During the last decades, different studies highlighted the benefits of acquiring channel state cognition based on the environmental context of mobile users or devices. Thanks to this cognition, cellular networks can optimize themselves and personalize the delivered services and in turn, offer a better quality of experience to users. This benefit for mobile networks will come only if the environments are detected with high accuracy, short delays and minimal implementation cost. However, accurate environment detection is challenging for mobile networks as reallife situations are numerous, complex and dynamic. In this paper, we investigate the detection of mobile users' environment, in real-life situations, using machine learning classification methods on time series data. To attain the highest accuracy, while using limited length of time series, we propose using a heuristic method to account for the typical user behavior when he or she changes environment. For this, a new module, called User Behavioral Optimizer, is investigated and combined with time series models. It detects erroneous user behaviour predictions output by the machine learning models and then corrects some of them. Experiments are done using real radio data, that has been massively gathered from diverse real situations of mobile users. Experiments show that machine learning on time series data using our behavioral optimizer and heuristic allows to detect indoor/outdoor with F 1 − score , up to around 94.8%.

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Cognition of user behavior can make future mobile networks more intelligent and flexible. Knowledge about users' habits can be used to personalize services and intelligently manage network resources. However, inferring this key information with a low-cost signaling implementation, and avoiding constant user interaction, is crucial for Mobile Network Operators (MNOs). With this motivation, this paper investigates the detection of the real-life mobile user environment using contextaware detection via multi-task learning (MTL).We propose models that are able to automatically detect up to eight distinct real-life user environments. We also improve the detection accuracy with the assistance of the mobility state profiling task. We associate both environment and mobility tasks because they correspond to the main attributes of user behavior and, additionally, both of them are correlated. Using MTL, the task of detecting environment corresponds to simultaneously answering the questions: "how and where mobile user consumes mobile services?".We build models using real-life radio data which is already available in network. This data has been massively gathered from multiple diversified situations of mobile users. Simulation results support our claim to detect several environment classes in network infrastructure with improved UED accuracy.