Machine learning-enabled MIMO-FBMC communication channel parameter estimation in IIoT: A distributed CS approach

Compressed Sensing(CS)is a Machine Learning(ML)method,which can be regarded as a single-layer unsupervised learning method.It mainly emphasizes the sparsity of the model.In this paper,we study an ML-based CS Channel Estimation(CE)method for wireless communications,which plays an important role in Industrial Internet of Things(IIoT)applications.For the sparse correlation between channels in Multiple Input Multiple Output Filter Bank MultiCarrier with Offset Quadrature Amplitude Modulation(MIMO-FBMC/OQAM)systems,a Distributed Compressed Sensing(DCS)-based CE approach is studied.A distributed sparse adaptive weak selection threshold method is proposed for CE.Firstly,the correlation between MIMO channels is utilized to represent a joint sparse model,and CE is transformed into a joint sparse signal reconstruction problem.Then,the number of correlation atoms for inner product operation is optimized by weak selection threshold,and sparse signal reconstruction is realized by sparse adaptation.The experiment results show that the proposed DCS-based method not only estimates the multipath channel components accurately but also achieves higher CE performance than classical Orthogonal Matching Pursuit(OMP)method and other traditional DCS methods in the time-frequency dual selective channels.

A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.

Wavelet Transform-Based Distributed Compressed Sensing in Wireless Sensor Networks

Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.

Sparse channel estimation for MIMO-OFDM systems using distributed compressed sensing

A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.

On the Energy Self-Sustainability of IoT via Distributed Compressed Sensing

This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.

A new backtracking-based sparsity adaptive algorithm for distributed compressed sensing

A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing(DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but the numbers of non-zero(significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing strong DCS algorithms.

A DISTRIBUTED COMPRESSED SENSING APPROACH FOR SPEECH SIGNAL DENOISING

Compressed sensing,a new area of signal processing rising in recent years,seeks to minimize the number of samples that is necessary to be taken from a signal for precise reconstruction.The precondition of compressed sensing theory is the sparsity of signals.In this paper,two methods to estimate the sparsity level of the signal are formulated.And then an approach to estimate the sparsity level directly from the noisy signal is presented.Moreover,a scheme based on distributed compressed sensing for speech signal denoising is described in this work which exploits multiple measurements of the noisy speech signal to construct the block-sparse data and then reconstruct the original speech signal using block-sparse model-based Compressive Sampling Matching Pursuit(CoSaMP) algorithm.Several simulation results demonstrate the accuracy of the estimated sparsity level and that this de-noising system for noisy speech signals can achieve favorable performance especially when speech signals suffer severe noise.

Stability of perfect and imperfect cylindrical shells under axial compression and torsion

Stability analyses of perfect and imperfect cylindrical shells under axial compression and torsion were presented. Finite element method for the stability analysis of perfect cylindrical shells was put forward through comparing critical loads and the first buckling modes with those obtained through theoretical analysis. Two typical initial defects, non-circularity and uneven thickness distribution, were studied. Critical loads decline with the increase of non-circularity, which exist in imperfect cylindrical shells under both axial compression and torsion. Non-circularity defect has no effect on the first buckling mode when cylindrical shell is under torsion. Unfortunately, it has a completely different buckling mode when cylindrical shell is under axial compression. Critical loads decline with the increase of thickness defect amplitude, which exist in imperfect cylindrical shells under both axial compression and torsion, too. A greater wave number is conducive to the stability of cylindrical shells. The first buckling mode of imperfect cylindrical shells under torsion maintains its original shape, but it changes with wave number when the cylindrical shell is under axial compression.

Dynamic Global-Principal Component Analysis Sparse Representation for Distributed Compressive Video Sampling

Video reconstruction quality largely depends on the ability of employed sparse domain to adequately represent the underlying video in Distributed Compressed Video Sensing (DCVS). In this paper, we propose a novel dynamic global-Principal Component Analysis (PCA) sparse representation algorithm for video based on the sparse-land model and nonlocal similarity. First, grouping by matching is realized at the decoder from key frames that are previously recovered. Second, we apply PCA to each group (sub-dataset) to compute the principle components from which the sub-dictionary is constructed. Finally, the non-key frames are reconstructed from random measurement data using a Compressed Sensing (CS) reconstruction algorithm with sparse regularization. Experimental results show that our algorithm has a better performance compared with the DCT and K-SVD dictionaries.

Research on Data Compression of WSN Based on Compressed Sensing

For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.

Experimental investigation on the energy evolution of dry and water-saturated red sandstones

In order to investigate the effect of water content on the energy evolution of red sandstone, the axial loading–unloading experiments on dry and water-saturated sandstone samples were conducted, and the distribution and evolution of elastic energy and dissipated energy within the rock were measured.The results show that the saturation process from dry to fully-saturated states reduces the strength, rigidity and brittleness of the rock by 30.2%, 25.5% and 16.7%, respectively. The water-saturated sample has larger irreversible deformation in the pre-peak stage and smaller stress drop in the post-peak stage.The saturation process decreases the accumulation energy limit by 38.9%, but increases the dissipated energy and residual elastic energy density, thus greatly reducing the magnitude and rate of energy release. The water-saturated sample has lower conversion efficiency to elastic energy by 3% in the prepeak region; moreover, the elastic energy ratio falls with a smaller range in the post-peak stage.Therefore, saturation process can greatly reduce the risk of dynamic disaster, and heterogeneous water content can lead to dynamic disaster possibly on the other hand.

A Novel Training Sequence Applied to DCS-Based Channel Estimation

Studies have indicated that the distributed compressed sensing based(DCSbased) channel estimation can decrease the length of the reference signals effectively. In block transmission, a unique word(UW) can be used as a cyclic prefix and reference signal. However, the DCS-based channel estimation requires diversity sequences instead of UW. In this paper, we proposed a novel method that employs a training sequence(TS) whose duration time is slightly longer than the maximum delay spread time. Based on proposed TS, the DCS approach perform perfectly in multipath channel estimation. Meanwhile, a cyclic prefix construct could be formed, which reduces the complexity of the frequency domain equalization(FDE) directly. Simulation results demonstrate that, by using the method of simultaneous orthogonal matching pursuit(SOMP), the required channel overhead has been reduced thanks to the proposed TS.

A survey on distributed compressed sensing： theory and applications

The compressed sensing （CS） theory makes sample rate relate to signal structure and content. CS samples and compresses the signal with far below Nyquist sampling frequency simultaneously. However, CS only considers the intra-signal correlations, without taking the correlations of the multi-signals into account. Distributed compressed sensing （DCS） is an extension of CS that takes advantage of both the inter- and intra-signal correlations, which is wildly used as a powerful method for the multi-signals sensing and compression in many fields. In this paper, the characteristics and related works of DCS are reviewed. The framework of DCS is introduced. As DCS＇s main portions, sparse representation, measurement matrix selection, and joint reconstruction are classified and summarized. The applications of DCS are also categorized and discussed. Finally, the conclusion remarks and the further research works are provided.

X-ray tomography analysis of aluminum alloy powder compaction

Aluminum alloy green compacts obtained by single-action compaction in lubricated and unlubricated dies were investigated by X-ray tomography.For the purpose of obtaining the relationship between the density of green compact and the gray value of reconstructed images,linear fitting was performed.The results show that these data have excellent linear relationship.Then,a new method for analysis of compaction process was presented in this paper.Detailed quantitative analysis of the density and compressive pressure distribution in green compacts was performed.The compressive pressure transmitted ratio and friction index were also discussed.It is found that the lubricated die wall has better effects on the density homogeneity at lower applied compressive pressure.During compaction process,the densification of powder particles in the die mainly occurs in the lower part of the green compact and the factor which mainly influences the friction between die wall and green compact is the applied compressive pressure.