Relevant support recovery algorithm in modulated wideband converter

Zhu, Jiawei; Li, Jie; Li, Jian; Li, Zhi

doi:10.1049/iet-com.2018.5474

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In addition, many MWC-based signal improvement algorithms are proposed, such as the iterative support detection method [9], and the sparse Bayesian algorithm [10]. Although the MMV class algorithms require fewer samples than the SMV class algorithms to achieve the same signal reconstruction accuracy, the MMV class algorithms [11,12,13], like the SMV class algorithms, also rely on the a priori information of signal sparsity, which is extremely difficult to be acquired in the actual complex electromagnetic environment.…”

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

Wang

Chen

Jia

et al. 2021

Preprint

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As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm, which is based on the advanced sub-Nyquist sampling framework, has the similar performance as MWC and even higher than MWC in some cases.

show abstract

Section: Introductionmentioning

confidence: 99%

Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

Wang

Chen

Jia

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, many MWC-based signal improvement algorithms are proposed, such as the iterative support detection method [15], and the sparse Bayesian algorithm [16]. Although the MMV class algorithms require fewer samples than the SMV class algorithms to achieve the same signal reconstruction accuracy, the MMV class algorithms [17][18][19], like the SMV class algorithms, also rely on the a priori information of signal sparsity, which is extremely difficult to be acquired in the actual complex electromagnetic environment.…”

mentioning

confidence: 99%

Wideband spectrum sensing based on advanced sub-Nyquist sampling structure

Wang

Chen

Jia

et al. 2022

EURASIP J. Adv. Signal Process.

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As the bandwidth increases, the high-speed sampling rate becomes the bottleneck for the development of wideband spectrum sensing. Wideband spectrum sensing with sub-Nyquist sampling attracts more attention and modulated wideband converter (MWC) is an attractive sub-Nyquist sampling system. For the purpose of breaking the system structure limit, an advanced sub-Nyquist sampling framework is proposed to simplify the MWC system structure, adopting the single sampling channel structure with a frequency shifting module to acquire the sub-Nyquist sampling values. In order to recover the signal support information, the sensing matrix must be built according to the only one mixing function. Most existing support recovery methods rely on some prior knowledge about the spectrum sparsity, which is difficult to acquire in practical electromagnetic environment. To address this problem, we propose an adaptive residual energy detection algorithm (ARED), which bypasses the need for the above-mentioned prior knowledge. Simulation results show that, without requiring the aforementioned prior knowledge, the ARED algorithm based on the advanced sub-Nyquist sampling framework has the similar performance as MWC and even higher than MWC in some cases using only one sampling channel.

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Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

Wang,

Lin,

Jia

2024

Electronics

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To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators.

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Relevant support recovery algorithm in modulated wideband converter

Cited by 3 publications

References 18 publications

Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

Wideband Spectrum Sensing Based on Advanced Sub-Nyquist Sampling Structure

Wideband spectrum sensing based on advanced sub-Nyquist sampling structure

Spatial Information Entropy-Assisted Integrated Sensing and Communication for Integrated Satellite-Terrestrial Networks

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