Zhuo Sun

Prior Structure-Based Sparsity Representation for Compressive Signal Feature Recovery

Compressive sampling is a promising solution to reduce required sampling rates for signal reconstruction. In many scenarios, such as cognitive radio and modulation recognition, there are only expecting to acquire useful features rather than original signals. To reconstruct these features from compressive measurements, Compressive Sensing (CS) requires features to be sparse and have a one-dimensional relationship with those measurements. Since most of features are nonlinearly transformed from signals, selecting one with high sparsity and then building a linear mapping between it and measurements become the main challenges. This work proposed a new method to find sparsity representations for signals based on their intra-structure. With this method, two common features, autocorrelation function and fourth order time-varying moment are respectively expressed as another two sparse representations called structure-based sparsity representations. Simulation shows that these representations can work effectively in reducing reconstruction iterations, computing consumption, and memory cost for sensing matrices.

Compressive signal reconstruction with noise pre-filtering in compressed domain

Compressive Sensing (CS) enables us to exactly reconstruct a signal from a small number of observations if it has a sparse representation in a known basis. But in the situation where the sparsity is not strictly satisfied as the signal contains Gaussian noise, the general practice tries to directly recover the noisy signal, and then filters out the noise from the recovered signal. Because the existence of noise may reduce the reconstruction accuracy, to overcome this drawback, we propose a noise reduction method which preprocesses the signal in compressed domain based on minimum mean square error (MMSE). The method can efficiently suppress the noise but still keep plenty of energy of the desired signal. Both analyses and simulations indicate that the modified method can improve the performance of reconstruction.

Modulation Classification of Linear Digital Signals Based on Compressive Sensing Using High-Order Moments

Compressed sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary. While for the classification of modulated signals, we only expect to acquire some characteristics rather than the original signal. However, to select the feature used for modulation classification with sparsity is the main challenge. In this paper, we propose a method to identify the linear modulation format of an unknown single carrier linear digital signal using compressive samples, without reconstructing the original signal. In our method, we construct a compositional feature of multiple high-order moments of the received data as the identification characteristic. From simulations we can see that the method is effective, even at a relatively low signal-to-noise ratio.

Research on Location Management Strategies of LEO Satellite Communication System

With the continuous development of satellite communications, satellite communications have gone into the personal communication period, the corresponding location management strategy is particularly important in order to achieve hand-held terminal and personal communication globalization. In this paper, it is studied that the location management strategy for Fuxing low-orbit satellite communication system and proposes a two-layer database structure which includes the central database and the local database, and defines the location update and the location paging process as well as the detailed process design. At the same time, a paging optimization strategy based on the planning sequence algorithm is proposed for the subscriber stations with different speeds. As a result, the algorithm can reduce the paging signaling cost by 8% to 40% or so, to achieve the optimization effect. Finally, the corresponding influencing factors are analyzed according to the simulation results.

Complex Networks Analysis Based on IP Data of Mobile Communication System

In order to analyze the IP label data from mobile communication gateway, this paper creates the network based on IP label data. By using the degree analysis method, the paper analyses the data and gets IP network degree distribution condition, fits deviation analysis and extracts the import node to obtain the size of the IP network and the network boundary. Then, the Eigenvector centrality analysis method is used to calculate the core of interconnected nodes. The IP label data network is partitioned and three pure data set is achieved according to the important nodes in combination with label propagation algorithm. Each divided data set can be used for further data analysis, including analyzing the user behaviour through a purer user behaviour data extracted from the complex network data and stripping the invalid data from the data set.

Energy Dissipation Balance Scheme in Dynamic Ad Hoc Networks

In order to balance energy allocation to extend the lifetime of dynamic ad hoc networks, the Relay Node Backup Power Control (RNBPC) scheme is proposed using Markov decision process. Through theoretical proof and simulations, an expected energy-balanced network can be achieved by collecting information of transmission probability of every node in the network and predict the future transmission situation in preprocess period. During preprocess period, we initialize the network by the combination of two proved schemes. Once we find the nodes which have heavy communication task, evaluated by the transmission probability, then the scheme searches the feasible backup relay node to share the communication task to avoid energy running out too quickly. Simulation results show that the RNBPC scheme can relieve traffic nodes and then balance the energy dissipation of every node as well to extend the lifetime of the whole network.

Signal Sensing with Sub-Nyquist Cyclic Feature

Compressive sensing can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary. While in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals. In this paper, we propose an effective method of digital communication signal detection based on the sub-Nyquist cyclic feature without the introduction of the reconstruction algorithm. By analyzing the difference between matrixes of cyclic autocorrelation for the two sceneries, it can be determined whether there exists signal or not. The simulation results show that the method performs well even at a low signal-to-noise ratio such as −10 dB, and complexity of both time and space is lowered considerably.

Feature-Based Digital Modulation Recognition Using Compressive Sampling

Compressive sensing theory can be applied to reconstruct the signal with far fewer measurements than what is usually considered necessary, while in many scenarios, such as spectrum detection and modulation recognition, we only expect to acquire useful characteristics rather than the original signals, where selecting the feature with sparsity becomes the main challenge. With the aim of digital modulation recognition, the paper mainly constructs two features which can be recovered directly from compressive samples. The two features are the spectrum of received data and its nonlinear transformation and the compositional feature of multiple high-order moments of the received data; both of them have desired sparsity required for reconstruction from subsamples. Recognition of multiple frequency shift keying, multiple phase shift keying, and multiple quadrature amplitude modulation are considered in our paper and implemented in a unified procedure. Simulation shows that the two identification features can work effectively in the digital modulation recognition, even at a relatively low signal-to-noise ratio.

Extracting fingerprint of wireless devices based on phase noise and multiple level wavelet decomposition

Wireless devices can be identified by the fingerprint extracted from the signal transmitted, which is useful in wireless communication security and other fields. This paper presents a method that extracts fingerprint based on phase noise of signal and multiple level wavelet decomposition. The phase of signal will be extracted first and then decomposed by multiple level wavelet decomposition. The statistic value of each wavelet coefficient vector is utilized for constructing fingerprint. Besides, the relationship between wavelet decomposition level and recognition accuracy is simulated. And advertised decomposition level is revealed as well. Compared with previous methods, our method is simpler and the accuracy of recognition remains high when Signal Noise Ratio (SNR) is low.

Recognition of OFDM Signal Based on Cyclic Cumulant Reconstruction with Sub-Nyquist Sampling

In recent years,to meet the challenge of spectrum sensing with ultra wide band and big data in cooperative and cognitive radio networks,the theory of compressed sensing is introduced in,which can solve the problem of high sampling rate requirement due to Shannon-Nyquist sampling theory. In this paper,considering the property of signals’ cyclostationarity, we innovatively propose a method in OFDM signal detection using sub-Nyquist samples. By doing sparsity analysis combined with detection necessities,we present a partial-scale reconstruction method to reduce the recovery iteration and lower the algorithm complexity. Furthermore,we find out an equivalent cyclic cumulant calculation method for OFDM signals to simplify the calculation and lower the high memory consumption during signal processing. From the simulations we can see the optimized method introduced in effectively eliminates the constraints for compressed detection of OFDM signals and possesses a far-reaching significance in further researches and applications.