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Improving physical-layer security for primary users in cognitive radio networks

IET COMMUNICATIONS 2017年第15期11卷 2303-2310页

作者： Xu, Ding Li, Qun Nanjing Univ Posts & Telecommun Wireless Commun Key Lab Jiangsu Prov Nanjing Jiangsu Peoples R China

In this study, the authors investigate the physical-layer security in a cognitive radio network where both the secondary user (SU) and the primary user (PU) are facing security threats from the malicious eavesdroppers. To protect the PU, the SU acts as a friendly jammer to interfere with the eavesdroppers by splitting a certain portion of the transmit power for sending the jamming noise. The problem of optimising SU scheduling, power allocation and power splitting ratio to maximise the SU ergodic secrecy rate subject to the PU secrecy outage constraint with imperfect channel state information available at the SU is investigated based on the dual optimisation method. In addition, a greedy algorithm is also proposed for minimising the PU secrecy outage probability. Simulation results indicate that the proposed algorithms are effective in improving the PU secrecy performance in terms of secrecy outage probability as well as providing secure communications for the SU.

关键词： cognitive radio telecommunication security jamming telecommunication scheduling telecommunication network reliability probability minimisation physical-layer security improvement cognitive radio network secondary user primary user malicious eavesdroppers jamming noise power allocation and power splitting ratio SU scheduling optimisation problem PU secrecy outage constraint dual optimisation method greedy algorithm PU secrecy outage probability minimisation

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Compressed Sensing for Wireless Communications: Useful Tips and Tricks

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2017年第3期19卷 1527-1550页

作者： Choi, Jun Won Shim, Byonghyo Ding, Yacong Rao, Bhaskar Kim, Dong In Hanyang Univ Dept Elect & Biomed Engn Seoul 04763 South Korea Seoul Natl Univ Inst New Media & Commun Seoul 151742 South Korea Seoul Natl Univ Sch Elect & Comp Engn Seoul 151742 South Korea Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92122 USA Sungkyunkwan Univ Sch Informat & Commun Engn Suwon 440746 South Korea

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to grasp simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips and tricks that wireless communication researchers need to know when designing CS-based wireless systems. First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct sub-problems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this paper will be a useful guide for wireless communication researchers and even non-experts to get the gist of CS techniques.

关键词： Compressed sensing sparse signal underdetermined systems wireless communication systems l(1)-norm greedy algorithm performance guarantee

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Generalized fusion algorithm for compressive sampling reconstruction and RIP-based analysis

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SIGNAL PROCESSING 2017年 139卷 36-48页

作者： Zaki, Ahmed Chatterjee, Saikat Rasmussen, Lars K. KTH Royal Inst Technol ACCESS Linnaeus Ctr Stockholm Sweden

We design a Generalized Fusion algorithm for Compressive Sampling (gFACS) reconstruction. In the gFACS algorithm, several individual compressive sampling (CS) reconstruction algorithms participate to achieve a better performance than the individual algorithms. The gFACS algorithm is iterative in nature and its convergence is proved under certain conditions using Restricted Isometry Property (RIP) based theoretical analysis. The theoretical analysis allows for the participation of any off-the-shelf or new CS reconstruction algorithm with simple modifications, and still guarantees convergence. We show modifications of some well-known CS reconstruction algorithms for their seamless use in the gFACS algorithm. Simulation results show that the proposed gFACS algorithm indeed provides better performance than the participating individual algorithms. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Compressive sampling greedy algorithm RIP analysis Fusion strategy

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Weighted Joint Sparse Representation for Removing Mixed Noise in Image

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IEEE TRANSACTIONS ON CYBERNETICS 2017年第3期47卷 600-611页

作者： Liu, Licheng Chen, Long Chen, C. L. Philip Tang, Yuan Yan Pun, Chi Man Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Joint sparse representation (JSR) has shown great potential in various image processing and computer vision tasks. Nevertheless, the conventional JSR is fragile to outliers. In this paper, we propose a weighted JSR (WJSR) model to simultaneously encode a set of data samples that are drawn from the same subspace but corrupted with noise and out-liers. Our model is desirable to exploit the common information shared by these data samples while reducing the influence of outliers. To solve the WJSR model, we further introduce a greedy algorithm called weighted simultaneous orthogonal matching pursuit to efficiently approximate the global optimal solution. Then, we apply the WJSR for mixed noise removal by jointly coding the grouped nonlocal similar image patches. The denoising performance is further improved by incorporating it with the global prior and the sparse errors into a unified framework. Experimental results show that our denoising method is superior to several state-of-the-art mixed noise removal methods.

关键词： greedy algorithm image denoising joint sparse representation (JSR) nonlocal similarity weighted sparse coding

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Offline-Enhanced Reduced Basis Method Through Adaptive Construction of the Surrogate Training Set

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JOURNAL OF SCIENTIFIC COMPUTING 2017年第2-3期73卷 853-875页

作者： Jiang, Jiahua Chen, Yanlai Narayan, Akil Univ Massachusetts Dartmouth Dept Math 285 Old Westport Rd N Dartmouth MA 02747 USA Univ Utah Sci Comp & Imaging SCI Inst 72 S Campus Dr Salt Lake City UT 84112 USA Univ Utah Dept Math 72 S Campus Dr Salt Lake City UT 84112 USA

The reduced basis method (RBM) is a popular certified model reduction approach for solving parametrized partial differential equations. One critical stage of the offline portion of the algorithm is a greedy algorithm, requiring maximization of an error estimate over parameter space. In practice this maximization is usually performed by replacing the parameter domain continuum with a discrete "training" set. When the dimension of parameter space is large, it is necessary to significantly increase the size of this training set in order to effectively search parameter space. Large training sets diminish the attractiveness of RBM algorithms since this proportionally increases the cost of the offline phase. In this work we propose novel strategies for offline RBM algorithms that mitigate the computational difficulty of maximizing error estimates over a training set. The main idea is to identify a subset of the training set, a "surrogate training set" (STS), on which to perform greedy algorithms. The STS we construct is much smaller in size than the full training set, yet our examples suggest that it is accurate enough to induce the solution manifold of interest at the current offline RBM iteration. We propose two algorithms to construct the STS: our first algorithm, the successive maximization method, is inspired by inverse transform sampling for non-standard univariate probability distributions. The second constructs an STS by identifying pivots in the Cholesky decomposition of an approximate error correlation matrix. We demonstrate the algorithm through numerical experiments, showing that it is capable of accelerating offline RBM procedures without degrading accuracy, assuming that the solution manifold has rapidly decaying Kolmogorov width.

关键词： Reduced basis method Surrogate parameter domain Cholesky decomposition Adaptivity greedy algorithm

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Novel transmit antenna selection strategy for massive MIMO downlink channel

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WIRELESS NETWORKS 2017年第8期23卷 2473-2484页

作者： Benmimoune, Mouncef Driouch, Elmahdi Ajib, Wessam Massicotte, Daniel Univ Quebec Montreal TRIME Lab Dept Comp Engn Montreal PQ Canada Univ Quebec Montreal Montreal PQ Canada Univ Quebec Montreal Dept Comp Sci Montreal PQ Canada Univ Quebec Trois Rivieres Dept Elect & Comp Engn Trois Rivieres PQ Canada

In this paper, we deal with the problem of acquiring the channel state information (CSI) at the transmitter in large-scale multiple input multiple output (MIMO) systems, so-called massive MIMO systems. Clearly, obtaining CSI plays a central role to provide high system performance. Even though, in frequency-division duplexed systems, acquiring this information requires a prohibitive amount of feedback, since it increases with the number of transmit antenna. In this work, we design an efficient transmit antenna selection strategy aware of the amount of required CSI for a point-to-multipoint transmission in massive MIMO systems. The proposed strategy provides high sum-rate with limited CSI feedback and limited computational complexity. Innovatively, the antenna selection in our strategy is performed in a decentralized fashion successively at the receiving users. Two schemes are proposed in this work to perform the antenna selection at each user. Next, taking into consideration that the large-scale MIMO transmitter suffers from imperfect knowledge of CSI, we design a new performance criterion. Computer simulations validate that, when the CSI is perfectly known, the proposed strategy is able to achieve high performance in terms of system sum-rate while a significant reduction in both CSI feedback overhead and computational complexity is observed. Moreover, assuming imperfect CSI, the new proposed criterion achieves higher performance when the estimation accuracy is low and at high SNR regime.

关键词： Massive MIMO Antenna selection Channel state information FDD greedy algorithm

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Backtracking-based matching pursuit method for distributed compressed sensing

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第13期76卷 14691-14710页

作者： Zhang, Yujie Qi, Rui Zeng, Yanni China Univ Geosci Sch Math & Phys Wuhan Peoples R China Univ Windsor Sch Comp Sci Windsor ON Canada Naval Univ Engn Sch Sci Wuhan Peoples R China Hubei Univ Econ Fac Stat Wuhan Peoples R China

In this paper, we study a distributed compressed sensing (DCS) problem in which we need to recover a set of jointly sparse vectors from the measurements. A Backtracking-based Adaptive Orthogonal Matching Pursuit (BAOMP) method to approximately sparse solutions for DCS is proposed. It is an iterative approach where each iteration consists of consecutive forward selection to adaptively choose several atoms and backward removal stages to detect the previous chosen atoms' reliability and then delete the unreliable atoms at each iteration. Also, unlike its several predecessors, the proposed method does not require the sparsity level to be known as a prior which makes it a potential candidate for many practical applications, when the sparsity of signals is not available. We demonstrate the reconstruction ability of the proposed algorithm on both synthetically generated data and image using Normal and Binary sparse signals, and the real-life electrocardiography (ECG) data, where the proposed method yields less reconstruction error and higher exact recovery rate than other existing DCS algorithms.

关键词： Distributed compressed sensing greedy algorithm Sparse signal reconstruction Iterative algorithm Orthogonal matching pursuit

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Continuous basis compressive time-delay estimation in overlapped echoes

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IET SIGNAL PROCESSING 2017年第5期11卷 566-571页

作者： Fereidoony, Foad Chamaani, Somayyeh Mirtaheri, Syed Abdullah Sebt, Mohammad Ali KN Toosi Univ Technol Elect & Comp Engn Tehran Iran

High-accuracy time-delay estimation is basically noted in several research areas. L1-minimisation is a compressive sensing (CS) approach which solves this problem with high resolution and accuracy in the case of spars signals. Band excluded orthogonal matching pursuit is another CS method which uses a greedy algorithm to retrieve time delays and has lower complexity compared with the L1-minimisation method;however, it is only applicable when the signals are well spaced or orthogonal. Moreover, both approaches are established on a discrete basis which inherently limits their accuracy for the constraint on the sampling rate of the system. To mitigate these challenges in this study, the authors first incorporate the L1-minimisation method in a greedy algorithm to achieve a high resolution in the discrete grid. In the next step, to overcome the limitation caused by the sampling rate and refine the obtained time delays, the algorithm is combined with a complex continuous basis pursuit (CCBP) by using a polar interpolation. Their simulation and experiment results show that the proposed combination of L1-minimisation-CCBP can recover time delays in very closely spaced echoes not only with high accuracy but also with low computational time and sampling rate.

关键词： compressed sensing echo delay estimation minimisation greedy algorithms signal sampling interpolation signal resolution iterative methods continuous basis compressive time-delay estimation overlapped echo High-accuracy time-delay estimation compressive sensing approach orthogonal matching pursuit CS method greedy algorithm system sampling rate discrete grid complex continuous basis pursuit polar interpolation L-1-minimisation-CCBP time delay recovery sparse signal

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Probability-Based Multi-hop Diffusion Method for Influence Maximization in Social Networks

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WIRELESS PERSONAL COMMUNICATIONS 2017年第4期93卷 903-916页

作者： Duy-Linh Nguyen Tri-Hai Nguyen Do, Trong-Hop Yoo, Myungsik Soongsil Univ Sch Elect Engn Seoul South Korea

Influence maximization is the problem of finding a subset of nodes that maximizes the spread of information in a social network. Many solutions have been developed, including greedy and heuristics based algorithms. While the former is very time consuming that might be impractical in many cases, the later is feasible in terms of computational time, but its influence spread is not guaranteed because of limitations in the algorithm. In this study, we propose a new heuristic algorithm which considers the propagation probabilities of nodes in the network individually and takes into account the effect of multi-hop neighbors, thus, it can achieve higher influence spread. A realistic network model with non-uniform propagation probabilities between nodes is assumed in our algorithm. We also examine the optimal number of hops of neighbors to be considered in the algorithm. Experiments using real-world social networks showed that our proposed method outperformed the previous heuristic-based approaches.

关键词： Propagation probability greedy algorithm Heuristic algorithm Influence maximization Social networks

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Recursive Dictionary-Based Simultaneous Orthogonal Matching Pursuit for Sparse Unmixing of Hyperspectral Data

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Transactions of Nanjing University of Aeronautics and Astronautics 2017年第4期34卷 456-464页

作者： Kong Fanqiang Guo Wenjun Shen Qiu Wang Dandan College of Astronautics Nanjing University of Aeronautics and Astronautics

The sparse unmixing problem of greedy algorithms still remains a great challenge at finding an optimal subset of endmembers for the observed data from the spectral library,due to the usually high correlation of the spectral *** such circumstances,a novel greedy algorithm for sparse unmixing of hyperspectral data is presented,termed the recursive dictionary-based simultaneous orthogonal matching pursuit(RD-SOMP).The algorithm adopts a block-processing strategy to divide the whole hyperspectral image into several *** each iteration of the block,the spectral library is projected into the orthogonal subspace and renormalized,which can reduce the correlation of the spectral *** RD-SOMP selects a new endmember with the maximum correlation between the current residual and the orthogonal subspace of the spectral *** endmembers picked in all the blocks are associated as the endmember sets of the whole hyperspectral ***,the abundances are estimated using the whole hyperspectral data with the obtained endmember *** can be proved that RD-SOMP can recover the optimal endmembers from the spectral library under certain *** results demonstrate that the RD-SOMP algorithm outperforms the other algorithms,with a better spectral unmixing accuracy.

关键词： hyperspectral unmixing greedy algorithm simultaneous sparse representation sparse unmixing

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