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As traditional spectrum sensing approaches unable to deal with the contradiction between detection accuracy and complexity in cognitive radio network, a novel q-weighed sequential cooperative energy detection method for spectrum sensing in time varying channel is proposed in this paper to achieve better performance with lower complexity. By adding the q- weighted log likelihood ratio (LLR) of the...
Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propose a novel covariance sketching scheme with reduced complexity for spatial-temporal data, whose covariance matrices satisfy the Kronecker product expansion model recently introduced by...
In diversity-based multiple antenna cognitive radio system, the performance of the existing cooperative spectrum sensing algorithms based on covariance matrix degrades seriously due to the channel correlation. In this paper, we formulate spectrum sensing as a multinomial distribution test problem, and then apply the Discrete Anderson Darling (DAD) test, one of discrete goodness of fit tests, to examining...
In this paper, we propose a low-complexity sensing algorithm based on the generalized likelihood ratio test that satisfy the complexity-performance compromise. The proposed algorithm can significantly reduce the complexity computation of the eigenvalues and enhance outstandingly the detection performance compared to the existing GLRT algorithm. Besides, the cooperative spectrum sensing has proved...
In this paper, we present a new structure for compressive sensing(CS)-based random access (RA) scheme for machine-type communications (MTC). In the proposed scheme, we consider a one-shot transmission in which users transmit their packets right away by spreading them with multiple spreading sequences (MS) over a frame without waiting for scheduling grant from the base station. The scheme is designed...
In this paper a new object-oriented segmentation method for high-resolution remote sensing images is proposed. To limit computational complexity, a preliminary superpixel representation of the image is obtained by means of a suitable watershed transform. Then, a region adjacency graph is associated with the superpixels, with edge weights accounting for region similarity/dissimilarity. The final segmentation...
Quadcopter, a popular Unmanned Aerial Vehicle (UAV), is able to land, take off, hover, and move on 3D trajectory. The ability requires accurate control of the rotors velocity based on input from its sensors. One of the control mechanisms is the altitude control. This paper presents a new algorithm to identify altitude change of a quadcopter based on image processing techniques. The algorithm is designed...
This paper presents an Sparsity Update Subspace Pursuit (SUSP) algorithm for compressed sparse signal reconstruction with unknown sparsity. From practical point of view, the sparsity information is usually unavailable in many applications. In particular, the compressed spectrum sensing application is considered in this paper . The proposed SUSP algorithm begins with sparsity estimation and iteratively...
In cognitive radio networks, the task of spectrum sensing is required to be reliable at low signal-to-noise ratios (SNRs). Spectral correlation is an effective approach to satisfy the requirement. The algorithms based on statistic spectral correlation profiles are a good method as shown in some previous works. In this paper, we propose an algorithm with maximum ratio combination for the profiles to...
Owing to the opportunistic nature of its operation principle, the performance of Dynamic Spectrum Access/Cognitive Radio (DSA/CR) systems depends on the spectrum occupancy pattern of primary systems. DSA/CR systems can monitor periodically the occupancy state of licensed channels in order to gain statistical information on their occupancy patterns, and exploit this information in decision-making processes...
Efficient spectrum sensing plays a vital role in opportunistic and dynamic spectrum access for cognitive radios. Several spectrum sensing algorithms based on energy detection, matched filtering and autocorrelation based feature detection have therefore been proposed in the literature. All these algorithms tend to have higher complexity for achieving better sensing performance, which requires higher...
Based on autocorrelation matrix theory of spectrum sensing for cognitive radio (CR). To solve the high cost of cooperative spectrum sensing, this paper proposed a spectrum sensing algorithm by step combination cooperative strategy, with the purpose of keeping balance between accuracy and velocity, the method chooses CR numbers and its users from all CR users randomly. Simulation results show that...
Cooperative spectrum sensing (CSS) schemes utilizing Compress Sampling (CS) enable efficient and reliable CSS for cognitive radio networks. Existing CSS schemes perform CS by random measurement matrixes, and thus suffer from a loss of efficiency. To counteract this drawback, this letter proposes a CS based CSS scheme using measurement matrix exploring the correlation of the primary spectrum. A reconstruction...
This paper presents a temporal method for direction of arrival (DOA) estimation for wideband sources by using an antenna array. Existing DOA estimation algorithms treat the problem in frequency domain by decomposing the wide spectrum of these signals in several narrowband signals, and use a narrowband DOA estimation methods to solve the problem. These methods suffer from high computational complexity...
Spectrum sensing is the key for Cognitive Radio (CR) systems such as IEEE 802.22 standard in improving the Quality of Service (QoS) of secondary user while avoiding harmful interference to primary user (PU) of the allocated spectrum. In this paper, we reviewed the traditional MF's (Matched Filter) properties and proposed a novel hybrid MF structure which is designed to perform spectrum sensing function...
Location template matching (LTM) is a source localization technique in solids that is robust to dispersion and multipath. This is possible since LTM compares the input with a database of signals made at known locations. With this in place, it is possible to employ LTM in situations where the surface of interest takes an irregular shape. However, one of the existing LTM approaches uses crosscorrelation...
We consider the issue of opportunistic spectrum access (OSA) in cognitive radio network. By modeling the channel occupancy states induced by the primary users as a discrete-time Markov process, the channel selection scheme of the secondary users at the MAC layer can be formulated as a partially observable Markov decision process (POMDP). Under the POMDP framework, myopic sensing (which is a kind of...
In cognitive radio systems, one of the main requirements is to detect the presence of the primary users' transmission, especially in weak signal cases. Cyclostationary detection is always used to solve weak signal detection, however, the computational complexity prevents it from wide usage. In this paper, a significant cycle frequency based feature detection algorithm has been proposed, in which only...
We propose a new compressive sensing scheme, based on codes of graphs, that allows for joint design of sensing matrices and low complexity reconstruction algorithms. The compressive sensing matrices can be shown to offer asymptotically optimal performance when used in combination with OMP methods. For more elaborate greedy reconstruction schemes, we propose a new family of list decoding and multiple-basis...
Low-complexity video encoding has been applicable to several emerging applications. Recently, distributed video coding (DVC) has been proposed to reduce encoding complexity to the order of that for still image encoding. In addition, compressive sensing (CS) has been applicable to directly capture compressed image data efficiently. In this paper, by integrating the respective characteristics of DVC...
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