Abstract
The number of Internet of Things (IoT) devices has experienced a large growth during the last decade, as well as the data volume gathered from remote sensors. Satellites are still a suitable communication method and may be preferable for a remote ubiquitous sensor network (USN), which sometimes are located in places without much communications infrastructure where coverage is the principal drawback. Alternatively, the proposed solution for this article aims at a near-vertical incidence skywave (NVIS) channel for high frequencies (HF) with a low-cost platform, allowing a low-power transmissions coverage area up to 250 km for USN. The HF standards are focused on generic communication channels not being robust for NVIS communications. In this article we study and test an alternative based on orthogonal frequency-division multiplexing (OFDM) modulations to make them more robust and less dependent on the channel NVIS communications. For that purpose, we test the HF standard modulations and a designed OFDM modulation to prove the robustness of each. This study has been tested between Barcelona and Tarragona, using different transmission power levels and modulation orders.
Highlights
It is not news that the number of sensors and mobile devices is increasing enormously every day in the current world
In this paper we present an alternative to the standards by the use of a wideband orthogonal frequency-division multiplexing (OFDM)
The mostarelevant results obtained from the test performed will be shown
Summary
It is not news that the number of sensors and mobile devices is increasing enormously every day in the current world. The infrastructure of the communications for these devices is very extended in areas with a high population. Some areas in the world do not have such infrastructure due to complex orography, which makes communications between the transmitter and receptor almost impossible. The environmental impact of satellite deployments [1] and their high cost has made researchers discover new methods of communications especially with the aim of collecting data through remote sensors for several scientific studies. Remote sensing became an extended study focus making use of new technologies such as light detection and ranging (LIDAR), artificial intelligence (AI) [2], machine learning [3], geocoding algorithms [4], deep convolutional neural networks [5] or multi-sensor fusion positioning [6], being part of some examples of the wide range of technologies that sensing uses
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