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In this work, we use two well-established machine learning algorithms i.e., Random Forest (RF) and XGBoost, to predict ambient temperature for a baseband’s board. After providing an overview of the related work, we describe how we train the two ML models and identify the optimal training and test datasets to avoid the problems of data under- and over-fitting. Given this train/test split, the trained RF and XGBoost models provide temperature predictions with an accuracy lower than one degree Celsius, i.e., far better than any other approach that we used in the past. Our feature importance assessments reveal that the temperature sensors contribute significantly more towards predicting the ambient temperature compared to the power and voltage readings. Furthermore, the RF model appears less volatile than XGBoost using our training data. As the results demonstrate, our predictive temperature models allow for an accurate error prediction as a function of baseband board sensors.

System memory errors have always been problematic; today, they cause more than forty percent of confirmed hardware errors in repair centers for both data centers and telecommunications network nodes. Therefore, it is somewhat expected that, in recent years, device manufacturers improved the hardware features to support hardware-assisted fault management implementation. For example, the new standard, DDR5, includes both data redundancy, the so-called Error Correcting Code (ECC), and physical redundancy, the post-package repair (PPR), as mandatory features. Production and repair centers mainly use physical redundancy to replace faulty memory rows. In contrast, field use still needs to be improved, mainly due to a need for integrated system solutions for network nodes. This paper aims to compensate for this shortcoming and presents a system solution for handling memory errors. It is a multi-technology proposition (mixed use of ECC and PPR) based on multi-layer (hardware, firmware, and software) error information exchange.

Fault management is an important function that impacts the design of any digital system, from the simple kiosk in a shop to a complex 6G network. It is common to classify fault conditions into different taxonomies using terms like fault or error. Fault taxonomies are often suitable for managing fault detection, fault reporting, and fault localization but often neglect to support all different functions required by a fault management process. A correctly implemented fault management process must be able to distinguish between defects and faults, decide upon ap-propriate actions to recover the system to an ideal state, and avoid an error condition. Fault management is a multi-disciplinary process where recovery actions are deployed promptly by com-bined hardware, firmware, and software orchestration. The importance of fault management processes significantly increases with modern nanometer technologies, which suffer the risk of so-called soft errors, a corruption of a bit cells that can happen due to spurious disturbance, like cosmic radiation. Modern fault management implementations must support recovery actions for soft errors to ensure a steady system. This paper describes an extended fault classification model that emphasizes fault management and recovery actions. We aim to show how the reliability-based fault taxonomy definition is more suitable for the overall fault management process.

Fault management is a key function to guarantee the quality of the service. Research has done a lot to improve fault supervision, and investigation is ongoing in fault prediction, thanks to the potentials of artificial intelligence and machine learning. In this study, we propose a fault management framework that puts an emphasis on fault recovery: a framework developed on multi-layer function and a fault recovery methodology distributed over several technological layers. The basic principle of our proposal is that the system's complexity exposes it to a higher probability of temporary error. Newfound attention to the fault recovery phase is the key to keeping the service's quality high and saving maintenance costs by decreasing the return rate. 

Processing capacity distribution has become widespread in the fog computing era. End-user services have multiplied, from consumer products to Industry 5.0. In this scenario, the services must have a very high-reliability level. But in a system with such displacement of hardware, the reliability of the service necessarily passes through the hardware design. Devices shall have a high quality, but they shall also efficiently support fault management. Hardware design must take into account all fault management functions and participate in creating a fault management policy to ensure that the ultimate goal of fault management is fulfilled, namely to increase a system's reliability. Efficiently and sustainably, both in the system's performance and the product's cost. This paper analyzes the hardware design techniques that efficiently contribute to the realization of fault management and, consequently, guarantee a high level of reliability and availability for the services offered to the end customer. We describe hardware requirements and how they affect the choice of devices in the hardware design of networking systems.