Currently, cellular networks are overloaded with mobile data traffic due to the rapid growth of mobile broadband subscriptions and the increasing popularity of applications for smartphones. One possible solution to alleviate this problem is the offloading of mobile data traffic from the primary access technology to the WiFi infrastructure to gain extra capacity and improve the overall network performance. As the strategy what and when to offload data is non-trivial, it is of vital importance to develop novel algorithms to guide this process. This paper addresses solutions for WiFi offloading in Long Term Evolution (LTE) cellular networks when performance needs exceed the capability of the LTE access. It then compares the performance of each access technology using different network performance metrics. In detail, an optimized Signal-to-noise ratio (SNR)-threshold based handover solution and extension to the 3 rd Generation Partnership Project (3GPP) standard for Access Network Discovery and Selection Function (ANDSF) framework for WiFi offloading is proposed. Our simulation results have shown that ANDSF discovery can be used to control the amount of offloading.

This dissertation is briefly organized as follows.Chapter 1 Introduction : provides a detailed overview of motivation, and the use cases that are relevant to this dissertation. It also describes the scientific methods used in this dissertation.Chapter 2 Summary and Contributions : presents a brief summary of the included papers in this dissertation and their scientific contributions which have been published in journals and international conferences.Chapter 3 Background : sketches some of the basic contextual background information that provides an overview of the background relevant to the reader and challenges that are dealt with in the research.Chapter 4 Related Work : presents a summary of the relevant state-of-the-art related works found in the literature for the three main use cases we presented in Chapter 1.Chapter 5 Conclusions : provides a summary of the research and highlights the main contributions of the dissertation. This chapter also provides considerations of the research and suggestions for promising future research directions.

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Long Short-Term Memory (LSTM) neural networks are a state-of-the-art techniques when it comes to sequence learning and time series prediction models. In this paper, we have used LSTM-based Recurrent Neural Networks (RNN) for building a generic prediction model for Transmission Control Protocol (TCP) connection characteristics from passive measurements. To the best of our knowledge, this is the first work that attempts to apply LSTM for demonstrating how a network operator can identify the most important system-wide TCP per-connection states of a TCP client that determine a network condition (e.g., cwnd) from passive traffic measured at an intermediate node of the network without having access to the sender. We found out that LSTM learners outperform the state-of-the-art classical machine learning prediction models. Through an extensive experimental evaluation on multiple scenarios, we demonstrate the scalability and robustness of our approach and its potential for monitoring TCP transmission states related to network congestion from passive measurements. Our results based on emulated and realistic settings suggest that Deep Learning is a promising tool for monitoring system-wide TCP states from passive measurements and we believe that the methodology presented in our paper may strengthen future research work in the computer networking community.