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Collaborative spectrum sensing is subject to the attack of malicious secondary user(s), which may send false reports. Therefore, it is necessary to detect potential attacker(s) and then exclude the attacker's report for spectrum sensing. Many existing attacker-detection schemes are based on the knowledge of the attacker's strategy and thus apply the Bayesian attacker detection. However, in practical...
Compressive sensing (CS) creates a new framework of signal reconstruction from a smaller set of incoherent projection compared with the traditional Nyquist-rate sampling theory. Recently, it has been shown that CS can solve some signal processing problems given incoherent measurements without reconstructing signals. Moreover, the number of measurements for most compressive signal processing application...
This work considers the problem of quickest detection of signals in a coupled system of N sensors, which receive continuous sequential observations from the environment. It is assumed that the signals, which are modeled a general Ito?? processes, are coupled across sensors, but that their onset times may differ from sensor to sensor. The objective is the optimal detection of the first time at which...
In this paper we consider the spectrum sensing performance and requirements for detecting legacy users (LU) in cognitive radios (CR) with periodic scanning. The performance and requirements are studied based on the temporal spectral occupancy statistics of the LU and the sensing signal to noise ratio levels in order to achieve a certain level of detection probability. We model the temporal statistics...
Besides sensing the environment variables, the application of localization in wireless sensor networks has became an important research subject. Unlike the other range-free localization schemes which are not effective in real time performance, we propose a real time algorithm, which determines the location of the moving object based on dynamically changing signal strength. The simulation results demonstrated...
Among variant kinds of spectrum sensing strategies in cognitive radio (CR) broadly studied before, cooperative sensing is the most popular one to detect the primary user accurately. This paper proposes a new cooperative spectrum sensing algorithm in which double threshold energy detection will be employed at local decision and different fusion rules will be used at the fusion center. Theoretic analysis...
Compressive sensing (CS) is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Instead of taking N periodic samples, we measure M ?? N inner products with random vectors and then recover the signal via a sparsity-seeking optimization or greedy algorithm. A new framework for CS based on unions of subspaces can improve signal recovery by including dependencies between...
According to dramatic increase of wireless communication demand, more spectrum resources are needed to support considerable and various wireless services. However, limited spectrum resources are regulatory assigned to licensed user and no interference needs to occur from unlicensed user to licensed user. In this paper, we propose efficient decision rule in order to get better chance to detect the...
Compressive sensing (CS) has developed as an enticing alternative to the traditional process of signal acquisition. For a length-N signal with sparsity K, merely M = O(K log N) ?? N random linear projections (measurements) can be used for robust reconstruction in polynomial time. Sparsity is a powerful and simple signal model; yet, richer models that impose additional structure on the sparse nonzeros...
Efficient and reliable spectrum sensing plays a critical role in cognitive radio networks. This paper proposes a cooperative sensing scheme that detects the existence of a common signal component in the signals received by multiple geographically distributed radios. The scheme assumes that signals received by different radios display strong coherence if they have a common source. Detection of this...
Energy efficiency is a critical consideration in the design of low cost sensor networks. This paper studies the problem of extending the lifetime of the sensor networks as far as possible while maintaining the quality of network coverage. A systematical analysis on the relationship between the network lifetime and cover sets alternation is given, and by introducing the concept of time weight factor,...
In this paper, a new method to detect the number of signals in the presence of color noise is proposed. Firstly, pseudocovariance matrix is constructed using the covariance of sensors. Then, the information theoretic criteria and Gerschgorin disks are used to estimate the number of signals. Simulation results show that the proposed AIC and MDL can work well in the presence of color noise, while the...
In this paper we describe a new cooperating sensing method using double threshold energy detection technique for cognitive radio. Each secondary cognitive user takes a local decision on spectrum occupancy based on two threshold energy detection and uses 1 bit information to convey its decision to the fusion center that collects decisions from all cooperating users who are able to detect presence or...
In this paper, a novel method combining cooperative spectrum sensing with quantized soft decision combining is introduced. In order to allow cognitive radios and cognitive networks to opportunistically use spectrum, it is a prerequisite that the license owner or primary user of the spectrum will not be harmfully interfered and the spectrum band will be vacated as soon as the primary user starts its...
The ultra wide-band cognitive radio networks, in recent years, have attracted the attention of the research due to their challenges concerning the signals detection over multiband. The aim of this paper is to address the ultra wide-band spectrum sensing, i.e. to detect the existence of spectral holes in ultra-wide frequency bands in order to improve the spectrum utilization and minimize the interference...
A new type of distributed constant false alarm rate(CFAR) scheme based on fuzzy logic and Ordered Data Variability CFAR(ODV-CFAR) algorithm is proposed in this paper. In this scheme, each ODV-CFAR detector computes the membership function value mapping to the false alarm space from the samples of reference cells, and transmits it to the fusion center. These values are combined according to fuzzy fusion...
Fire is a kind of disaster threatening the social wealth and humanity's safety. The fire detection is the special type signal's detection, system must have the ability of automatic adjust the operational parameters to adapt to the environment change. Traditional fire detection systems' intellectualized degree are low, the error alarm and the leakage take place frequently. In order to reduce the rates...
In this paper we consider the problem of sampling far below the Nyquist rate signals that are sparse linear superpositions of shifts of a known, potentially wide-band, pulse. This signal model is key for applications such as Ultra Wide Band (UWB) communications or neural signal processing. Following the recently proposed Compressed Sensing methodology, we study several acquisition strategies and show...
As an enabling functionality of overlay cognitive radio networks, spectrum sensing needs to reliably detect licensed signal in the band of interest. To achieve reliable sensing, we propose a linear fusion scheme for distributed spectrum sensing to combine the sensing results from multiple spatially distributed cognitive radios. The optimal linear fusion design is formulated into a nonconvex optimization...
In this paper, we solve the problem of detecting the entries of a sparse finite-alphabet signal from a limited amount of data, for instance obtained by compressive sampling. While existing methods either rely on the sparsity property, the finite-alphabet property, or none of those properties to solve the under-determined system of linear equations, we capitalize on both the sparsity and the finite-alphabet...
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