New short-length single-direction frame structures are proposed for 5G time division duplex (TDD) systems, where the transmit direction [i.e., either downlink (DL) or uplink (UL)] can be independently chosen at each cell in every frame. Accordingly, high flexibility is provided to match the per-cell DL/UL traffic asymmetries and full exploitation of dynamic TDD is allowed. As a downside, interference management becomes crucial. In this regard, this paper proposes a procedure for dynamic TDD in dense multiple-input multiple-output small cell networks, where the transmit direction selected per small cell (SC) is dynamically optimized together with the user scheduling and transmit precoding. We focus on the maximization of a general utility function that takes into account the DL/UL traffic asymmetries of each user and the interference conditions in the network. Although the problem is non-convex, it is decomposed thanks to the interference-cost concept and then efficiently solved in parallel. Simulation results show gains in DL and UL average rates for different traffic asymmetries and SC/user densities as compared to existing dynamic TDD schemes thanks to the proposed joint optimization. The gains become more significant when there is high interference and limited number of antennas.

Social Prescribing is a mechanism by which primary care team members can refer patients to community groups to improve their health and well-being. It integrates health, social care, and community, allowing patients to actively improve their health and well-being by participating in community initiatives and activities. These activities have traditionally been part of community life in European countries, and the benefits need to be consistently recognized.

Accurate and precise wireless infrastructure-based positioning systems become crucial as industries move towards flexible, portable, and autonomous transportation systems such as Automated Guided Vehicles (AGVs). Multipath-dominant dynamic environments like industries present significant challenges for wireless signal propagation and affect wireless positioning accuracy due to the interplay of reflected signals from obstacles. The achievable indoor positioning accuracy of the target AGV can be enhanced by fusing the measurements from the wireless infrastructure with the target's onboard sensor data. Nevertheless, the lack of correspondence between the wireless infrastructure and the target's onboard sensors causes the measurements from these two systems to arrive at irregular time steps. Using asynchronous measurements in the data fusion process can degrade the overall positioning accuracy of the target AGV. This paper proposes a novel deep learning-based data fusion approach to deal with asynchronous measurements from the wireless infrastructure Ultra-Wideband (UWB) and the target's onboard Inertial Measurement Unit (IMU) sensor to achieve enhanced positioning accuracy of the target AGV. In particular, a two-stage cascaded Deep Neural Network (DNN) is proposed to deal with the asynchronized measurements from UWB and IMU sensors. The first stage of the DNN is used to obtain the initial position estimate of the AGV by processing the measurements from UWB. Subsequently, the second stage of the DNN fuses the initial position estimate of the AGV with the IMU sensor data to obtain the final enhanced position estimate. The proposed approach is validated with real-world experiments in an indoor industrial scenario using UWB technology in channel 2 ($3.7-4.2~\rm{GHz}$) and an IMU sensor placed on an AGV. Moreover, the achievable positioning accuracy and the computational runtime to provide the position estimates with the proposed approach are analyzed. The experimental results show that the proposed approach achieves a mean absolute error of less than $\rm{10}~\rm{cm}$, outperforming the considered baseline methods, Extended Kalman Filter (EKF) and Long Short-Term Memory (LSTM).

This paper addresses the energy-latency tradeoff in distributed application offloading, in which an energy-limited handset offloads totally or partially an application to one or several virtual machines (VMs) located in remote locations or access points (APs) close to the mobile terminal (MT). One of the APs (the serving AP) provides radio access to the MT and is connected to the VMs through nonideal backhaul (BH) links. In this setting, we optimize the offloading strategy (including the joint optimization of radio and computational resources) to minimize the energy consumption at the MT subject to a maximum latency constraint. In addition, we propose robust designs to cope with imperfect acquisition of the channel state information (CSI) and the BH parameters. Our findings show that as far as the energy-latency tradeoff is concerned, the optimal order of activation of the VMs does not depend on their processing capabilities but the delays of the BH links. However, once a VM is selected to participate in the processing, the optimal amount of processing allocated to such VM depends on its computational capabilities as well as on the features (capacity and delay) of the BH link. Additionally, offloading decisions become more conservative as the uncertainty in CSI and BH parameters increases.

In industrial environments, the wireless infrastructure is functional for offering services such as communication and positioning of industrial assets. However, the frequently occurring Non-Line-of-Sight (NLoS) conditions in industrial scenarios cause the wireless receiver to have positional information from a limited and varying number of wireless transmitters between consecutive time steps, leading to ambiguities in wireless infrastructure-based positioning. In this paper, we propose PosGNN, a novel data fusion solution based on the Graph Neural Network (GNN) approach that allows us to estimate the position of the User Equipment (UE) by fusing the positional information from the available wireless transmitters at each time step with the UE sensor technology. The performance of the proposed method is assessed using an experimental setup of Ultra-Wideband (UWB) technology as wireless infrastructure at $3.7 - \text{4.2}\,\text{GHz}$ frequency band, the Inertial Measurement Unit (IMU) as UE-side sensor, and the Automated Guided Vehicle (AGV) as the target UE to be positioned. The experimental results demonstrate the exceptional performance of our approach over the conventional model-based approach, Extended Kalman Filter (EKF), and the data-driven approach, Deep Neural Network (DNN), achieving an average positioning error of less than $\text{15}\,\text{cm}$ in harsh industrial environments.

In this paper, we design linear precoders for the downlink of a visible light communication (VLC) system that simultaneously serves multiple users. Instead of using phosphor-coated white light-emitting diodes (PWLEDs), we focus on Red-Green-Blue light-emitting diodes (RGB-LEDs) that allow modulating three separate data streams on the three primary colors of the RGB-LEDs. For this system, we design a zero-forcing (ZF) precoder that maximizes the weighted sum rate for a multilevel pulse amplitude modulation (M-PAM). The precoding design in RGB-based systems presents some challenges due to the system constraints, such as the limited power, the non-negative amplitude constraints per light-emitting diode (LED), and the need to guarantee white light emission while transmitting with RGB-LEDs. For comparison purposes, we also consider the ZF design for a PWLED-based system and evaluate the performance of both a PWLED- and an RGB-based system.