Fixed Satellite Services (FSS) used to be alone at the C-Band spectrum in most countries. Since the deployment of 5G in many countries (i.e., 3.3–3.6 GHz), FSS is not the exclusive system in the C-Band anymore. In order to minimize the detrimental interference for the FSS to allowable levels, regional exclusion zones of maximum radiated power in 5G base stations (BS) are proposed and evaluated. In this paper, a measurement campaign has been carried out, and an analysis of the interference has been studied. A filtering model, namely Filter to Remove Broadband Interference 5G (FIREBRING), is proposed and analyzed concerning the carrier-to-noise ratio (C/N). Moreover, this paper focuses on the evaluation of the 5G interference into the FSS. The proposed solution deployed an Low-Noise Block (LNB) with a band frequency of 3.7 to 4.2 GHz to test the satellite down-conversion signal at the receiver. The paper offered a complete analysis of the 5G signal, taking into account the implications of out-of-band emissions, potentially LNB saturation into FSS receiver, and the repercussions of the deployment of the 5G BS active antenna systems. With the LNB and down-converter in place, it can be found that the signal interference between 1.450 GHz and 1.550 GHz, is nearly 18 dB.

In the next few years, Low Earth Orbit (LEO) constellations will become key enablers for the deployment of global Internet of Things (IoT) services. Due to their proximity to Earth, LEO satellites can directly communicate with ground nodes and, thus, serve as mobile gateways for IoT devices deployed in remote areas lacking terrestrial infrastructure. Within this Direct-to-Satellite IoT (DtS-IoT) context, LoRa (Long Range) technology, capable of providing long range connectivity to power-constrained devices, has received great attention. However, serious scalability issues have been observed in LoRa-based DtS-IoT networks when a high number of LoRa devices perform uplink transmissions driven by the straightforward Aloha protocol during the short visibility periods of the passing-by satellites. In this paper, we evaluate some Aloha-based protocols suitable for this kind of networks and present a new adaptive variant that allows LoRa devices to dynamically adjust their uplink transmission rates in order to make the network work near its optimal operating point. Simulation results show that our proposal is able to significantly improve the network throughput in overloaded scenarios without the need for coordination among LoRa devices, listening to the channel nor gateway support.

As the number of satellite networks increases, the radio spectrum is becoming more congested, prompting the need to explore higher frequencies. However, it is more difficult to operate at higher frequencies due to severe impairments caused by varying atmospheric conditions. Hence, radio channel forecasting is crucial for operators to adjust and maintain the link’s quality. This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is composed of; (i) forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) statistical channel simulator that generates a time-series path-loss random process, intended for system design and research. Both approaches capitalize on real-measurements obtained from Al-phaSat’s Q/V-band transmitter at different geographic latitudes. The results show that model-based deep learning can outperform simple statistical and deep learning methods by at least 50%. Moreover, the model is capable of incorporating varying rain and elevation angle profiles.

This paper presents results of a narrowband measurement campaign conducted inside a Boeing 737-400 aircraft, the objective being the development of a propagation prediction model which can be used in the deployment of in-cabin wireless networks. The measurements were conducted at three different frequency bands: 1.8, 2.1, and 2.45 GHz, representative of several wireless services. Both a simple, empirical, inverse distance power law and a deterministic, site-specific model were investigated. Parameters for the empirical model were extracted from the measurements at different locations inside the cabin: aisle and seats. Additionally, a statistical characterization of the multipath scenario created by the transmitted signal and the various cabin elements is presented. The deterministicmodel, based on Physical Optics (PO) techniques, provides a reasonable match with the empirical results. Finally, measurements and modeling results are provided for the penetration loss into the cabin (or out of the cabin), representative of interference scenarios.

The use of indoor wireless devices has substantially increased in recent years. This escalation is due to the expansion of traditional communication devices, such as mobile phones and laptop computers, to less traditional ones, such as wirelessly communicating sensor nodes present in smart homes, office buildings, and industrial environments. The placement of these nodes varies considerably from one application to the next, but, when the nodes are placed indoors and within the vicinity of human height, a body's movement can cause significant time-varying channel conditions. The movement is even more compelling in such networks because of the power constraints involved [1]. Thus, an accurate study on the impact of a moving body and the characteristics of indoor propagation channels is important.