Cell identity allocation and optimisation of handover parameters in self-organised LTE femtocell networks

A thesis submitted to the University of Bedfordshire in partial ful lment of the requirements for the degree of Doctor of PhilosophyFemtocell is a small cellular base station used by operators to extend indoor service coverage and enhance overall network performance. In Long Term Evolution (LTE), femtocell works under macrocell coverage and combines with the macrocell to constitute the two-tier network. Compared to the traditional single-tier network, the two-tier scenario creates many new challenges, which lead to the 3rd Generation Partnership Project (3GPP) implementing an automation technology called Self-Organising Network (SON) in order to achieve lower cost and enhanced network performance. This thesis focuses on the inbound and outbound handovers (handover between femtocell and macrocell); in detail, it provides suitable solutions for the intensity of femtocell handover prediction, Physical Cell Identity (PCI) allocation and handover triggering parameter optimisation. Moreover, those solutions are implemented in the structure of SON. In order to e ciently manage radio resource allocation, this research investigates the conventional UE-based prediction model and proposes a cell-based prediction model to predict the intensity of a femtocell's handover, which overcomes the drawbacks of the conventional models in the two-tier scenario. Then, the predictor is used in the proposed dynamic group PCI allocation approach in order to solve the problem of PCI allocation for the femtocells. In addition, based on SON, this approach is implemented in the structure of a centralised Automated Con guration of Physical Cell Identity (ACPCI). It overcomes the drawbacks of the conventional method by reducing inbound handover failure of Cell Global Identity (CGI). This thesis also tackles optimisation of the handover triggering parameters to minimise handover failure. A dynamic hysteresis-adjusting approach for each User Equipment (UE) is proposed, using received average Reference Signal-Signal to Interference plus Noise Ratio (RS-SINR) of the UE as a criterion. Furthermore, based on SON, this approach is implemented in the structure of hybrid Mobility Robustness Optimisation (MRO). It is able to off er the unique optimised hysteresis value to the individual UE in the network. In order to evaluate the performance of the proposed approach against existing methods, a System Level Simulation (SLS) tool, provided by the Centre for Wireless Network Design (CWiND) research group, is utilised, which models the structure of two-tier communication of LTE femtocell-based networks

Seamless coverage for the next generation wireless communication networks

Data demand has exponentially increased due to the rapid growth of wireless and mobile devices traffic in recent years. With the advent of the fifth generation, 5G, and beyond networks, users will be able to take advantage of additional services beyond the capability of current wireless networks while maintaining a highquality experience. The exploitation of millimeter-wave (mm-wave) frequency in 5G promises to meet the demands of future networks with the motto of providing high data rate coverage with low latency to its users, which will allow future networks to function more efficiently. However, while planning a network using mm-wave frequencies, it is important to consider their small coverage footprints and weak penetration resistance. Heterogeneous network planning with the dense deployment of the small cells is one way of overcoming these issues, yet, without proper planning of the integrated network within the same or different frequencies could lead to other problems such as coverage gaps and frequent handovers; due to the natural physics of mm-wave frequencies. Therefore this thesis focuses on bringing ultra-reliable low-latency communication for mm-wave indoor users by increasing the indoor coverage and reducing the frequency of handovers. Towards achieving this thesis’s aim, a detailed literature review of mm-wave coverage is provided in Chapter 2. Moreover, a table that highlights the penetration loss of materials at various frequencies is provided as a result of thorough research in this field, which will be helpful to the researchers investigating this subject. According to our knowledge, this is the first table presenting the most studies that have been conducted in this field. Chapter 3 examines the interference effect of the outdoor base station (BS) inside the building in the context of a heterogeneous network environment. A single building model scenario is created, and the interference analysis is performed to observe the effects of different building materials used as walls. The results reveal the importance of choosing the material type when outdoor BS is close to the building. Moreover, the interference effect of outdoor BS should be minimized when the frequency re-use technique is deployed over very short distances. Chapter 4 presents two-fold contributions, in addition to providing a comprehensive handover study of mm-wave technology. The first study starts with addressing the problem of modelling users’ movement in the indoor environment. Therefore, a user-based indoor mobility prediction via Markov chain with an initial transition matrix is proposed, acquired from Q-learning algorithms. Based on the acquired knowledge of the user’s mobility in the indoor environment, the second contribution of this chapter provides a pre-emptive handover algorithm to provide seamless connection while the user moves within the heterogeneous network. The implementation and evaluation of the proposed algorithm show a reduction in the handover signalling costs by more than 50%, outperforming conventional handover algorithms. Lastly, Chapter 5 contributes to providing robust signal coverage for coverage blind areas and implementing and evaluating the proposed handover algorithm with the intelligent reflective surface. The results show a reduction in the handover signalling costs by more than 33%, outperforming conventional handover algorithms with the pre-emptive handover initiation

Quality aspects of Internet telephony

Internet telephony has had a tremendous impact on how people communicate. Many now maintain contact using some form of Internet telephony. Therefore the motivation for this work has been to address the quality aspects of real-world Internet telephony for both fixed and wireless telecommunication. The focus has been on the quality aspects of voice communication, since poor quality leads often to user dissatisfaction. The scope of the work has been broad in order to address the main factors within IP-based voice communication. The first four chapters of this dissertation constitute the background material. The first chapter outlines where Internet telephony is deployed today. It also motivates the topics and techniques used in this research. The second chapter provides the background on Internet telephony including signalling, speech coding and voice Internetworking. The third chapter focuses solely on quality measures for packetised voice systems and finally the fourth chapter is devoted to the history of voice research. The appendix of this dissertation constitutes the research contributions. It includes an examination of the access network, focusing on how calls are multiplexed in wired and wireless systems. Subsequently in the wireless case, we consider how to handover calls from 802.11 networks to the cellular infrastructure. We then consider the Internet backbone where most of our work is devoted to measurements specifically for Internet telephony. The applications of these measurements have been estimating telephony arrival processes, measuring call quality, and quantifying the trend in Internet telephony quality over several years. We also consider the end systems, since they are responsible for reconstructing a voice stream given loss and delay constraints. Finally we estimate voice quality using the ITU proposal PESQ and the packet loss process. The main contribution of this work is a systematic examination of Internet telephony. We describe several methods to enable adaptable solutions for maintaining consistent voice quality. We have also found that relatively small technical changes can lead to substantial user quality improvements. A second contribution of this work is a suite of software tools designed to ascertain voice quality in IP networks. Some of these tools are in use within commercial systems today

Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks

Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time. The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks. The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile users’ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks. The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages users’ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability. The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage. The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs. The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users. The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks

Outdoor location tracking of mobile devices in cellular networks

This paper presents a technique and experimental validation for anonymous outdoor location tracking of all users residing on a mobile cellular network. The proposed technique does not require any intervention or cooperation on the mobile side but runs completely on the network side, which is useful to automatically monitor traffic, estimate population movements, or detect criminal activity. The proposed technique exploits the topology of a mobile cellular network, enriched open map data, mode of transportation, and advanced route filtering. Current tracking algorithms for cellular networks are validated in optimal or controlled environments on a small dataset or are merely validated by simulations. In this work, validation data consisting of millions of parallel location estimations from over a million users are collected and processed in real time, in cooperation with a major network operator in Belgium. Experiments are conducted in urban and rural environments near Ghent and Antwerp, with trajectories on foot, by bike, and by car, in the months May and September 2017. It is shown that the mode of transportation, smartphone usage, and environment impact the accuracy and that the proposed AMT location tracking algorithm is more robust and outperforms existing techniques with relative improvements up to 88%. Best performances were obtained in urban environments with median accuracies up to 112 m

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable