LTE-advanced self-organizing network conflicts and coordination algorithms

Self-organizing network (SON) functions have been introduced in the LTE and LTEAdvanced standards by the Third Generation Partnership Project as an excellent solution that promises enormous improvements in network performance. However, the most challenging issue in implementing SON functions in reality is the identification of the best possible interactions among simultaneously operating and even conflicting SON functions in order to guarantee robust, stable, and desired network operation. In this direction, the first step is the comprehensive modeling of various types of conflicts among SON functions, not only to acquire a detailed view of the problem, but also to pave the way for designing appropriate Self-Coordination mechanisms among SON functions. In this article we present a comprehensive classification of SON function conflicts, which leads the way for designing suitable conflict resolution solutions among SON functions and implementing SON in reality. Identifying conflicting and interfering relations among autonomous network management functionalities is a tremendously complex task. We demonstrate how analysis of fundamental trade-offs among performance metrics can us to the identification of potential conflicts. Moreover, we present analytical models of these conflicts using reference signal received power plots in multi-cell environments, which help to dig into the complex relations among SON functions. We identify potential chain reactions among SON function conflicts that can affect the concurrent operation of multiple SON functions in reality. Finally, we propose a selfcoordination framework for conflict resolution among multiple SON functions in LTE/LTEAdvanced networks, while highlighting a number of future research challenges for conflict-free operation of SON

Hybrid Spectrum Allocation Scheme in Wireless Cellular Networks

Mobile services have seen a major upswing driven by the bandwidth hungry applications thus leading to higher data rate requirements on the wireless networks. Spectrum being the most precious resource in the wireless industry is of keen interest. Various spectrum assignment and frequency reuse schemes have been proposed in literature. However in future networks, dynamic schemes that adapt to spatio-temporal variation in the environment are desired. We thus present a hybrid spectrum assignment scheme which adapts its allocation strategies depending on user distribution in the system. Results show that the proposed dynamic spectrum assignment strategy improves spectrum utilization thereby providing a higher data rate for the users

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

Artificial Immune Systems - Models, algorithms and applications

Copyright © 2010 Academic Research Publishing Agency.This article has been made available through the Brunel Open Access Publishing Fund.Artificial Immune Systems (AIS) are computational paradigms that belong to the computational intelligence family and are inspired by the biological immune system. During the past decade, they have attracted a lot of interest from researchers aiming to develop immune-based models and techniques to solve complex computational or engineering problems. This work presents a survey of existing AIS models and algorithms with a focus on the last five years.This article is available through the Brunel Open Access Publishing Fun

A novel design approach for 5G massive MIMO and NB-IoT green networks using a hybrid Jaya-differential evolution algorithm

Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era