Optimización de problemas de varios objetivos desde un enfoque de eficiencia energética aplicado a redes celulares heterogéneas 5G usando un marco de conmutación de celdas pequeñas

This Ph.D. dissertation addresses the Many-Objective Optimization Problem (MaOP) study to reduce the inter-cell interference and the power consumption for realistic Centralized, Collaborative, Cloud, and Clean Radio Access Networks (C-RANs). It uses the Cell Switch-Off (CSO) scheme to switch-off/on Remote Radio Units (RRUs) and the Coordinated Scheduling (CS) technique to allocate resource blocks smartly. The EF1-NSGA-III (It is a variation of the NSGA-III algorithm that uses the front 1 to find extreme points at the normalization procedure extended in this thesis) algorithm is employed to solve a proposed Many-Objective Optimization Problem (MaOP). It is composed of four objective functions, four constraints, and two decision variables. However, the above problem is redefined to have three objective functions to see the performance comparison between the NSGA-II and EF1-NSGA-III algorithms. The OpenAirInterface (OAI) platform is used to evaluate and validate the performance of an indoor coverage system because most of the user-end equipment of next-generation cellular networks will be in an indoor environment. It constitutes the fastest growing 5G open-source platform that implements 3GPP technology on general-purpose computers, fast Ethernet transport ports, and Commercial-Off-The-Shelf (COTS) software-defined radio hardware. This document is composed of five contributions. The first one is a survey about testbed, emulators, and simulators for 4G/5G cellular networks. The second one is the extension of the KanGAL's NSGA-II code to implement the EF1-NSGA-III, adaptive EF1-NSGA-III (A-EF1-NSGA-III), and efficient adaptive EF1-NSGA-III (A$^2$-EF1-NSGA-III). They support up to 10 objective functions, manage real, integer, and binary decision variables, and many constraints. The above algorithms outperform other works in terms of the Inverted Generational Distance (IGD) metric. The third contribution is the implementation of real-time emulation methodologies for C-RANs using a frequency domain representation in OAI. It improves the average computation time 10-fold compared to the time domain without using Radio Frequency hardware and avoids their uncertainties. The fourth one is the implementation of the Coordination Scheduling (CS) technique as a proof-of-concept to validate the advantages of frequency domain methodologies and to allocate resource blocks dynamically among RRUs. Finally, a many-objective optimization problem is defined and solved with evolutionary algorithms where diversity is managed based on crowded-distance and reference points to reduce the power consumption for C-RANs. The solutions obtained are considered to control the scheduling task at the Radio Cloud Center (RCC) and to switch RRUs.Este disertación aborda el estudio del problema de optimización de varios objetivos (MaOP) para reducir la interferencia entre células y el consumo de energía para redes de acceso de radio en tiempo real, colaborativas, en la nube y limpias (C-RAN). Utiliza el esquema de conmutacion de celdas (CSO) para apagar / encender unidades de radio remotas (RRU) y la técnica de programación coordinada (CS) para asignar bloques de recursos de manera inteligente. El algoritmo EF1-NSGA-III (es una variación del algoritmo NSGA-III que usa el primer frente de pareto para encontrar puntos extremos en el procedimiento de normalización extendido en esta tesis) se utiliza para resolver un problema de optimización de varios objetivos (MaOP) propuesto. Se compone de cuatro funciones objetivos, cuatro restricciones y dos variables de decisión. Sin embargo, el problema anterior se redefine para tener tres funciones objetivas para ver la comparación de rendimiento entre los algoritmos NSGA-II y EF1-NSGA-III. La plataforma OpenAirInterface (OAI) se utiliza para evaluar y validar el rendimiento de un sistema de cobertura en interiores porque la mayoría del equipos móviles de las redes celulares de próxima generación estarán en un entorno interior. Ella constituye la plataforma de código abierto 5G de más rápido crecimiento que implementa la tecnología 3GPP en computadoras de uso general, puertos de transporte Ethernet rápidos y hardware de radio definido por software comercial (COTS). Este documento se compone de cinco contribuciones. La primera es una estudio sobre banco de pruebas, emuladores y simuladores para redes celulares 4G / 5G. El segundo es la extensión del código NSGA-II de KanGAL para implementar EF1-NSGA-III, EF1-NSGA-III adaptativo (A-EF1-NSGA-III) y EF1-NSGA-III adaptativo eficiente (A $^ 2$ -EF1-NSGA-III). Admiten hasta 10 funciones objetivas, gestionan variables de decisión reales, enteras y binarias, y muchas restricciones. Los algoritmos anteriores superan a otros trabajos en términos de la métrica de distancia generacional invertida (IGD). La tercera contribución es la implementación de metodologías de emulación en tiempo real para C-RAN utilizando una representación de dominio de frecuencia en OAI. Mejora el tiempo de cálculo promedio 10 veces en comparación con el dominio del tiempo sin usar hardware de radiofrecuencia y evita sus incertidumbres. El cuarto es la implementación de la técnica de Programación de Coordinación (CS) como prueba de concepto para validar las ventajas de las metodologías de dominio de frecuencia y asignar bloques de recursos dinámicamente entre las RRU. Finalmente, un problema de optimización de muchos objetivos se define y resuelve con algoritmos evolutivos en los que la diversidad se gestiona en función de la distancia de crouding y los puntos de referencia para reducir el consumo de energía de las C-RAN. Las soluciones obtenidas controlan la tarea de programación en Radio Cloud Center (RCC) y conmutan las RRU.Proyecto personal: Redes celulares de próxima generaciónDoctorad

A Multiobjective Computation Offloading Algorithm for Mobile Edge Computing

In mobile edge computing (MEC), smart mobile devices (SMDs) with limited computation resources and battery lifetime can offload their computing-intensive tasks to MEC servers, thus to enhance the computing capability and reduce the energy consumption of SMDs. Nevertheless, offloading tasks to the edge incurs additional transmission time and thus higher execution delay. This paper studies the trade-off between the completion time of applications and the energy consumption of SMDs in MEC networks. The problem is formulated as a multiobjective computation offloading problem (MCOP), where the task precedence, i.e. ordering of tasks in SMD applications, is introduced as a new constraint in the MCOP. An improved multiobjective evolutionary algorithm based on decomposition (MOEA/D) with two performance enhancing schemes is proposed.1) The problem-specific population initialization scheme uses a latency-based execution location initialization method to initialize the execution location (i.e. either local SMD or MEC server) for each task. 2) The dynamic voltage and frequency scaling based energy conservation scheme helps to decrease the energy consumption without increasing the completion time of applications. The simulation results clearly demonstrate that the proposed algorithm outperforms a number of state-of-the-art heuristics and meta-heuristics in terms of the convergence and diversity of the obtained nondominated solutions

Learning-based tracking area list management in 4G and 5G networks

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksMobility management in 5G networks is a very challenging issue. It requires novel ideas and improved management so that signaling is kept minimized and far from congesting the network. Mobile networks have become massive generators of data and in the forthcoming years this data is expected to increase drastically. The use of intelligence and analytics based on big data is a good ally for operators to enhance operational efficiency and provide individualized services. This work proposes to exploit User Equipment (UE) patterns and hidden relationships from geo-spatial time series to minimize signaling due to idle mode mobility. We propose a holistic methodology to generate optimized Tracking Area Lists (TALs) in a per UE manner, considering its learned individual behavior. The k -means algorithm is proposed to find the allocation of cells into tracking areas. This is used as a basis for the TALs optimization itself, which follows a combined multi-objective and single-objective approach depending on the UE behavior. The last stage identifies UE profiles and performs the allocation of the TAL by using a neural network. The goodness of each technique has been evaluated individually and jointly under very realistic conditions and different situations. Results demonstrate important signaling reductions and good sensitivity to changing conditions.This work was supported by the Spanish National Science Council and ERFD funds under projects TEC2014-60258-C2-2-R and RTI2018-099880-B-C32.Peer ReviewedPostprint (author's final draft

Evolutionary Game Theoretic Multi-Objective Optimization Algorithms and Their Applications

Multi-objective optimization problems require more than one objective functions to be optimized simultaneously. They are widely applied in many science fields, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conicting objectives. Most of the real world multi-objective optimization problems are NP-Hard problems. It may be too computationally costly to find an exact solution but sometimes a near optimal solution is sufficient. In these cases, Multi-Objective Evolutionary Algorithms (MOEAs) provide good approximate solutions to problems that cannot be solved easily using other techniques. However Evolutionary Algorithm is not stable due to its random nature, it may produce very different results every time it runs. This dissertation proposes an Evolutionary Game Theory (EGT) framework based algorithm (EGTMOA) that provides optimality and stability at the same time. EGTMOA combines the notion of stability from EGT and optimality from MOEA to form a novel and promising algorithm to solve multi-objective optimization problems. This dissertation studies three different multi-objective optimization applications, Cloud Virtual Machine Placement, Body Sensor Networks, and Multi-Hub Molecular Communication along with their proposed EGTMOA framework based algorithms. Experiment results show that EGTMOAs outperform many well known multi-objective evolutionary algorithms in stability, performance and runtime

Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%