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Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

In commercial networks, autonomous user nodes operating on batteries are assumed to be selfish to consume their energy solely to maximize their own benefits, e.g., data throughputs. In this letter a two-user cooperative game is proposed to perform the power allocation for selfish cooperative communication networks. In the game, one selfish user node could trade its transmission power for the other ones cooperative relaying directly, and both user nodes are willing to achieve an optimal data-rate increase through cooperative relaying. To find the Nash bargaining solution (NBS) of the game, a low-complexity numerical particle swarm optimizer (PSO) algorithm is also developed. Simulation results indicate that the NBS of the game is efficient, in that both users could experience better performance than they work independently, and fair, in that the degree of cooperation of a user node only depends on how much contribution its partner can make to improve its own performance.

WiFi-based indoor localization techniques are critical for location-based services. Among them, fingerprint-based method gains considerable interest due to its high accuracy and low equipment requirement. One of the major challenges faced by fingerprint-based position system is that in some places there are not enough access points (AP) to provide features for accurate location. To address that, we propose a novel fingerprint-based system using only a single AP. We propose a novel phase decomposition method to obtain the phase of multipath provided by a AP and use the decomposed phase as a fingerprint after the feature exaction by principal component analysis (PCA). Performance in the laboratory, meeting room, and corridor is investigated, and our system is also compared with a RSSI-based and a CSI-based fingerprint localization system. As the experimental results suggest, the minimum mean distance error is 0.6 m in the laboratory, 0.45 m in the meeting room, and 1.08 m in the corridor, outperforming the other two systems.

In recent years, due to the rapidly growing capacities of physical layer, device-free passive detection holds great importance for a broad range of application. Most recent works focus on motion detection, intrusion detection, and vital sign with commodity WiFi devices in the indoor environment. Conventional device-free motion detection techniques, which utilize received signal strength (RSS), may suffer from coarse granularity and high variability problems. In resorting to the finer-grained channel state information (CSI), we propose PhaseMode, a novel approach for device-free motion detection leveraging CSI phase difference data between adjacent antenna pairs. We implement our approach on commercial WiFi devices and validate its performance. We conduct experiments in different test periods of three indoor environments; the results show that the proposed scheme achieves an average accuracy over 99.4% of motion detection in different scenarios.

Queue-aware energy-efficient scheduling and power allocation with feedback reduction in small-cell networks SCIENCE CHINA Information Sciences 61, 048301 (2018); A suboptimal joint bandwidth and power allocation for cooperative relay networks: a cooperative game theoretic approach SCIENCE CHINA Information Sciences 56, 072304 (2013); An energy-efficient geographic routing based on cooperative transmission in wireless sensor networks SCIENCE CHINA Information Sciences 56, 072302 (2013); Energy-efficient power allocation for non-regenerative OFDM relay links SCIENCE CHINA Information Sciences 56, 022306 (2013);. RESEARCH PAPER. SCIENCE CHINA Information Sciences

SUMMARYWireless nodes operating on batteries are always assumed to be selfish to consume their energy solely to maximize their own benefits. Thus, the two network objectives, that is, system efficiency and user fairness should be considered simultaneously. To this end, we propose two game theoretic mechanisms, that is, the signal‐to‐noise ratio (SNR) game and the data‐rate game to stimulate cooperation among selfish user nodes for cooperative relaying. Considering one node could trade its transmission power for its partner's relaying directly, the strategy of a node is defined as the amount of power that it is willing to contribute for relaying purpose. In the SNR game, selfish nodes are willing to achieve SNR increases at their receivers, while in the data‐rate game the nodes are willing to achieve data‐rate gains. We prove that each of the games has a unique Nash bargaining solution. Simulation results show that the Nash bargaining solution lead to fair and efficient resource allocation for both the cooperative partner nodes in the Pareto optimal sense, that is, both the nodes could experience better performance than they work independently and the degree of cooperation of a node only depends on how much contribution its partner can make to improve its own performance. Copyright © 2012 John Wiley & Sons, Ltd.

This paper proposes a cooperative game to perform a fair and efficient resource allocation for the time division multiple access (TDMA) based cooperative communication networks. In the considered system, two selfish user nodes can act as a source as well as a relay for each other. A transmission node with energy limitation is willing to seek cooperative relaying only if the datarate achieved through cooperation is not lower than that achieved without cooperation by consuming the same amount of energy. The cooperative strategy of a node can be defined as the number of data-symbols and power that it is willing to contribute for relaying purpose. We formulate this two-node fair and efficient resource sharing problem as a bargaining game. Since the Nash bargaining solution (NBS) to the game is computationally complex to obtain, a low-complexity algorithm to search the suboptimal NBS is proposed. Simulation results show that the NBS results are fair in that both nodes could experience better performance than if they work independently. And the NBS results are efficient in that the performance loss of the game to that of the maximal overall rate scheme is small while the maximal-rate scheme is unfair.