Location and tracking needs are becoming more prominent in industrial environments nowadays. Process optimization, traceability or safety are some of the topics where a positioning system can operate to improve and increase the productivity of a factory or warehouse. Among the different options, solutions based on ultra-wideband (UWB) have emerged during recent years as a good choice to obtain highly accurate estimations in indoor scenarios. However, the typical harsh wireless channel conditions found inside industrial environments, together with interferences caused by workers and machinery, constitute a challenge for this kind of system. This paper describes a real industrial problem (location and tracking of forklift trucks) that requires precise internal positioning and presents a study on the feasibility of meeting this challenge using UWB technology. To this end, a simulator of this technology was created based on UWB measurements from a set of real sensors. This simulator was used together with a location algorithm and a physical model of the forklift to obtain estimations of position in different scenarios with different obstacles. Together with the simulated UWB sensor, an additional inertial sensor and optical sensor were modeled in order to test its effect on supporting the location based on UWB. All the software created for this work is published under an open-source license and is publicly available.

Indoor positioning systems based on radio frequency inherently present multipath-related phenomena. This causes ranging systems such as ultra-wideband (UWB) to lose accuracy when detecting secondary propagation paths between two devices. If a positioning algorithm uses ranging measurements without considering these phenomena, it will face critical errors in estimating the position. This work analyzes the performance obtained in a localization system when combining location algorithms with machine learning techniques applied to a previous classification and mitigation of the propagation effects. For this purpose, real-world cross-scenarios are considered, where the data extracted from low-cost UWB devices for training the algorithms come from a scenario different from that considered for the test. The experimental results reveal that machine learning (ML) techniques are suitable for detecting non-line-of-sight (NLOS) ranging values in this situation.

Long Term Evolution (LTE) is expected to substitute the Global System for Mobile Communications (GSM) as the radio access technology for railway communications. Recently, especial attention has been devoted to high-speed trains since this particular environment poses challenging problems in terms of performance simulation and measurement. In order to severely decrease the cost and complexity of high-speed measurement campaigns, we have proposed a technique to induce effects caused by highly-time varying channels on Orthogonal Frequency-Division Multiplexing (OFDM) signals while conducting measurements at low speeds. In this work, we evaluate this technique by comparing the results of LTE measurements at different velocities as well as by simulations. Additionally, we use this technique to show the performance of LTE for high-speed train scenarios up to 600 km/h. To accomplish this, we use both a controlled high-speed measurement setup as well as a channel model developed according to the guidelines of the ITU Radiocommunication Sector (ITU-R) for the evaluation of radio interface technologies for IMT- Advanced systems.

Joint source-channel coding for discrete-time analog sources is an appealing transmission approach because of its extremely low delay and complexity. When the users access the channel orthogonally, analog transmission of correlated information over fading multiple access channels (MACs) using modulo-like mappings provides better performance than uncoded transmission. In this paper, we propose a simplified decoder for modulo mappings in possibly non-orthogonal MAC scenarios with a single-antenna users and a multiple-antenna receiver. Sphere decoding is investigated to reduce the computational complexity when the number of users is large. In addition, affordable strategies are proposed to optimize the mapping parameters according to the channel conditions and the source correlation. The obtained results show that the use of modulo mappings is suitable when the number of antennas at the receiver is larger than the number of users and for high correlation between user data.

Analog transmission of correlated information over Gaussian broadcast channels (BCs) using analog joint source- channel coding (JSCC) is addressed. This communication strategy has attractive advantages, such as low complexity, minimal delay, graceful degradation, and adaptation to time-varying environments. In this paper, we focus on the optimization of parametric continuous mappings that satisfy individual quality of service requirements over Gaussian BCs. This optimization is based on the balancing of the user distortions to ensure the feasibility of the resulting optimization problems. An analysis of the overall distortion corresponding to the considered mappings is carried out to design algorithms that determine the optimal values for the mappings parameters. Results show that the proposed analog JSCC scheme provides near optimal performance and an adequate balancing of the individual distortions.

This work focuses on distributed linear precoding when users transmit correlated information over a fading Multiple-Input and Multiple-Output Multiple Access Channel. Precoders are optimized in order to minimize the sum-Mean Square Error (MSE) between the source and the estimated symbols. When sources are correlated, minimizing the sum-MSE results in a non-convex optimization problem. Precoders for an arbitrary number of users and transmit and receive antennas are thus obtained via a projected steepest-descent algorithm and a low-complexity heuristic approach. For the more restrictive case of two single-antenna users, a closed-form expression for the minimum sum-MSE precoders is derived. Moreover, for the scenario with a single receive antenna and any number of users, a solution is obtained by means of a semidefinite relaxation. Finally, we also consider precoding schemes where the precoders are decomposed into complex scalars and unit norm vectors. Simulation results show a significant improvement when source correlation is exploited at precoding, especially for low SNRs and when the number of receive antennas is lower than the number of transmitting nodes.