Values Of Signal Power Research Articles

A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing.

Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.

Intelligent Reflecting Surface-Aided Spectrum Sensing for Cognitive Radio

Spectrum sensing is a key enabling technique for cognitive radio (CR), which provides essential information on the spectrum availability. However, due to severe wireless channel fading and path loss, the primary user (PU) signals received at the CR or secondary user (SU) can be practically too weak for reliable detection. To tackle this issue, we consider in this letter a new intelligent reflecting surface (IRS)-aided spectrum sensing scheme for CR, by exploiting the large aperture and passive beamforming gains of IRS to boost the PU signal strength received at the SU to facilitate its spectrum sensing. Specifically, by dynamically changing the IRS reflection over time according to a given codebook, its reflected signal power varies substantially at the SU, which is utilized for opportunistic signal detection. Furthermore, we propose a weighted energy detection method by combining the received signal power values over different IRS reflections, which significantly improves the detection performance. Simulation results validate the performance gain of the proposed IRS-aided spectrum sensing scheme, as compared to different benchmark schemes.

A novel methodology to estimate the state-of-health and remaining-useful-life of a Li-ion battery using discrete Fourier transformation

In this work, a multiphysics-based modeling approach is followed, which helps to diagnose the state-of-health (SoH) of the battery by using the discrete Fourier transform (DFT) analysis. The experimentally validated electrochemical model is considered based on the pseudo-2-Dimensional (P2D) porous electrode model. A constant current with a small perturbation (sinusoidal) on it is applied to the cell for testing purposes. The cell testing pulses of different frequencies are applied to the cell, and output responses are measured for the same. The change in battery output voltage response is observed with the contributing factors (SEI layer resistance, active material volume fraction reduction of anode) in the electrochemical model to observe the change in battery output voltage response. The spectrum analysis of the output voltage response is carried out to show the effect of aging on the battery using the DFT algorithm. Mathematical relationships between signal power and cell degrading parameters are formulated. Finally, a methodology is developed based on the obtained signal power values and the cell degrading factors, which concludes the present SoH of the cell and helps to give remarks to the remaining-useful-life (RUL) of the cell.

Experimental measurements on the effect of vehicle movement direction on received signal power in V2V communication

Vehicle-to-Vehicle (V2V) communication has been a popular topic in recent years. V2V communication channel measurements in different environments and scenarios have been performed to reveal the effect of different parameters such as vehicles, buildings, or trees. However, vehicle movement direction is generally neglected when the measurement data are analyzed. In this study, V2V channel measurements were carried out in open roads similar to a highway but with less traffic. The measurement data are divided into two groups: the vehicles approaching each other and moving away from each other. The effect of vehicle movement direction on the received signal power is indicated by comparing the received signal power values of these two groups. The results show that the received signal power differs depending on vehicle movement direction. However, according to our findings, it is not clear which movement direction causes more path loss. The authors suggest that vehicle movement direction should be taken into account in analyzing, modeling, and simulations of V2V communication channel.

Dynamic Radio Map Using Statistical Hypothesis Testing

Private fifth-generation mobile communication networks (5G) have garnered considerable attention for providing various communication services. In private 5G systems, such as those employed in smart factories, it is necessary to appropriately manage the interference power among diverse systems because each nonpublic operator deploys a gNodeB in the local area and utilizes a similar allocated frequency. Several researchers have discussed the use of a radio map, which stores the spatial distribution of received signal power values as a tool for managing intra-system interference. Radio maps are typically applied to television white space, where the location of the base station (BS) is fixed and the average value of the received signal power exhibits time-invariant characteristics. However, in private 5G networks, the estimation accuracy using a conventional radio map is lowered because the location of the BS may be changed when the factory is rearranged. In this study, a database server is used to infer a significant difference in the average values of the received signal power by means of Welch’s ${t}$ -test; the server then updates the radio map based on the test results. The simulation results indicate that the proposed method can detect changes in the BS location and update the radio map accurately.

Space-Frequency-Interpolated Radio Map

This paper presents a novel method for radio map construction that simultaneously interpolates the received signal power values over space and frequency domains. Radio maps can be used to improve spectrum management and for localization systems, which are related to wireless systems in general such as cellular systems, Internet of Things (IoT) networks, and vehicular communications. Researchers have shown that crowdsourcing using spatial interpolation techniques can be used to accurately construct a radio map to improve these applications; however, because of the limitation in the spatial domain, conventional methods can build a radio map for only those frequencies at which sensing can be performed. Our proposed method focuses on the fact that the shadowing values show strong correlation over a wide range of frequency domains. The main idea is to treat the shadowing values obtained over various frequencies as the ones over target frequency. Using the actual datasets obtained over 870, 2115, and 3500 MHz in a cellular system, we show that the proposed method can accurately generate a radio map, even if no (or few) datasets are available in the target frequency.

Adaptive power allocation based beamforming algorithm for multiple input and multiple output nonorthogonal multiple access system throughput optimization

AbstractThe combination of nonorthogonal multiple access (NOMA) and multiple input and multiple output (MIMO) systems is considered a promising approach for 5G mobile communication networks. In addition, techniques such as user selection and beamforming could be applied to improve some parameters of system performance. In this context, we propose a joint user selection, adaptive power allocation, and beamforming algorithm for the downlink of massive MIMO NOMA systems in order to maximize the throughput of users. To this end, we present a novel optimal beamforming solution scheme based on the adaptive computation of signal power values to maximize system throughput, considering the less interfering users. To evaluate the performance of the proposed algorithm, simulations were performed considering different power allocation algorithms.

Blind Algorithm Development for Peak to Average Power Ratio Reduction in OFDM Systems under Frequency Selective Channels

One major drawback of orthogonal frequency division multiplexing (OFDM) system is peak to average power ratio (PAPR). This effect causes high power amplifier (HPA) to introduce intermodulation and out of band radiation as the signal goes through, thus degrades the performance of OFDM systems. This paper proposes blind algorithms which takes advantage of signal transformation technique and signal distortion technique. Simulation results show that at complementary cumulative distribution function (CCDF) level of 10-3 , the proposed algorithm achieved 3.2 dB PAPR improvement compared to discrete Fourier transform with interleaved frequency division multiple access (DFT-IFDMA) based algorithm. The bit error rate (BER) performance has degraded by 2 dB compared to the original OFDM signal with no distortion under frequency selective channel (FCS) at BER of 10-4 . These presented results, mark this algorithm as a better candidate for PAPR reduction algorithm in long term evolution (LTE) network. Under AWGN channels, the proposed algorithm performs better both in low and high signal power values. Under frequency selective channels, the existing and proposed algorithm converges after 10 dB of signal to noise power values. The low BER transmissions at low signal power values signify energy efficiency, ideal for portable wireless devices with limited battery power.

The Quality of the Propagation Prediction with Radio Mobile for Tetra Communication Systems

Abstract We present a short analysis of the prediction quality offered by Radio Mobile application when used for frequency band of TETRA systems in Romania. The simulation results provided by Radio Mobile were compared against own measurements made in multiple locations in Sibiu city. The correlation coefficient between simulated and measured values of signal power was of 0.907 while the maximum variation between data in the strings was 9.6 dB and the average standard deviation was 4.4 dB. These results indicate that the radio propagation model ‘Two-ray Irregular Terrain Model Point to Point’ (ITM) provides a prediction capability that offers a reliable planning of the radio coverage of TETRA systems.

Comparative study of wavelet denoising in myoelectric control applications

Here, the wavelet analysis has been investigated to improve the quality of myoelectric signal before use in prosthetic design. Effective Surface Electromyogram (SEMG) signals were estimated by first decomposing the obtained signal using wavelet transform and then analysing the decomposed coefficients by threshold methods. With the appropriate choice of wavelet, it is possible to reduce interference noise effectively in the SEMG signal. However, the most effective wavelet for SEMG denoising is chosen by calculating the root mean square value and signal power values. The combined results of root mean square value and signal power shows that wavelet db4 performs the best denoising among the wavelets. Furthermore, time domain and frequency domain methods were applied for SEMG signal analysis to investigate the effect of muscle-force contraction on the signal. It was found that, during sustained contractions, the mean frequency (MNF) and median frequency (MDF) increase as muscle force levels increase.

Wavelet analysis-based evaluation of electromyogram signal using human machine cooperation

The human body is a combination of interacting systems that can be analysed using engineering principles. It is well known that surface electromyogram signals easily acquired from surface of skin of the body using non-invasive electrodes are composed with variety of noises. Hence methods to remove noise become most significant for surface electromyogram (sEMG) signal before performing processing and analysis. In this study, wavelet analysis has been used to analyse quality of effectiveness of surface electromyogram signal. The surface electromyogram signals were estimated with the following steps: first, the obtained signal was decomposed using wavelet transform; then, decomposed coefficients were analysed by threshold methods. Daubechies wavelets (db2-db14) family for efficiently removing noise from the recorded surface electromyogram signals has been used. However, the most essential wavelet for surface electromyogram denoising is chosen by calculating the root mean square value and signal power values from different voluntary contraction motions. The combined results of root mean square value and signal power shows that wavelet db4 performs denoising best among the wavelets. Furthermore, the statistical technique of analysis of variance (ANOVA) for experimental and best wavelet coefficient was analysed to investigate the effect of muscle-force relationship for ensuring class separability.

Performances of Erbium-Doped Fiber Amplifier at 980-nm Pump Power in Various Parameter Combinations

Optical fiber amplifiers are dominating the loss compensation in fiber optic communications systems, especially the Erbium doped types for communication spectrum based on silica fibers. The gain of the EDFA can be maximized by using appropriate design parameters and appropriate value of the material properties. This paper describes the derivation of the equations that govern the pump power, input signal propagation, gain and optimum fiber length of the amplifier. By using these deduced expressions, new tools have been developed using C programming and MATLAB and then the performances of EDFA pumped with 980-nm signal have been analyzed. The analysis results that the gain of the amplifier increases with the length of the amplifier, and also with the pump signal power. At higher values of signal power the gain becomes saturated. The gain has a nonlinear relation with the amplifier length for a particular pump power. From the experiments, it has been found that for a particular length the saturation gain increases with the carrier density of the amplifier, but at longer length the gain is almost independent of the carrier density. It has also been found that the pump threshold power increases linearly with the length and carrier density of the amplifier. The tools developed in the analysis process could be used by researchers in future for further investigation of the performance of EDFA in various parameter sets or, for finding a parameter set for specific target performance of the EDFA.

Skin Microcirculation in Primary Arterial Hypertension

Laser Doppler Fluxometry (LDF) was used to assess basic flow (PP) of microcirculation (mc) of dorsal hand skin in 10 healthy persons of both sex (k) – of age 41,3 ± 7,18 years and 10 persons suffering from primary arterial hypertension (nt) – of age 41,3 ± 10,95 years. In obtained record it has beed evaluated: basic flow (PP – in arbitray units of perfusion – PU) and, by analysing frequency spectrum – heart rhythm (RS) and its variation (ZRS), respiratory rhythm (RO), myogenic rhythm of precapillary sphincters tone (RM) and its amplitude (RMa), neurogenic rhythm of precapillary sphincters tone (RN) and its amplitude (NRa) and endothelium-dependent rhythm of precapillary sphincters tone (RE) and its amplitude (REa). The rhythm was assessed in cycles per minute (Hz) and the amplitude by value of signal power in arbitrary units. Means (x¯ ) and standard deviations (± SD) were defined; differences were assessed using t-Student test. In the group of individuals, suffering from arterial hypertension, the decrease of heart rhythm variability, acceleration of respiratory rhythm and neurogenic rhythm on the microcirculation level was observed, what testifies to higher sympathetic activity. In people with arterial hypertension such regularity was not observed, which can prove the endothelium dysfunction. LDF enables complex, noninvasive assessment of circulatory system functioning that can be widely used in clinical

Second-order statistics of selection macro-diversity system operating over Gamma shadowed κ-μ fading channels

In this article, infinite-series expressions for the second-order statistical measures of a macro-diversity structure operating over the Gamma shadowed κ-μ fading channels are provided. We have focused on MRC (maximal ratio combining) combining at each base station (micro-diversity), and selection combining (SC), based on output signal power values, between base stations (macro-diversity). Some numerical results of the system's level crossing rate and average fading duration are presented, in order to examine the influence of various parameters such as shadowing and fading severity and number of the diversity branches at the micro-combiners on concerned quantities.

Second-order statistics of SC macrodiversity system operating over Gamma shadowed Nakagami- m fading channels

This paper derives the second-order statistics of SC (selection combining) macrodiversity operating over the Gamma shadowed Nakagami- m fading channels. Macrodiversity system of SC type consists of two microdiversity systems and selection (switching) is based on their output signal power values. Each microdiversity system is of MRC (maximal ratio combining) type with arbitrary number of branches in the presence of correlative Nakagami- m fading. We have derived the infinite-series expressions for LCR (level crossing rate) and AFD (average fading duration) at the output of this system. Numerical results are also presented in order to show the influence of various parameters such as number of the diversity branches at the microcmbiners, fading severity and level of correlation between those branches on the system's statistics, and then compared to the previously published results from this area.

The Effect of a Finite-Width Decision Threshold on Binary Differentially Coherent PSK Systems

A binary PCM regenerator must decide which of two signal states was transmitted. This decision is normally based on whether some particular voltage is above or below a certain threshold at some particular time. In a real regenerator, the voltage in question must differ from this threshold value by some finite amount in order to cause the device to respond properly. The purpose of this calculation is to examine the effect of this “dead zone” on the probability of error in a differentially coherent PSK system both in the case where the signal is limited in amplitude (after the noise is added) and when it is not. It is found that when the expected value of signal power, S, exceeds the threshold value, T, by more than 6 db and 10 db in the limited and unlimited cases, respectively, the effect on error probability is less than the effect of a 1-db degradation in signal-to-noise ratio. However, for smaller values of signal-to-threshold (S/T) the degradation becomes large. Numerical values for error probability as a function of signal-to-noise ratio are presented for S/T = ∞, 12, 9, 6, 4, and 3 db for both cases.