Environmental consideration provides new trends in wireless communication network known as green communication. The main object of green communication is to save as much as possible the energy consumption of the communication system. In this paper, the authors have investigated the green distributed nonlinear state estimation problem in wireless sensor networks (WSNs), which will be seamlessly integrated with the forthcoming 5G communication system. A distributed signal reconstruction algorithm is proposed by employing compressive sensing and consensus filter to solve sparse signal reconstruction issue in WSNs with energy efficiency considered. In particular, the pseudo-measurement (PM) technology is introduced into the cubature Kalman filter (CKF), and a sparsity constraint is imposed on the nonlinear estimation by CKF. In order to develop a distributed reconstruction algorithm to fuse the random linear measurements from the nodes in WSNs, the PM embedded CKF is formulated into the information form, and then the derived information filter is combined with the consensus filter, while the square-root version is further developed to improve the performance and strengthen power saving capability. The simulation results demonstrate that the sparse signal can be reconstructed with much fewer nodes in decentralized manner and all the nodes can reach a consensus, while providing some attractive benefits to the green 5G communication system.INDEX TERMS Green, compressive sensing, nonlinear, cubature Kalman filter, square-root decomposition, consensus filter, wireless sensor networks (WSNs).

This article presents a distributed Bayesian reconstruction algorithm for wireless sensor networks to reconstruct the sparse signals based on variational sparse Bayesian learning and consensus filter. The proposed approach is able to address wireless sensor network applications for a fusion-center-free scenario. In the proposed approach, each node calculates the local information quantities using local measurement matrix and measurements. A consensus filter is then used to diffuse the local information quantities to other nodes and approximate the global information at each node. Then, the signals are reconstructed by variational approximation with resultant global information. Simulation results demonstrate that the proposed distributed approach converges to their centralized counterpart and has good recovery performance.

When using Rock Quality Designation (RQD) method to evaluate rock mass integrity, the premises are often neglected and there are some limitations in RQD method. This study proposes a hole section statistical method and a rock block coefficient to supplement and improve the limitations of RQD method. The accuracy is verified by applying it to a reservoir.

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In this paper, we proposed a new noise speech recognition method based on the compressive sensing theory. Through compressive sensing, our method increases the anti-noise ability of speech recognition system greatly, which leads to the improvement of the recognition accuracy. According to the experiments, our proposed method achieved better recognition performance compared with the traditional isolated word recognition method based on DTW algorithm.