Big Data Computing and Communications: First International by Yu Wang, Hui Xiong, Shlomo Argamon, XiangYang Li, JianZhong

By Yu Wang, Hui Xiong, Shlomo Argamon, XiangYang Li, JianZhong Li

This publication constitutes the lawsuits of the 1st foreign convention on enormous facts Computing and Communications, BigCom 2015, held in Taiyuan, China, in August 2015. The forty-one papers awarded during this quantity have been conscientiously reviewed and chosen from seventy four submissions. They have been equipped in topical sections named: instant communique and networks; database and massive facts; shrewdpermanent mobile and sensing software; safety and privateness; structure and purposes; sensor networks and RFID; social networks and advice; sign processing and development reputation; and routing and source management.

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Additional resources for Big Data Computing and Communications: First International Conference, BigCom 2015, Taiyuan, China, August 1-3, 2015, Proceedings

Example text

A 1 -regularized least-absolutes algorithm ( 1 -LA) is investigated based on the Linear Programming to recover the dynamic sparse channel in non-Gaussian impulsive noise scenarios[11]. Moreover, a geometric mixed norm approach is proposed for shallow water acoustic channel estimation and tracking, which reformulates the wellknown 1 - 2 sparse optimization metric as an equivalent mixture of the 2 norm of the complex squared root of the channel coefficients and a fourth-order complex function based on the estimation error, and employs a gradient descent algorithm to estimate and track sparse coefficients[16].

Wang Algorithm 1.. 0 -CSKF 1: Initialization ˆ 0 = 0 and Σ0 = 100 Set h 2: Prediction ˆ k−1|k−1 ˆ k|k−1 = ξ h h Σk|k−1 = ξ 2 Σk−1|k−1 + σu2 I 3: Measurement Update H 2 Kk = Σk|k−1 AH k Ak Σk|k−1 Ak + σw I ˆ k|k−1 + Kk yktr − Ak h ˆ k|k = h ˆ k|k−1 h 4: −1 Σk|k = (I − Kk Ak ) Σk|k−1 Constrained estimate ˜ ik and Σ ˜ki denote the 0 norm constrained estimate and its covariance, thus, Let h ˆ k|k and Σ ˜ 0k = h ˜k0 = Σk|k . initialized with h ˜ =H ˜ tan , or H ˜ =H ˜ tanh , or H ˜ =H ˜ gaussian ˜ =H ˜ 2order , or H Let H for i = 1, 2, .

The CSI corresponding to the pilot subcarriers is first estimated, and that corresponding to the data subcarriers is obtained c Springer International Publishing Switzerland 2015 Y. Wang et al. ): BigCom 2015, LNCS 9196, pp. 28–42, 2015. 1007/978-3-319-22047-5 3 Dynamic Sparse Channel Estimation Using 0 -constrained Kalman Filter 29 by interpolation. This is called Pilot-Assisted Channel Estimation (PACE) for which a tremendous research effort is yet being made [9]. Numerous experimental studies undertaken by researchers in the recent past have shown that wireless channels are often characterized by multipath propagation and tend to exhibit sparse structures with a few significant paths in many wideband OFDM systems, such as underwater acoustic systems, digital television systems and residential ultra-wideband systems [1].

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