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.

Abstract
The increasing complexity of recent Wi-Fi amendments is making the use of traditional algorithms and heuristics unfeasible to address the Rate Adaptation (RA) problem. This is due to the large combination of configuration parameters along with the high variability of the wireless channel. Recently, several works have proposed the usage of Reinforcement Learning (RL) techniques to address the problem. However, the proposed solutions lack sufficient technical explanation. Also, the lack of standard frameworks enabling the reproducibility of results and the limited availability of source code, makes the fair comparison with state of the art approaches a challenge. This paper proposes a framework, named RateRL, that integrates state of the art libraries with the well-known Network Simulator 3 (ns-3) to enable the implementation and evaluation of RL-based RA algorithms. To the best of our knowledge, RateRL is the first tool available to assist researchers during the implementation, validation and evaluation phases of RL-based RA algorithms and enable the fair comparison between competing algorithms.

Abstract
Underwater wireless communications play a significant role in the Blue Economy, supporting the operations of sensing platforms like Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). These platforms require reliable and fast communications to transmit the extensive data gathered without surfacing. Yet, the ocean poses challenges to signal propagation, restricting communications to high bitrate at short ranges via optical and RF, or low bitrate at long distances using acoustic communications. This paper introduces Aquacom, a multimodal manager for underwater communications that integrates acoustic, RF, and optical communnications, ensuring seamless handover between technologies and link aggregation to enhance network performance. Upon validation in freshwater tank lab tests, Aquacom demonstrated the capability for switching interfaces without data loss and effective link aggregation through the simultaneous use of multiple wireless interfaces.

Abstract
The exploration of the ocean has got an increasing interest, including activities such as offshore wind farms and deep-sea mining. However, the ocean environment and the high cost of operations, namely for manned missions, have led to the development of Autonomous Underwater Vehicles (AUVs) and other sensing platforms. AUVs play a vital role in these environments, relying on communications systems to operate and exchange sensor data. Yet, reliable and energy-efficient broad-band wireless communications underwater remain an unsolved challenge, despite the recent advances in the field. We present a novel multimodal approach, named DURIUS, that considers the movement of the AUV to convey the sensor data and selects the most suitable underwater wireless communications technology - acoustic, optical or radio - according to the underwater context, targeting maximum performance and minimum energy consumption. Our analytical results show that DURIUS increases data throughput and reduces energy consumption when compared with the state of the art approaches.

Abstract

Abstract
With the growing connectivity demands, Unmanned Aerial Vehicles (UAVs) have emerged as a prominent component in the deployment of Next Generation On-demand Wireless Networks. However, current UAV positioning solutions typically neglect the impact of Rate Adaptation (RA) algorithms or simplify its effect by considering ideal and non-implementable RA algorithms. This work proposes the Rate Adaptation aware RL-based Flying Gateway Positioning (RARL) algorithm, a positioning method for Flying Gateways that applies Deep Q-Learning, accounting for the dynamic data rate imposed by the underlying RA algorithm. The RARL algorithm aims to maximize the throughput of the flying wireless links serving one or more Flying Access Points, which in turn serve ground terminals. The performance evaluation of the RARL algorithm demonstrates that it is capable of taking into account the effect of the underlying RA algorithm and achieve the maximum throughput in all analysed static and mobile scenarios.