Low Cost Sparse Network Monitoring Based on Block Matrix Completion

Author(s):  
Kun Xie ◽  
Jiazheng Tian ◽  
Gaogang Xie ◽  
Guangxing Zhang ◽  
Dafang Zhang
2018 ◽  
Vol 17 (7) ◽  
pp. 1595-1608 ◽  
Author(s):  
Kun Xie ◽  
Lele Wang ◽  
Xin Wang ◽  
Gaogang Xie ◽  
Jigang Wen

2020 ◽  
Vol 28 (3) ◽  
pp. 958-971
Author(s):  
Kun Xie ◽  
Yuxiang Chen ◽  
Xin Wang ◽  
Gaogang Xie ◽  
Jiannong Cao ◽  
...  

2014 ◽  
Vol 12 (3) ◽  
pp. 776-782
Author(s):  
Hongxin Cao ◽  
Y. W. Yang ◽  
Y. Liu ◽  
Z. Y. Zhang ◽  
Y. L. Chen ◽  
...  

Author(s):  
Shun Li ◽  
Siwei Sun ◽  
Danping Shi ◽  
Chaoyun Li ◽  
Lei Hu

As perfect building blocks for the diffusion layers of many symmetric-key primitives, the construction of MDS matrices with lightweight circuits has received much attention from the symmetric-key community. One promising way of realizing low-cost MDS matrices is based on the iterative construction: a low-cost matrix becomes MDS after rising it to a certain power. To be more specific, if At is MDS, then one can implement A instead of At to achieve the MDS property at the expense of an increased latency with t clock cycles. In this work, we identify the exact lower bound of the number of nonzero blocks for a 4 × 4 block matrix to be potentially iterative-MDS. Subsequently, we show that the theoretically lightest 4 × 4 iterative MDS block matrix (whose entries or blocks are 4 × 4 binary matrices) with minimal nonzero blocks costs at least 3 XOR gates, and a concrete example achieving the 3-XOR bound is provided. Moreover, we prove that there is no hope for previous constructions (GFS, LFS, DSI, and spares DSI) to beat this bound. Since the circuit latency is another important factor, we also consider the lower bound of the number of iterations for certain iterative MDS matrices. Guided by these bounds and based on the ideas employed to identify them, we explore the design space of lightweight iterative MDS matrices with other dimensions and report on improved results. Whenever we are unable to find better results, we try to determine the bound of the optimal solution. As a result, the optimality of some previous results is proved.


Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1377
Author(s):  
Mingjun Ding ◽  
Xiaodong Yang ◽  
Rui Hu ◽  
Zhitao Xiao ◽  
Jun Tong ◽  
...  

Large-scale symmetric arrays such as uniform linear arrays (ULA) have been widely used in wireless communications for improving spectrum efficiency and reliability. Channel state information (CSI) is critical for optimizing massive multiple-input multiple-output(MIMO)-based wireless communication systems. The acquisition of CSI for massive MIMO faces challenges such as training shortage and high computational complexity. For millimeter wave MIMO systems, the low-rankness of the channel can be utilized to address the challenge of training shortage. In this paper, we compared several channel estimation schemes based on matrix completion (MC) for symmetrical arrays. Performance and computational complexity are discussed and compared. By comparing the performance in different scenarios, we concluded that the generalized conditional gradient with alternating minimization (GCG-Alt) estimator provided a low-cost, robust solution, while the alternating direction method of multipliers (ADMM)-based hybrid methods achieved the best performance when the array response was perfectly known.


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