IoT-based management platform for real-time spectrum and energy optimization of broadcasting networks

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

From MFN to SFN : performance prediction through machine learning

In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R-2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97

Análisis del comportamiento de la ganancia de SFN para DTMB

En los últimos años se ha venido desplegando en Cuba el servicio de Televisión Digital Terrestre (TDT) de acuerdo con el estándar DTMB en su esquema para redes de múltiples frecuencias (MFN). Sin embargo, como parte de la evolución de esta tecnología, algunos países han migrado hacia el despliegue de redes de una sola frecuencia (SFN), pues este esquema provee un uso más eficiente del espectro radioeléctrico. Estudios sobre SFN muestran que es posible con este esquema conseguir una distribución más homogénea de la calidad de la señal recibida y, además, las señales provenientes de transmisores diferentes pueden ser combinadas de forma constructiva para obtener una ganancia en la recepción. No obstante, algunos autores consideran que un aumento de la intensidad total de la señal recibida, no siempre se corresponde con una mejor recepción. Es por esto que se han considerado parámetros propios de la recepción como: relación señal a ruido (SNR) y razón de modulación errónea (MER), en lugar de la intensidad de la señal recibida para evaluar la ganancia. En este artículo se presenta un análisis, a partir de los resultados de mediciones de laboratorio, que permite caracterizar la ganancia de SFN (SFNG) para DTMB, considerando el parámetro MER como medida de la calidad de la señal recibida. Además, se presentan los resultados obtenidos de evaluar la capacidad de recepción de un receptor comercial en SFN con presencia de multitrayectos con valores de retardo cercanos a la duración del intervalo de guarda

Assessment of white spaces quality in rural areas : a large-scale spectrum survey

Spectrum is a valuable resource for broadcasting and wireless communications. Despite the fact that most of the sub-1-GHz spectrum is granted, spectrum surveys in suburban areas reveal a poor usage efficiency (less than 20% on average). Cognitive Radio for 56 next generation radio is a potential technology for dynamically exploit the spectrum white spaces, particularly in low populated rural areas. However, not all the empty spectrum is useful for providing broadband wireless connectivity guaranteeing a certain Quality of Service (QoS) or without causing harmful interference to the primary licensed services. In this paper we perform a large scale -spectrum survey and measurement campaign in two rural areas. We also quantify the white spaces and its real potential for secondary services. Our research reveals that, on average, less than 11% of the spectrum is in use in the surveyed rural areas. However, in one area only 60% of the white spaces can be used for a commercial service

Experimental assessment of harmful interference between broadcasting and distributed cognitive radio networks in real scenarios

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