Cooperative Compressive Spectrum Research Articles

RETRACTED: Localization algorithm of energy efficient radio spectrum sensing in cognitive internet of things radio networks

Abstract Spectrum sensing is a key problem of implementing cognitive radio (CR). Some malicious users falsify data, which reduces the performance of spectrum sensing. To this end, trustful cluster-based cooperative compressed spectrum sensing (TCBCSS) algorithm is proposed orienting to malicious user environment. Maximum likelihood (ML) is firstly used for estimating the distance from the cluster to the primary user, and the distance is compared with the preset threshold value so as to seek for the trustful cluster. Then, the channel condition is detected using the compressed spectrum sensing algorithm according to the information provided by the trustful cluster. The simulation result indicates that the proposed TCBCSS algorithm could cope with the malicious user environment effectively, detect the malicious user accurately, and maintain high spectrum detection rate.

Efficient cooperative compressive spectrum sensing by identifying multi-candidate and exploiting deterministic matrix

Cooperative spectrum sensing exploits the spatial diversity to improve the detection of occupied channels in cognitive radio networks (CRNs). Cooperative compressive spectrum sensing (CCSS) utilizing the sparsity of channel occupancy further improves the efficiency by reducing the number of reports without degrading detection performance. In this paper, we firstly and mainly propose the referred multi-candidate orthogonal matrix matching pursuit (MOMMP) algorithms to efficiently and effectively detect occupied channels at fusion center (FC), where multi-candidate identification and orthogonal projection are utilized to respectively reduce the number of required iterations and improve the probability of exact identification. Secondly, two common but different approaches based on threshold and Gaussian distribution are introduced to realize the multi-candidate identification. Moreover, to improve the detection accuracy and energy efficiency, we propose the matrix construction based on shrinkage and gradient descent (MCSGD) algorithm to provide a deterministic filter coefficient matrix of low t-average coherence. Finally, several numerical simulations validate that our proposals provide satisfactory performance with higher probability of detection, lower probability of false alarm and less detection time.

A Novel Decentralized Scheme for Cooperative Compressed Spectrum Sensing in Distributed Networks

Compressed sensing (CS) recently turns out to be an effective approach to alleviate the sampling bottleneck in wideband spectrum sensing. However, the computation overhead incurred by compressed reconstruction is nontrivial, especially in a power-constrained cognitive radio (CR). Moreover, additional information, which is generally unavailable in practice, is needed in conventional CS-based wideband spectrum sensing schemes to improve the reconstruction quality as well as the detection performance. To address these issues, this paper proposes a novel decentralized scheme for cooperative compressed spectrum sensing in distributed CR networks. Our key observation is that the sparse signals are unnecessary to be reconstructed since the task of spectrum sensing is only interested in the spectrum occupancy status. The major novelty of the proposed scheme relates to the use of Karcher mean as a statistic indicating the spectrum occupancy status, thereby eliminating the compressed reconstruction stage and significantly reducing the computational complexity. Considering limited communication resources per CR, a decentralized implementation based on alternating direction method of multipliers is presented to calculate the Karcher mean via one-hop communications only. The superior performance of the proposed scheme is demonstrated by comparing with several existing decentralized schemes in terms of detection performance, communication overhead, and computational complexity.

Fast anomaly detection using Boxplot rule for multivariate data in cooperative wideband cognitive radio in the presence of jammer

ABSTRACTThis work presents a new centralized cooperative compressive spectrum sensing scheme for wideband cognitive radios (CRs), which combines intelligent graphical Boxplot rule detector and compressive sampling technique. The received signal at each CR sensing receiver in the presence of jamming attack signal and adding white Gaussian noise is transformed into a digital signal using an analog‐to‐information converter via random‐demodulator. The proposed approach consists of the anomaly detection using Boxplot rule technique applied to the compressed measurements obtained from each CR user collected in matrix form called sampling matrix. Cooperation among CR users allows the CR users to detect anomalies or attacks in the presence of noise and jamming attack signal. Furthermore, as an important solution, cooperation makes it possible between all CRs to sample more compressively at every CR, that is, every CR user gives minimum number of samples denoted by Ns. Two hypotheses are proposed in this paper H0 and H1 to know if there is anomalies problem using one of the robust graphical techniques. The results show that the proposed technique performs well in addition to its low complexity. Copyright © 2014 John Wiley & Sons, Ltd.

A Fast Reweighted Alternating Direction Method for Wideband Spectrum Sensing

In this paper, we propose a fast algorithm based on Reweighted Alternating Direction Method of Multiplier (R-ADMM) for cooperative compressive wideband spectrum sensing. The R-ADMM alternately updates the recovered signal matrix, the Lagrangian multiplier and the residue, and all update rules only involve matrix or vector multiplications and summations. To seek joint sparse solutions in a fully distributed scheme, multiple cognitive users collaborate during the sensing period by enforcing consensus among local spectral estimates. Meanwhile, adding weight in the target term and suppressing non-zero elements with large weights for getting the solution close to minimumnorm. The experimental results show that detection probability and detection speed of the algorithm has been improved under low SNR.

인지 무선 네트워크에서 상관관계를 갖는 다중 신호를 위한 협력 베이지안 압축 스펙트럼 센싱

본 논문에서는 분산된 광대역 인지 무선 네트워크 환경에서 상관관계를 갖는 다중 신호를 위한 협력 압축 스펙트럼 센싱 기법을 제안한다. 압축 센싱 (Compressed Sensing)은 나이퀴스트율 (Nyquist Rate) 이하로 샘플링된 신호를 높은 확률로 복구할 수 있는 신호처리 기법으로 기존의 광대역 스펙트럼 센싱을 위해서 필요로 했던 고속의 아날로그-디지털 변환기 구현 문제를 해결할 수 있다. 압축 센싱에서는 압축된 신호를 원본 신호로 정확하게 복구하는 복구 알고리즘을 설계하는 것이 하나의 핵심 문제이다. 본 논문에서는 나이퀴스트율 이하로 압축된 신호의 복구 성능을 높이기 위하여 연속된 다중 입력 신호로 구성된 Multiple Measurement Vector 모델을 이용하였고, 입력 신호 사이의 시간적 상관관계를 이용하는 협력 베이지안 복구 알고리즘을 제안한다. In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

WIDE-BAND SECURE COMPRESSED SPECTRUM SENSING FOR COGNITIVE RADIO SYSTEMS

Cooperative wide-band spectrum sensing has been considered to enable cognitive radio operation of wireless regional area networks (WRAN) in the UHF and VHF TV broadcasting bands. In this paper, cooperative compressed spectrum sensing is considered to enable fast sensing of the wide-band spectrum. The speed and accuracy of spectrum sensing are improved by further optimization of the compressed sensing receiver, which is done blindly without any prior knowledge of the sensed signal. Enhanced compressed spectrum sensing algorithms are proposed for the cases of individual spectrum sensing and cooperative spectrum sensing. The cooperative signal reconstruction process is modifled to optimally combine the received measurements at the fusion center. A low complexity authentication mechanism, which is inherent to cooperative compressed spectrum sensing, is proposed to make the cognitive radio system immune to adversary attacks.

Belief Propagation Based Cooperative Compressed Spectrum Sensing in Wideband Cognitive Radio Networks

Wideband spectrum sensing in heterogenous cognitive radio networks has two significant challenges to tackle. One is the spectrum acquisition in the wideband scenario due to the limited sampling capability; the other is how to collaborate among the secondary users. Compressed spectrum sensing provides a powerful approach to acquire wideband signal. Moreover, most cooperative spectrum sensing methods assume that all the secondary users experience the same occupancy of primary users, which may be infeasible in a heterogenous spectrum environment where secondary users at different locations may be affected by different primary users. In this paper, we propose a probabilistic graphical model to represent and fuse multi-prior information from one hop neighboring secondary users. Belief propagation (BP) is used for the statistical inference of the spectrum occupancy. Numerical simulation results demonstrate that the proposed BP based cooperative compressed spectrum sensing can effectively achieve cooperation in heterogenous environments and improve performance of compressed spectrum sensing under a low sampling rate and low signal-to-noise ratio (SNR), compared with the other distributed cooperative compressed sensing methods.