Terrestrial Radio Research Articles

Scedasticity descriptor of terrestrial wireless communications channels for multipath clustering datasets

Fifth-generation (5G) wireless systems increased the bandwidth, improved the speed, and shortened the latency of communications systems. Various channel models are developed to study 5G. These channel models reproduce the stochastic properties of multiple-input multiple-output (MIMO) antennas by generating wireless multipath components (MPCs). The MPCs that have similar properties in delay, angles of departure, and angles of arrival form clusters. The multipaths and multipath clusters serve as datasets to understand the properties of 5G. These datasets generated by the Cooperation in Science and Technology 2100 (COST 2100), International Mobile Telecommunications-2020 (IMT-2020), quasi deterministic radio channel generator (QuaDRiGa), and wireless world initiative new radio II (WINNER II) channel models are tested for their homoscedasticity based on Johansen's procedure. Results show that the COST 2100, QuaDRiGa, and WINNER II datasets are heteroscedastic, while the IMT-2020 dataset is homoscedastic.

Joint User Grouping and Resource Allocation for LEO Satellite Multicast

Similar to the dilemma encountered by terrestrial wireless networks, low Earth orbit (LEO) satellite networks are also facing the problem of increasing pressure caused by the growth of wireless traffic. Wireless multicast technology is an effective technique on improving the spectrum efficiency by sending the same signal to multiple users who request the same service. In this article, the LEO satellite transmission system is considered and a maximum system capacity problem is formulated. To solve the original nonconvex problem efficiently, a joint user grouping and resource allocation scheme is proposed. There are two parts in the scheme: on the one hand, we propose an improved K-means++ algorithm for user grouping; on the other hand, we proposed a joint power and bandwidth allocation algorithm using the two stage iteration method. Compared with the baseline approach, our proposed scheme can improve the capacity 10% higher at least.

Ground-to-Air Tropospheric Mapping Function for Very Low Elevation Angles

This paper proposes a real-time capable tropospheric delay model for terrestrial radio navigation systems used for aviation. Its functional scope ranges from elevation angles below the local horizon to zenith. Flight experiments were carried out to quantify the improvement in service coverage achieved for measurements close to and below the local horizon, with aircraft distances in the order of 250 km. Mismodeling tropospheric delays at these elevation angles and aircraft distances can result in slant-range error several order of magnitude greater than the accuracy of terrestrial nav-aids, and adequately accounting them translates into improved accuracy and tighter integrity bounds. The proposed method uses a polynomial representation of the atmospheric refractivity instead of the traditional exponential distribution used in GNSS. The resulting method provides tropospheric corrections accurate to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 0.5 cm for elevation angles above 5 degrees, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> 15 cm at an elevation of 0.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> . In addition, the proposed model offers a reduction in the horizontal position error in terms of an order of magnitude from 14 meters (for uncompensated tropospheric delay) to below 1.6 cm for all flight segments, that will enable terrestrial systems to support increasingly demanding performance based navigation systems.

Flavored electronic nicotine delivery systems ads (2019–2020) on traditional U.S. Media by audience demographics

The tobacco industry has historically targeted flavored products to specific U.S. consumer segments, including young people, women, and systemically marginalized groups based on race, ethnicity, or sexual/gender identity. Existing research on target marketing is focused on cigarettes and smokeless tobacco. In contrast, studies of target marketing of electronic nicotine delivery systems (ENDS)—a growing segment of the U.S. tobacco product market – are much more limited. We analyzed data on 496 ENDS ads and audience demographics to explore the extent to which flavored ENDS ads on cable television (n = 25 ads), terrestrial radio (n = 412 ads), and in print consumer magazines (n = 59 ads) are targeted to different demographic groups based on age, sex, and race/ethnicity. We observed flavor-related content in one-quarter to one-third of ENDS ad occurrences during 2019–2020. Across all media outlets examined, audience age was an important factor in explaining the likelihood of ENDS ads containing flavor-related content. For example, within a television channel, there were 3.82 [95% confidence interval (CI): 1.39–10.49] times greater odds that an ENDS ad contained flavor-related content versus not for every 1% increase in the proportion of U.S. youth ages 6–17 watching a television show. In addition, there were 2.13 [95 %CI: 1.30–3.51] and 1.61 [95 %CI:1.60–1.63] times greater odds that an ENDS ad contained flavor-related content versus not in cable television and radio stations, respectively, for every 1% increase in the proportion of male audience members. Race/ethnicity was an important explanatory factor for the presence of flavor-related content on radio but not television ENDS ads. Our findings suggest differences in target marketing of flavored ENDS by media outlet and audience demographics.

A Software for RFI Analysis of Radio Environment around Radio Telescope

Radio astronomy uses radio telescopes to detect very faint emissions from celestial objects. However, human-made radio frequency interference (RFI) is currently a common problem faced by most terrestrial radio telescopes, and it is getting worse with the development of the economy and technology. Therefore, it is essential to monitor and evaluate interference during the planning, construction, and operation stages of the radio telescope and protect the quiet radio environment around the radio astronomical site. In this paper, we present a software for an RFI analysis of the radio environment around the telescope. In this software, information has been collected, including the location of the site; the technical specifications, such as aperture and the frequency range of the radio telescopes; and the terrain around the site. The software and its modules are composed of telescope, geographic, and meteorological databases, and analysis modules of terrestrial and space-based RFI. Combined with the propagation characteristics of radio waves, we can analyze and evaluate RFI on the ground and in space around the radio telescope. The feasibility of the software has been proved by the experimental implementation of the propagation properties and RFI source estimation. With this software, efficient technical support can be expected for protecting the radio environment around the telescope, as well as improving site selection for planned radio astronomical facilities.

STARFIRE: An algorithm for estimating radio frequency interference in orbits around Earth

Ground-based 21-cm experiments targeting the global signal from the periods of Cosmic Dawn (CD) and Epoch of Reionization (EoR) are susceptible to adverse effects presented by i) the ionosphere ii) antenna chromaticity induced by objects in its vicinity iii) terrestrial radio frequency interference (RFI). Terrestrial RFI is particularly challenging as the FM radio band spanning over 88-108 MHz lies entirely within the frequency range of the CD/EoR experiments (∼40–200 MHz). Multiple space-based experiments have been proposed to operate in the radio-quiet zone on the lunar farside. An intermediate option in cost and complexity is an experiment operating in space in an orbit around Earth, which readily alleviates the first two challenges. However, the effect of RFI in Earth’s orbit on the detection of global signal needs to be quantitatively evaluated. We present STARFIRE – Simulation of TerrestriAl Radio Frequency Interference in oRbits around Earth – an algorithm that provides an expectation of FM seeded RFI at different altitudes over Earth. Using a limited set of publicly available FM transmitter databases, which can be extended by the user community, we demonstrate the use of the STARFIRE framework to generate a three-dimensional spatio-spectral cube of RFI as would be measured in Earth orbit. Applications of STARFIRE include identifying minimum RFI orbits around Earth, producing RFI spectra over a particular location, and generating RFI heatmaps at specific frequencies for a range of altitudes. STARFIRE can be easily adapted for different frequencies, altitudes, antenna properties, RFI databases, and combined with astrophysical sky-models. This can be used to estimate the effect of RFI on the detection of global 21-cm signal from Earth-orbit, and hence for sensitivity estimates and experiment design of an Earth orbiting CD/EoR detection experiment.

A TETRA-Based System for Remote Health Monitoring of First Responders: Peak AoI Assessment in Direct and Trunked Mode

In this article, we study peak age of information (PAoI) performance of a novel Internet of Things (IoT) solution for remote health monitoring of first responders over terrestrial trunked radio (TETRA) links. The solution features a set of sensors embedded in a smart garment that periodically records and sends physiological parameters of first responders to a remote agent. The received data are analyzed by the remote agent, which feeds back notifications and warnings to the first responders in the form of electrotactile stimuli. The communication in the system is performed over the TETRA short data service (SDS), which is the default option for the development of third-party applications and which has rather limited capabilities. The choice of PAoI as the parameter of interest is motivated by its suitability to measure data freshness in IoT applications with periodic monitoring. We derive closed-form expressions of PAoI for different packet-management schemes allowed by the TETRA standard and verify the analytical results through extensive simulations under varying message generation rates. Our results provide important insights on the expected PAoI performance, which can be used for the system design guidelines. To the best of our knowledge, this is the first work that analyzes AoI performance of the TETRA networks.

Deep Reinforcement Learning for Joint Trajectory Planning, Transmission Scheduling, and Access Control in UAV-Assisted Wireless Sensor Networks.

Unmanned aerial vehicles (UAVs) can be used to relay sensing information and computational workloads from ground users (GUs) to a remote base station (RBS) for further processing. In this paper, we employ multiple UAVs to assist with the collection of sensing information in a terrestrial wireless sensor network. All of the information collected by the UAVs can be forwarded to the RBS. We aim to improve the energy efficiency for sensing-data collection and transmission by optimizing UAV trajectory, scheduling, and access-control strategies. Considering a time-slotted frame structure, UAV flight, sensing, and information-forwarding sub-slots are confined to each time slot. This motivates the trade-off study between UAV access-control and trajectory planning. More sensing data in one time slot will take up more UAV buffer space and require a longer transmission time for information forwarding. We solve this problem by a multi-agent deep reinforcement learning approach that takes into consideration a dynamic network environment with uncertain information about the GU spatial distribution and traffic demands. We further devise a hierarchical learning framework with reduced action and state spaces to improve the learning efficiency by exploiting the distributed structure of the UAV-assisted wireless sensor network. Simulation results show that UAV trajectory planning with access control can significantly improve UAV energy efficiency. The hierarchical learning method is more stable in learning and can also achieve higher sensing performance.

Towards the application of neuromorphic computing to satellite communications

Artificial intelligence (AI) has recently received significant attention as a key enabler for future 5G-and-beyond terrestrial wireless networks. The applications of AI to satellite communications is also gaining momentum to realize a more autonomous operation with reduced requirements in terms of human intervention. The adoption of AI for satellite communications will set new requirements on computing processors, which will need to support large workloads as efficiently as possible under harsh environmental conditions. In this context, neuromorphic processing (NP) is emerging as a bio-inspired solution to address pattern recognition tasks involving multiple, possibly unstructured, temporal signals and/or requiring continual learning. The key merits of the technology are energy efficiency and capacity for on-device adaptation. In this paper, we highlight potential use cases and applications of NP to satellite communications. We also explore major technical challenges for the implementation of space-based NP focusing on the available NP chipsets.

Evaluation of Refractivity Gradient and k-factor within the Lower Troposphere of Maiduguri and Enugu under Two Climatic Zones in Nigeria

Estimation of radio refractivity is important in the planning and design of terrestrial radio communication links for the availability and accessibility of strong networks and signals. This paper investigates the refractivity gradient, effective earth radius factor (k), and the geo-climatic factor K in the first 1km of the troposphere of two selected stations (Maiduguri and Enugu) under different climatic zones in Nigeria. The indirect method of measuring radio refractivity was employed in this study to take measurements over the two selected stations. Vertical profile values of pressure (hPa), Temperature (°C), and Relative humidity (%) within the first 1 km were extracted from MERRA MAIMCPASM V5.20 database profile obtained from a satellite sounding instrument by NASA in the United States. MatLab programming language was used to evaluate the refractivity gradient, k-factor, and geo-climatic factor using the equations recommended by ITU. The results showed that Enugu was predominantly sub-refractive due to the tropical savannah climate while Maiduguri encountered both sub-refractive and normal refractive conditions due to the hot semi-arid climate, and unstable and extreme weather conditions in the region.

The impact of elements and advertising broadcasting intensity exposure in terrestrial radio

Advertising is appealing to most organisations since it provides information, entertains, and communicates, especially on the radio. Advertising can boost sales, but it must be carefully structured to be effective. Radio advertisers must create engaging messaging to be effective. Selective attention, Selective perception, and Selective retention are the three ways people pick mass media content. Selective attention occurs when people focus on interesting messages. Selective perception means people interpret messages according to their ability. Selective retention is when a person only remembers the message they want. Radio advertising has several qualities and tools, which can be divided into Message Style, Message Structure, and Presenter. Radio advertising messages are readily forgotten since they function on the right side of the brain and excite more impulse nets, so they must be repeated. According to current studies, improving the Radio Advertising Components (Message delivery style, Message Structure, and Message Conveyor) by 0.125 times will enhance audience attention. Under the same conditions, increasing radio ad frequency and duration by 0.0512 times decreases audience interest. Increased radio advertising intensity decreases audience interest. Nonetheless, this may happen because field surveys suggest audiences are happier or more interested in listening to high-quality radio ads by paying special attention to radio ad aspects. Radio advertisement broadcasting is intense and plays low-quality ads, which displeases audiences.

RESONANCE, REVERBERATION, AND RHYTHMS: CONCEPTUALIZING VIBRATORY POWER IN DIGITAL MEDIA

This panel explores the theories, concepts, and methodologies appropriate to the exploration of the “vibratory power” of transduction where sound and other digitally mediated forms of reverberation conjure affective and corporeal bonds of resonance and solidarity. The first three papers engage in a critical interrogation of sound as epistemology and ontologies in digital networked media, with a focus on streaming internet radio. More specifically, these papers examine diverse uses and experiences of streaming ‘radio’ and its deployments for community-building, emotional sustenance, and an online reconstitution of place. In their explorations, these papers consider which methodologies are appropriate and useful to examine the affordances of streaming radio, enabling in turn analysis of informing ideas of ‘community’, intimacy, territorialization/deterritorialization, and questions of what comprises ‘radioness’ itself. Is radio still radio if it is online? What happens to the "community" that is conjured into being by terrestrial broadcast radio when the "station" is internet only?’ What does thinking from place mean when the tangibility of place is physically transcended and thus broken up and reconstituted as elements of locality, of community? The fourth and final power transposes the “perspective of frequency, force and affective tone” in transduction to the wider field of social media practices during the early stages of COVID. In this paper, the obsessive-compulsive ritual of “doomscrolling” becomes the practice through which rhythms of resonance and reverberation become imprinted upon the flows of everyday life through the transductive modulations of digital networked media.

Predicting the performance of radio over free space optics system using machine learning techniques

The efficiency and reliability of radio over free space optical communication may be significantly degraded by different weather conditions, thus leading to decrease and increase of signal to noise ratio (SNR) and bit error rate (BER) of the recovered signal respectively. Therefore, real time optical performance estimation is of utmost importance to enable the awareness of channel conditions and for achieving high quality of service. In this work, a terrestrial radio over free space optics (RoFSO) system has been investigated by employing 32-QAM and 64-QAM OFDM modulation schemes under rain, haze and clear weather conditions. Performance of the system is further estimated by employing artificial neural networks (ANN), k-nearest neighbours (KNN) and decision tree (DT) machine learning (ML) techniques with root mean square error (RMSE) and coefficient of determination (R2) as performance metrices. Atmospheric attenuation and different internal system parameters act as input features with BER and SNR of the received signal as the modelling targets. The best performing model has been found to be by applying an ANN approach with lower RMSE values for SNR and BER as 1.87 and 1.26 respectively. The value of R2 predicted by the model are 0.97139 and 0.97802 for SNR and BER data sets respectively.

On the Degrees of Freedom of a Propagation-Delay Based Multicast X Channel with Two Transmitters and Arbitrary Receivers

Compared with the unicast scenario, X channels with multicast messaging can support richer transmission scenarios. The transmission efficiency of the wireless multicast X channel is an important and open problem. This article studies the degrees of freedom of a propagation-delay based multicast X channel with two transmitters and arbitrary receivers, where each transmitter sends K different messages and each receiver desires K - 1 of them from each transmitter. The cyclic polynomial approach is adopted for modeling and analysis. The DoF upper bound is analyzed and shown to be unreachable. Then a suboptimal scheme with one extra time-slot cycle is proposed, which uses the cyclic interference alignment method and achieves a DoF of K - 1. Finally, the feasibility conditions in the Euclidean space are derived and the potential applications are demonstrated for underwater acoustic and terrestrial radio communications.

Lev Petrovich Pitaevskii

Lev Petrovich Pitaevskii

Aerial-assisted task offloading approach over multilayer satellite edge computing

Satellite communication technology can solve the signal covering issues in special areas, such as deserts and oceans, which is difficult to handle with in traditional terrestrial wireless networks. Mobile edge computing (EMC) is crucial to help satellite communication to improve latency and energy efficiency performance. In this paper, we model a satellite constellation edge computing architecture with three layers and propose a task offloading deployment approach to balance the computation task. Simulation results show that the proposed algorithm can greatly reduce the execution delay of missions, improve the utilization of satellite resources, and increase the mission success rate compared with the existing strategies.

Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks

Non-terrestrial network (NTN) services using low-Earth-orbit (LEO) satellites are expanding. Interference management of NTN services with other terrestrial wireless services is emerging as a critical issue owing to the inherent international and vast coverage nature of NTN. This study develops a multi-agent deep reinforcement learning (DRL) framework to establish a multi-beam uplink channel allocation strategy that minimizes interference with incumbent stations under the given quality of service (QoS) constraints. We propose a novel framework with the sequential training of agents in a specific order to overcome the inherent non-stationarity of multi-agent DRL. To improve learning efficiency, we design the training sequence in accordance with reward function and initial state. As a result, taking actions in the order of the largest interference to the incumbent station provides superior performance to taking actions in an arbitrary order. Moreover, the proposed channel allocation performs close to the optimal exhaustive search and outperforms conventional greedy graph coloring method.

A review of routing algorithms for underwater wireless sensor networks

With the rise of the upsurge of exploiting marine resources and the rapid development of the research of terrestrial wireless sensor networks, the research of underwater wireless sensor networks (UWSNs,) has become a new research hotspot. In UWSNs, routing protocol plays a very important role as one of the important components. This paper introduces several classical routing algorithms from the following categories: location, sector, reinforcement learning, and intelligent optimization algorithms. Finally, the paper also summarizes and prospects the research direction of underwater wireless sensor network routing algorithms.

STATISTICAL CHARACTERISTICS OF TWO-DIMENSIONAL AUTOCORRELATION FUNCTIONS IN NOISE-LIKE COMPLEX SIGNALS

For various terrestrial and satellite radio systems, the task of detecting complex signals under conditions of a priori uncertainty, when their parameters, including the modulation law, is unknown, is relevant. Detection of such signals can be combined with estimation (measurement) of their parameters, such as carrier frequency and delay time. A method for calculating two-dimensional autocorrelation functions of noise-like complex signals has been developed and their types have been studied depending on the type of pseudo-random sequences on the basis of which they are formed, as well as the modes of emission of these signals in radio systems. Regularities have been revealed in the statistical characteristics of side peaks of various types of complex signals depending on the length of pseudo-random sequences and radiation modes. The developed methodology for calculating the two-dimensional correlation function of complex signals made it possible to identify the relationship between the values of the side peaks of the correlation functions of pseudo-random sequences used in their formation and the peaks of the modules of their two-dimensional correlation function. This approach allows, without a detailed calculation of the two-dimensional correlation functions themselves in various modes of emission of complex signals, to determine their statistical characteristics using the corresponding parameters of the distribution functions of the side peaks of the correlation functions. The two-dimensional correlation function of various complex signals was calculated and their statistical characteristics were studied.

Power Amplifier Design for 25.1 km Long-Distance Transmission Under 214 km/h High-Speed Movement of Air-to-Ground Wireless Self-Assembled Network Nodes

In this paper, we design a power amplifier for this network protocol to combat the Doppler effect caused by the high-speed movement of network nodes and the rapid signal fading failure caused by the long-distance transmission of network nodes, and to extend the communication distance between network nodes, to realize the long-distance transmission under the high-speed movement of network nodes. The overall design of the power amplifier for bi-directional transmission structure, power amplifier transmitter, and low-noise amplifier receiver is used in a four-stage cascade current series negative feedback structure mode, where the signal transmitter selected RFPA5208 and MAX4003 chip, the signal receiver selected TQP3M9037 and SKY16602 chip. The above overall design enables the self-assembled nodes to ensure stable topology and achieve long-spacing transmission even in the high-speed mobile state. The test results show that the output power is 41.5dBm-44.3dBm, the gain is 44dB-45dB, and the noise is 1.6dB when the frequency band range is 2.4GHz-2.5GHz. The experimental results show that when the self-assembling nodes maintain the relative speed of 214km/h, the longest transmission distance of the point-to-point of the amorphous flat terrestrial wireless self-assembling network reaches 16300m, and the longest transmission distance of the point-to-point of the amorphous flat air-to-ground wireless self-assembling network reaches 25100m. The power amplifier designed in this paper has good application value. It provides an individual research basis for future research on the framework of a low-altitude economic fly-by-wire network.