Abstract

Abstract
Many natural signals exhibit quasi-periodic behaviors and are conveniently modeled as combinations of several harmonic sinusoids whose relative frequencies, magnitudes, and phases vary with time. The waveform shapes of those signals reflect important physical phenomena underlying their generation, requiring those parameters to be accurately estimated and modeled. In the literature, accurate phase estimation and modeling have received significantly less attention than frequency or magnitude estimation. This paper first addresses accurate DFT-based phase estimation of individual sinusoids across six scenarios involving two DFT-based filter banks and three different windows. It has been shown that bias in phase estimation is less than 0.001 radians when the SNR is equal to or larger than 2.5 dB. Using the Cram & eacute;r-Rao lower bound as a reference, it has been demonstrated that one particular window offers performance of practical interest by better approximating the CRLB under favorable signal conditions and minimizing performance deviation under adverse conditions. This paper describes the development of a shift-invariant phase-related feature that characterizes the harmonic phase structure. This feature motivates a new signal processing paradigm that greatly simplifies the parametric modeling, transformation, and synthesis of harmonic signals. It also aids in understanding and reverse engineering the phasegram. The theory and results are discussed from a reproducible perspective, with dedicated experiments supported by code, allowing for the replication of figures and results presented in this paper and facilitating further research.

Abstract
Telecommunications and computer vision have evolved independently. With the emergence of high-frequency wireless links operating mostly in line-of-sight, visual data can help predict the channel dynamics by detecting obstacles and help overcoming them through beamforming or handover techniques. This paper proposes a novel architecture for delivering real-time radio and video sensing information to O-RAN xApps through a multi-agent approach, and introduces a new video function capable of generating blockage information for xApps, enabling Integrated Sensing and Communications. Experimental results show that the delay of sensing information remains under 1 ms and that an xApp can successfully use radio and video sensing information to control the 5G/6G RAN in real-time.

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.

Abstract
In wireless communications, the need to cover operation areas, such as seaports, is at the forefront of discussion, especially regarding network capacity provisioning. Radio network planning typically involves determining the number of fixed cells, considering link budgets and deploying them geometrically centered across targeted areas. This paper proposes a solution to determine the optimal position for a mobile cell, considering 3GPP pathloss models. The obtained position for the mobile cell maximises the aggregate network capacity offered to a set of User Equipments (UEs), with gains up to 187% compared to the positioning of the mobile cell at the UEs’ geometrical center. The proposed solution can be used by network planners and integrated into network optimisation tools. This has the potential to reduce costs associated with the radio access network planning by enhancing flexibility for on-demand deployments.

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.