Radio Signals Research Articles

Enhancing radio signal classification under low SNR conditions using deep residual networks with channel attention mechanisms

This paper delves into the advancement of deep residual networks (ResNets) integrated with channel attention mechanisms for the classification of radio signals under conditions of low Signal-to-Noise Ratio (SNR). Utilizing an expansive dataset of radio signals, this paper introduces a novel architecture, MyResNet1, that combines residual learning with channel-wise attention, allowing the model to concentrate on essential features for precise classification. My, investigations exhibit notable improvements in classification accuracy, especially in challenging low SNR scenarios, highlighting the potential of attention-augmented deep residual networks in radio signal processing. Furthermore, this studyexplores various optimization strategies, including data augmentation and regularization techniques, to enhance the models performance and robustness. My findings contribute significantly to cognitive radio technologies and illuminate the potential of deep learning in sophisticated signal classification tasks, aligned with recent explorations in automatic modulation recognition (AMR) through deep learning and autoencoder-based methodologies for enhancing I/Q channel interactions.

The Fast and Reliable Detection of Multiple Narrowband FH Signals: A Practical Framework.

Frequency hopping (FH) is a well-known technique that is commonly used in communication systems owing to its many advantages, including its strong anti-jamming capability. In this technique, basically, radio signals are transmitted by switching the carrier between different frequency channels. As a result, the FH signal is not stationary; hence, its spectrum is expected to change over time. Therefore, the task of detection and parameter estimation of FH signals is very challenging in practice. To address this challenge, the study presented in this article proposes a method that detects and estimates the parameters of multiple narrowband FH signals. In the proposed method, first, short-time Fourier transform (STFT) is utilized to analyze FH signals, and a practical binarization process based on thresholding is used to detect FH signals. Then, a new algorithm is proposed to ensure that the center frequencies of the detected signals are successfully separated. Next, another algorithm is proposed to estimate the parameters of the detected signals. After estimating the parameters for the entire spectrum, an approach is used to detect FH signals. Lastly, the hop-clustering process is applied to separate the hops into groups without time overlap. The simulation results show that the proposed method can be an efficient way for the fast and accurate parameter estimation and detection of multiple narrowband FH signals.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

Modulated Radio Frequency Stealth Waveforms for Ultra-Wideband Radio Fuzes.

The increasingly complex electromagnetic environment of modern warfare and the proliferation of intelligent jamming threaten to reduce the survival rate of radio fuzes on the battlefield. Radio frequency (RF) stealth technology can fundamentally improve the anti-interception and reconnaissance capabilities of radio fuzes, thereby lessening the probability of them being intercepted, recognized, and jammed by the enemy. In this paper, an RF stealth waveform based on chaotic pulse-position modulation is proposed for ultra-wideband (UWB) radio fuzes. Adding a perturbation signal based on the Tent map ensures that the chaotic sequences have sufficiently long periods despite hardware byte limitations. Measuring the approximate entropy and sequence period shows that the Tent map with the addition of perturbation signals can maintain good randomness under byte constraints, closely approximating the Tent map with ideal precision. Simulations verify that the proposed chaotic mapping used to modulate the pulse position of an ultra-wideband radio fuze signal results in superior detection, anti-interception, and anti-jamming performance.

MHC-I and PD-L1 Expression is Associated with Decreased Tumor Outgrowth and is Radiotherapy-inducible in the Murine Head and Neck Squamous Cell Carcinoma Model MOC1

IntroductionCombined radiotherapy and immune checkpoint inhibition is a potential treatment option for head and neck squamous cell carcinoma (HNSCC). Immunocompetent mouse models can help to successfully develop radio- immunotherapy combinations and to increase our understanding of the effects of radiotherapy on the tumor microenvironment for future clinical translation. Therefore, the aim of this study was to develop a homogeneous, reproducible HNSCC model originating from the Mouse Oral Cancer 1 (MOC1) HNSCC cell line, and to explore the radiotherapy-induced changes in its tumor microenvironment, using flow cytometry and PD-L1 microSPECT/CT imaging.Materials and MethodsIn vivo growing tumors originating from the parental MOC1 line were used to generate single cell derived clones. These clones were screened in vitro for their ability to induce programmed cell death ligand 1 (PD-L1) and major histocompatibility complex class I (MHC-I) following IFNγ exposure. Clones with different IFNγ sensitivity were inoculated in C57BL/6 mice and assessed for tumor outgrowth. The composition of the tumor microenvironment of a stably growing (non)irradiated MOC1-derived clone was assessed by immunohistochemistry, flow cytometry and PD-L1 microSPECT/CT.ResultsLow in vitro inducibility of MHC-I and PD-L1 by IFNγ was associated with increased tumor outgrowth of MOC1 clones in vivo. Flow cytometry analysis of cells derived from a stable in vivo growing MOC1 clone MOC1.3D5low showed expression of MHC-I and PD-L1 on several cell populations within the tumor. Upon irradiation, MHC-I and PD-L1 increased on leukocytes (CD45.2+) and cancer associated fibroblasts (CD45.2−/EpCAM−/CD90.1+). Furthermore, PD-L1 microSPECT/CT showed increased tumor uptake of radiolabeled PD-L1 antibodies with a heterogeneous spatial distribution of the radio signal, which co-localized with PD-L1+ and CD45.2+ areas.DiscussionPD-L1 and MHC-I inducibility by IFNγ in vitro is associated with tumor outgrowth of MOC1 clones in vivo. In tumors originating from a stably growing MOC1-derived clone, expression of these immune-related markers was induced by irradiation shown by flow cytometry on several cell populations within the tumor microenvironment such as immune cells and cancer associated fibroblasts. PD-L1 microSPECT/CT showed increased tumor uptake following radiotherapy, and autoradiography showed correlation of uptake with areas that are heavily infiltrated by immune cells. Knowledge of radiotherapy-induced effects on the tumor microenvironment in this model can help optimize timing and dosage for radio- immunotherapy combination strategies in future research.

Method for remote measurement of specific conductivity and moisture of subsurface soil horizons

The aim of the research is to develop a radar method for determining the physical and chemical parameters of subsurface soil horizons, providing rapid determination of moisture and specific conductivity in the area of the plant root system. The proposed method is based on a set of Fresnel equations which describe reflection of electromagnetic waves from the interface between dielectric media in vertical and horizontal polarization of the probing signal. For the practical implementation of the method, it is proposed to use two unmanned aerial vehicles that form a bistatic radar system which irradiates the Earth's surface obliquely in order to create the Brewster's effect and increase the fraction of the radio signal reflected from subsurface horizons. The percentage of moisture and the specific conductivity of soil are calculated from the measured values of the imaginary part of the complex permittivity. The required accuracy of moisture and conductivity measurements is achieved by two-step calibration of the measuring device. The values of the moisture content and specific conductivity of soil obtained by radar at a frequency of 469 MHz are in good agreement with the results of measuring these parameters using the soil moisture meter TDR 150 Spectrum Technologies, Inc.

Bit weight-aware high-fidelity digital RoF fronthaul based on dual-drive MZM and chirp-dispersion Interaction.

Digital radio-over-fiber (D-RoF) quantizes the wireless waveform to improve the noise tolerance in fronthaul links. Unlike conventional data transmission, the quantization bits exhibit different weights, offering a new strategy to protect the high-weight bits. By introducing a dual-drive MZM (DD-MZM)-based optical transmitter, the interaction between frequency chirp and chromatic dispersion (CD) results in eye closure/open. Such an effect would potentially achieve higher fidelity of the reconstructed wireless signal in the SNR-limited region. In this paper, we first establish a theoretical model for chirp and dispersion-induced eye closure/open, characterizing the influence of fiber length and baud rate on the constellation interval. We then propose two protection schemes for 4-level pulse amplitude modulation (PAM-4), in which the high- and low-weight bits are interleaved and loaded onto the upper or lower arms of DD-MZM to leverage the negative or positive chirp. Furthermore, we extend to high-order modulation formats and propose two uneven optical PAM-8 schemes with different frequency chirps, which protect one or two of the three bits in each PAM-8 symbol, respectively. The chirp-dispersion interaction-enabled uneven optical PAM-4/8 schemes are experimentally demonstrated in intensity modulation and direct detection (IM-DD) D-RoF system. We compare the performance with chirp-free PAM-4/8 schemes generated by differential-driven mode. Then, we adopt the µ-law quantizer to verify the effectiveness of the proposed schemes and the Lloyd-Max algorithm-based quantizer to maximize the signal-to-noise ratio (SNR). With the help of chirp-dispersion interaction-enabled uneven optical PAM-4/8 schemes, the experimental results show 11.3-dB and 4.8-dB SNR gains of the wireless signal after 5-km fiber transmission, respectively.

Power-over-fiber-based optical wireless communication systems towards 6G

This paper reports two implementations of power-over-fiber (PoF) solutions applied to radio-over-fiber (RoF) and optical wireless communication (OWC) systems, in the context of an industrial environment. We employ a conventional 62.5-µm multimode fiber (MMF) to deliver optical power to different communication links based on RoF, free-space optics (FSO), and visible light communication (VLC) technologies aiming beyond 5G (B5G) and 6G applications. First, a 3.5-GHz 5G New Radio (5G NR) signal is transmitted throughout a 20-km single-mode optical fiber (SMF) link using RoF technology. Regarding the PoF system, a 5-W optical power is transmitted through a 100-m MMF link. A photovoltaic power converter (PPC) and a DC/DC converter are employed to convert the power from the optical to the electrical domain and adjust the voltage level, respectively, with the purpose of energizing a remote RoF module. The attainable optical and electrical power transmission efficiencies (OPTE and PTE) are 80% and 19%, respectively. Posterior, a second PoF system is implemented to power a hybrid RoF/FSO/VLC B5G system, comprising a 200-m MMF and an additional DC/DC converter. Over 10.5 W of optical power is transmitted to feed an electrical amplifier (EA) and a white LED from the VLC link. In this configuration, we achieve 78% and 18.5% of OPTE and PTE, respectively. Furthermore, a performance investigation based on the root mean square error vector magnitude (EVMRMS) metric is conducted to evaluate the signal using the implemented PoF systems and a conventional electrical power supply. In the first implementation, a throughput of 600 Mbps is achieved with 100-MHz bandwidth without performance degradation, when compared to the conventional-powered RoF system, whereas, in the second implementation, 60-Mbps throughput is achieved when employing the FSO and VLC technologies simultaneously, demonstrating the applicability and potential of the PoF technique for B5G and 6G industrial communications.

Firing pattern manipulation of neuronal networks by deep unfolding‐based model predictive control

AbstractThe complexity of neuronal networks, characterized by interconnected neurons, presents significant challenges in control due to their nonlinear and intricate behaviour. This paper introduces a novel method designed to generate control inputs for neuronal networks to regulate the firing patterns of modules within the network. This methodology is built upon temporal deep unfolding‐based model predictive control, a technique rooted in the deep unfolding method commonly used in wireless signal processing. To address the unique dynamics of neurons, such as zero gradients in firing times, the method employs approximations of input currents using a sigmoid function during its development. The effectiveness of this approach is validated through extensive numerical simulations. Furthermore, control experiments were conducted by reducing the number of input neurons to identify critical features for control. Various selection techniques were utilized to pinpoint key input neurons. These experiments shed light on the importance of specific input neurons in controlling module firing within neuronal networks. Thus, this study presents a tailored methodology for managing networked neurons, extends temporal deep unfolding‐based model predictive control to nonlinear systems with reset dynamics, and demonstrates its ability to achieve desired firing patterns in neuronal networks.

Evaluation of fragility fracture risk using deep learning based on ultrasound radio frequency signal.

It was essential to identify individuals at high risk of fragility fracture and prevented them due to the significant morbidity, mortality, and economic burden associated with fragility fracture. The quantitative ultrasound (QUS) showed promise in assessing bone structure characteristics and determining the risk of fragility fracture. To evaluate the performance of a multi-channel residual network (MResNet) based on ultrasonic radiofrequency (RF) signal to discriminate fragility fractures retrospectively in postmenopausal women, and compared it with the traditional parameter of QUS, speed of sound (SOS), and bone mineral density (BMD) acquired with dual X-ray absorptiometry (DXA). Using QUS, RF signal and SOS were acquired for 246 postmenopausal women. An MResNet was utilized, based on the RF signal, to categorize individuals with an elevated risk of fragility fracture. DXA was employed to obtain BMD at the lumbar, hip, and femoral neck. The fracture history of all adult subjects was gathered. Analyzing the odds ratios (OR) and the area under the receiver operator characteristic curves (AUC) was done to evaluate the effectiveness of various methods in discriminating fragility fracture. Among the 246 postmenopausal women, 170 belonged to the non-fracture group, 50 to the vertebral group, and 26 to the non-vertebral fracture group. MResNet was competent to discriminate any fragility fracture (OR = 2.64; AUC = 0.74), Vertebral fracture (OR = 3.02; AUC = 0.77), and non-vertebral fracture (OR = 2.01; AUC = 0.69). After being modified by clinical covariates, the efficiency of MResNet was further improved to OR = 3.31-4.08, AUC = 0.81-0.83 among all fracture groups, which significantly surpassed QUS-SOS (OR = 1.32-1.36; AUC = 0.60) and DXA-BMD (OR = 1.23-2.94; AUC = 0.63-0.76). This pilot cross-sectional study demonstrates that the MResNet model based on the ultrasonic RF signal shows promising performance in discriminating fragility fractures in postmenopausal women. When incorporating clinical covariates, the efficiency of the modified MResNet is further enhanced, surpassing the performance of QUS-SOS and DXA-BMD in terms of OR and AUC. These findings highlight the potential of the MResNet as a promising approach for fracture risk assessment. Future research should focus on larger and more diverse populations to validate these results and explore its clinical applications.

Simulation of radio signals from cosmic-ray cascades in air and ice as observed by in-ice Askaryan radio detectors

Simulation of radio signals from cosmic-ray cascades in air and ice as observed by in-ice Askaryan radio detectors

Infrared-radio-follow-up observations for detection of the magnetic radio emission of extra solar planets: a new window to detect exoplanets

There are several methods for indirectly detecting exoplanets, such as transit, radial velocity, astrometry, and the conventional gravitational microlensing approach. These methods rely on observing the effects of exoplanets on the emission or motion of observed stars. All these techniques have focused on the optical or infrared domains. However, an alternative method for exoplanet detection via microlensing events involves planets orbiting the source star, creating a binary source system. In this study, we explore a novel approach to detecting and studying exoplanets exclusively through their radio emissions resulting from magnetospheric processes. We propose utilizing the Roman telescope as a survey observer to detect microlensing events. Subsequently, we investigate the potential for detecting planetary radio signals through follow-up observations of these microlensing events in the radio band using the SKA telescope. This method is viable due to the comparable radio emission levels of exoplanets and their parent stars, unlike optical and infrared emissions. We conduct a Monte Carlo simulation to replicate the observations by the Nancy Roman Telescope, followed by a follow-up observation in radio frequencies using the SKA telescope. We determine that approximately 1,317 exoplanets exhibit detectable signals by the SKA telescope during the 7-season observations by the Nancy Roman Telescope. This result indicates that such a method cannot only facilitate the direct detection of exoplanets but also enable the measurement of their magnetic field strength through analysis of their radio emissions.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

Measuring the amplitudes of the received symbols of the shift keyed radio signals

In high-speed discrete information transmission systems, multi-position signals with various types of phase and amplitude shift keying are widely used. There are multi-level amplitude shift keyed (ASK), amplitude and phase-shift keyed (APSK) and quadrature amplitude modulation (QAM) signals. When demodulating the amplitude code of such signals, the amplitude of the received symbol is determined, and its calibrated value is compared with the specified threshold levels. It is proposed to directly calculate the minimum and maximum amplitudes of the symbols at the output of the demodulator, with a subsequent definition of decision thresholds. It is noteworthy that there is no need to calibrate the amplitude characteristics of the reception path. The logarithmic ratio of the amplitudes of the received and previous symbols is further calculated. And if its value falls within the specified boundaries, independent of the signal and noise, then a decision is made about whether the amplitudes of these symbols belong to the maximum or minimum values. These obtained values are averaged after that and used to set thresholds for decision-making in the demodulator. The results of the statistical simulation confirm the high efficiency of the proposed technical solution.

FireSonic: Design and Implementation of an Ultrasound Sensing-Based Fire Type Identification System.

Accurate and prompt determination of fire types is essential for effective firefighting and reducing damage. However, traditional methods such as smoke detection, visual analysis, and wireless signals are not able to identify fire types. This paper introduces FireSonic, an acoustic sensing system that leverages commercial speakers and microphones to actively probe the fire using acoustic signals, effectively identifying fire types. By incorporating beamforming technology, FireSonic first enhances signal clarity and reliability, thus mitigating signal attenuation and distortion. To establish a reliable correlation between fire type and sound propagation, FireSonic quantifies the heat release rate (HRR) of flames by analyzing the relationship between fire-heated areas and sound wave propagation delays. Furthermore, the system extracts spatiotemporal features related to fire from channel measurements. The experimental results demonstrate that FireSonic attains an average fire type classification accuracy of 95.5% and a detection latency of less than 400 ms, satisfying the requirements for real-time monitoring. This system significantly enhances the formulation of targeted firefighting strategies, boosting fire response effectiveness and public safety.

Reference values of carotid intima-media thickness and arterial stiffness in Chinese adults based on ultrasound radio frequency signal: A nationwide, multicenter study.

Carotid intima-media thickness (IMT) and diameter, stiffness, and wave reflections, are independent and important clinical biomarkers and risk predictors for cardiovascular diseases. The purpose of the present study was to establish nationwide reference values of carotid properties for healthy Chinese adults and to explore potential clinical determinants. A total of 3053 healthy Han Chinese adults (1922 women) aged 18-79years were enrolled at 28 collaborating tertiary centers throughout China between April 2021 and July 2022. The real-time tracking of common carotid artery walls was achieved by the radio frequency (RF) ultrasound system. The IMT, diameter, compliance coefficient, β stiffness, local pulse wave velocity (PWV), local systolic blood pressure, augmented pressure (AP), and augmentation index (AIx) were then automatically measured and reported. Data were stratified by age groups and sex. The relationships between age and carotid property parameters were analyzed by Jonckheere-Terpstra test and simple linear regressions. The major clinical determinants of carotid properties were identified by Pearson's correlation, multiple linear regression, and analyses of covariance. All the parameters of carotid properties demonstrated significantly age-related trajectories. Women showed thinner IMT, smaller carotid diameter, larger AP, and AIx than men. The β stiffness and PWV were significantly higher in men than women before forties, but the differences reversed after that. The increase rate of carotid IMT (5.5μm/year in women and 5.8μm/year in men) and diameter (0.03mm/year in both men and women) were similar between men and women. For the stiffness and wave reflections, women showed significantly larger age-related variations than men as demonstrated by steeper regression slopes (all P for age by sex interaction <0.05). The blood pressures, body mass index (BMI), and triglyceride levels were identified as major clinical determinants of carotid properties with adjustment of age and sex. The age- and sex-specific reference values of carotid properties measured by RF ultrasound for healthy Chinese adults were established. The blood pressures, BMI, and triglyceride levels should be considered for clinical application of corresponding reference values.

Обобщенная математическая модель радиосигнала в различных базисах

The purpose of this article is to develop a generalized mathematical model of a radio signal in various bases. The proposed model allows us to generalize the known models of radio signals in basis functions of sine/cosine, basis functions of spline characters, bases of spline-Vilenkin-Chrestenson functions, bases of spline-Pontryagin-Vilenkin-Chrestenson functions, etc., constructed on the basis of spline-algebraic harmonic methods analysis. For the generalized model, special cases are formulated for the considered spaces of basis functions. Block diagrams and timing diagrams are presented that explain the main stages of the formation and processing of radio signals in various bases. Using the simulation method in the Matlab environment, the noise immunity of the proposed radio signals was assessed. Analysis of the obtained results showed that radio signals generated on the basis of a generalized mathematical model in various noise immunity bases under AWGN conditions are comparable (and even have a slight advantage) with similar signals generated in the bases of classical exponential functions. An assessment of structural secrecy is presented. It is shown that the structural secrecy of radio signals generated in various bases is ensured by the formation of signals in a priori unknown bases, the parameters of which: group, ring, spline order and number of the basis function change in accordance with a given code word, also unknown to potential unauthorized processing systems. Structural secrecy manifests itself in the incorrect choice of basis when processing signal data for reception, which entails an expansion of the signal spectrum. As a result, incorrect estimation of signal parameters and incorrect setting of the demodulator occurs.

Deep hybrid transformer network for robust modulation classification in wireless communications

Modulation classification is a research hot-spot in the field of machine learning and the knowledge-based systems of wireless communication, involving the identification of different types of wireless signals. However, classification targets like radio signals usually involve long-sequence and large-scale data, and the classification of modulation types is affected by environmental noises, resulting in unsatisfactory performance. To overcome these challenges, we propose a novel Deep Hybrid Transformer Network (DH-TR) that makes full use of the intrinsic properties of multi-head self-attention and different neural modules, facilitating the identification of modulation types from global to local perspectives. In particular, DH-TR uses a convolution stem to extract local features inherent in In-phase and Quadrature (IQ) data, based on which the Gated Recurrent Unit (GRU) is used to capture the step-wise sequential patterns of these signals. Afterward, the self-attention based Transformer branch is employed to learn the global and long-term dependencies among the signal patches. With this innovative hybrid design, DH-TR performs well in processing sequential signal data and can better capture the complex relationships between signals. We validate DH-TR’s effectiveness through extensive experiments on four benchmark datasets, demonstrating its superior performance in modulation classification with higher accuracy and robustness to noise compared to existing methods.

State Space Modelling for detecting and characterising gravitational waves afterglows

We propose the usage of an innovative method for selecting transients and variables. These sources are detected at different wavelengths across the electromagnetic spectrum spanning from radio waves to gamma-rays. We focus on radio signals and use State Space Models, which are also referred to as Dynamic Linear Models. State Space Models (and more generally parametric auto-regressive models) have been the mainstay of economic modelling for some years, but rarely they have been used in Astrophysics.The statistics currently used to identify radio variables and transients are not sophisticated enough to distinguish different types of variability. These methods simply report the overall modulation and significance of the variability, and the ordering of the data in time is insignificant. State Space Models are much more advanced and can encode not only the amount and significance of the variability but also properties, such as slope, rise or decline for a given time t.In this work, we evaluate the effectiveness of State Space Models for transient and variable detection including classification in time-series astronomy. We also propose a method for detecting a transient source hosted in a variable active galaxy, whereby the time-series of a static host galaxy and the dynamic nature of the transient in the galaxy are intertwined. Furthermore, we examine the hypothetical scenario where the target transient we want to detect is the gravitational wave source GW170817 (or similar).

Resource allocation for relaying cognitive network with energy harvesting

This paper investigates a relay-based cognitive wireless-powered communication network with multiple secondary receivers and a secondary relaying network. In the secondary network, a relay adopts an energy model to harvest energy from radio frequency signals of the primary and secondary sources and subsequently utilizes the harvested energy to cooperate with the source for information transmission to multiple Internet-of-things users over time-division multiple access. The research group formulated a novel optimization problem that jointly optimizes the power and time fraction for energy and information transmission to maximize the sum throughput of the secondary network. The authors first converted the non-convex problem to a more computationally tractable problem and then proposed an iterative algorithm in which closed-form solutions are obtained at each iteration. Thus, simulation results verify and demonstrate the effectiveness of the proposed approach.