Accurate wireless network planning is crucial for the deployment of new wireless services. This usually requires the consecutive evaluation of many candidate solutions, which is only feasible for simple path loss models, such as one-slope models or multi-wall models. However, such path loss models are quite straightforward and often do not deliver satisfactory estimations, eventually impacting the quality of the proposed network deployment. More advanced models, such as Indoor Dominant Path Loss models, are usually more accurate, but as their path loss calculation is much more time-consuming, it is no longer possible to evaluate a large set of candidate deployment solutions. Out of necessity, a heuristic network planning algorithm is then typically used, but the outcomes heavily depend on the quality of the heuristic. Therefore, this paper investigates the use of Machine Learning to approximate a complex 5G path loss model. The much lower calculation time allows using this model in a Genetic Algorithm-based network planning algorithm. The Machine Learning model is trained for two buildings and is validated on three other buildings, with a Mean Absolute Error below 3 dB. It is shown that the new approach is able to find a wireless network deployment solution with an equal, or smaller, amount of access points, while still providing the required coverage for at least 99.4% of the receiver locations and it does this 15 times faster. Unlike a heuristic approach, the proposed one also allows accounting for additional design criteria, such as maximal average received power throughout the building, or minimal exposure to radiofrequency signals in certain rooms.

New frequency bands, such as the mmWave at 28 GHz for fifth Generation networks in Frequency Range 2, are being introduced to accomplish the required throughput for new services such as remote surveillance, object tracking, and factory automation. These high-frequency bands are sensible to reflections and diffraction. Therefore, accurate path loss calculation relies on advanced models and ray tracing. However, this is time-consuming, and evaluating a large set of candidate solutions is no longer possible when planning the optimal number and location of base stations in the network planning process. This paper investigates the use of machine learning to approximate a complex mmWave ray-tracing-based path loss model in indoor scenarios. The much lower calculation time allows us to approximate the ray-tracer path loss estimation well and to apply a genetic algorithm for realizing network planning. The machine learning model is trained and validated for two buildings and tested with another, with an average of the Mean Absolute Error of 2.8 dB over all cases. It is shown that the combination of Machine Learning and Genetic Algorithm is able to find a network deployment in the FR2 band accounting for the minimum number of access points and minimum electromagnetic exposure, while still providing a predefined coverage percentage inside the building.

Currently in LACETEL, Research & Development Telecommunications Institute, Cuba, it is under development a DTMB tool, built on the hardware of commercial TV receivers. The present work focuses on the development of the needed functionalities over two different hardware platforms. It is covered the main functionalities implemented so far, the monitoring of signal parameters, its presentation on screen and the workflow for statistics processing. Since the result is to transform TV receivers into measuring instruments, it is also described the process followed for calibration and adjustment of the parameters being measured by the tools.

Broadcasting networks are an efficient means for delivering media content to a high density of users, because their operational cost is almost independent of the size of their audience for a given coverage area. However, when the propagation conditions are better than the worst-case design, the energy efficiency is suboptimal. In this paper, we present the results of a trial to emulate the performance of a dynamic broadcasting network with adaptive radiated power in a real broadcasting scenario. We assess the radiated power of the broadcasting network in a Cuban environment by means of a monitoring device. The power consumption of the dynamic broadcasting network with adaptive radiated power is assessed and compared with traditional broadcasting for different implementation margins. To emulate the performance of the dynamic broadcasting network with adaptive radiated power, we consider a commercial Digital Terrestrial Multimedia Broadcast (DTMB) transmitter in Havana, Cuba. Testbed hardware is designed and developed to measure the fading with a commercial receiver and emulate the signal reception under adaptive power conditions. The dynamic broadcasting network performance is assessed following the general guidelines and techniques for the evaluation of digital terrestrial television broadcasting systems recommended in the ITU-R BT.2035-2 report.

Spectrum scarcity has become a challenge for the growing demand of wireless services. Paradoxically, although most of the sub-1-GHz spectrum is assigned it is underutilized in the space and time domain. Several surveys demonstrate a spectrum usage efficiency lower than 20%. Cognitive Radio technologies has been proposed as a long-term solution for tackling spectrum scarcity and inefficiency. However, a major concern has been the interference to and from the primary licensed service. In this paper we investigate the coexistence between broadcasting digital television and LTE-A distributed cognitive radio in real scenarios. Our research revealed that, for the worst case the LTE-A distributed cognitive radio causes a degradation of the broadcasting service QoE higher than 60%. For the LTE-A network there is up to 5 dB C/I margin that does not worsen its QoS, being a potential resource for networking optimization and interference mitigation.

This paper is the design of a Radio Environment Map (REM) with a real-time tool to sense the radiofrequency spectrum and optimally places with Surrogate Modelling and Sequential Experimental Design tools a total of 72 SDR sensors in the selected area, using LoRa and/or NB-IoT technologies for networking. It permits the regulatory body to check the correct use of the assigned spectrum and constitutes a communication alternative in case of a catastrophic event, such as a hurricane or an earthquake, where radio and TV broadcasting play an important role in keeping people informed after such meteorological event. The radiobroadcast services use large antennas and high towers, making them vulnerable to such events. Regardless of the chosen technology, the IoT monitoring network will be more robust, since it uses small antennas and lower towers, and often a given area is covered by multiple base stations. The tool can be used to deploy new services in the nonserved area (e.g., 4G in the 700 MHz band at a lower cost or using TVWS techniques to provide communications and internet connection) and optimal interference management.