Recent Advances in Machine Learning for Network Automation in the O-RAN

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation using ML in O-RAN. We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support for ML techniques. The survey then explores challenges in network automation using ML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects where ML techniques can benefit.Peer reviewe

Lead pollution in an urban community: whose health priority?

Objective: A community-based research project is used as a case study to debate the concept of community participation in setting research priorities. Background: A multidisciplinary team of researchers in health, engineering, and ecosystem management at the American University of Beirut (AUB) conducted a pilot study, funded by IDRC, to investigate lead pollution in a densely populated community in Beirut, Lebanon. It was hypothesized that lead pollution is a major health problem, mainly due to leaded gasoline. Thirty households were visited, where a member of the household was interviewed and blood and environmental samples were collected and analyzed for lead levels. Several meetings with community representatives and interviews with 28 members of the community were also conducted to identify the community’s environmental health (EH) priorities. Observations: The community specified the emissions from vehicles using diesel fuel, poor water quality, and municipal waste disposal among the leading EH priorities. Leaded gasoline and lead pollution did not make the list. Household cooperation with the lead study was low. Contrary to expectations, blood lead levels and concentration of lead in air/water/dust/food samples were within acceptable international standards. Discussion: This study is a clear example of the frequent mismatch between researchers’ priorities and that of the community under investigation. The community’s priorities were all sensual and tangible- smell (diesel, solid waste), color (water, diesel), smoke (diesel), and taste (water). Out-of-sight and non-tangible EH problems such as lead pollution fell from the community’s sphere of concern. Researchers face the dilemma of imposing their own agenda or changing direction mid-stream. Is it ethical to educate and raise community awareness about a problem to later investigate it? Or is it a moral duty? How can researchers balance their own needs and objectives with that of the community? The Ecosystem Approach to Environmental Health presents some answers but its application faces the same challenges

Using Real-Time Kinematics Algorithm in Mission Critical Communication for Accurate Positioning and Time Correction over 5G and Beyond Networks

—At 5G and beyond networks, accurate localization services and nanosecond time synchronization are crucial to enabling mission-critical wireless communications technologies and techniques such as autonomous vehicles and distributed multiple-input and multiple-output (MIMO) antenna systems. This paper investigates how to improve wireless time synchronization by studying time correction based on the Real-Time Kinematics (RTK) positioning algorithm. Using the multiple Global Navigation Satellite System (GNSS) receiver references and the proposed binary GNSS satellite formation to reduce the effect of the ionosphere and troposphere delays and recede the measurement phase-range and pseudorange errors. As a result, it improves user equipment's (UE) localization and measures the time difference between the Base Station (BS) and the UE local clocks. The results show that the positioning accuracy has been increased, and a millimetre accuracy has been achieved while attaining the sub-nanosecond time error (TE) between the UE's and BS local clocks

Prognosis methods of stress corrosion cracking under harsh environmental conditions

Stress corrosion cracking (SCC) under harsh environmental conditions still poses a significant challenge, despite extensive research efforts. The intricate interplay among mechanical, chemical, and electrochemical factors hinders the accurate prognosis of material degradation and remaining service life. Furthermore, the demand for real-time monitoring and early detection of SCC defects adds further complexity to the prognostication process. Therefore, there is an urgent need for comprehensive review papers that consolidate current knowledge and advancements in prognosis methods. Such reviews would facilitate a better understanding and resolution of the challenges associated with SCC under harsh environmental conditions. This work aims to provide a comprehensive overview of various prognosis methods utilized for the assessment and prediction of SCC in such environments. The paper will delve into the following sections: exacerbating harsh environmental conditions, non-destructive testing (NDT) techniques, electrochemical techniques, numerical modeling, and machine learning. This review is inclined to serve as a valuable resource for researchers and practitioners working in the field, facilitating the development of effective strategies to mitigate SCC and ensure the integrity and reliability of materials operating in challenging environments. Despite considerable research, stress corrosion cracking in harsh environments remains a critical issue, complicated by the interplay of mechanical, chemical, and electrochemical factors. This review aims to consolidate current prognosis methods, including non-destructive testing, electrochemical techniques, numerical modeling, and machine learning. Key findings indicate that while traditional methods offer limited reliability, emerging computational approaches show promise for real-time, accurate predictions. The paper also briefly discusses notable SCC failure cases to underscore the urgency for improved prognosis techniques. This work aspires to fill knowledge gaps and serve as a resource for developing effective SCC mitigation strategies, thereby ensuring material integrity in challenging operational conditions

Recent Advances in Machine Learning for Network Automation in the O-RAN

The evolution of network technologies has witnessed a paradigm shift toward open and intelligent networks, with the Open Radio Access Network (O-RAN) architecture emerging as a promising solution. O-RAN introduces disaggregation and virtualization, enabling network operators to deploy multi-vendor and interoperable solutions. However, managing and automating the complex O-RAN ecosystem presents numerous challenges. To address this, machine learning (ML) techniques have gained considerable attention in recent years, offering promising avenues for network automation in O-RAN. This paper presents a comprehensive survey of the current research efforts on network automation usingML in O-RAN.We begin by providing an overview of the O-RAN architecture and its key components, highlighting the need for automation. Subsequently, we delve into O-RAN support forML techniques. The survey then explores challenges in network automation usingML within the O-RAN environment, followed by the existing research studies discussing application of ML algorithms and frameworks for network automation in O-RAN. The survey further discusses the research opportunities by identifying important aspects whereML techniques can benefit