by T CR max ). • 2-If a UE's N CR count is smaller than a threshold N CR medium , then the UE's mobility state is determined as "Normal". If the UE's N CR is greater than N CR medium but less than N CR high , the state is determined as "Medium". Finally, if the UE's N CR is greater than N CR high , then the state is determined as "High".

III. DATASET COLLECTION IN CROWDSOURCING MODE

In machine learning domain, data collection is the first main step for building the desired knowledge about the user environment. For this goal, we opt for a dataset composed of radio signals (RSRP , CQI), temporal features (T A, Time), a Mobility Indicator (M I), and finally the environment label when it is known. Thus, our dataset is composed of a vector of following 6 features:

• Time: recording time of signal or burst data arrival (ms).

• RSRP : the average received power of the Reference Signal (RS). The RSRP value lies between -140 dBm to -44 dBm [START_REF]Requirements for support of radio resource management[END_REF]. • CQI: Channel Quality Indicator that is used to indicate the most appropriate transmission modulation and coding scheme to be used [START_REF]Physical layer procedures[END_REF]. • T A: Timing Advance is used to control UL signal transmission timing [START_REF]Medium Access Control (MAC) protocol specification[END_REF]. • M I: the number of the Cell ID changes (N CID) in a sliding window of a given duration (T CR max ) [START_REF]User Equipment (UE) procedures in idle mode[END_REF], [START_REF]Radio Resource Control (RRC); Protocol specification[END_REF]. • Label: Indoor or Outdoor label in case it exists. To estimate the value of mobility indicator, we derive the value of sliding window duration T CR max . In urban environments, considering macro-cells, we assume a typical separation distance of around 900m between two base stations. Assuming this distance, we compute that a mobile user with a typical average speed of 30km/h moves to an other cell at least once at 100s, excepting a few rare cases. Consequently, T CR max = 100s of history on visited cells (UE History Information) is sufficient for starting to observe cell ID changes.

However, this is valid only for urban environments. Figure 1 plots the time to cross a cell vs. typical user speed for three environment types -urban and suburban macrocell and small cell -assuming a trajectory model where a user follows a straight line. We assume a typical separation distance between two base stations of 1.5 km for suburban environments and 350 m for small cell deployments. As expected, we observe that the crossing time is a function of the environment. For a speed of 30km/h, T CR max value is around 100s. T CR max has a lower value in the case of small cell deployments because such cell-types have smaller radius. The crowdsourcing mode for data collection is a recent concept that is used by a large researcher community as well as big firms like Google, Netflix and Amazon. Actually, such data collection mode allows to get data more quickly, cheaply and in large quantities more than ever before. But most importantly, it allows to reflect better the users' behaviors by providing a huge diversity of their experiences. This, in turn, improves the ML performance because with this data collection mode we get more highly representative data.

In our case, our dataset, collected using the crowdsourcing mode, has been gathered since October 2017 on wards, 24h/7, with an average of 1 measurement per 15 seconds while the mobile phone session is active and 1 measurement per 2 minutes otherwise. Thus, we have collected around 2M lines of data per user. This number is still growing. In this paper, we used 250K lines of data which is specific to LTE networks. This dataset is made of 30% of labeled data and 70% of unlabeled data. The collection has been performed in many different indoor and outdoor environments. Indoor corresponds to the following locations: at home, in restaurant, in cafe, at work or in other types of building, etc. Whereas, outdoor is associated to forest, streets, parks, mountain and beach, to a pedestrian, a running user, or a user in car moving with high speed, etc. The gathering was done in many cities and places like countryside, small cities, metropolis, and different countries, but for this paper we are only studying data collected in France. This long collection period allows us to have data reflecting all weather types.

IV. MOBILITY AND DISTANCE IMPACT ON IOD

In this section, we study the impact of adding two additional input features referred to as M I and T A to existing RSRP and CQI on IOD performance. M I and T A represent respectively the mobility type and the distance between user and eNB. To allow fair comparison, the evaluation is done with the classical ML algorithms, used in [START_REF] Ray | Localization of LTE measurement records with missing information[END_REF], i.e., Support Vector Machine (SVM), with a supervised Deep Learning and with a clustering algorithm.

RSRP and CQI are radio metrics directly linked to the mobile environment as they represent the extent of environment attenuation. But, using them is not enough to correctly classify some ambiguous points. For example, consider a mobile user travelling in train. The user is considered as outdoor meanwhile the received signal strength is bad because of not only the high speed of the train (Doppler effect), but also because of the surrounding structures. Indeed, the metal windows and carriages of train cause a significant attenuation of radio signal power. For example, in 2 GHz frequency band, the penetration losses from train carriages are usually in the range of 20 to 35 dB [START_REF] Laiyemo | High speed moving networks in future wireless systems[END_REF]. Figure 2 shows the indoor and outdoor Cumulative Distribution Functions (CDFs) derived from the crowdsourced data. The blue curve with full-line represents the data taken in normal speed: only from static locations and low speed points. The blue dotted curve depicts the data collected from all mobile locations (normal, medium, high). We note that the dotted line is closer to the indoor CDF. This leads to superimposed points located at the beginning of the tails of both the CDFs. These overlapped points are coming mainly from either deep indoor positions or high or medium speed mobile positions, thus, creating ambiguity.

Figure 3 shows the CDF of mobility indicator values for different environments (car, pedestrian, buildings, train, mall, bus) for T CR max = 100s. As expected, the curves imply that the number of cell ID changes (N CID) is correlated with the environment type. Indeed, the indoor user (e.g. in buildings) either doesn't change cells or changes very few when he is located at borders of multiple cells. However, when he moves outdoor (e.g in transportation), the number of handovers increases as he covers large distances. Note that the figure implies that N CID is smaller in pedestrian case than in mall case. One reason is that in some cases relatively longer walks occur in malls. Consequently, the insertion of T A and M I should eliminate the ambiguities as they provide additional information that is relevant to the IOD system. Indeed, with them the IOD system exploits information on the distance of mobile users from the base station and is aware of their mobility type, respectively. On one hand, T A would help to classify the ambiguous points which correspond to, for e.g., measurement points with low RSRP , but near to eNB. On the other hand, the user mobility highly correlated to the user environment will ease the classification of outdoor measurement points with low RSRP , for example, while inside a high speed train. The indoor user moves slowly as compared to outdoor where he can move more quickly. Therefore, using both the additional signals can help to classify the ambiguous measurement points and improve the overall performance of the supervised classifier. However, a question may be asked: how much do these parameters contribute to IOD performance? Figure 4 depicts the relative optimal ordering of the four input features related to their relevance for IOD. The order of these items is obtained using "Extra-Trees-Classifier" algorithm. It reveals that RSRP will contribute most to the IOD performance. The ranking scores of T A and M I are close. They thus impact the IOD performance almost identically. Furthermore, both signals together will contribute a little higher than RSRP and CQI combined. Thus, an improvement in IOD performance is expected by introducing these two additional parameters. For the evaluation, the comparison is done in three cases where different structures of learning datasets are examined: the first one contains only RSRP and CQI, the second one includes in addition the timing advance, but not the mobility indicator and the third one includes all data. In each case, the model is trained and evaluated on labeled data which is split into two subsets composed of 70% of data for training and 30% of data for tests and validation. As shown in the table I the user mobility is correlated to the IOD issue. Added to RSRP and CQI, T A and M I enhance the classical machine learning performance with up to 8% of gain, approximately. We note also that Deep Learning (DL) outperforms the other classical machine learning algorithms.

V. DEEP LEARNING-BASED SEMI-SUPERVISED

APPROACHES

Assuming that we have a sufficiently powerful learning algorithm, one of the most reliable ways to get better performance is to feed the algorithm with more data. Indeed, the quality of the model is generally constrained by the quality and the volume of the training data. DL and other modern nonlinear machine learning techniques get better with more data. Thus, there is a need to look for a way to enlarge volume of the training data. The idea is then to use unlabeled data, which is easy to obtain, and mix it with available labeled data, which is costly to obtain, for classifier training. Hybrid and semi-supervised approaches are the best candidates for this.

In this section, we compare and discuss the IOD performance using semi-supervised IOD approaches. As investigated in [START_REF] Zhu | Introduction to semisupervised learning[END_REF], [START_REF] Zhu | Semi-supervised learning literature survey[END_REF], [START_REF] Zhou | Tri-training: Exploiting unlabeled data using three classifiers[END_REF], [START_REF] Zhu | Semi-supervised learning with graphs[END_REF], [START_REF] Radu | A semi-supervised learning approach for robust indoor-outdoor detection with smartphones[END_REF], we consider the 3 classic following approaches of hybrid learning that make use of both the labeled and the unlabeled data:

• Cluster-then-label: a clustering method is used to label the unlabeled data. • Co-training: multiple supervised classifiers learn from each other's outputs. • Self-training: a supervised classifier trained on a small labeled dataset learns iteratively from its own classification of additional unlabeled data. We evaluate the above 3 ways of learning using our dataset. Let S T otal be the total dataset made of:

S T otal = S Labeled ∪ S U nlabeled
where S Labeled ∈ R 6 is the subset of the labeled data and S U nlabeled ∈ R 5 is the subset of the unlabeled data. Note that Card (S U nlabeled ) ≈ 3 × Card (S Labeled ) in case of our collected data, where Card() gives the number of data points. For the performance evaluation on new environments unknown to the classifier we use S T est ∈ R 6 , where S T est ⊂ S T otal and Card (S T est ) ≈ 1 3 × Card (S Labeled ). A. Cluster-then-Label

Our proposed system of Cluster Then Label (CTL) approach is composed of two main modules described in Figure 5. The first module handles the unlabeled data by applying a clustering algorithm on S T otal to make emerge 2 clusters: indoor and outdoor. We use labels of S Labeled , as well as the priori information that users are much more indoor than outdoor, to associate labels to S U nlabeled . Then an optimizer is used to correct the wrong labels, as much as it can, during the clustering phase. For correction, we use the idea that a user can not change his environment twice in 30 seconds. This is because a user cannot change its environment two times so quickly. The second module uses S Labeled and also newly labeled data of S U nlabeled to train a supervised classifier. We evaluate the CTL method with different clustering algorithms (K-Means, Expectation Maximization, Hierarchical Clustering, Bayesian Gaussian Mixture (BGM)). BGM showed better performance and was retained. Deep feed forward neuronal Network (DL) was used as the supervised classifier.

B. Co-Training

In general, the Co-training (CT) approach explores the results of two or more classifiers at the same time. There are many implementations of the CT according to the needs and the use cases. However the most common one splits the dataset vertically according to features (signals in our case) and thus forming feature-based sub-datasets. As shown in Figure 6, two DL classifiers are trained on S Labeled data. Then each data instance in S U nlabeled is classified by the two classifiers and the intersection result with high classification probability is used to retrain and improve a final DL. The assumption is that the classifiers working with different sets of features are able to complement each other. The main issue of CT is how to split data vertically to form the subsets that have the same amount of information. The idea is that if there are 2 primary features and 2 other secondary ones, then we will build subsets in a way that each subset has a primary feature and a secondary one. This guarantees that each subset and the associated classifier has its fair share of effective features to attain good performance.

C. Self-training

The Self-Training (ST) approach is one of the semisupervised learning methods that alternatively repeats classifier training and labeling unlabeled data in training set. The main issue with ST approach is the amplification of error while labeling the unlabeled data. That means if the current trained classifier makes errors while classifying the unlabeled data then the wrong label of the unlabeled data will provide an inaccurate information for the classifier of the next step [START_REF] Lee | Deep neural network self-training based on unsupervised learning and dropout[END_REF]. By iterating these two steps, the overall error of the final classifier will become larger. To remedy this error amplification phenomenon and to have a generic classifier, we propose a data selection system between the two phases (Figure 7). To eliminate the wrongly labeled data, we again apply the assumption that a user can not change his environment twice in 30 seconds. A label is therefore considered wrong if in 30s the user goes from environment 1 to environment 2 and then from environment 2 to environment 1 again. Thus, we eliminate this labeling error. We also delete data that was classified with a low classification probability. That means, we eliminate data that was classified with a classification probability lower than 65%. This threshold of 65% was fixed after a statistical study to avoid both risks of over-fitting or error amplification. For the IOD study, we set Deep Learning hyper-parameters obtained from a GridSearch algorithm (which simply is an exhaustive search through a manually specified subset of the hyper-parameter for Deep Learning). We have used both scikitlearn and keras in python for the implementation. The DL module is a feed forward neuronal network (fully connected) with 8 hidden Layers using tanh as the activation function. Actually, tanh is one of the widely used activation function while designing neural networks today. It is used mainly in classification tasks which will lead to faster training process and convergence. As for the last layer (the output layer) we used a sigmoid activation function to smooth the results since we look for a binary classification either 0 or 1 (for indoor/outdoor environments). This experimentation performed on the S Labeled data, provides a DL model with 95.30% of F 1 -score. The model is saved and will serve as an initialization for the next training steps.

The 3 learning approaches -CTL, CT and ST -are evaluated on S T otal by computing each-time the F 1 -score and the accuracy of each approach. The performance evaluation is carried out on both S T est and S Labeled . As shown in table II, CTL has the lowest F 1 -score compared to CT and ST. This is explained by the fact that more the unlabeled data volume increases, the more the performance of the supervised DL gets limited to the clustering performance and errors. In our case, the BGM cluster used has a F 1-score of 79.71%, which gives a low F 1-score of CTL. CT and ST show close performances with slightly better performance of the latter since both the DL classifiers trained with their own tagged data subsets provide the same F 1 -score of average of 85%. However, CT is very complex and greedy in resource use. CT takes lot of training time as it deals with 3 neuronal networks. Therefore ST is the best choice for IOD system since on the first phase (trained only on labeled data) as well as the last phase (trained on both labeled and unlabeled data) has showed the best performances reaching an F 1 -score of 96.18%. In this section, we discuss the impact of data volume and optimization of the semi-supervised self-training. Here, we address an important question: how much percentage of labeled data is needed for target satisfactory performance of the IOD system?

To answer this question, a study of the impact of the labeled data and unlabeled data volume on the training model is conduced. For this, F 1 -score is evaluated for various S U nlabeled and S Labeled which leads to two scenarios:

• Scenario 1: S Labeled is fixed and the volume of the S U nlabeled is variable. The ST training performance is evaluated progressively according to the percentage of unlabeled data which reaches at maximum 72.69%. • Scenario 2: S U nlabeled is fixed and the volume of the S Labeled is variable. The ST training performance is evaluated progressively according to the percentage of labeled data which reaches at maximum 27.31%. Results of both scenarios are shown in Figure 8. The figure plots the F 1 -score values of ST versus the size ratio between S Labeled or S U nlabeled and S T otal . The double Xaxis refers then to the percentage of labeled data (the bottom X-axis) or unlabeled data (the top X-axis) related to the total volume of data. As expected, the addition of unlabeled data improves the IOD system performances. In scenario 1, ST uses all the labeled data and a variable part of unlabeled data. F 1 -score increases with the size of S U nlabeled . However, there is only moderate improvement. By using all of the labeled data ST starts already at 95% to converge toward 96, 18%. This state corresponds to the case where all the unlabeled data is used. The information brought by all S Labeled data is sufficiently rich. This is unlike the scenario II, where the F 1-score augmentation is more pronounced. Availability of less S Labeled is realistic assumption as collecting labeled data is expensive. In any case the labeled data contains more relevant information. If the mobile operator targets an error percentage of 5% for IOD (namely F 1 -score = 95%), the red curve indicates that a distribution of 20% and 80% of labeled and unlabeled data respectively is sufficient for the training phase. Consequently, the mobile network operators wanting to implement IOD inside their network may use similar percentages of labeled and unlabeled data during the updating phase of IOD learning model. They may need to manually label only 20% of collected data. During online labeling it enables first to alleviate the network overhead by limiting the amount of UL signalling (all labels) sent to eNB and, secondly, reduces the complexity and the required time for tagging data. In this paper we studied the IOD problem from the network side. We used a ML approach using 3GPP signals. The inputs are: RSRP , CQI, T A, M I. We first showed the importance of this judicious choice of inputs. The addition of T A and M I to the (RSRP, CQI) couple has shown an improvement of 10% in the overall performance of IOD. A diversified partially labeled dataset was used for evaluation. It allows to be as close as possible to the real behaviors of mobile users in daily life. Secondly, in order to exploit also the unlabeled data, a comparative study of semi-supervised approaches was conducted. It showed that Self-Training approach is the best one for IOD. The ST training model obtained with a sharing of (20%,80%) between labeled and unlabeled data provides a F 1 -score of 95%. Such an evaluation -namely the required sharing between labeled and unlabeled data for a target IOD performance -could be of interest to operators. Avoiding to tag all data strongly reduces the labeling efforts and constraints for the operators wanting to implement IOD algorithm. ST can thus perform well without requiring a complete labeling of data. In future, we plan to extend our work to user behavior contextualization by investigating other user behavior attributes.

Fig. 1 .

 1 Fig. 1. Time to cross a cell (s) vs. user speed (km/h)

Fig. 2 .

 2 Fig. 2. Empirical CDFs for measured RSRP . (full) outdoor static and low speed, (dotted) outdoor variable speed.

Fig. 3 .

 3 Fig. 3. Cumulated Distribution Function of the mobility indicator vs. Environment -one user

Fig. 4 .

 4 Fig. 4. Feature ranking based on cumulative information brought by them.

Fig. 5 .

 5 Fig. 5. The Cluster-then-label semi supervised approach model

Fig. 6 .

 6 Fig. 6. The Co-Training (CT) semi supervised approach model We analyzed the features in consideration with different machine learning techniques. According to Figure 4 we divided the whole dataset vertically to two subsets composed of (1) [RSRP, CQI, Class] and (2) [T A, M I, Class]. This vertical division ensures the same information weight ( 50%), so we offer a fair opportunity of equal learning to both the DL classifiers. After the training phase of these two classifiers, we apply the same vertical division on the S U nlabeled set in order to predict their labels. Each data instance is classified by the two different classifiers. For the final step, only the intersection between resulting labels of two classifiers is kept. The kept part of S U nlabeled added to S labeled are used to train and improve the final DL.

Fig. 7 .

 7 Fig. 7. The Self-taught/Self training semi supervised approach model VI. EXPERIMENTAL SETUP AND RESULTS In ML language we call hyper-parameters the set of parameters that are fixed before the training phase. By contrast, the values of other parameters are derived via training. In Deep Learning, the hyper-parameter optimization is the biggest challenge because of their high number. On top of that, individual models can be very slow to train mainly when training from scratch. Actually, there are two ways for training neural networks: (1) either to train from scratch or (2) to use a pre-trained model/ architecture/ weights/ to initialize training. The multi-layer neural network requires a large amount of computational resources for training. Thus, the pre-trained option is needed for faster training of the neural network. Using weights from pre-training models suffers from a problem that it may aggravate error amplification because misconception generated during previous self-training phase is propagated to the next self-training phase. To avoid the overfitting we set a dropout layer to regularize the learning in the next-training phase.For the IOD study, we set Deep Learning hyper-parameters obtained from a GridSearch algorithm (which simply is an exhaustive search through a manually specified subset of the hyper-parameter for Deep Learning). We have used both scikitlearn and keras in python for the implementation. The DL module is a feed forward neuronal network (fully connected) with 8 hidden Layers using tanh as the activation function. Actually, tanh is one of the widely used activation function while designing neural networks today. It is used mainly in classification tasks which will lead to faster training process and convergence. As for the last layer (the output layer) we used a sigmoid activation function to smooth the results since we look for a binary classification either 0 or 1 (for indoor/outdoor environments). This experimentation performed on the S Labeled data, provides a DL model with 95.30% of F 1 -score. The model is saved and will serve as an initialization for the next training steps.The 3 learning approaches -CTL, CT and ST -are evaluated on S T otal by computing each-time the F 1 -score and the accuracy of each approach. The performance evaluation is

Fig. 8 .

 8 Fig. 8. Data volume Impact: (Blue Line) Scenario 1: Variation of the S U nlabaled volume. (Red Line) Scenario 2: Variation of S Labeled volume

TABLE I CLUSTERING

 I AND SUPERVISED CLASSIFICATION PERFORMANCE: ACCURACY & F1-SCORE VS. TIME ADVANCE & MOBILITY INDICATOR