Optimization of Mobility Parameters using Fuzzy Logic and Reinforcement Learning in Self-Organizing Networks

In this thesis, several optimization techniques for next-generation wireless networks are proposed to solve different problems in the field of Self-Organizing Networks and heterogeneous networks. The common basis of these problems is that network parameters are automatically tuned to deal with the specific problem. As the set of network parameters is extremely large, this work mainly focuses on parameters involved in mobility management. In addition, the proposed self-tuning schemes are based on Fuzzy Logic Controllers (FLC), whose potential lies in the capability to express the knowledge in a similar way to the human perception and reasoning. In addition, in those cases in which a mathematical approach has been required to optimize the behavior of the FLC, the selected solution has been Reinforcement Learning, since this methodology is especially appropriate for learning from interaction, which becomes essential in complex systems such as wireless networks. Taking this into account, firstly, a new Mobility Load Balancing (MLB) scheme is proposed to solve persistent congestion problems in next-generation wireless networks, in particular, due to an uneven spatial traffic distribution, which typically leads to an inefficient usage of resources. A key feature of the proposed algorithm is that not only the parameters are optimized, but also the parameter tuning strategy. Secondly, a novel MLB algorithm for enterprise femtocells scenarios is proposed. Such scenarios are characterized by the lack of a thorough deployment of these low-cost nodes, meaning that a more efficient use of radio resources can be achieved by applying effective MLB schemes. As in the previous problem, the optimization of the self-tuning process is also studied in this case. Thirdly, a new self-tuning algorithm for Mobility Robustness Optimization (MRO) is proposed. This study includes the impact of context factors such as the system load and user speed, as well as a proposal for coordination between the designed MLB and MRO functions. Fourthly, a novel self-tuning algorithm for Traffic Steering (TS) in heterogeneous networks is proposed. The main features of the proposed algorithm are the flexibility to support different operator policies and the adaptation capability to network variations. Finally, with the aim of validating the proposed techniques, a dynamic system-level simulator for Long-Term Evolution (LTE) networks has been designed

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Multi-Connectivity Management and Orchestration Architecture Integrated With 5g Multi Radio Access Technology Network

The significant growth in the number of devices and the tremendous boost in network/user traffic types and volume as well as the efficiency constraints of 4G innovations have encouraged industry efforts and also financial investments towards defining, developing, and releasing systems for the fifth generation. The 5G of mobile broadband wireless networks with multiple Radio Access Technologies (Multi-RATs) have actually been designed to satisfy the system and service requirements of the existing as well as the coming applications. The multi-RAT access network is considered the key enabling technology to satisfy these requirements based on low latency, high throughput. To utilize all available network resources efficiently, research activities have been proposed on multi-connectivity to connect, split, steer, switch, and orchestrate across multiple RATs. Recently, multi-connectivity management and orchestration architecture standardization has just started; therefore, further study and research is needed. This project proposed a multi-connectivity management and orchestration architecture integrated with 5G, Long-Term Evolution (LTE), and Wireless LANs (WLAN) technologies. The simulations experiments conducted to measure the Quality of Experience (QoE) by provisioning network resources efficiently, which are: data rate, latency, bit error rate. The results show that the 5G requirements have been achieved with latency and throughput around 1ms and 200 Mbps, respectively

View on 5G Architecture: Version 2.0

The 5G Architecture Working Group as part of the 5GPPP Initiative is looking at capturing novel trends and key technological enablers for the realization of the 5G architecture. It also targets at presenting in a harmonized way the architectural concepts developed in various projects and initiatives (not limited to 5GPPP projects only) so as to provide a consolidated view on the technical directions for the architecture design in the 5G era. The first version of the white paper was released in July 2016, which captured novel trends and key technological enablers for the realization of the 5G architecture vision along with harmonized architectural concepts from 5GPPP Phase 1 projects and initiatives. Capitalizing on the architectural vision and framework set by the first version of the white paper, this Version 2.0 of the white paper presents the latest findings and analyses with a particular focus on the concept evaluations, and accordingly it presents the consolidated overall architecture design

Mobile Robots Navigation

Mobile robots navigation includes different interrelated activities: (i) perception, as obtaining and interpreting sensory information; (ii) exploration, as the strategy that guides the robot to select the next direction to go; (iii) mapping, involving the construction of a spatial representation by using the sensory information perceived; (iv) localization, as the strategy to estimate the robot position within the spatial map; (v) path planning, as the strategy to find a path towards a goal location being optimal or not; and (vi) path execution, where motor actions are determined and adapted to environmental changes. The book addresses those activities by integrating results from the research work of several authors all over the world. Research cases are documented in 32 chapters organized within 7 categories next described

Predicting and Recovering Link Failure Localization Using Competitive Swarm Optimization for DSR Protocol in MANET

Portable impromptu organization is a self-putting together, major construction-less, independent remote versatile hub that exists without even a trace of a determined base station or government association. MANET requires no extraordinary foundation as the organization is unique. Multicasting is an urgent issue in correspondence organizations. Multicast is one of the effective methods in MANET. In multicasting, information parcels from one hub are communicated to a bunch of recipient hubs all at once, at a similar time. In this research work, Failure Node Detection and Efficient Node Localization in a MANET situation are proposed. Localization in MANET is a main area that attracts significant research interest. Localization is a method to determine the nodes’ location in the communication network. A novel routing algorithm, which is used for Predicting and Recovering Link Failure Localization using a Genetic Algorithm with Competitive Swarm Optimization (PRLFL-GACSO) Algorithm is proposed in this study to calculate and recover link failure in MANET. The process of link failure detection is accomplished using mathematical modelling of the genetic algorithm and the routing is attained using the Competitive Swarm optimization technique. The result proposed MANET method makes use of the CSO algorithm, which facilitates a well-organized packet transfer from the source node to the destination node and enhances DSR routing performance. Based on node movement, link value, and endwise delay, the optimal route is found. The main benefit of the PRLFL-GACSO Algorithm is it achieves multiple optimal solutions over global information. Further, premature convergence is avoided using Competitive Swarm Optimization (CSO). The suggested work is measured based on the Ns simulator. The presentation metrix are PDR, endwise delay, power consumption, hit ratio, etc. The presentation of the proposed method is almost 4% and 5% greater than the present TEA-MDRP, RSTA-AOMDV, and RMQS-ua methods. After, the suggested method attains greater performance for detecting and recovering link failure. In future work, the hybrid multiway routing protocols are presented to provide link failure and route breakages and liability tolerance at the time of node failure, and it also increases the worth of service aspects, respectively