Abstract
Deep learning methods have been shown to be competitive solutions for modulation classification tasks, but suffer from being computationally expensive, limiting their use on embedded devices. We propose a new deep neural network architecture which employs known structures, depth-wise separable convolution and residual connections, as well as a compression methodology, which combined lead to a tiny and fast algorithm for modulation classification. Our compressed model won the first place in ITU's AI/ML in 5G Challenge 2021, achieving 61.73? compression over the challenge baseline and being over 2.6? better than the second best submission. The source code of this work is publicly available at github.com/ITU-AI- ML-in-5G-Challenge/ITU-ML5G-PS-007-BacalhauNet.

Abstract
In order to achieve the full potential of the Internet-of-Things, connectivity between devices should be ubiquitous and efficient. Wireless mesh networks are a critical component to achieve this ubiquitous connectivity for a wide range of services, and are composed of terminal devices (i.e., nodes), such as sensors of various types, and wall powered gateway devices, which provide further internet connectivity (e.g., via Wi-Fi). When considering large indoor areas, such as hospitals or industrial scenarios, the mesh must cover a large area, which introduces concerns regarding range and the number of gateways needed and respective wall cabling infrastructure, including data and power. Solutions for mesh networks implemented over different wireless protocols exist, like the recent Bluetooth Low Energy (BLE) 5.1. While BLE provides lower power consumption, some wall-power infrastructure may still be required. Alternatively, if some nodes are battery powered, concerns such as lifetime and packet delivery are introduced. We evaluate a scenario where the intermediate nodes of the mesh are battery powered, using a BLE relay of our own design, which acts as a range extender by forwarding packets from end-nodes to gateways. We present the relay's design and experimentally determine the packet forwarding efficiency for several scenarios and configurations. In the best case, up to 35% of the packets transmitted by 11 end-nodes can be forwarded to a gateway by a single relay under continuous operation. A battery lifetime of 1 year can be achieved with a relay duty cycle of 20%.

Abstract
Future telecommunications aim to be ubiquitous and efficient, as widely deployed connectivity will allow for a variety of edge/fog based services. Challenges are numerous, e.g., spectrum overuse, energy efficiency, latency and bandwidth, battery life and computing power of edge devices. Addressing these challenges is key to compose the backbone for the future Internet-of-Things (IoT). Among IoT applications are Indoor Positioning System and indoor Real-Time-Location-Systems systems, which are needed where GPS is unviable. The Bluetooth Low Energy (BLE) 5.1 specification introduced Direction Finding to the protocol, allowing for BLE devices with antenna arrays to derive the Angle-of-Arrival (AoA) of transmissions. Well known algorithms for AoA calculation are computationally demanding, so recent works have addressed this, since the low-cost of BLE devices may provide efficient solutions for indoor localization. In this paper, we present a system topology and algorithms for self-localization where a receiver with an antenna array utilizes the AoAs from fixed battery powered beacons to self-localize, without a centralized system or wall-power infrastructure. We conduct two main experiments using a BLE receiver of our own design. Firstly, we validate the expected behaviour in an anechoic chamber, computing the AoA with an RMSE of 10.7 degrees conduct a test in an outdoor area of 12 by 12 meters using four beacons, and present pre-processing steps prior to computing the AoAs, followed by position estimations achieving a mean absolute error of 3.6 m for 21 map positions, with a minimum as low as 1.1 m.

Abstract
This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around 15 degrees for a 1 m transmission length, increasing to 22 degrees for a shorter distance of 0.5 m but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of 5 mm (6 mm) and 0.157 rad (0.174 rad) for a distance between the transmitter and receiver of 1 m and 0.5 m, respectively.

Abstract

This study presents the first application-ready demonstration of radio-frequency (RF) switches based on memristors fabricated through a combination of electrochemical exfoliation and liquid-liquid interfacial assembly (EC-LL). This 2D layer fabrication method yields uniform, low-defect bilayer MoS2 nanosheet networks without relying on high-temperature processes or hazardous gases typical of chemical vapor deposition (CVD), offering a low-cost and environmentally friendly route towards CMOS-compatible integration. Remarkably, the resulting devices exhibit robust unipolar resistive switching which simplifies biasing requirements and reduces power consumption. Reproducibility with retention of 104 sec, and endurance of 100 cycles is reported. RF measurements confirm reliable operation at millimeter wave (mmWave) frequencies across 10–110 GHz, demonstrating low insertion loss (0.42–0.9 dB), isolation >18 dB, and an intrinsic cut-off frequency of ~5.4 THz. Integration into Reconfigurable Intelligent Surface Unit Cells (RIS-UCs) further showcases the technology’s utility in next-generation mmWave communication systems, including 5G/6G and satellite applications. Simulations of a 24×24-element RIS panel confirm high gain (>21.6 dBi) and efficient beam steering (-60º, 60º degrees) over the 26.8–29.1 GHz band, while the ultra-low switching energy (~330 pJ per unit cell) enables zero static power consumption—critical for scalable and sustainable 6G infrastructure. This work establishes a new benchmark by delivering the first solution-processed, application-suitable 2D material in solid-state RF switches combining non-volatility, high-frequency operation, and CMOS integration potential. It marks a significant step toward reconfigurable, energy-efficient wireless communication platforms.