Biblio

The fifth generation of mobile technology (5G) is positioned to address the demands and business contexts of 2020 and beyond. Therefore, in 5G, there is a need to push the envelope of performance to provide, where needed, for example, much greater throughput, much lower latency, ultra-high reliability, much higher connectivity density, and higher mobility range. A crucial point in the effective provisioning of 5G Next Generation Mobile Networks (NGMN) lies in the efficient error control and in more details in the utilization of Forward Error Correction (FEC) codes on the application layer. FEC is a method for error control of data transmission adopted in several mobile multicast standards. FEC is a feedback free error recovery method where the sender introduces redundant data in advance with the source data enabling the recipient to recover from different arbitrary packet losses. Recently, the adoption of FEC error control method has been boosted by the introduction of powerful Application Layer FEC (AL-FEC) codes. Furthermore, several works have emerged aiming to address the efficient application of AL-FEC protection introducing deterministic or randomized online algorithms. In this work we propose a novel AL-FEC scheme based on online algorithms forced by the well stated AL-FEC policy online problem. We present an algorithm which exploits feedback capabilities of the mobile users regarding the outcome of a transmission, and adapts the introduced protection respectively. Moreover, we provide an extensive analysis of the proposed AL-FEC algorithm accompanied by a performance evaluation against common error protection schemes.

In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.