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.

The radio spectrum is a limited resource. Demand for wireless communication services is increasing exponentially, stressing the availability of radio spectrum to accommodate new services. TV white space (TVWS) technologies allow a dynamic usage of the spectrum. These technologies provide wireless connectivity, in the channels of the very high frequency and ultra high frequency television broadcasting bands. In this paper, we investigate and compare the coverage range, network capacity, and network energy efficiency for TVWS technologies and LTE. We consider Ghent, Belgium, and Boyeros, Havana, Cuba, to evaluate a realistic outdoor suburban and rural area, respectively. The comparison shows that TVWS networks have an energy efficiency 9-12 times higher than LTE networks.

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.

In this paper, we investigate the coexistence of cognitive radio networks on TV white spaces for rural and suburban connectivity. Although experimental models and laboratory measurements defined the maximum interference threshold for TV white space technologies for general use cases, our research found that in real wireless rural and suburban scenarios, severe interference to the broadcasting services might occur. This is particularly relevant when the traffic load of the telecom base stations (BSs) exceeds 80% of their maximum capacity. We propose a dynamic management algorithm for minimizing the interference, based on a centralized access control architecture for cognitive radio wireless networks. In an experimental emulation for assessing the impact of cognitive radio interference on the broadcasting service’s QoE, our method reduced the perceived video distortion by the broadcasting users by at least 50% and 27.5% in a rural and suburban scenario, respectively, while the spectrum usage is increased by just 8%.

Despite the recent advances in broadband penetration and accessibility, broadcasting networks continue to be the most efficient way for delivering media content to large areas independently of the user density. Nevertheless, a convergence of broadcasting services into the broadband networks is foreseen. In this context, Layered Division Multiplexing (LDM) allows the joint provision of unicast, multicast, and broadcast services in mobile cells infrastructure. Despite the success of LDM in broadcasting infrastructure, its practical application in Long Term Evolution networks providing Evolved Multimedia Broadcast Multicast Services (LTE eMBMS) and 5G-MBMS is more difficult. In this paper, we experimentally quantify the cell-interference and its impact on the network performance and Quality of Service. Our experiments reveal that the inter-cell interference margin for LTE eMBMS is about 3 dB higher compared to traditional LTE networks. If a layered architecture is incorporated this higher inter-cell interference would cause a reduction of approximately 19% of the Enhanced Layer (Lower Layer) coverage.

We investigate the feasibility of Internet of Things (IoT) technology to monitor and improve the energy efficiency and spectrum usage efficiency of broadcasting networks in the Ultra‐High Frequency (UHF) band. Traditional broadcasting networks are designed with a fixed radiated power to guarantee a certain service availability. However, excessive fading margins often lead to inefficient spectrum usage, higher interference, and power consumption. We present an IoT‐based management platform capable of dynamically adjusting the broadcasting network radiated power according to the current propagation conditions. We assess the performance and benchmark two IoT solutions (i.e., LoRa and NB‐IoT). By means of the IoT management platform the broadcasting network with adaptive radiated power reduces the power consumption by 15% to 16.3% and increases the spectrum usage efficiency by 32% to 35% (depending on the IoT platform). The IoT feedback loop power consumption represents less than 2% of the system power consumption. In addition, white space spectrum availability for secondary wireless telecommunications services is increased by 34% during 90% of the time.