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Weidong Wang - One of the best experts on this subject based on the ideXlab platform.

  • stochastic encoding based distributed Blind Estimation for deterministic vector signal
    Vehicular Technology Conference, 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • VTC Spring - Stochastic Encoding based Distributed Blind Estimation for Deterministic Vector Signal
    2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks
    IEEE Access, 2019
    Co-Authors: Wentao Zhang, Li Chen, Min Qin, Weidong Wang
    Abstract:

    To realize the Internet of Things, one of the essential elements is wireless sensor networks which can sense the physical conditions of the environment. The ubiquitous sensing is achieved by a large number of spatially dispersed sensors and distributed Estimation technology. However, the low-cost sensors are insufficient to support conventional distributed Estimation schemes. Since most conventional schemes include channel training process, the resource consumption of which is enormous. Thus, one key challenge in designing a feasible distributed Estimation scheme is to reduce resource consumption from channel training. We tackle the challenge by proposing a distributed Blind Estimation scheme. The proposed scheme consists of two components: random transmission and statistical inference. Specifically, assuming sensors contain only two states that are active and inactive. The random transmission strategy turns the sensing value into a parameter to govern the sensor states. At the fusion center, statistical inference method is used to recover the sensing value. The specific design of the inference method involves the distribution approximation and clustering, which are accomplished by Gaussian mixture model and expectation-maximization principle. By the proposed scheme, the channel information is no longer needed in distributed Estimation. Therefore, it is more energy-efficient and more applicable to the complicated wireless environment compared with conventional schemes. Besides, we investigate the impacts of the number of sensors and quantization on the Estimation performance. Finally, simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

Wentao Zhang - One of the best experts on this subject based on the ideXlab platform.

  • stochastic encoding based distributed Blind Estimation for deterministic vector signal
    Vehicular Technology Conference, 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • VTC Spring - Stochastic Encoding based Distributed Blind Estimation for Deterministic Vector Signal
    2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks
    IEEE Access, 2019
    Co-Authors: Wentao Zhang, Li Chen, Min Qin, Weidong Wang
    Abstract:

    To realize the Internet of Things, one of the essential elements is wireless sensor networks which can sense the physical conditions of the environment. The ubiquitous sensing is achieved by a large number of spatially dispersed sensors and distributed Estimation technology. However, the low-cost sensors are insufficient to support conventional distributed Estimation schemes. Since most conventional schemes include channel training process, the resource consumption of which is enormous. Thus, one key challenge in designing a feasible distributed Estimation scheme is to reduce resource consumption from channel training. We tackle the challenge by proposing a distributed Blind Estimation scheme. The proposed scheme consists of two components: random transmission and statistical inference. Specifically, assuming sensors contain only two states that are active and inactive. The random transmission strategy turns the sensing value into a parameter to govern the sensor states. At the fusion center, statistical inference method is used to recover the sensing value. The specific design of the inference method involves the distribution approximation and clustering, which are accomplished by Gaussian mixture model and expectation-maximization principle. By the proposed scheme, the channel information is no longer needed in distributed Estimation. Therefore, it is more energy-efficient and more applicable to the complicated wireless environment compared with conventional schemes. Besides, we investigate the impacts of the number of sensors and quantization on the Estimation performance. Finally, simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

Helmut Bolcskei - One of the best experts on this subject based on the ideXlab platform.

  • Blind Estimation of symbol timing and carrier frequency offset in wireless ofdm systems
    IEEE Transactions on Communications, 2001
    Co-Authors: Helmut Bolcskei
    Abstract:

    Orthogonal frequency-division multiplexing (OFDM) systems are highly sensitive to synchronization errors. We introduce an algorithm for the Blind Estimation of symbol timing and carrier frequency offset in wireless OFDM systems. The proposed estimator is an extension of the Gini-Giannakis (see IEEE Trans. Commun., vol.46, p.400-411, 1998) estimator for single-carrier systems. It exploits the cyclostationarity of OFDM signals and relies on second-order statistics only. Our method can be applied to pulse shaping OFDM systems with arbitrary time-frequency guard regions, OFDM based on offset quadrature amplitude modulation, and biorthogonal frequency-division multiplexing systems. We furthermore propose the use of different subcarrier transmit powers (subcarrier weighting) and periodic transmitter precoding to achieve a carrier frequency acquisition range of the entire bandwidth of the OFDM signal, and a symbol timing acquisition range of arbitrary length. Finally, we provide simulation results demonstrating the performance of the new estimator.

Li Chen - One of the best experts on this subject based on the ideXlab platform.

  • stochastic encoding based distributed Blind Estimation for deterministic vector signal
    Vehicular Technology Conference, 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • VTC Spring - Stochastic Encoding based Distributed Blind Estimation for Deterministic Vector Signal
    2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 2020
    Co-Authors: Wentao Zhang, Li Chen, Weidong Wang
    Abstract:

    In large-scale wireless sensor networks (WSN), a large number of spatially dispersed sensors and distributed signal Estimation schemes provide ubiquitous sensing. However, low-cost sensors are insufficient to support conventional distributed Estimation schemes, since the channel training process causes an enormous resource consumption in the large-scale WSN. This paper proposes a distributed Blind Estimation scheme that consists of two components: stochastic coding and statistical inference. The stochastic coding turns the desired vector signal into statistical parameters to govern the quantized symbols. At the fusion center (FC), statistical inference based on unsupervised clustering algorithms is utilized to recover the vector signal. The channel information is not required in the proposed distributed Estimation. Besides, we investigate the asymptotic properties of the Estimation error. Simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

  • Statistical Inference-Based Distributed Blind Estimation in Wireless Sensor Networks
    IEEE Access, 2019
    Co-Authors: Wentao Zhang, Li Chen, Min Qin, Weidong Wang
    Abstract:

    To realize the Internet of Things, one of the essential elements is wireless sensor networks which can sense the physical conditions of the environment. The ubiquitous sensing is achieved by a large number of spatially dispersed sensors and distributed Estimation technology. However, the low-cost sensors are insufficient to support conventional distributed Estimation schemes. Since most conventional schemes include channel training process, the resource consumption of which is enormous. Thus, one key challenge in designing a feasible distributed Estimation scheme is to reduce resource consumption from channel training. We tackle the challenge by proposing a distributed Blind Estimation scheme. The proposed scheme consists of two components: random transmission and statistical inference. Specifically, assuming sensors contain only two states that are active and inactive. The random transmission strategy turns the sensing value into a parameter to govern the sensor states. At the fusion center, statistical inference method is used to recover the sensing value. The specific design of the inference method involves the distribution approximation and clustering, which are accomplished by Gaussian mixture model and expectation-maximization principle. By the proposed scheme, the channel information is no longer needed in distributed Estimation. Therefore, it is more energy-efficient and more applicable to the complicated wireless environment compared with conventional schemes. Besides, we investigate the impacts of the number of sensors and quantization on the Estimation performance. Finally, simulation results demonstrate the effectiveness of the proposed Blind Estimation scheme.

Zhou De-qian - One of the best experts on this subject based on the ideXlab platform.