Visting Researchers
Ayat Zaki Hindi, Luxembourg Institute for Science and Technology
Dates: 12-12-2023
Title: Uplink coexistence for high throughput UAVs in cellular networks
Abstract:
During the visit, Dr. Ayat Zaki Hindi delved into the cutting-edge field of cellular-connected Unmanned Aerial Vehicles (UAVs), with a focus on scenarios where UAVs equipped with cameras streamed live events. These UAVs shared the cellular network with numerous ground user equipment (gUEs), employed by event attendees to upload content. This scenario posed a significant challenge as the network had to cater to the high data rate demands of both UAVs and gUEs. To navigate this complexity, Dr. Hindi’s work investigated interference mitigation techniques utilizing UAV beamforming capabilities and introduced an innovative UAV cell-selection algorithm designed to reduce UAV interference with the serving cell.
Findings from Dr. Hindi’s research highlighted a remarkable increase in uplink data rates by 32% for gUEs in densely populated cells, while also achieving a desired UAV throughput of 20 Mbps. This enhancement necessitated a 50% rise in UAV transmission power and resulted in a minor decrease, up to 13%, in data rates for gUEs in adjacent cells.
Moreover, Dr. Hindi explored the practical scenario of UAVs equipped with panel antennas, which could direct a fixed number of beams. The research determined the optimal number of beams required to ensure UAV performance without adversely affecting the network’s ability to serve high-density areas. The study concluded that a minimum of two beams was crucial for maintaining UAV performance, with the use of four beams significantly improving energy efficiency.
Expanding to consider the operation of multiple UAVs in close proximity, Dr. Hindi presented a UAV admission strategy aimed at preserving their performance while limiting their impact on terrestrial network users. This visit covered the trajectory of Dr. Hindi’s research, from initial conceptualization to the development of solutions poised to transform the integration of UAVs into cellular networks, ensuring high efficiency and minimal disruption.
Nicola Piovesan, Huawei Technologies, France
Dates: 14-09-2023
Title: A Journey Towards Energy Efficiency: Exploring Machine Learning Models for Mobile Network Optimization
Abstract:
In his visit, Dr. Nicola Piovesan shared insights into the transformative impacts of the fifth generation (5G) radio technology, which enhances our daily experiences by enabling greater automation through increased capacity, massive connectivity, and ultra-reliable low-latency communications. He emphasized that despite the remarkable advancements and the approximately fourfold improvement in energy efficiency over 3GPP Long Term Evolution (LTE) deployments, a critical challenge remains: current 5G New Radio (NR) networks still exhibit up to three times higher energy consumption. This issue leads to increased carbon emissions and higher operational costs for network operators.
Dr. Piovesan’s discussions focused on the potential of big data and machine learning to improve 5G energy efficiency. He shared his work on how data collected from thousands of base stations could be leveraged to develop accurate machine learning models to assess the energy consumption of multi-carrier base stations. The insights derived from this work have been foundational in creating a realistic and analytically tractable power consumption model, which is vital for theoretical analyses, standardization, development, and optimization efforts.
Moreover, Dr. Piovesan discussed with us about the future potential of large language models in autonomously generating explainable models, which could significantly advance the pursuit of more energy-efficient networks. The visit provided an opportunity to discuss the innovative strategies and methods that are being developed to address the challenges of 5G energy efficiency, marking a significant step towards fostering more sustainable and cost-effective mobile network operations.
Matteo Bernabe, Huawei Technologies France / EURECOM
Dates: 19-11-2023 / 25-11-2023
Title: Optimal SSB Beam Planning and UAV Cell Selection for 5G Connectivity on Aerial Highways
Abstract:
During his visit, Matteo Bernabe shared insights on integrating Unmanned Aerial Vehicles (UAVs) into urban environments with 5G technology. He outlined a key challenge: in urban areas, UAVs often encounter similar signal strengths from multiple nearby cells, complicating the selection of an optimal serving cell and causing downlink transmission interference.
Matteo discussed the Aerial Highways concept—predetermined paths for UAV missions. He noted that multiple UAVs flying closely within these paths and the introduction of massive MIMO (mMIMO) features significantly impact connectivity and performance. He discussed a heuristic metric and algorithm for optimally segmenting the Aerial Highway and identifying the best serving cell for each segment. Additionally, he introduced a strategy using a codebook and centralized algorithm for efficient beam selection, aiming to solve the combinatorial problem of cell association.
Matteo’s contributions highlighted that through careful design and planning of terrestrial networks, improvements in signal to interference plus noise ratio (SINR) and throughput for UAV communications can be achieved without adding complexity to the network or affecting ground users’ performance. His visit offered a valuable solution for network vendors and operators planning to ensure optimal 5G connectivity for UAVs within Aerial Highways.
Dates: June 3–6, 2024
Title: Supporting Enhanced Aerial Highway Coverage via Terrestrial LTE and NR Cellular Networks
Abstract:
During his second visit, Matteo expanded on the concept of aerial highways for UAV navigation and their integration within cellular networks. We discussed algorithms to optimize LTE and NR networks, addressing vertical tilt adjustments and mMIMO beamforming for enhanced aerial coverage without degrading ground services. This work demonstrates the importance of pre-defining aerial highways to optimize coverage and balance power distribution effectively. The results obtained by Matteo after the visit underline the necessity of leveraging advanced NR networks to overcome the limitations of LTE and ensure reliable aerial
coverage in diverse environments. This second visit of Matteo further solidified our collaboration and directly contributed to the outputs of the project. We submitted a journal paper on the topicl Highways.
Henri Alam, Huawei Technologies France/EURECOM, France
Dates: November 11–15, 2024
Title: Optimizing Integrated Terrestrial and Non-Terrestrial Networks Performance with Traffic-Aware Resource Management
Abstract:
Henri’s visit focused on collaborative work to develop an integrated TN-NTN framework aimed at enhancing coverage and energy efficiency. Together, we continued to advance the BLASTER algorithm, which dynamically manages bandwidth, UE association, power control, and BS activation to balance energy savings and Quality of Service (QoS). Our discussions lead to practical solutions for managing fluctuating traffic demands and optimizing resource allocation in modern cellular networks. Henri’s work during this visit significantly strengthened our ongoing collaboration and directly supported the project output detailed earlier in this report. We submitted a journal paper on the topic.
Mohammed Benzaghta, Universitat Pompeu Fabra, Spain
Dates: October 21–25, 2024
Title: Optimizing Mobility Management in Aerial Highways
Abstract:
During Mohammed’s visit, we engaged in in-depth discussions on mobility management optimization, focusing on the trade-offs between handover ping-pongs (HPPs) and radio link failures (RLFs) in heterogeneous scenarios. Leveraging advanced channel modeling and TuRBO optimization, the discussions explored strategies for improving HO efficiency across varying speeds and UE distributions. These interactions led to new algorithm implementations, which have yield promising results that outperform 3GPP benchmarks and highlight the benefits of per-cell optimization and transfer learning. The work has progressed towards a journal paper that will detail these findings and their implications for mobility management in future networks.