Self-encoded spread spectrum with iterative detection under pulsed-noise jamming

Duraisamy, Poomathi; Nguyen, Lim

doi:10.1109/jcn.2013.000050

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The existing anti-pulse jamming methods mainly include the ''hidden'' method based on spread spectrum technology and the ''shearing'' method based on filtering technology. The authors in [9] proposed a kind of self-encoding spread spectrum technology, which can effectively reduce the symbol error rate (SER) of system under periodic pulse jamming. [10] proposed two self-adaptive filtering algorithms, namely, the incremental and diffusion affine projection sign algorithms, which can reduce the system bit error rate under pulse jamming environment.…”

Section: Introductionmentioning

confidence: 99%

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Zhou

Niu

2020

IEEE Access

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Pulse jamming is one of the common malicious jamming patterns that can significantly reduce the of wireless communication's reliability. This paper investigates the problem of anti-jamming communication in a random pulse jamming environment. In order to obtain the countermeasure in time domain, the Markov decision process (MDP) is employed to model and analyze the above problem, and a time-domain anti-pulse jamming algorithm (TDAA) based on reinforcement learning is proposed. The proposed algorithm learns from the dynamic interaction with the jamming environment to gradually approximate the optimal time-domain strategy. The optimal strategy enables the transmitter to switch between two states, i.e. ''active'' and ''silent'', to avoid random pulse jamming. In addition, a state estimation and adjustment method for the random pulse jamming environment is introduced to improve the robustness of the proposed TDAA. Simulation results show that, compared with continuous transmission, the proposed TDAA can effectively reduce the jamming collision ratio and significantly improve the normalized throughput. And compared with transmitting terminal Q-learning algorithm (TTQA), the proposed TDAA has higher time utilization ratio and normalized throughput.INDEX TERMS Anti-jamming, Markov decision process, Q-learning, reinforcement learning, random pulse jamming.

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Section: Introductionmentioning

confidence: 99%

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Zhou

Niu

2020

IEEE Access

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“…It is shown that using biphase modulation results in waveforms that have a large time‐bandwidth product and very low input signal‐to‐noise ratio (SNR) and thus inherently have an LPI by a radiometer . Self‐encoded spread spectrum system is a novel spread spectrum scheme presented by Lim Nguyen in which neither PN sequences nor chaotic sequences are needed . Alternatively, the spreading code is obtained from the digital information source.…”

Section: Introductionmentioning

confidence: 99%

Under‐sampling spectrum‐sparse signals based on active aliasing for low probability detection

Wang

et al. 2015

Security Comm Networks

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Because of the broadcast nature of the wireless communication medium, their security issues are receiving extensive concern. In this paper, we propose a spectrum‐sparse signal based on multi‐carrier modulation for secure wireless communication. The proposed signal is a summation of multiple sub‐band signals over several equally spaced carriers. The power spectrum density of the proposed signal can be reduced to a level even lower than the background noise, provided the number of sub‐bands is sufficiently large. Thus, the proposed signal possesses a favorable low probability detection property similar to that of spread spectrum signals. At the intended receiver, an under‐sampling method based on active aliasing is proposed. In this way, the intended receiver can coherently collect the transmitted signal power over all sub‐bands and extract the information reliably. We also derive a closed‐form expression for bit error rate performance of the proposed scheme in additive white Gaussian channel with binary phase shift keying modulation. Finally, numerical and simulation results to testify the effectiveness of the proposed scheme are generated for both binary phase shift keying and eight‐phase shift keying modulations in additive white Gaussian channel. Copyright © 2015 John Wiley & Sons, Ltd.

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“…It has been shown that iterative detection (ID) can exploit the modulation memory of SESS signals to achieve not only signal gain in additive white Gaussian noise (AWGN) channels, but also time diversity gain with significant bit‐error‐rate (BER) performance in fading channels [3, 13, 14]. The ID in SESS signals also has the potential to completely mitigate the effect of jamming [4]. It has been shown that by scrambling the spreading code, the ID can be used to provide code phase information [11].…”

Section: Introductionmentioning

confidence: 99%

Multipath error mitigation using self‐encoded spread spectrum for navigation

Duraisamy

Jang

Nguyen

2013

IET Radar, Sonar & Navigation

Self Cite

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Self-encoded spread spectrum (SESS) is a novel modulation technique that acquires its spreading sequence from the random input data stream rather than using the traditional pseudo-random-code generators. It has been shown that the memory in SESS can improve the system performance significantly in fading channel. In this study the authors analyse the tracking error of SESS system in the presence of multipath Rayleigh fading channels. Code Shuffling is introduced to randomise the spreading code, which in turn mitigates the effect of reflected path component and enhances the accuracy and performance of iterative detection (ID) against multipath effects. The performance comparison of SESS with C/A code is investigated with the main focus on short multipath delay scenarios. The results demonstrate that ID can significantly mitigate the pseudorange multipath error without any bandwidth expansion.

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Self-encoded spread spectrum with iterative detection under pulsed-noise jamming

Cited by 5 publications

References 14 publications

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

A Countermeasure Against Random Pulse Jamming in Time Domain Based on Reinforcement Learning

Under‐sampling spectrum‐sparse signals based on active aliasing for low probability detection

Multipath error mitigation using self‐encoded spread spectrum for navigation

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