In this paper, we present an auto-encoder-based machine learning framework for self organizing networks (SON). Traditional machine learning approaches, for example, K Nearest Neighbor, lack the ability to be precisely predictive. Therefore, they can not be extended for sequential data in the true sense because they require a batch of data to be trained on. In this work, we explore artificial neural network-based approaches like the autoencoders (AE) and propose a framework. The proposed framework provides an advantage over traditional machine learning approaches in terms of accuracy and the capability to be extended with other methods. The paper provides an assessment of the application of autoencoders (AE) for cell outage detection. First, we briefly introduce deep learning (DL) and also shed light on why it is a promising technique to make self organizing networks intelligent, cognitive, and intuitive so that they behave as fully self-configured, self-optimized, and self-healed cellular networks. The concept of SON is then explained with applications of intrusion detection and mobility load balancing. Our empirical study presents a framework for cell outage detection based on an autoencoder using simulated data obtained from a SON simulator. Finally, we provide a comparative analysis of the proposed framework with the existing frameworks.

The fifth-generation mobile network (5G), as the fundamental enabler of Industry 4.0, has facilitated digital transformation and smart manufacturing through AI and cloud computing (CC). However, B5G is viewed as a turning point that will fundamentally transform existing global trends in wireless communication practices as well as in the lives of masses. B5G foresees a world where physical–digital confluence takes place. This study intends to see the world beyond 5G with the transition to 6G assuming the lead as future wireless communication technology. However, despite several developments, the dream of an era without latency, unprecedented speed internet, and extraterrestrial communication has yet to become a reality. This article explores main impediments and challenges that the 5G–6G transition may face in achieving these greater ideals. This article furnishes the vision for 6G, facilitating technology infrastructures, challenges, and research leads towards the ultimate achievement of “technology for humanity” objective and better service to underprivileged people.

The number of elderly people increases quickly in many countries, under the global population aging situation. It is an upsetting fact that many elderly people are suffering from the dementia, which seriously obstructs their independent living and travel. It is a pervasive problem that the demented elderly individuals are easy to get lost or go into danger during alone travel in daily life. Therefore we propose a novel mobile system named "Canderoid" to monitor independent outdoor travel of the elderly individuals remotely, with aid from the caretaker. The system is composed mainly of an android terminal (Wanderoid), an MQTT broker, and a platform on caretaker side. In the system, an android terminal named "Wanderoid" is implemented on a smartphone to capture the travelling status, using built-in smartphone sensors (i.e. camera with an adhesive fish-eye lens, compass and GPS). The terminal device is a normal smartphone, with a fish-eye lens attached on the camera. The sensor data are transferred to the platform of caretaker after capturing. The data transmission work relies on a message pushing architecture, which deals with mobile IP address changing and enables remote manipulation of the smartphone terminal, by introducing the MQTT broker. Then the caretaker platform can interpret sensor data and real-timely present the travelling status using snapshot taken by the fish-eye camera, street view and map. A reliability test, energy dissipation test and usability test are carried out on the prototype to verify that the system is effective, easy-to-use, reliable and energy-saving, from the viewpoints of both technology and human factors.

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In this paper, we suggest a new research direction and a future vision for Self-Healing (SH) in Self-Organizing Networks (SONs). The problem we wish to solve is that traditional SH solutions may not be sufficient for the future needs of cellular network management because of their reactive nature, i.e., they start recovering after detecting already occurred faults instead of preparing for possible future faults in a pre-emptive manner. The detection delays are especially problematic with regard to the zero latency requirements of 5G networks. To address this problem, existing SONs need to be upgraded from reactive to proactive response. One of the dimensions in SH research is to employ more holistic context information that includes, e.g., user location and mobility information, in addition to traditional context information mostly gathered from sources inside the network. Such extra information has already been found useful in SH. In this paper, we suggest how user context information can not only be incorporated in SH but also how future context could be predicted based on currently available information. We present a user mobility case study as an example to illustrate our idea.

Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

The aim of this paper is to present an experimental platform developed for 3GPP Long Term Evolution (LTE) networks where the effectiveness of self-optimization algorithms can be tested in a realistic environment. Several Self-organizing networks (SON) use cases are implemented in realistic scenarios. The system is developed to provide self-optimization functionalities in the framework of Self organizing networks (SON). The experimental system shown is built on top of radio network simulators and commercial network management products. In this work different commercial network management products like Radio Network Optimizer, Radio Network Simulator and Operational Subsystem (OSS) Middleware are integrated to provide automatic and efficient SON solutions, thus reducing human effort on the one hand and improving network performance in terms of coverage, capacity and service quality on the other. The network performance impacts of remote electrical antenna tilt (RET) optimization and transmission power optimization could be shown with the tool. Average cell throughput and per physical resource block (PRB) throughput are shown for constant bit rate (CBR) service before and after optimization. Coverage improvements are seen in improvements in handover (HO) failure statistics. Robustness and convergence time of optimization algorithms are studied.