DTMB monitoring tool based on a commercial set-top box

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

et al. 2022

Self Cite

Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.

Section: A Study Areamentioning

confidence: 99%

Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Santana¹,

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

et al. 2022

Self Cite

“…The software algorithm to set-up and adjust the tuner aims to optimize the Signal-to-Noise-Ratio, minimize the distortions, and avoid saturation by adjusting an Automatic Gain Control System (AGC). Although the reception quality is improved, the measurements can be distorted [7]. Hence, the measurement algorithm must be calibrated in order to achieve the highest precision allowed by the receiver hardware.…”

Section: A Emulation Setup and Configurationmentioning

confidence: 99%

“…1 shows a block diagram of a dynamic broadcasting network design. In the digital TV receiver, software running in the background in the middleware can measure the signal level variations (e.g., [7]). This information is transmitted to the broadcasting network via a dedicated feedback network.…”

Section: Introductionmentioning

confidence: 99%

Emulation of a Dynamic Broadcasting Network with Adaptive Radiated Power in a Real Scenario

2018 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

Santana³

et al. 2018

Self Cite

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.

“…The IoT network has to be designed and optimized to overlap with the broadcasting network coverage. STBs include a System on Chip (SoC) with enough computational performance to implement complex measurement tasks [26]. A measurement device implemented by software in a commercial DTMB (Digital Terrestrial Multimedia Broadcast) STB reported a dispersion of 2 dB for signal level measurements (absolute value) and Signal-toNoise Ratio (SNR) measurements (relative value) [26].…”

Section: Collaborative Network Designmentioning

confidence: 99%

IoT‐Based Management Platform for Real‐Time Spectrum and Energy Optimization of Broadcasting Networks

Wireless Communications and Mobile Computing

Pupo³

et al. 2018

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.