In this paper, we present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications. In particular, we provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology in order for the latter to execute optimal safe performance which is measured through certain Key Performance Indicators (KPIs). The network consists of a set of fixed Base Stations (BS) which are equipped with antennas, which one can control by adjusting their vertical tilt angle. The aforementioned process is called Remote Electrical Tilt (RET) optimization. Recent research has focused on performing this RET optimization by employing Reinforcement Learning (RL) strategies due to the fact that they have self-learning capabilities to adapt in uncertain environments. The term safety refers to particular constraints bounds of the network KPIs in order to guarantee that when the algorithms are deployed in a live network, the performance is maintained. In our proposed architecture the safety is ensured through model-checking techniques over combined discrete system models (automata) that are abstracted through the learning process. We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions, and those that are allowed and blocked according to the safety specification.

Abstract--Artificial intelligence (AI) will play an increasing role in cellular network deployment, configuration and management. This paper examines the security implications of AI-driven 6G radio access networks (RANs). While the expected timeline for 6G standardization is still several years out, pre-standardization efforts related to 6G security are already ongoing and will benefit from fundamental and experimental research. The Open RAN (O-RAN) describes an industry-driven open architecture and interfaces for building next generation RANs with AI control. Considering this architecture, we identify the critical threats to data driven network and physical layer elements, the corresponding countermeasures, and the research directions. The steady increase in the number of connected devices and the heterogeneous types of communications performance demands have driven the wireless business and research and development (R&D) efforts.

IT equipment consists of products such as Personal computers (PCs), servers, monitors, storage devices etc. Software comprises of computer programs, firmware and applications. The IT & business services segment is further classified into consulting, custom solutions development, outsourcing services etc. The telecommunication equipment segment consists of telecom equipments such as switches, routers etc. The carrier services segment comprises of operations related revenue spent by telecom service provider on acquiring telecom capacity, primarily from overseas carrier. How Important Is Machine Learning as a Service (MLaaS)?

Many of today's innovative technologies, such as cloud computing, edge computing, the endpoint and 5G, all change the way we communicate with each other. Following the pandemic and the consequential impact on the UK economy, all organizations will have to rely heavily on the implementation of new technologies including these in order to get back on their feet. However, adjusting to this new way of working can provide unique challenges. For example, telecommunication companies and operators that adopt 5G technology need to develop entirely new revenue streams as well as lay down new infrastructure, embrace Artificial Intelligence (AI) and Machine Learning (ML), and change their business models. John Day is Sales Engineering Leader, UK&I and Nordics at Commvault. The coronavirus pandemic has created some major setbacks for telecommunications companies as they work to roll out 5G networks.

With the advent of Artificial Intelligence (AI)-empowered communications, industry, academia, and standardization organizations are progressing on the definition of mechanisms and procedures to address the increasing complexity of future 5G and beyond communications. In this context, the International Telecommunication Union (ITU) organized the first AI for 5G Challenge to bring industry and academia together to introduce and solve representative problems related to the application of Machine Learning (ML) to networks. In this paper, we present the results gathered from Problem Statement~13 (PS-013), organized by Universitat Pompeu Fabra (UPF), which primary goal was predicting the performance of next-generation Wireless Local Area Networks (WLANs) applying Channel Bonding (CB) techniques. In particular, we overview the ML models proposed by participants (including Artificial Neural Networks, Graph Neural Networks, Random Forest regression, and gradient boosting) and analyze their performance on an open dataset generated using the IEEE 802.11ax-oriented Komondor network simulator. The accuracy achieved by the proposed methods demonstrates the suitability of ML for predicting the performance of WLANs. Moreover, we discuss the importance of abstracting WLAN interactions to achieve better results, and we argue that there is certainly room for improvement in throughput prediction through ML.

Studying the implicit regularization effect of the nonlinear training dynamics of neural networks (NNs) is important for understanding why over-parameterized neural networks often generalize well on real dataset. Empirically, existing works have shown that weights of NNs condense on isolated orientations with a small initialization. The condensation dynamics implies that NNs can learn features from the training data with a network configuration effectively equivalent to a much smaller network during the training. In this work, we show that the multiple roots of activation function at origin is a key factor to understanding the condensation at the initial stage of training. Our experiments suggest that the maximal number of condensed orientations is twice of the multiplicity. Our theoretical analysis confirms experiments for two cases, one is for the activation function of multiplicity one and the other is for the one-dimensional input. This work makes a step towards understanding how small initialization implicitly leads NNs to condensation at initial stage of training, which lays a solid foundation for the future study of the nonlinear dynamics of NNs and its implicit regularization effect at a later stage of training.

Network activities recognition has always been a significant component of intrusion detection. However, with the increasing network traffic flow and complexity of network behavior, it is becoming more and more difficult to identify the specific behavior quickly and accurately by user network monitoring software. It also requires the system security staff to pay close attention to the latest intrusion monitoring technology and methods. All of these greatly increase the difficulty and complexity of intrusion detection tasks. The application of machine learning methods based on supervised classification technology would help to liberate the network security staff from the heavy and boring tasks. A finetuned model would accurately recognize user behavior, which could provide persistent monitoring with a relative high accuracy and good adaptability. Finally, the results of network activities recognition by J48 and Na\"ive Bayes algorithms are introduced and evaluated.

The digital sphere is raining technologies. The influence of artificial intelligence is taking center stage with every possible improvement. Technology is changing almost all industries including banking and finance, healthcare, automobile, telecommunication, manufacturing, defense and military, entertainment and media, education, etc. The sub-domains of Artificial Intelligence such as machine learning, natural language processing, data analytics, and image analytics are also rolling out profitable use cases in diverse sectors. Besides, artificial intelligence is serving the business purpose by leveraging end-to-end automation processes. Therefore, Analytics Insight has listed the top 50 business use cases of artificial intelligence in diverse sectors. Predictive analytics is a gift to healthcare. Sometimes, we come across patients who say they underwent an unnecessary surgery due to a lack of predictions on what was coming. Fortunately, artificial intelligence is changing the fate of such burdensome risks and avoidable surgeries.

There are numerous potential use cases for ML in telecommunications networks (see Figure 1). In the area of system monitoring, anomaly detection systems are crucial for identifying performance issues and problematic network behavior. Proactively predicting the degradation of key performance indicators, and identifying the likely root cause, can help reduce and prevent outages. In the area of managed services, ML models can improve trouble ticket management by effectively classifying, prioritizing, and escalating incidents. Capacity planning and customer retention can be improved through explainable churn prediction.

Live traffic analysis at the first aggregation point in the ISP network enables the implementation of complex traffic engineering policies but is limited by the scarce processing capabilities, especially for Deep Learning (DL) based analytics. The introduction of specialized hardware accelerators i.e., Tensor Processing Unit (TPU), offers the opportunity to enhance the processing capabilities of network devices at the edge. Yet, to date, no packet processing pipeline is capable of offering DL-based analysis capabilities in the data-plane, without interfering with network operations. In this paper, we present FENXI, a system to run complex analytics by leveraging TPU. The design of FENXI decouples forwarding operations and traffic analytics which operates at different granularities i.e., packet and flow levels. We conceive two independent modules that asynchronously communicate to exchange network data and analytics results, and design data structures to extract flow level statistics without impacting per-packet processing. We prototyped and evaluated FENXI on general-purpose servers considering both adversarial and realistic network conditions. Our analysis shows that FENXI can sustain 100 Gbps line rate traffic processing requiring only limited resources, while also dynamically adapting to variable network conditions.