Dr Rúben Borralho

—Network coverage is an increasing concern for the Quality of Service (QoS) targets of new mobile technologies. New solutions designed to fulfill the requirements of the existing fifth-generation (5G) and upcoming sixth-generation (6G) emerging scenarios are based on deploying a high number of network access points (APs), which tend to considerably degrade coverage and cell-edge performance due to added interference and increase the energy consumption of cellular systems. In this paper, we present new results on our recently proposed novel concept of cell-sweeping that aims to minimize the coverage dead-spots and improve cell-edge user performance. More specifically, the concept is explored further in this paper analyzing the impact of different cell-sweeping configurations and evaluating the potential benefits towards achieving energy efficiency. By means of system level computer simulations, it is shown that cell-sweeping provides energy savings of 11% and 26.5% for a similar average and cell-edge user throughput performance, respectively, when compared to the conventional static cell deployment in a typical urban macro cell scenario.

Seamless and ubiquitous coverage are key factors for future cellular networks. Despite capacity and data rates being the main topics under discussion when envisioning the Fifth Generation (5G) and beyond of mobile communications, network coverage remains one of the major issues since coverage quality highly impacts the system performance and end-user experience. The increasing number of base stations and user terminals is anticipated to negatively impact the network coverage due to increasing interference. Furthermore, the "ubiquitous coverage" use cases, including rural and isolated areas, present a significant challenge for mobile communication technologies. This survey presents an overview of the concept of coverage, highlighting the ways it is studied, measured, and how it impacts the network performance. Additionally, an overlook of the most important key performance indicators influenced by coverage, which may affect the envisioned use cases with respect to throughput, latency, and massive connectivity, are discussed. Moreover, the main existing developments and deployments which are expected to augment the network coverage, in order to meet the requirements of the emerging systems, are presented as well as implementation challenges.

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.

The exponential growth of the network elements and data traffic exchange in the last few years elevated the need of network providers for optimized and cost-efficient solutions regarding network management and monitorization. Solutions such as drive-tests (DTs) are becoming extremely expensive with the vast extension and complexity of nowadays mobile networks. Therefore, this paper provides a solution for optimized networkcontext knowledge acquisition, towards the self-organizing networks (SONs) concept. The presented framework incorporates an entire scheme for network Traces processing and positioning, based on network measurements and fingerprinting techniques. This framework enables a series of different use cases for network management and optimization, with real-time data processing capabilities within the network Traces collection interval (15 minutes), and achieving a median positioning error of 90 m.