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Publications

Publications by CTM

2024

CONVERGE: A Vision-Radio Research Infrastructure Towards 6G and Beyond

Authors
Teixeira, FB; Ricardo, M; Coelho, A; Oliveira, HP; Viana, P; Paulino, N; Fontes, H; Marques, P; Campos, R; Pessoa, LM;

Publication
2024 JOINT EUROPEAN CONFERENCE ON NETWORKS AND COMMUNICATIONS & 6G SUMMIT, EUCNC/6G SUMMIT 2024

Abstract
Telecommunications and computer vision have evolved separately so far. Yet, with the shift to sub-terahertz (sub-THz) and terahertz (THz) radio communications, there is an opportunity to explore computer vision technologies together with radio communications, considering the dependency of both technologies on Line of Sight. The combination of radio sensing and computer vision can address challenges such as obstructions and poor lighting. Also, machine learning algorithms, capable of processing multimodal data, play a crucial role in deriving insights from raw and low-level sensing data, offering a new level of abstraction that can enhance various applications and use cases such as beamforming and terminal handovers. This paper introduces CONVERGE, a pioneering vision-radio paradigm that bridges this gap by leveraging Integrated Sensing and Communication (ISAC) to facilitate a dual View-to-Communicate, Communicate-to-View approach. CONVERGE offers tools that merge wireless communications and computer vision, establishing a novel Research Infrastructure (RI) that will be open to the scientific community and capable of providing open datasets. This new infrastructure will support future research in 6G and beyond concerning multiple verticals, such as telecommunications, automotive, manufacturing, media, and health.

2024

Joint Mobile Cell Positioning and Scheduler Selection in Locations Characterised by Substantial Obstacles

Authors
Correia, PF; Coelho, A; Ricardo, M;

Publication
CoRR

Abstract

2024

Positioning of a Next Generation Mobile Cell to Maximise Aggregate Network Capacity

Authors
Correia, PF; Coelho, A; Ricardo, M;

Publication
CoRR

Abstract

2024

Traffic and Obstacle-Aware UAV Positioning in Urban Environments Using Reinforcement Learning

Authors
Shafafi, K; Ricardo, M; Campos, R;

Publication
IEEE ACCESS

Abstract
Unmanned Aerial Vehicles (UAVs) are suited as cost-effective and adaptable platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs). Implementing aerial networks in disaster management scenarios and crowded areas can effectively enhance Quality of Service (QoS). Maintaining Line-of-Sight (LoS) in such environments, especially at higher frequencies, is crucial for ensuring reliable communication networks with high capacity, particularly in environments with obstacles. The main contribution of this paper is a traffic- and obstacle-aware UAV positioning algorithm named Reinforcement Learning-based Traffic and Obstacle-aware Positioning Algorithm (RLTOPA), for such environments. RLTOPA determines the optimal position of the UAV by considering the positions of ground users, the coordinates of obstacles, and the traffic demands of users. This positioning aims to maximize QoS in terms of throughput by ensuring optimal LoS between ground users and the UAV. The network performance of the proposed solution, characterized in terms of mean delay and throughput, was evaluated using the ns-3 simulator. The results show up to 95% improvement in aggregate throughput and 71% in delay without compromising fairness.

2024

CONVERGE: A Vision-Radio Research Infrastructure Towards 6G and Beyond

Authors
Teixeira, FB; Ricardo, M; Coelho, A; Oliveira, HP; Viana, P; Paulino, N; Fontes, H; Marques, P; Campos, R; Pessoa, LM;

Publication
Joint European Conference on Networks and Communications & 6G Summit, EuCNC/6G Summit 2024, Antwerp, Belgium, June 3-6, 2024

Abstract

2024

Mobile Node Emulator for 5G Integrated Access and Backhaul Networks

Authors
Cojocaru, I; Coelho, A; Ricardo, M;

Publication
20th International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2024, Paris, France, October 21-23, 2024

Abstract
The Integrated Access and Backhaul (IAB) 5G network architecture, introduced in 3GPP Release 16, leverages a shared 5G spectrum for both access and backhaul networks. Due to the novelty of IAB, there is a lack of suitable implementations and performance evaluations. This paper addresses this gap by proposing EMU-IAB, a mobility emulator for private standalone 5G IAB networks. The proposed emulation environment comprises a 5G Core Network, an IAB-enabled Radio Access Network (RAN), leveraging the Open-RAN (O-RAN) architecture. The RAN includes a fixed IAB Donor, a mobile IAB Node, and multiple User Equipments (UEs). The mobility of the IAB Node is managed through EMU-IAB, which allows defining the path loss of emulated wireless channels. The validation of EMU-IAB was conducted under a realistic IAB node mobility scenario, addressing different traffic demand from the UEs. © 2024 IEEE.

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