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In this letter, ridge regression, which is a regularization method, is explored for relaxing the requirements on the number and the maximum amplitude of training samples in digital predistortion. An effective method is proposed to determine the ridge parameter, which is essential in ridge regression and is used to regularize the predistorter identification. Experiments demonstrate that the required number and maximum amplitude of training samples can be relaxed by 95% and 30%, respectively, with tolerable linearization performance loss.

With the rapid growth of digital music today, due to the complexity of the music itself, the ambiguity of the definition of music category, and the limited understanding of the characteristics of human auditory perception, the research on topics related to automatic segmentation of music is still in its infancy, while automatic music is still in its infancy. Segmentation is a prerequisite for fast and effective retrieval of music resources, and its potential application needs are huge. Therefore, topics related to automatic music segmentation have important research value. This paper studies an improved algorithm based on negative entropy maximization for well-posed speech and music separation. Aiming at the problem that the separation performance of the negative entropy maximization method depends on the selection of the initial matrix, the Newton downhill method is used instead of the Newton iteration method as the optimization algorithm to find the optimal matrix. By changing the descending factor, the objective function shows a downward trend, and the dependence of the algorithm on the initial value is reduced. The simulation experimental results show that the algorithm can separate the source signal well under different initial values. The average iteration time of the improved algorithm is reduced by 26.2%, the number of iterations is reduced by 69.4%, and the iteration time and the number of iterations are both small. Fluctuations within the range better solve the problem of sensitivity to the initial value. Experiments have proved that the new objective function can significantly improve the separation performance of neural networks. Compared with the existing music separation methods, the method in this paper shows excellent performance in both accompaniment and singing in separated music.

In vivo, left-handed DNA duplex (usually refers to Z-DNA) is mainly formed in the region of DNA with alternating purine pyrimidine (APP) sequence and plays significant biological roles. It is well known that d(CG)n sequence can form Z-DNA most easily under negative supercoil conditions, but its essence has not been well clarified. The study on sequence dependence of Z-DNA stability is very difficult without modification or inducers. Here, by the strong topological constraint caused by hybridization of two complementary short circular ssDNAs, left-handed duplex part was generated for various sequences, and their characteristics were investigated by using gel-shift after binding to specific proteins, CD and Tm analysis, and restriction enzyme cleavage. Under the strong topological constraint, non-APP sequences can also form left-handed DNA duplex as stable as that of APP sequences. As compared with non-APP sequences, the thermal stability difference for APP sequences between Z-form and B-form is smaller, which may be the reason that Z-DNA forms preferentially for APP ones. This result can help us to understand why nature selected APP sequences to regulate gene expression by transient Z-DNA formation, as well as why polymer with chirality can usually form both duplexes with left- or right-handed helix., Graphical Abstract Graphical AbstractBy formation of the LR chimera from two complementary ssDNA circles, we found that non-APP sequences can form left-handed DNA duplex, and APP sequences prefer to form Z-DNA due to its smaller difference in free energy between Z-form and B-form.

This paper firstly introduces the basic knowledge of music, proposes the detailed design of a music retrieval system based on the knowledge of music, and analyzes the feature extraction algorithm and matching algorithm by using the features of music. Feature extraction of audio data is the important research of this paper. In this paper, the main melody features, MFCC features, GFCC features, and rhythm features, are extracted from audio data and a feature fusion algorithm is proposed to achieve the fusion of GFCC features and rhythm features to form new features under the processing of principal component analysis (PCA) dimensionality reduction. After learning the main melody features, MFCC features, GFCC features, and rhythm features, based on the property that PCA dimensionality reduction can effectively reduce noise and improve retrieval efficiency, this paper proposes vector fusion by dimensionality reduction of GFCC features and rhythm features. The matching retrieval of audio features is an important task in music retrieval. In this paper, the DTW algorithm is chosen as the main algorithm for retrieving music. The classification retrieval of music is also achieved by the K-nearest neighbor algorithm. In this paper, after implementing the research and improvement of algorithms, these algorithms are integrated into the system to achieve audio preprocessing, feature extraction, feature postprocessing, and matching retrieval. This article uses 100 different kinds of MP3 format music as the music library and randomly selects 4 pieces each time, and it tests the system under different system parameters, recording duration, and environmental noise. Through the research of this paper, the efficiency of music retrieval is improved and theoretical support is provided for the design of music retrieval software integration system.

Data mining technology is an effective knowledge mining and data relationship induction technology based on massive data, which is widely used in data analysis in many fields. In order to improve the utilization effect of students’ performance and meet the teaching needs of modern education, data mining technology can be applied to the existing performance database to mine the data information and treatment. Data mining technology is used to analyse and process the data stored in the student achievement management system, which provides the basis for improving the teaching quality and optimizing the teaching resources. Based on the analysis of the relevant data of large-scale English test results, this paper finds out the relevant rules that affect college English test results, forms the corresponding performance prediction rules, uses data mining technology to more comprehensively analyse the factors that affect students’ performance, establishes a model, and uses data mining tools to mine and analyse students’ English test data. It is of great practical significance to select the model with high accuracy, further optimize the parameters, make good use of the data, and then take targeted measures to guide the teaching reform, help students make more efficient learning plans, and improve and perfect the existing problems in teaching.

When multiple single-antenna half-duplex (HD) nodes communicate with a full-duplex (FD) base station equipped with multiple transmit and receive antennas, the uplink nodes would generate inter-node interference (INI) on the downlink nodes. We propose a base station assisted INI suppression scheme. The scheme is to design a proper amplify-and-forward matrix at the base station so that the sum achievable rate of the uplink and downlink is maximized. We derive the close-form expression of the amplify-and-forward matrix, which is the product of a parameter optimization and a zero-forcing beamforming (ZFBF) matrices. Finally, we investigate the performance of the proposed INI suppression scheme in a single cell.

To improve the accuracy of the nonlinear distortion correction for the radio frequency (RF) power amplifier (PA), it is necessary to precisely obtain the reverse function of the PA nonlinear model. However, the direct inversion of the PA nonlinear model involves solving a high-order univariate polynomial, which is difficult to apply in engineering. In this study, based on the envelope memory polynomial (EMP) model, the high-order terms of the nonlinear model are approximated by their previously calculated values through iterations and considered as known constants in the polynomial solution finding process, thereby resulting in a significant reduction in computational complexity. Compared with the direct inversion method, model of a 9th-order nonlinear, the proposed method reduces the calculation time in the coordinated rotation digital computer (CORDIC) algorithm by at least 80%. The simulation results show that for a long-term evolution (LTE) downlink signal, the results obtained by the proposed simplified method agree well with those obtained by direct inversion method.

A novel digital predistortion (DPD) architecture is proposed to linearize power amplifiers (PAs) in a phased array applicable to the wireless communication systems. First, the composite signal of the array outputs over the air is captured by an extra observation chain deployed in this architecture. Although the observed signal may differ from the composite signal of the array pointing to an arbitrary target, it contains all the nonlinearities of the PAs inside the array. Thus, the nonlinearities of the PAs in the array can be simply characterized from the observed signal by using a single nonlinear model. Based on this nonlinear model, an indirect learning method is then adopted to estimate the reverse nonlinear model coefficients, i.e., the DPD model coefficients, for linearization of the PA array. Moreover, a testbed is designed with a PA array composed of four identical PAs and experiments are performed on a 256-QAM waveform of 100-MHz bandwidth to evaluate the effectiveness of the proposed method. Experimental results have validated that the adjacent channel power ratio is improved from -31 dBc to -48 dBc and the error vector magnitude is improved from 5% to 0.8%.

In modern wireless communication systems, the inherent nonlinearities of power amplifiers(PAs) result in severe distortion and spectral regrowth. To deal with this problem, we propose a nonlinear feedback linear zed power amplifier (NFLPA) to compensate for the PA‘s nonlinearity with an extra radio frequency RF feedback chain. By accurately capturing the nonlinear distortion in advance, the distorted signal is subsequently attenuated and coupled with the original input signal to effectively linearize the PA output. Furthermore, the linearization performance of the proposed NFLPA is often restricted by the non-ideal electronic components, which introduce extra loop delay mismatch existing in the feedback chain. To conduct a quantitative analysis of the linearization performance with the presence of loop delay mismatch, the orthogonal frequency division multiplexing (OFDM) modulation scheme is utilized to analyze the residual nonlinear power of the NFLPA output, the expression of which is derived in terms of loop delay mismatch, transmitted signal power, and signal bandwidth. Simulations reveal that the deployment of the NFLPA significantly enhances the linearization performance. Moreover, the degradation effect of loop delay mismatch is simultaneously evaluated based on the adjacent channel leakage ratio (ACLR) performance via simulations.

In the presence of some eavesdroppers, cooperative jamming cancellation (CJC) plays a significant role in physical layer security for wireless communications. The assumption that the cooperative jamming is cancelled ideally with perfect frequency and timing synchronization is commonly used in the past literatures. Due to the instability of crystal oscillators and the relative motion between the cooperative node and the receiving node, residual frequency offset always exists between these two nodes, resulting in performance degradation during CJC. In this paper, a scenario in which all nodes equipped with single antenna is considered. To explicitly analyze the effect of frequency offset on CJC, we model frequency offset as an inter-frequency interference (IFI) in the frequency domain, and evaluate the CJC performance by jamming cancellation ratio (JCR), which is derived as the closed-form expression. Theoretical and simulation results show that, for the additive white Gaussian noise (AWGN) channel, when the ideal received jamming-to-noise ratio (JNR) is 60dB, the jamming bandwidth is 10MHz, and the sampling period of analog-digital converter (ADC) is 100ns, the maximum tolerable frequency offsets that incur 0.1dB, 1dB, and 3dB JCR degradation are 0.82Hz, 2.74Hz, and 5.37Hz, respectively.

In recent years, the information security environment is undergoing great changes. On one hand, with the development of technology and the advancement of network integration, network involves in a larger and larger scale, and its structure is becoming more and more complex. On the other hand, the development of attack technology has also taken a qualitative leap. The ability of violent cracking and the scale of botnets are no longer the primary factors that determine the attack effect. On the contrary, attackers are more likely to adopt information-driven complex combination attacks with clear attack targets. These changes have brought great challenges to network security defense. Therefore, from the perspective of building a secure network environment, this paper studies network vulnerability assessment in the field of computer network security, and proposes a method of network vulnerability assessment based on attack graph and security metrics. This method models and constructs the attack graph for the possible network attack behaviors. Besides, taking it as the analysis model, the risk assessment technology is taken to determine the indicators and calculating methods of security metrics so as to realize the evaluation of network vulnerability. Finally, the discussion was conducted based on the results, and a follow-up study was suggested.

This paper theoretically analyzes the performance of distributed-antenna orthogonal frequency division multiplexing (OFDM) systems with multiple carrier frequency offsets (CFO) using zero-forcing (ZF) detection. Considering both the large-scale fading and the multipath fading of the wireless channel, we derive the average power of the inter-carrier interference (ICI) due to the CFOs and thus obtain the expression for the signal to interference and noise ratio (SINR). By analyzing the underlying characteristics of the ZF detection, we address the reason for the inaccuracy of the conventional SINR expression and propose a novel modified SINR expression with better precision, and hence obtain a closed-form expression for the bit error rate (BER) performance with the presence of multiple CFOs. It is shown by the analytical and simulation results that the analytical SINR obtained by the conventional expression appears lower than actually while the analytical SINR and BER obtained by the expressions presented in this paper are more accurate and closer to the corresponding simulation results.

Analog self-interference (SI) cancellation is a key process to enable full-duplex wireless communications, which generates a cancelling signal (a copy of the SI) at the local receiver and subtracts from the received signal to cancel the SI imposed by the local transmitter. However, due to the inherent nonlinearities of analog canceller (AC), the SI cancellation performance is often restricted by introducing extra nonlinear distortion to the receiver. In this letter, an observation architecture is proposed to capture the AC nonlinear distortion by deploying an auxiliary chain identical to the AC and a linear cancellation approach. The captured nonlinear distortion could be used as a reference in the digital domain to mitigate the AC-induced nonlinear interference at the receiver. Experiments verify that the proposed architecture is capable of capturing the weak AC-induced distortion and suppressing it to close the receiver noise floor.

The implementation of teaching evaluation comprehensive evaluation system and effective management on teaching quality can scientifically manage the development of teachers’ teaching activities. At present, the teaching quality evaluation of teachers in higher vocational colleges has not yet realized algorithm operation based on quantum behavior particle swarm optimization. Therefore, it is necessary to study the teaching quality evaluation system consistent with the characteristics of higher vocational colleges. Quantum particle swarm optimization can construct a system model that can evaluate the quality of teaching. The system includes various functions like management and evaluation, which can be closely combined with the actual needs of each college. User name and password can effectively improve the security performance of the system. Convenient and fast database management operation, as well as intelligent and friendly database management interface, can ensure the timeliness and accuracy of query results. Research shows that, based on the practical application of quantum behavior particle swarm optimization in the field of teaching quality evaluation, the means of teaching evaluation are more scientific.

Ultra dense heterogeneous networks (UDHN) is one of the leading ideas in fifth generation (5G) mobile communication systems to improve the spectrum efficiency and the energy efficiency. In future 5G systems, users will be able to use the diverse radio access technologies (RATs) simultaneously. However, one of crucial issues in spectral sharing 5G UDHN is the co-channel interference among the small cell base stations (SCBSs) in different networks, such as cellular and WiFi networks. In order to mitigate the co-channel interference in the receiver, a co-channel interference suppression method is proposed in this paper. In this method, the covariance matrix of the interference is first approximated by the difference between the covariance matrices of the received signal and the desired signal. Subsequently, diagonal loading is adopted to generate the estimated covariance matrix of the interference to improve the robustness of the method. Simulation results are presented to demonstrate the advantage of the proposed algorithm.

Massive multiple-input multiple-output (MIMO) plays a key role in the next generation communication systems. For time-division-duplex (TDD) massive MIMO systems, channel reciprocity calibration (RC) is generally needed to cope with the reciprocity mismatch between the uplink and downlink channel state information. Currently, evaluating the capacity of TDD massive MIMO systems with imperfect RC mainly relies on exhausting Monte Carlo simulation and very few works can be found to concisely characterize the achievable capacity of TDD massive MIMO systems with imperfect RC. In this study, by adopting a SVD method, a low-complexity closed-form analytical expression is derived for the capacity lower bound of TDD massive MIMO systems with zero-forcing (ZF) beamforming and hardware dismatch, which succinctly explains and quantifies the effect of calibration error, the number of antennas and users on the capacity. We also demonstrate that the average power of multiuser interference resulting from imperfect calibration can be approximated as additive white gaussian noise (AWGN), whose power is proportional to the number of user and decreases with the number of antenna. The proposed theoretical results agree with simulation results fairly well, and can be used to design and evaluate the performance of practical massive systems without relying on exhausting simulations.

The effect of amplifier nonlinearity is analyzed for M-QAM full-duplex communications in additive white Gaussian noise channels. Two classical memoryless nonlinear amplifier models, i.e., a traveling-wave tube amplifier and a solid-state power amplifier, are considered. An analytical expression for the bit error ratio is derived using a characteristic function and numerical integration. Theoretical results show perfect agreement with those obtained by simulation. The analytical results could be used to evaluate the degradation in performance of an M-QAM full-duplex system caused by amplifier nonlinearity, and will be helpful in finding the optimal static operating point of an amplifier.

In this paper, an adaptive step size algorithm is proposed to reduce peak-to-average power ratio (PAR) of OFDM signals. The error vector magnitude (EVM) and spectral mask constraints are considered. Unlike the existing cognitive clipping method, the proposed scheme adjusts the EVM threshold with adaptive step size, which is proportionate to the ratio between PAR of each OFDM symbol and that required by system. Compared with the original method, the new algorithm offers more than 0.2dB PAR reduction at 10e-4 complementary cumulative distribution function (CCDF) level and 20% iteration reduction for 256 subcarriers QPSK WiMAX signal.

In this paper, an asynchronous layered space-time architecture is proposed to realize the spatial multiplexing over the Ricean multiple-input multiple-output (MIMO) channels. Based on reducing the correlation of spatial shaping pulses between the multiplexed streams, this approach can be used to solve the problem of detection in the ill-conditioned channel matrix. It is shown that at the receiver, the reduction of the correlation enables the requirement on the number of the receive antennas to be removed by the zero forcing (ZF) detector compared with the conventional synchronous layered space-time architecture. First, this paper presents how to exploit the correlation of spatial shaping pulses between the multiplexed streams. Then deriving the exact closed-form expression of error rate for the proposed scheme, this paper finds that the maximum possible diversity gain can be achieved based on the independent layered architecture by the ZF detector at the cost of limited multiplexing gain.