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5G networks and beyond introduce a larger number of Network Elements (NEs) and functions than former cellular generations. The increase in NEs will, thus, result in significantly increasing the Management-Plane (M-Plane) data collected from the NEs. Therefore, the conventional centralized Network Management Systems (NMSs) will face fundamental challenges in processing the M-Plane data. In this paper, we present the concept of Quality of Monitoring (QoM) as a solution, which is able to reduce the M-Plane data already at the NEs. First, QoM aggregates the raw M-Plane data into Key Performance Indicators (KPIs). To these KPIs, the QoM applies a data-driven algorithm to define information loss limits for QoM classes specific for each KPI time series. Then, the QoM applies the classes for compressing the KPI data utilizing a lossy-compression method, which is a derivative of the Piece-Wise Constant Approximation (PWCA) algorithm. To evaluate the performance of the QoM solution, we use M-Plane raw data from a live LTE network and calculate four KPIs, while each KPI has different statistical characteristics. We also define three QoM classes named Exact, Optimized, and Sharp. For all KPIs, the class Optimized has a higher compression rate than the class Exact, while the class Sharp has the highest compression rate. Assuming that, for example, NEs of a network produce 280 MB of raw data containing information that needs to be transferred to the network operations center; we use KPIs to represent the information contents of the data, and QoM solution to transfer the data over the network. As a result, the QoM solution achieves an estimated 95% compression gain from the raw data in transfer.

The post-pandemic future will offer tremendous opportunity and challenge from transformation of the human experience linking physical, digital and biological worlds: 6G should be based on a new architecture to fully realize the vision to connect the worlds. We explore several novel architecture concepts for the 6G era driven by a decomposition of the architecture into platform, functions, orchestration and specialization aspects. With 6G, we associate an open, scalable, elastic, and platform agnostic het-cloud, with converged applications and services decomposed into micro-services and serverless functions, specialized architecture for extreme attributes, as well as open service orchestration architecture. Key attributes and characteristics of the associated architectural scenarios are described. At the air-interface level, 6G is expected to encompass use of sub-Terahertz spectrum and new spectrum sharing technologies, air-interface design optimized by AI/ML techniques, integration of radio sensing with communication, and meeting extreme requirements on latency, reliability and synchronization. Fully realizing the benefits of these advances in radio technology will also call for innovations in 6G network architecture as described.

Big data analytics and machine learning applications are often used to detect and classify anomalous behaviour in telecom network measurement data. The accuracy of findings during the analysis phase greatly depends on the quality of the training dataset. If the training dataset contains data from network elements (NEs) with high number of failures and high failure rates, such behaviour will be assumed as normal. As a result, the analysis phase will fail to detect NEs with such behaviour. Effective post-processing techniques are needed to analyse the anomalies, to determine the different kinds of anomalies, as well as their relevance in real-world scenarios. Manual post-processing of anomalies detected in an Anomaly Detection experiment is a cumbersome task, and ways to automate this process are not much researched upon. There exists no universally accepted method for effective classification of anomalous behaviour. High failure ratios have traditionally been considered as signs of faults in NEs. Operators use well-known key performance indicators (KPIs) such as drop call ratio and handover failure ratio to identify misbehaving NEs. The main problem with these KPIs based on failure ratios is their unstable nature. This paper proposes a method of measuring the significance of failures. The usage of this method is proposed in two stages of anomaly detection: training set filtering (pre-processing stage) and classification of anomalies (post-processing stage) using an automated process.

In this paper, we propose a concept for distributed Management Plane data computation and its delivery in the cellular networks. Architecture for proposed concept is described. Calculation of Key Performance Indicators is distributed to the cellular network edge, close to the managed network elements which reduces the volume of the Management Plane traffic. In this concept, further aggregation and refinement of data is done in the nodes located in the operator’s cloud, close to consumers of Management Plane data. Distribution of calculation to the network edge reduces load at the network operator’s central database. This paper presents an analysis to the benefits of the proposed concept. Efficient on-demand type streaming data delivery model allows network management functions to be plugged in to receive Management Plane data directly without database access. A demonstrator system has been implemented. The feasibility of the implementation is evaluated in terms of resource consumption and latency.