Adaptive multi-tone jamming suppression for DSSS communications based on compressive sensing

Xin, Jia; Zhang, Yongshun; Kou, Baohua

doi:10.1109/cisp.2015.7408087

Cited by 2 publications

(2 citation statements)

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“…Multi-frequency interference interferes with multiple carriers and is characterized as a frequency-division, time-division, and comprehensive multi-frequency interference [36]. Among these, multi-tone interference (MTI) is a superposition of multiple single-tone interference (STI), where STI accommodates a singly element-sparse interference in the frequency domain.…”

Section: Interference Analysismentioning

confidence: 99%

“…Based on structured compressive sensing, Liu et al proposed a novel cancellation scheme, which could recover and mitigate the sparse narrowband interference accurately at the receiver [18]. Zhang & Jia developed an improved block sparse Bayesian learning method to estimate and mitigate the narrowband interference in communication systems; their method reconstructed the interference from the compressed signal and then suppressed the interference in the time domain [19].…”

mentioning

confidence: 99%

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Maintaining the Status Quo: Simultaneous Estimation and Elimination for Multiple Interference in Transform Domain Vehicular Communication

Wang

Han

Wang

et al. 2022

IEEE Trans. Veh. Technol.

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Wireless communication in vehicular networks is highly vulnerable to electromagnetic interference, which seriously undermines the communication effectiveness. Thus, interference estimation and elimination are essential in vehicular communications. To solve the multitasking problem of interference estimation and cancelation within the transformation domain of vehicular communications where they are divided into the consistency and inconsistency based on the different conditions of spectrum sensing, the present study proposes an iterative Bayesian sparse learning approach for simultaneous interference recovery and elimination which applies multiple Bayesian learning for measured signals, and the structured sparse learning method for simultaneous interference mitigation with signal recovery. Furthermore, the updating optimization based on Bayesian estimation framework is performed to facilitate the learning process and improve the performance of interference estimation and signal reconstruction. Simulation results show that the proposed algorithms achieved more rapid convergence and more accurate recovery (31.84% and 27.88% higher in spectrum sensing with consistency and inconsistency, respectively) than conventional algorithms. During all iterations, the proposed algorithms, because of updating optimization, were more robust than conventional ones, but the computational complexity remained largely the same. To explore the correlation between the parameters and the interference recovery performance, we performed experiments, which revealed linear, logarithmic, and exponential regression correlations for particular forms of interference. We also compared the demodulation performance of the proposed algorithm with existing methods, and it turned out the proposed algorithm also obtained higher gains on average in the bit error rate.

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Section: Interference Analysismentioning

confidence: 99%

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confidence: 99%