Weak Signal Propagation Research Articles

Insights on Polar Day Antarctica Radio Propagation Using Amateur Radio Beacons on Circumnavigating Balloons

We deployed six pico balloons with 20 m transmitters (14.09 MHz) from Neumayer Station III in the 2022 Antarctic summer. Our objective was to evaluate ionospheric propagation in lower latitudes. Leveraging the Weak Signal Propagation Reporter (WSPR) protocol, we transmitted and received telemetry data on a global scale. Each balloon remained airborne for over a month, with one completing eight circumnavigations of the southern hemisphere, transmitting WSPR beacon data for 98 days. Our analysis focused on signal propagation characteristics in the polar ionosphere and surrounding regions, considering factors such as location relative to the WSPR network and solar elevation angles. Alignment between solar elevation angles at transmitting and receiving stations indicated a relationship with signal reception; lower solar elevation angles proved crucial for long-range propagation. We discovered that, beyond a solar angle of 60 degrees above the horizon, no decodes were recorded beyond 7500 km. Most signal spots were observed within a 1000–5000 km range and solar elevation angles ranging from 1 to 80 degrees. Over Antarctica, spot occurrences peaked around 4 UTC, particularly during the early hours of the day. Our findings demonstrate the usefulness of pico balloons for propagation studies, providing insights into the WSPR network’s coverage over Antarctica and surrounding lower latitudes.

A Survey on IoT Positioning Leveraging LPWAN, GNSS, and LEO-PNT

Location data is an important piece of information in many Internet of Things (IoT) applications. Global Navigation Satellite Systems (GNSS) have been established as the standard for large-scale localization. However, the rapidly increasing need to locate IoT devices in recent years has exposed several shortcomings of traditional GNSS approaches. These limitations include the weak signal propagation in indoor and dense environments, the inability to calculate or obtain a location remotely, and a high energy consumption. Therefore, several industries have shown an increasing demand for alternative and innovative positioning solutions that are more suited in an IoT context. Hence, we conduct a survey on state-of-the-art, large-scale and energy-efficient positioning techniques for IoT applications. More specifically, we analyze the performance of terrestrial-based Low Power Wide Area Network (LPWAN) techniques, novel GNSS solutions, and innovative positioning techniques leveraging Low Earth Orbit (LEO) satellite constellations. A comparison is made in terms of 16 dimensions including energy consumption, positioning accuracy, coverage, and scalability. The analysis shows that interoperability between technologies is key to enable energy-efficient communication and positioning applications in the emerging market of satellite IoT.

Neuronal morphology and network properties modulate signal propagation in multi-layer feedforward network

The propagation and detection of weak signals play a vital role in the central nervous system's information processing. In this paper, a biophysical two-compartment model is adopted to investigate how the neuronal morphology and network properties modulate signal propagation in a multi-layer feedforward network (FFN). The numerical simulation results show that neurons with larger dendrites have higher firing rates and better responses to weak signals. Similarly, the output layer of FFN constructed by larger-dendrite neurons also exhibits better responses. A suitable chaotic current is necessary for the propagation of weak signals. Excessively strong or weak chaotic current leads to propagation failure. Sparse connection and weak synaptic strength optimize the responses of the output layer, which is consistent with real biological networks observed in the brain. It is found that weak signal propagation in FFN is highly correlated with the regulation of firing rate. Our results may provide novel insights into the modeling of complex networks and network function implementation.

Adaptive detection for resolvable multipath signal under heterogeneous clutter

In the urban environment, the radar is easily affected by multipath (MP) returns. The problem of detecting point-like targets in the urban environment with the existence of resolvable MP returns under the background of heterogeneous clutter is addressed. Two adaptive detectors have been devised with a combination of prior information on the environment and echoes of target. The detectors can use the MP echoes in all MP prediction range cells for detection or in some available paths for signal detection by the available path selection algorithm that eliminates the weak signal propagation paths. Remarkably, the proposed decision schemes exhibit the desirable constant false alarm rate property with respect to the unknown parameters of the heterogeneous interference. The performance assessment, conducted on simulated data in terms of detection probability also in comparison with conventional solutions, highlights the effectiveness of the MP exploitation, while the MP signal is strong and shows the detection performance enhancement potential.

A Systematic Study of 7 MHz Greyline Propagation Using Amateur Radio Beacon Signals

This paper investigates 7 MHz ionospheric radio wave propagation between pairs of distant countries that simultaneously lie on the terminator. This is known as greyline propagation. Observations of amateur radio beacon transmitters recorded in the Weak Signal Propagation Reporter (WSPR) database are used to investigate the times of day that beacon signals were observed during the year 2017. The WSPR beacon network consists of thousands of automated beacon transmitters and observers distributed over the globe. The WSPR database is a very useful resource for radio science as it offers the date and time at which a propagation path was available between two radio stations, as well as their precise locations. This paper provides the first systematic study of grey-line propagation between New Zealand/Eastern Australia and UK/Europe. The study shows that communications were predominantly made from the United Kingdom (UK) to New Zealand at around both sunset and sunrise times, whereas from New Zealand to the UK, communication links occurred mainly during UK sunrise hours. The lack of observations at the UK sunset time was particularly evident during the UK summer. The same pattern was found in the observations of propagation from Eastern Australia to UK, and from New Zealand and Eastern Australia to Italy and the surrounding regions in Europe. The observed asymmetry in reception pattern could possibly be due to the increase in electromagnetic noise across Europe in the summer afternoon/evening from thunderstorms.

First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks

AbstractWe demonstrate a novel method for observing Large Scale Traveling Ionospheric Disturbances (LSTIDs) using high frequency (HF) amateur radio reporting networks, including the Reverse Beacon Network (RBN), Weak Signal Propagation Reporter Network (WSPRNet), and PSKReporter. LSTIDs are quasi‐periodic variations in ionospheric densities with horizontal wavelengths >1,000 km and periods between 30 and 180 min. On Nov 3, 2017, LSTID signatures were observed simultaneously over the continental United States in amateur radio, SuperDARN HF radar, and GNSS Total Electron Content with a period of ∼2.5 hr, propagation azimuth of ∼163°, horizontal wavelength of ∼1680 km, and phase speed of ∼1,200 km hr−1. SuperMAG SME index enhancements and Poker Flat Incoherent Scatter Radar measurements suggest the LSTIDs were driven by auroral electrojet intensifications and Joule heating. This novel measurement technique has applications in future scientific studies and for assessing the impact of LSTIDs on HF communications.

Reconfigurable Intelligent Surface for Low-Latency Edge Computing in 6G

Edge computing, as one of the key technologies in 6G networks, establishes a distributed computing environment by deploying computation and storage resources in proximity to end users. However, the dense deployment of base stations, cellular dead zones, and high dynamics of mobile devices may cause serious interference issues and weak signal propagation, which will severely affect the transmission efficiency of edge computing and cannot support low-latency applications and services. Reconfigurable intelligent surface (RIS) is a new technology that can enhance the spectral efficiency and suppress interference of wireless communication by adaptively configuring massive low-cost passive reflecting elements. In this article, we introduce RIS into edge computing to support low-latency applications, where edge computing can alleviate the heavy computation pressure of mobile devices with ubiquitously distributed computing resources, and RIS can enhance the quality of the wireless communication link by intelligently altering the radio propagation environment. To elaborate the effectiveness of RIS for edge computing, we then propose a deep-reinforcement-learning-based computation offloading scheme to minimize the total offloading latency of mobile devices. Numerical results indicate that the RIS-aided scheme can improve wireless communication data rate and reduce task execution latency.

Propagation characteristics of weak signal in feedforward Izhikevich neural networks

The feedforward neural network is widely applied in various machine learning architectures, in which the synaptic weight between layers plays an important role in the weak signal propagation. In this paper, the five-layer Izhikevich neural networks with excitatory or excitatory–inhibition neurons are employed to study the effect of Gaussian white noise and synaptic weight between layers on the weak signal transmission characteristics of the subthreshold excitatory postsynaptic currents signal imposed on the input layer. It can be found that there is an optimum value of noise intensity (a medium noise intensity) at which the weak signal can be stably propagated in the five-layer Izhikevich neural networks. The spike timing precision (STP) under the optimal noise intensity will become maximum by increasing the synaptic weight. The noise intensity and synaptic weight corresponding to the maximum value of the STP in the excitatory–inhibition network are smaller than those in excitatory–inhibition network. For the smaller or the bigger noise intensity, however, the STP will become very small, and the weak signal cannot be transmitted from the input layer to the output layer. Furthermore, the weak signal is propagated from the input layer to the output layer and enhanced under a larger synaptic weight in the feedforward neural networks.

Effects of electromagnetic induction on signal propagation and synchronization in multilayer Hindmarsh-Rose neural networks

The feed-forward neural networks are the basis and have been widely applied on modern deep learning models, wherein connection strength between neurons plays a critical role in weak signal propagation and neural synchronization. In this paper, a four-variable Hindmarsh–Rose (HR) neural model is presented by introducing an additive variable as magnetic flow which changes the membrane potential via a memristor. The improved HR neurons in the feed-forward multilayer (four and eight layers) networks are investigated. The effects of electromagnetic radiation, synaptic weight and noise intensity on the propagation of the subthreshold excitatory postsynaptic current (EPSC) signal and the neural synchronization are discussed. It is found that when the system is in a weak magnetic field, the subthreshold EPSC signal can be successfully transmitted to the post-layers. Moreover, the neural synchronization of each layer is affected by electromagnetic radiation in the network, and with the help of noise the constant input current will transmit to the post-layers in a stable periodic synchronous form. Our findings provide a possible mechanism for enhancing the subthreshold signal propagation and triggering the neural synchronization in the nervous system.

Effects of sine-Wiener noise on signal propagation in a randomly connected neural network

We investigate the effects of sine-Wiener (SW)-noise on signal propagation in a randomly connected neural network based on Izhikevich neuron model in detail, in which the axonal conduction delays of synapses, the linkage probability between neurons and the ratio between excitatory and inhibitory neurons of the network are set similarly with the mammalian neocortex. It is found that the SW-noise can enhance the propagation of weak signal in the network. Besides the parameters of SW-noise, the characteristic parameters of the network also play important roles in signal propagation. Furthermore, it is found that the neural network has its sensitive frequency that can optimally enhance the propagation of weak signal when the signal’s frequency is close to the network’s sensitive frequency. In summary, the results here suggest that the SW-noise with suitable self-correlation time and intensity can facilitate the propagation of weak signal in the randomly connected neural network.

Vibrational resonance without tuning in a neuronal parallel array

This paper investigates the propagation of the weak aperiodic signal in a parallel array of FitzHugh–Nagumo (FHN) neurons with heterogenous aperiodic high-frequency (HAHF) disturbances. Different from the traditional vibrational resonance, where the optimal amplitudes of the HF driving signal should be tuned for individual elements, the ability of the proposed averaging parallel array network for weak signal detection can be optimized at a fixed amplitude of the HAHF disturbance, regardless of the nature of the input signal. Local connections in the parallel array network are found to be important for the propagation of weak signal in parallel array. Besides, the characteristics of high-frequency signal such as heterogeneity and frequency, can also modulate the propagation of aperiodic signal in parallel array.

High‐Frequency Communications Response to Solar Activity in September 2017 as Observed by Amateur Radio Networks

AbstractNumerous solar flares and coronal mass ejection‐induced interplanetary shocks associated with solar active region AR12673 caused disturbances to terrestrial high‐frequency (HF, 3–30 MHz) radio communications from 4–14 September 2017. Simultaneously, Hurricanes Irma and Jose caused significant damage to the Caribbean Islands and parts of Florida. The coincidental timing of both the space weather activity and hurricanes was unfortunate, as HF radio was needed for emergency communications. This paper presents the response of HF amateur radio propagation as observed by the Reverse Beacon Network and the Weak Signal Propagation Reporting Network to the space weather events of that period. Distributed data coverage from these dense sources provided a unique mix of global and regional coverage of ionospheric response and recovery that revealed several features of storm time HF propagation dynamics. X‐class flares on 6, 7, and 10 September caused acute radio blackouts during the day in the Caribbean with recovery times of tens of minutes to hours, based on the decay time of the flare. A severe geomagnetic storm with Kpmax = 8+ and SYM‐Hmin = −146 nT occurring 7–10 September wiped out ionospheric communications first on 14 MHz and then on 7 MHz starting at ∼1200 UT 8 September. This storm, combined with affects from additional flare and geomagnetic activity, contributed to a significant suppression of effective HF propagation bands both globally and in the Caribbean for a period of 12 to 15 days.

Weak signal detection and propagation in diluted feed-forward neural network with recurrent excitation and inhibition

Reliable signal propagation across distributed brain areas provides the basis for neural circuit function. Modeling studies on cortical circuits have shown that multilayered feed-forward networks (FFNs), if strongly and/or densely connected, can enable robust signal propagation. However, cortical networks are typically neither densely connected nor have strong synapses. This paper investigates under which conditions spiking activity can be propagated reliably across diluted FFNs. Extending previous works, we model each layer as a recurrent sub-network constituting both excitatory (E) and inhibitory (I) neurons and consider the effect of interactions between local excitation and inhibition on signal propagation. It is shown that elevation of cellular excitation–inhibition (EI) balance in the local sub-networks (layers) softens the requirement for dense/strong anatomical connections and thereby promotes weak signal propagation in weakly connected networks. By means of iterated maps, we show how elevated local excitability state compensates for the decreased gain of synchrony transfer function that is due to sparse long-range connectivity. Finally, we report that modulations of EI balance and background activity provide a mechanism for selectively gating and routing neural signal. Our results highlight the essential role of intrinsic network states in neural computation.

Ionospheric Sounding Using Real‐Time Amateur Radio Reporting Networks

AbstractAmateur radio reporting networks, such as the Reverse Beacon Network (RBN), PSKReporter, and the Weak Signal Propagation Network, are powerful tools for remote sensing the ionosphere. These voluntarily constructed and operated networks provide real‐time and archival data that could be used for space weather operations, forecasting, and research. The potential exists for the study of both global and localized effects. The capability of one such network to detect space weather disturbances is demonstrated by examining the impacts on RBN‐observed HF propagation paths of an X2.9 class solar flare detected by the GOES 15 satellite. Prior to the solar flare, the RBN observed strong HF propagation conditions between multiple continents, primarily Europe, North America, and South America. Immediately following the GOES 15 detection of the solar flare, the number of reported global RBN propagation paths dropped to less than 35% that of prior observations. After the flare, the RBN showed the gradual recovery of HF propagation conditions.

Stochastic resonance in feedforward acupuncture networks

Effects of noises and some other network properties on the weak signal propagation are studied systematically in feedforward acupuncture networks (FFN) based on FitzHugh–Nagumo neuron model. It is found that noises with medium intensity can enhance signal propagation and this effect can be further increased by the feedforward network structure. Resonant properties in the noisy network can also be altered by several network parameters, such as heterogeneity, synapse features, and feedback connections. These results may also provide a novel potential explanation for the propagation of acupuncture signal.

All-optical controlled switching in centrally coupled circular array of nonlinear optical fibers

We show that, in a nonlinear centrally coupled circular array of evanescently coupled fibers, the coupling dynamics of a weak signal beam can be efficiently influenced by a high-power control beam that induces nonlinear defects. When the intense control beam is launched into the central core and one core in the periphery, then localized solitons are formed and cause the fibers with induced defects (defected fibers) to decouple from the other array elements. In the presence of a high-intensity control beam, the propagation of weak signal is restricted to the defected optical fibers. The weak signal periodically couples between the induced defects. This oscillatory behavior depends on the sign of the (Kerr-type) nonlinearity and the initial phase difference between the control fields injected to the central and one of the peripheral fibers. This all-optical network has the advantage of routing and switching the weak signal field in a controlled way by adjusting the parameters of intense control field.

A WiFi-ZigBee Building Area Network Design of High Traffics AMI for Smart Grid

A WiFi-ZigBee hybrid BAN solution, namely WiZBAN, is proposed and implemented to cater for the development of high traffic AMI for smart grid application. It is important to highlight that the major challenge of WiZBAN is to handle the high density environment which results in heavy traffic loading and weak signal propagation. To overcome the captioned problem, Vertical Backbone Communication (VBC) and Horizontal Floor Communication (HFC) are defined for WiZBAN. The WiZBAN consists of WiZBAN Gateway (WiZGW), WiZBAN Meter Hub (WiZBAN) and WiZBAN In Home Display (WiZIHD) which caters for the smart grids services including smart metering and demand response. The WiZGW is the entrance of WiZBAN and connects WiZBAN to utilities. The WiZGW also teams up with WiZMH to enables VBC. On the other hand, WiZMH serves as the interception point of VBC and HFC. It interacts with smart meters and sets up the HFC together with WiZIHD to provide the user interface for end users. To shorten the transmission time, WiFi is adopted for VBC while ZigBee is applied to HCF to overcome the weak signal propagation. To investigate the performance of WiZBAN, a case study has been conducted based on an existing 23 floor residential building. From the measured and simulated results, the average round trip delay of demand response and smart metering are found to be 0.6 s and 9 s respectively.

Optimization of weak signal propagation in a feedforward network

The ability of weak signal detection and transduction of neurons is of great importance. Although the subject hotly debated in single neuron and complex networks [1], it has been partly addressed in feedforward networks [2,3]. In our previous work [2], we determined the conditions for weak rhythmic signal propagation through a feedforward network where the neurons are constructed with detailed biophysical modeling approaches. It is shown that the optimal propagation of weak rhythmic signals through feedforward neuronal networks depends significantly on the level of intrinsic noise, the forcing frequency and the inter-layer link density. Here, we used the same network structure in [2], which involves 10 layers with N=200 neurons in each one and 10% inter-layer link density (Fig1a). For synaptic transmission, alpha-synapse type is used to model the conductance variations in the post-synaptic neuron resulting from the binding of neurotransmitter released from the pre-synaptic neuron. Release time of neurotransmitters τsyn and coupling constant gsyn are critical parameters of this type of synapses. A measure Qi is chosen to quantify the signal transmission efficiency, which gives the existence of the input signal frequency at any layers’ output, similar to Signal to Noise Ratio (SNR). Model details and methods can be found in [2]. To extend our findings, we first investigated the role of fraction of forced neurons forming the first layer on the propagation of weak rhythmic activity. We consider different fractions of periodically forced neurons (chosen randomly) and compute Q10 (Fourier coefficients of output layer) over a broad frequency range (Fig 1b). Evidently, the larger the fraction of neurons subject to the weak forcing, the better the outreach of the signal through the network. This can be appreciated most clearly for the optimal angular forcing frequency, ω≈0.4ms–1[2]. Interestingly, the depicted curves show only marginal improvement in Q10 if more than 50% of the neurons forming the first layer are forced. This finding suggests that the propagation of weak signals across feedforward neuronal networks is rather robust to variations in the coverage of the initial input. We also examined how synaptic mechanisms effect the propagation of weak rhythmic activity through the network. For this purpose, a periodic sinusoidal force with the optimal angular frequency ω≈0.4ms–1 is introduced to all neurons in the first layer as shown in Fig​Fig1a,1a, then we computed Q10/Q1gain factor with respect to systematically changed values of synaptic parameters. Results are presented in Fig​Fig1c.1c. It is clearly seen that signal transmission can be modulated by the synaptic transmission parameters. There is an optimal τsyn range, i.e. 1.5 =1 for gsyn >0.4 nS, indicating the efficient transmission of the weak signal. The underlying mechanism can be understood by comparing synaptic and membrane time constants with respect to each other, because they together determine the operation mode of neurons, i.e. coincidence detector or temporal integrator [3]. Fig 1c also shows that weak signal can be transmitted to the output layer for the coupling constants of gsyn >0.4 nS regardless ofτsyn, implying that the synaptic coupling must be higher than a critical value. Figure 1 (a) Schematic illustration of a 10 layer feedforward network, (b) Fourier coefficient Q10 in dependence on ω for different fractions of forced neurons forming the first layer, (c) The dependence of the gain factor Q10 / Q1with respect to the synaptic ...

Vibrational resonance in feedforward network

This paper investigates vibrational resonance in multi-layer feedforward network (FFN) based on FitzHugh-Nagumo (FHN) neuron model. High-frequency stimuli can improve the input-output linearity of firing rates, especially for the inputs with low firing rate. For FFN network, it is found that high-frequency disturbances play important roles in enhancing the propagation of weak signal through layers. Synfire-enhanced phenomenon of signal propagation is also observed in multi-layers network, when the signal transmission is affected by high-frequency disturbances. Network connections are found to be important for the propagation of weak signal. Besides that, the characteristics of high-frequency stimuli such as heterogeneity and frequency can also modulate the propagation of neural code through layers.

Weak signal propagation through noisy feedforward neuronal networks

We determine under which conditions the propagation of weak periodic signals through a feedforward Hodgkin-Huxley neuronal network is optimal. We find that successive neuronal layers are able to amplify weak signals introduced to the neurons forming the first layer only above a certain intensity of intrinsic noise. Furthermore, we show that as low as 4% of all possible interlayer links are sufficient for an optimal propagation of weak signals to great depths of the feedforward neuronal network, provided the signal frequency and the intensity of intrinsic noise are appropriately adjusted.