CHAOTIC COMPRESSIVE SENSING OF TV –UHF BAND IN IRAQ USING CHEBYSHEV GRAM SCHMIDT SENSING  MATRIX

Cognitive radio (CR) is a promising technology for solving spectrum sacristy problem. Spectrum sensing is the main step of CR. Sensing the wideband spectrum produces more challenges. Compressive sensing (CS) is a technology used as spectrum sening in CR to solve these challenges. CS consists of three stages: sparse representation, encoding and decoding. In encoding stage sensing matrix are required, and it plays an important role for performance of CS. The design of efficient sensing matrix requires achieving low mutual coherence . In decoding stage the recovery algorithm is applied to reconstruct a sparse signal. İn this paper a new chaotic matrix is proposed based on Chebyshev map and modified gram Schmidt (MGS). The CS based proposed matrix is applied for sensing real TV signal as a PU. The proposed system is tested under two types of recovery algorithms. The performance of CS based proposed matrix is measured using recovery error (Re), mean square error (MSE), and probability of detection (Pd) and evaluated by comparing it with Gaussian, Bernoulli and chaotic matrix in the literature. The simulation results show that the proposed system has low Re and high Pd under low SNR values and has low MSE with high compression.

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Spectrum sensing of wideband signals based on cyclostationary and compressive sensing

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v20.i3.pp1361-1368 ◽

2020 ◽

Vol 20 (3) ◽

pp. 1361

Author(s):

Ali Mohammad A. AL-Hussain ◽

Maher Khudair Mahmood Al Azawi

Keyword(s):

Compressive Sensing ◽

Spectrum Sensing ◽

Compression Ratio ◽

High Speed ◽

Processing Time ◽

Sampling Rate ◽

Probability Of Detection ◽

Signal Spectrum ◽

Sensing Matrix ◽

Recovery Algorithm

Compressive sensing is a powerful technique used to overcome the problem of high sampling rate when dealing with wideband signal spectrum sensing which leads to high speed analogue to digital convertor (ADC) accompanied with large hardware complexity, high processing time, long duration of signal spectrum acquisition and high consumption power. Cyclostationary based detection with compressive technique will be studied and discussed in this paper. To perform the compressive sensing technique, Discrete Cosine Transform (DCT) is used as sparse representation basis of received signal and Gaussian random matrix as a sensing matrix, and then 𝓁1- norm recovery algorithm is used to recover the original signal. This signal is used with cyclostationary detector. The probability of detection as a function of SNR with several compression ratio and processing time versus compression ratio are used as performance parameters. The effect of the recovery error of reconstruction algorithm is presented as a function of probability of detection.

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Adaptive Sparse Recovery of Moving Targets for Distributed MIMO Radar

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.933.450 ◽

2014 ◽

Vol 933 ◽

pp. 450-455

Author(s):

Hui Yu ◽

Guang Hua Lu ◽

Hai Long Zhang

Keyword(s):

High Resolution ◽

Sparse Recovery ◽

Mimo Radar ◽

Moving Target ◽

Recovery Algorithm ◽

Low Snr ◽

Computation Efficiency ◽

Target Imaging ◽

Moving Target Imaging ◽

Recovery Error

The high resolution and better recovery performance with distributed MIMO radar would be significantly degraded when the target moves at an unknown velocity. In this paper, we propose an adaptive sparse recovery algorithm for moving target imaging to estimate the velocity and image jointly with high computation efficiency. With an iteration mechanism, the proposed method updates the image and estimates the velocity alternately by sequentially minimizing the norm and the recovery error. Numerical simulations are carried out to demonstrate that the proposed algorithm can retrieve high-resolution image and accurate velocity simultaneously even in low SNR.

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Guaranteed Stability of Sparse Recovery in Distributed Compressive Sensing MIMO Radar

International Journal of Antennas and Propagation ◽

10.1155/2015/421740 ◽

2015 ◽

Vol 2015 ◽

pp. 1-10 ◽

Cited By ~ 3

Author(s):

Yu Tao ◽

Gong Zhang ◽

Jindong Zhang

Keyword(s):

Compressive Sensing ◽

Matching Pursuit ◽

Mimo Radar ◽

Radar System ◽

Sensing Matrix ◽

Joint Measurement ◽

Low Snr ◽

The Face ◽

Distributed Compressive Sensing ◽

Average Coherence

Low SNR condition has been a big challenge in the face of distributed compressive sensing MIMO radar (DCS-MIMO radar) and noise in measurements would decrease performance of radar system. In this paper, we first devise the scheme of DCS-MIMO radar including the joint sparse basis and the joint measurement matrix. Joint orthogonal matching pursuit (JOMP) algorithm is proposed to recover sparse targets scene. We then derive a recovery stability guarantee by employing the average coherence of the sensing matrix, further reducing the least amount of measurements which are necessary for stable recovery of the sparse scene in the presence of noise. Numerical results show that this scheme of DCS-MIMO radar could estimate targets’ parameters accurately and demonstrate that the proposed stability guarantee could further reduce the amount of data to be transferred and processed. We also show the phase transitions diagram of the DCS-MIMO radar system in simulations, pointing out the problem to be further solved in our future work.

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To Analysis the Effects of Compressive Sensing on Music Signal with variation in Basis & Sensing Matrix

2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) ◽

10.1109/iceca.2018.8474822 ◽

2018 ◽

Cited By ~ 1

Author(s):

Khushboo Nibheriya ◽

Vivek Upadhyaya ◽

Ashok Kumar Kajla

Keyword(s):

Compressive Sensing ◽

Sensing Matrix ◽

Music Signal

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Hierarchical Multi-User Detection for Highly Overloaded Uplink Grant-Free NOMA

10.36227/techrxiv.17090294.v1 ◽

2021 ◽

Author(s):

Shah Mahdi Hasan ◽

Kaushik Mahata ◽

Md Mashud Hyder

Keyword(s):

Phase 1 ◽

Sparse Recovery ◽

Second Phase ◽

Sensing Matrix ◽

Recovery Algorithm ◽

Spreading Sequence ◽

Active User ◽

Machine Type Communication ◽

Two Phases ◽

Limited Spectrum

Grant-Free Non Orthogonal Multiple Access (NOMA) offers promising solutions to realize uplink (UL) massive Machine Type Communication (mMTC) using limited spectrum resources, while reducing signalling overhead. Because of the sparse, sporadic activities exhibited by the user equipments (UE), the active user detection (AUD) problem is often formulated as a compressive sensing problem. In line of that, greedy sparse recovery algorithms are spearheading the development of compressed sensing based multi-user detectors (CS-MUD). However, for a given number of resources, the performance of CS-MUD algorithms are fundamentally limited at higher overloading of NOMA. To circumvent this issue, in this work, we propose a two-stage hierarchical multi-user detection framework, where the UEs are randomly assigned to some pre-defined clusters. The active UEs split their data transmission frame into two phases. In the first phase an UE uses the sinusoidal spreading sequence (SS) of its cluster. In the second phase the UE uses its own unique random SS. At phase 1 of detection, the active clusters are detected, and a reduced sensing matrix is constructed. This matrix is used in Phase 2 to recover the active UE indices using some sparse recovery algorithm. Numerical investigations validate the efficacy of the proposed algorithm in highly overloaded scenarios.

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