Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Leng, Mei; Wu, Yik-Chung

doi:10.1109/tsp.2011.2160857

Cited by 69 publications

(38 citation statements)

References 26 publications

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“…At each round of message exchange, every variable node computes the output message to factor nodes according to (12) and (13). After receiving the message from neighboring variable nodes, each factor computes its output message according to (9) and (10). Such iteration is terminated when (14) converges or the maximum number of iteration is reached.…”

Section: Bp Message Computationmentioning

confidence: 99%

Fully distributed clock skew and offset estimation in wireless sensor networks

Du¹,

Wu²

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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In this paper, we propose a fully distributed algorithm for joint clock skew and offset estimation in wireless sensor networks. With the proposed algorithm, each node can estimate its clock skew and offset by communicating only with its neighbors. Such algorithm does not require any centralized information processing or coordination. Simulation results show that estimation mean-square-error at each node converge to the centralized Cramér-Rao bound with only a few number of message exchanges.

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Section: Bp Message Computationmentioning

confidence: 99%

Fully distributed clock skew and offset estimation in wireless sensor networks

Du¹,

Wu²

2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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“…Then, the information block can be de-mapped. 9 The computational complexity of ML is O((N t N r ) 2 L), where L denotes the size of the constellation points [29]. The complexity increases when L is large, which causes problems when ML based on ABPM is applied.…”

Section: B ML Detectionmentioning

confidence: 99%

Antenna Beam Pattern Modulation With Lattice-Reduction-Aided Detection

Ramirez-Gutierrez

Zhang

Elmirghani

2016

IEEE Trans. Veh. Technol.

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This paper introduces a novel transmission design for antenna beam pattern modulation (ABPM) with a low complexity decoding method. The concept of ABPM was first presented with the optimal maximum likelihood (ML) decoding. However, an ML detector may not be viable for practical systems when the constellation size or the number of antennas is large such as in massive multiple input multiple output (MIMO) systems. Linear detectors, on the other hand, have lower complexity but inferior performance.In this paper, we present the antenna pattern selection with a lattice reduction (LR) aided linear detector for ABPM to reduce the detection complexity with the bit error rate (BER) performance approaching that of ML while conserving low complexity. Simulation results show that even with this suboptimal detection, performance gain is achieved by the proposed scheme compared to different spatial modulation techniques using ML detection. In addition, to validate the results, an upper bound expression for BER is provided for ABPM with ML detection.

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“…Since the mean and maximum of a Gaussian distribution are the same, µ * equals the centralized joint maximum likelihood (ML) estimator under non-informative prior. [8,12] and variance of random delay σ 2 i = 0.05 is assumed to be identical for all nodes. 5000 Monte-carlo simulation trials were performed to obtain the average performance of each point in all the figures presented in this section.…”

Section: Asynchronous Bp Convergence Analysismentioning

confidence: 99%

Distributed Clock Skew and Offset Estimation in Wireless Sensor Networks: Asynchronous Algorithm and Convergence Analysis

2013

IEEE Trans. Wireless Commun.

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Abstract-In this paper, we propose a fully distributed algorithm for joint clock skew and offset estimation in wireless sensor networks based on belief propagation. In the proposed algorithm, each node can estimate its clock skew and offset in a completely distributed and asynchronous way: some nodes may update their estimates more frequently than others using outdated message from neighboring nodes. In addition, the proposed algorithm is robust to random packet loss. Such algorithm does not require any centralized information processing or coordination, and is scalable with network size. The proposed algorithm represents a unified framework that encompasses both classes of synchronous and asynchronous algorithms for network-wide clock synchronization. It is shown analytically that the proposed asynchronous algorithm converges to the optimal estimates with estimation mean-square-error at each node approaching the centralized Cramér-Rao bound under any network topology. Simulation results further show that the convergence speed is faster than that corresponding to a synchronous algorithm.

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Low-Complexity Maximum-Likelihood Estimator for Clock Synchronization of Wireless Sensor Nodes Under Exponential Delays

Cited by 69 publications

References 26 publications

Fully distributed clock skew and offset estimation in wireless sensor networks

Fully distributed clock skew and offset estimation in wireless sensor networks

Antenna Beam Pattern Modulation With Lattice-Reduction-Aided Detection

Distributed Clock Skew and Offset Estimation in Wireless Sensor Networks: Asynchronous Algorithm and Convergence Analysis

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