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

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

Using AI in wireless communication system for resource management and optimisation

The existence of Artificial Intelligence (AI) can be seen in everyday scenarios. Nowadays, the produced data by both machine and human is overwhelming in which exceeded the ability of humans to understand and digest to make decisions depending on that data. Thus, a hand of help from AI is needed to overcome such challenges. The 5G LTE communication system is a promising solution to provide a high user experience in terms of the provided speed, amount of data, and cost. However, and due to its complexity, the technology of LTE needs some improvement in terms of resource management and optimization. With the aid of AI, these two challenges can be overcome. In this paper, the AI represented by improved Q-learning algorithm with the Self-Organizing Network (SON) concept in LTE will be used to manage and optimize Handover (HO) parameters and process in the system. The ns-3 simulator result shows that AI managed to improve and optimize the LTE system performance

A Secured Software Defined Network Architecture for Mini Net using POX Controller

SDN (software-defined networks) is a new technology that stems from numerous network security enhancements. It handles network data in a flexible manner using highly secure frameworks. The secure SDN model's purpose is to ensure data security. The proposed idea to be executed is a robust firewall protection in a mini net employing a POX controller. In order to deal with network-induced dangers, the huge network connectivity influenced environment requires additional protection. The proposed effort focuses on creating a secure SDN simulation architecture that is managed by Open Source POX Controller. Through a POX-controlled traffic management system and a Fingerprint-enabled authentication technique, the system provides multilayer security. The enhanced security is achieved by assessing network traffic as either elephant or mouse flow and selecting the appropriate security level based on data complexity. Mininet is run in a virtual cloud, where protocols and tools are tested and supported by a virtual machine (VM). The novelty is to produce a secure SDN topology was created using a python-based POX controller in the suggested technique. It also provides a low-cost solution as well as rapid development in conjunction with industrial networks

Efficient Load Balancing Algorithm in Long Term Evolution (LTE) Heterogeneous Network Based on Dynamic Cell Range Expansion Bias

The traditional scheme for load balancing in a homogeneous Long Term Evolution (LTE) Network where User Equipment (UEs) associate to a node with the strongest received signal strength is not practical for LTE Heterogeneous Network (LTE HetNet) due to power disparity between the nodes. Therefore, dynamic Cell Range Expansion (CRE) based load-balancing schemes were employed by several scholars to address the challenges in the LTE HetNet. However, the fairness index in achieving the desired average user throughput and UE offloading effect is relatively low. In this work, an efficient load-balancing algorithm for LTE HetNet based on dynamic Cell Range Expansion (CRE) was developed to improve the fairness of the network for the desired throughput and UE offloading effect. The simulation results achieve a throughput gain improvement of up to 11%, while the fairness index improves by 6% compared to the existing algorithm. Further, the UEs offloading effect shows a significant improvement of 3% relative to the existing algorithm. Keywords: Fairness Index; Cell Range Expansion; Load Balancing; LTE Heterogeneous Network; Throughpu