Reconstruction of sub cross-correlation cancellation technique for unambiguous acquisition of BOC(kn, n) signals

In order to solve the problem of ambiguous acquisition of BOC signals caused by its property of multiple peaks,an unambiguous acquisition algorithm named reconstruction of sub cross-correlation cancellation technique(RSCCT)for BOC(kn,n)signals is proposed.In this paper,the principle of signal decomposition is combined with the traditional acquisition algorithm structure,and then based on the method of reconstructing the correlation function.The method firstly gets the sub-pseudorandom noise(PRN)code by decomposing the local PRN code,then uses BOC(kn,n)and the sub-PRN code cross-correlation to get the sub cross-correlation function.Finally,the correlation peak with a single peak is obtained by reconstructing the sub cross-correlation function so that the ambiguities of BOC acquisition are removed.The simulation shows that RSCCT can completely eliminate the side peaks of BOC(kn,n)group signals while maintaining the narrow correlation of BOC,and its computational complexity is equivalent to sub carrier phase cancellation(SCPC)and autocorrelation side-peak cancellation technique(ASPeCT),and it reduces the computational complexity relative to BPSK-like.For BOC(n,n),the acquisition sensitivity of RSCCT is 3.25 dB,0.81 dB and 0.25 dB higher than binary phase shift keying(BPSK)-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.91,3.0 and 3.7 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.For BOC(2n,n),the acquisition sensitivity of RSCCT is 5.5 dB,1.25 dB and 2.69 dB higher than BPSK-like,SCPC and ASPeCT at the acquisition probability of 90%,respectively.The peak to average power ratio is 1.02,1.68 and 2.12 times higher than ASPeCT,SCPC and BPSK-like at SNR=–20 dB,respectively.

The influences of large earthquake signals on the recovery of surface waves from ambient noise cross-correlation functions

Ambient noise tomography(ANT)has been widely used to image crust and upmost mantle structures.ANT assumes that sources of ambient noise are diffuse and evenly distributed in space and the energy of different modes is equipartitioned.At present,the sources of the primary and the secondary microseisms are well studied,but there are only a few on the studies of long-period ambient noise sources.In this study,we study the effects of large earthquake signals on the recovery of surface waves from seismic ambient noise data recorded by seismic stations from the US permanent networks and Global Seismographic Network(GSN).Our results show that large earthquake signals play an important role on the recovery of long-period surface waves from ambient noise cross-correlation functions.Our results are consistent with previous studies that suggest the contribution of earthquake signals to the recovery of surface waves from cross-correlations of ambient noise is dominant at periods larger than 20–40 s.

Na^(+)/K^(+)-ATPase:ion pump,signal transducer,or cytoprotective protein,and novel biological functions

Na^(+)/K^(+)-ATPase is a transmembrane protein that has important roles in the maintenance of electrochemical gradients across cell membranes by transporting three Na^(+)out of and two K^(+)into cells.Additionally,Na^(+)/K^(+)-ATPase participates in Ca^(2+)-signaling transduction and neurotransmitter release by coordinating the ion concentration gradient across the cell membrane.Na^(+)/K^(+)-ATPase works synergistically with multiple ion channels in the cell membrane to form a dynamic network of ion homeostatic regulation and affects cellular communication by regulating chemical signals and the ion balance among different types of cells.Therefo re,it is not surprising that Na^(+)/K^(+)-ATPase dysfunction has emerged as a risk factor for a variety of neurological diseases.However,published studies have so far only elucidated the important roles of Na^(+)/K^(+)-ATPase dysfunction in disease development,and we are lacking detailed mechanisms to clarify how Na^(+)/K^(+)-ATPase affects cell function.Our recent studies revealed that membrane loss of Na^(+)/K^(+)-ATPase is a key mechanism in many neurological disorders,particularly stroke and Parkinson's disease.Stabilization of plasma membrane Na^(+)/K^(+)-ATPase with an antibody is a novel strategy to treat these diseases.For this reason,Na^(+)/K^(+)-ATPase acts not only as a simple ion pump but also as a sensor/regulator or cytoprotective protein,participating in signal transduction such as neuronal autophagy and apoptosis,and glial cell migration.Thus,the present review attempts to summarize the novel biological functions of Na^(+)/K^(+)-ATPase and Na^(+)/K^(+)-ATPase-related pathogenesis.The potential for novel strategies to treat Na^(+)/K^(+)-ATPase-related brain diseases will also be discussed.

AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy

With the advent of the Industry 5.0 era,the Internet of Things(IoT)devices face unprecedented proliferation,requiring higher communications rates and lower transmission delays.Considering its high spectrum efficiency,the promising filter bank multicarrier(FBMC)technique using offset quadrature amplitude modulation(OQAM)has been applied to Beyond 5G(B5G)industry IoT networks.However,due to the broadcasting nature of wireless channels,the FBMC-OQAMindustry IoT network is inevitably vulnerable to adversary attacks frommalicious IoT nodes.The FBMC-OQAMindustry cognitive radio network(ICRNet)is proposed to ensure security at the physical layer to tackle the above challenge.As a pivotal step of ICRNet,blind modulation recognition(BMR)can detect and recognize the modulation type of malicious signals.The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes.A novel FBMC BMR algorithm is proposed with the transform channel convolution network(TCCNet)rather than a complicated two-dimensional convolution.Firstly,this is achieved by designing a low-complexity binary constellation diagram(BCD)gridding matrix as the input of TCCNet.Then,a transform channel convolution strategy is developed to convert the image-like BCD matrix into a serieslike data format,accelerating the BMR process while keeping discriminative features.Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8%and 40%over the traditional inphase/quadrature(I/Q)-based and constellation diagram(CD)-based methods at a signal noise ratio(SNR)of 12 dB,respectively.Moreover,the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network(CD-AlexNet)and I/Q-Convolutional Long Deep Neural Network(I/Q-CLDNN)algorithms,respectively.

Eigenvalue Modeling and Cross-Correlation Analysis for 6. 0- 6. 4 GHz MIMO Channel

In Multiple-Input Multiple-Output( MIMO) system, the number of positive channel matrix eigenvalues is directly related to system performance. In order to characterize and model channel matrix eigenvalues,channel measurements at 6. 0- 6. 4GHz by using 4 × 4 MIMO structure were conducted in a typical classroom environment. Based on measured data, the eigenvalues were modeled as Log-Normal distributed random variables and parameterized. Furthermore, Cross-Correlation( CC) coefficients of eigenvalues were estimated. The measurement results show that,under both Light-Of-Sight( LOS) and NonLight-Of-Sight( NLOS) scenarios,eigenvalues are highly de-correlated so that CC can be ignored for eigenvalue model.

Study of ultra-wideband radar signals-generated technology using two-channel signal generator

Synthesis of ultra-wideband （UWB） linear frequency modulation radar signals is a very important technology for microwave imaging, target identification and detection of low radar-cross-section （RCS） targets. A new method of UWB radar signals generation with two-channel signal generator is presented. The realization structure is given; the principle and errors of signal synthesis are analyzed. At the same time, an automatic .adjustment measure of signal phase is proposed because of phase discontinuity of waveform in this method. The simulation experiment and analysis results indicate that radar signals with large instantaneous bandwidth can be generated by means of this method on the condition that the high-speed digital devices are limited.

Effects of signal modulation and coloured cross-correlation of coloured noises on the diffusion of a harmonic oscillator

The diffusion in a harmonic oscillator driven by coloured noises ξ（t） and η（t） with coloured cross-correlation in which one of the noises is modulated by a biased periodic signal is investigated. The exact expression of diffusion coefficient d as a function of noise parameter, signal parameter, and oscillator frequency is derived. The findings in this paper are as follows. 1） The curves of d versus noise intensity D and d versus noises cross-correlation time z3 exist as two different phases. The transition between the two phases arises from the change of the cross-correlation coefficient A of the two Ornstein-Uhlenbeck （O-U） noises. 2） Changing the value of T3, the curves of d versus Q, the intensity of colored noise that is modulated by the signal, can transform from a phase having a minimum to a monotonic phase. 3） Changing the value of signal amplitude A, d versus Q curves can transform from a phase having a minimum to a monotonic phase. The above-mentioned results demonstrate that a like noise-induced transition appears in the model.

Transfer characteristics of dynamic biochemical signals in non-reversing pulsatile flows in a shallow Y-shaped microfluidic channel: signal filtering and nonlinear amplitude-frequency modulation

The transports of the dynamic biochemical signals in the non-reversing pulsatile flows in the mixing microchannel of a Y-shaped microfluidic device are ana- lyzed. The results show that the mixing micro-channel acts as a low-pass filter, and the biochemical signals are nonlinearly modulated by the pulsatile flows, which depend on the biochemical signal frequency, the flow signal frequency, and the biochemical signal transporting distance. It is concluded that, the transfer characteristics of the dynamic biochemical signals, which are transported in the time-varying flows, should be carefully considered for better loading biochemical signals on the cells cultured on the bottom of the microfluidic channel.

Stochastic resonance in a gain-noise model of a single-mode laser driven by pump noise and quantum noise with cross-correlation between real and imaginary parts under direct signal modulation

Stochastic resonance （SR） is studied in a gain-noise model of a single-mode laser driven by a coloured pump noise and a quantum noise with cross-correlation between real and imaginary parts under a direct signal modulation. By using a linear approximation method, we find that the SR appears during the variation of signal-to-noise ratio （SNR） separately with the pump noise self-correlation time τ, the noise correlation coefficient between the real part and the imaginary part of the quantum noise λq, the attenuation coefficient γ＇ and the deterministic steady-state intensity I0. In addition, it is found that the SR can be characterized not only by the dependence of SNR on the noise variables of and λq, but also by the dependence of SNR on the laser system variables of γ and I0. Thus our investigation extends the characteristic quantity of SR proposed before.

A method of multi-channel reference signals acquiring in broadband ANC

在一个胁腹数组在上一无人在水下车辆( UUV )，有宽带的自我产生的噪音和在频率和空间领域的有颜色的光谱性质是影响弱信号察觉的性能的主要因素，象物理降噪和活跃噪音取消一样的适应噪音取消( ANC )的技术经常因此在实践被使用。因为 ANC 基于关联，在性能的改进来自在参考信号和主要信号之间的更好的关联。充分利用胁腹数组的特征和信息的特征从水中听音器获得了，为为适应噪音取消的引用信号获得的一个新方法被建议，在哪个多信道的引用信号被为到达( DOA )和在数组元素的邻近的产量之间的 differencing 的一个给定的方向的精确推迟获得。建议方法的有效性通过为与小另外的计算负担显示出好性能的模拟和湖实验建模的系统被验证。

Dispersion of Dynamic Biochemical Signals in Steady Flow in a Shallow Y-type Microfluidic Channel

This paper presents an analysis of dispersion of dynamic biochemical signals in steady flow in a shallow Y-type microfluidic channel. A method is presented to control the flow widths of two steady flows in the Y-type microchannel from two inlets.The transfer function for the Y-type microchannel is given by solving the governing equation for the Taylor-Aris dispersion in the microchannel. The amplitude-frequency and phase-frequency relations are provided which show that a shallow Y-type microchannel acts as a low-pass filter. The transports of different dynamic biochemical signals are investigated. In comparison with a fully mixing microfluidic channel, the magnitudes of the dynamic signals at the outlets in a Y-type microchannel are much smaller than those in a fully mixing microchannel, which demonstrates that the amplitude attenuation in a Y-type microchannel is larger than that of a fully mixing microchannel due to the transverse molecular diffusion. In order to control the desired signal in a microchannel, the solution of the inverse problem for the channel is also presented.

Single channel signal component separation using Bayesian estimation

A Bayesian estimation method to separate multicomponent signals with single channel observation is presented in this paper. By using the basis function projection, the component separation becomes a problem of limited parameter estimation. Then, a Bayesian model for estimating parameters is set up. The reversible jump MCMC （Monte Carlo Markov Chain） algorithmis adopted to perform the Bayesian computation. The method can jointly estimate the parameters of each component and the component number. Simulation results demonstrate that the method has low SNR threshold and better performance.

Single channel source separation of radar fuze mixed signal based on phase difference analysis

A new method based on phase difference analysis is proposed for the single-channel mixed signal separation of single-channel radar fuze.This method is used to estimate the mixing coefficients of de-noised signals through the cumulants of mixed signals,solve the candidate data set by the mixing coefficients and signal analytical form,and resolve the problem of vector ambiguity by analyzing the phase differences.The signal separation is realized by exchanging data of the solutions.The waveform similarity coefficients are calculated,and the time鈥攆requency distributions of separated signals are analyzed.The results show that the proposed method is effective.

Reduction of signal reflection along through silicon via channel in high-speed three-dimensional integration circuit

The through silicon via （TSV） technology has proven to be the critical enabler to realize a three-dimensional （3D） gigscale system with higher performance but shorter interconnect length. However, the received digital signal after trans- mission through a TSV channel, composed of redistribution layers （RDLs）, TSVs, and bumps, is degraded at a high data-rate due to the non-idealities of the channel. We propose the Chebyshev multisection transformers to reduce the signal reflec- tion of TSV channel when operating frequency goes up to 20 GHz, by which signal reflection coefficient （$11） and signal transmission coefficient （$21） are improved remarkably by 150% and 73.3%, respectively. Both the time delay and power dissipation are also reduced by 4% and 13.3%, respectively. The resistance-inductance-conductance-capacitance （RLGC） elements of the TSV channel are iterated from scattering （S）-parameters, and the proposed method of weakening the signal reflection is verified using high frequency simulator structure （HFSS） simulation software by Ansoft.

Signal Detection and Channel Estimation in OTFS

Orthogonal time frequency space(OTFS)modulation is a recently proposed modulation scheme that exhibits robust performance in high-Doppler environments.It is a two-dimensional modulation scheme where information symbols are multiplexed in the de⁃lay-Doppler(DD)domain.Also,the channel is viewed in the DD domain where the chan⁃nel response is sparse and time-invariant for a long time.This simplifies channel estima⁃tion in the DD domain.This paper presents an overview of the state-of-the-art approaches in OTFS signal detection and DD channel estimation.We classify the signal detection ap⁃proaches into three categories,namely,low-complexity linear detection,approximate max⁃imum a posteriori(MAP)detection,and deep neural network(DNN)based detection.Simi⁃larly,we classify the DD channel estimation approaches into three categories,namely,separate pilot approach,embedded pilot approach,and superimposed pilot approach.We compile and present an overview of some of the key algorithms under these categories and illustrate their performance and complexity attributes.

Measuring Absolute Delay of RNSS Signal Channel Using Digital Envelope Detection

The absolute delay caused by equipment of radio navigation satellite service (RNSS) signal channel must be calibrated in the application of positioning. The measurement accuracy of absolute delay will affect the positioning precision of RNSS user. An absolute delay measurement technique using digital envelope detection was developed for RNSS signal transmission channel. The RNSS transmission signal of navigation satellite and the one pulse per second (1PPS)generated by satellite time keeping system were sampled synchronously. With sampling data of 1PPS,the reference point of the absolute delay can be decided at first,and then sampling data of RNSS transmission signal were truncated. The truncated data were processed using digital envelop detection algorithm to search the phase converting points of RNSS signal. Finally,the absolute delay of RNSS signal transmitting channel was calculated. Uncertainty of measurement with proposed technique is lower than 0. 2 ns as the sampling frequency is 10 GHz.

Deep Learning Based Signal Detector for OFDM Systems

In this paper,we propose a novel deep learning(DL)-based receiver design for orthogonal frequency division multiplexing(OFDM)systems.The entire process of channel estimation,equalization,and signal detection is replaced by a neural network(NN),and hence,the detector is called a NN detector(N^(2)D).First,an OFDM signal model is established.We analyze both temporal and spectral characteristics of OFDM signals,which are the motivation for DL.Then,the generated data based on the simulation of channel statistics is used for offline training of bi-directional long short-term memory(Bi-LSTM)NN.Especially,a discriminator(F)is added to the input of Bi-LSTM NN to look for subcarrier transmission data with optimal channel gain(OCG),which can greatly improve the performance of the detector.Finally,the trained N^(2)D is used for online recovery of OFDM symbols.The performance of the proposed N^(2)D is analyzed theoretically in terms of bit error rate(BER)by Monte Carlo simulation under different parameter scenarios.The simulation results demonstrate that the BER of N^(2)D is obviously lower than other algorithms,especially at high signal-to-noise ratios(SNRs).Meanwhile,the proposed N^(2)D is robust to the fluctuation of parameter values.

Read channel for signal waveform modulation multi-level disc

A novel read channel for signal waveform modulation multi-level disc is presented in this paper. This read channel employs timing recovery system and partial response maximum likelihood detector. Compared to the previous read channel composed of level detection and run-length detection, the present read channel shows superiority in capacity increase and robust performance. Especially, relying on the partial response maximum likelihood detection, lower bit error rate can be obtained.

Empirical Studies on Topographical Influences on Crbc/Akbc Terrestrial Television Stations’ Signals in Akpabuyo Local Council Area, Nigeria

Dissemination of information to citizens is a relevant component of governance. Expectedly, viewers tuned to broadcast stations within and outside their localities, expecting their receivers to faithfully reproduce the exact features of the transmitted signal. Akpabuyo is a dense forest zone near the creeks leading to the Atlantic ocean in Cross River State, Nigeria. The location has distinct environmental characteristics that made Akpabuyo Area Council, a challenging location to propagate electromagnetic waves;and therefore recipe for further investigation. Radio frequency analyzer, with 24 channels spectrum, ranging between 46 - 870 MHz (model: RO.VE.R.-“DLM3-T”) was deployed to capture signals from terrestrial television stations (TV). CATV measured signal of TV stations in dB, dBμV and dBmV. Its frequency ranged from 40 - 860 MHz;while varying from channel 1 to channel 69. Measurements taken from Akpabuyo L.G.A. showed the following results: the signal strength received from VHF Channel 11 ranged from 20 dBμV to 49 dBμV. From recorded empirical statistics from the study, 50% of the area received signal from this station above 30 dBμV, while other regions had signals below this value;representing the fringe zone of the frequency. The results obtained from the study relatively showed acceptance with Egli’s model. The study recorded a steady fluctuation between 17 dBμV and 19 dBμV from both propagating stations. However, Channel 27 signal at 519 MHz, had very weak signal coverage in Akpabuyo Local Government Area;with signal strength dropped to as low as 13 dB in many parts of the rural area. The study discovered that the state’s broadcasting stations, both at UHF and VHF channels did not transmit successfully across this densely forest (rural) location. Remedial measures such as installing Repeater stations at different locations as signal booster were recommended.

5G Oriented Exponential Effective Signal to Interference plus Noise Ratio Mapping Algorithm Based on Channel Classification

In the future the fifth generation( 5 G) communication systems,channel models may be very complicated and it is difficult to calculate equivalent signal to interference plus noise ratio( SINR)of a random fading channel. Therefore,methods for the calculation of equivalent SINR of a random fading channel are very necessary.In this paper,an enhanced algorithm on the exponential effective SINR mapping( EESM) model for random fading channels was proposed. First, the optimal adjustment parameters of typical channel fading models including extended pedestrian A( EPA)model,extended vehicular A( EVA) model and extended typical urban( ETU) model were obtained by simulation. Then the proposed solution was used to actualize channel classification according to the maximum multipath delay and the average power of the random channel. The solution can determine the typical channel closest to random channel for obtaining the optimal adjustment value of EESM. The evaluation results indicate that the proposed one can improve the whole system throughput significantly and meanwhile the accuracy of the link prediction algorithm is also guaranteed.