Cooperative Spectrum Sensing Method Using Sub-band Decomposition with DCT for Cognitive Radio System

Falih, Muntasser S.; Abdullah, Hikmat N.

doi:10.1007/978-3-030-38752-5_36

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Spectrum sensing is the first step in CR life cycle to detect the presence or absence of PU. The following equation represents the detection hypotheses 1

italicyf ((), n) goodbreak= ({, \begin{matrix} w ((), n); H_{0} \\ s ((), n) goodbreak+ w ((), n); H_{1} \end{matrix})

where yf ( n ) is the received sample, w ( n ) is the additive white Gaussian noise (AWGN) samples collected from the transmission channel, s ( n ) is the PU signal sample, and H 0 and H 1 are null and power hypothesis, respectively.…”

Section: Compressive Sensing In Cognitive Radiomentioning

confidence: 99%

“…The massive increase and development in wireless technology applications such as wireless LAN, cellular networks, sensor networks, and so forth cause congestion in electromagnetic spectrum resources 1,2 . A cognitive radio (CR) is a wireless technology that can automatically detect the available spectrum and changes its parameter accordingly.…”

Section: Introductionmentioning

confidence: 99%

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Compressive sensing based on novel chaotic matrix for cognitive radio

Abed

Abdullah

2022

Int J Communication

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Summary Cognitive radio (CR) is an emerging technology for optimum spectrum utilization. Spectrum sensing (SS) is the first step of CR. Important challenge in the next generations is wideband SS. Compressive sensing (CS) can be used as an SS in CR to solve this challenge. Sensing matrix (SM) plays an important role in CS. For better performance, SM must have low mutual coherence. The existing matrices in the literature are random and chaotic. All random matrices have a good performance, but they have problems of large memory storage, complexity, sharing bandwidth, and low security. Chaotic matrices (CMs) are the best but still have some problems including the use of sample distance in chaotic sequence that consumes high storage resources, low performance and compression under low signal‐to‐noise ratio (SNR), and low security. In this paper, we propose a new 1‐D chaotic map for constructing a novel SM. The proposed map has simple structure, wide range chaotic behavior, and high security. Its chaotic behavior is confirmed using Lyapunov exponent, bifurcation, and trajectory. The CS performance based on novel CM is measured using absolute error ( Abserrtrue), mutual coherence, memory cost, computational complexity, and MSE (mean square error), while evaluated through comparisons with matrices in the literature. The simulation and mathematical results show that the proposed map is a hyperchaotic with efficient chaotic behavior in wide range of parameters and low memory storage and complexity. The results also show that it has low Abserr, MSE, and mutual coherence at low SNR with high compression.

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“…Spectrum sensing is the first step in CR life cycle to detect the presence or absence of PU. The following equation represents the detection hypotheses 1

italicyf ((), n) goodbreak= ({, \begin{matrix} w ((), n); H_{0} \\ s ((), n) goodbreak+ w ((), n); H_{1} \end{matrix})

Section: Compressive Sensing In Cognitive Radiomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Compressive sensing based on novel chaotic matrix for cognitive radio

Abed

Abdullah

2022

Int J Communication

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“…Spectrum sensing can be defined as the process of primary user (PU) detection in the specific channel by the secondary user (SU) or unlicensed user to decide the channel status that is either occupied or free. The spectrum sensing process should be accurate enough to avoid harmful interference to the PU [2]. The traditional sensing techniques are energy detector, cyclostationary and matched filter.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Compressive Sensing of Tv –Uhf Band in Iraq Using Chebyshev Gram Schmidt Sensing Matrix

Abed

Abdullah²

2021

Iraqi Journal of ICT

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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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Modified Gram Schmidt Based Chaotic Matrix for Compressive Sensing in Cognitive Radio

Abed

Abdullah

2021

2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

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Cooperative Spectrum Sensing Method Using Sub-band Decomposition with DCT for Cognitive Radio System

Cited by 4 publications

References 8 publications

Compressive sensing based on novel chaotic matrix for cognitive radio

Compressive sensing based on novel chaotic matrix for cognitive radio

Chaotic Compressive Sensing of Tv –Uhf Band in Iraq Using Chebyshev Gram Schmidt Sensing Matrix

Modified Gram Schmidt Based Chaotic Matrix for Compressive Sensing in Cognitive Radio

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