Spectrum sensing for OFDM signals using pilot induced cyclostationarity in the presence of cyclic frequency offset

Chopra, Ribhu; Ghosh, Debashis; Mehra, D. K.

doi:10.1016/j.phycom.2017.07.008

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Remarkably, the maximum CFO considered here (

≃ 10 normal%

of the true CF value), represents a much larger value than that one taken into account in the previous works e.g. in [27, 29], where the maximum CFO did not exceed 5% of the true CF value.…”

Section: Numerical Resultsmentioning

confidence: 52%

See 1 more Smart Citation

Multi‐cycle spectrum sensing for OFDM signals under cyclic frequency offsets in cognitive vehicular networks

Tani¹,

Chiti²,

Fantacci³

et al. 2020

IET Communications

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“…Remarkably, the maximum CFO considered here (

≃ 10 normal%

of the true CF value), represents a much larger value than that one taken into account in the previous works e.g. in [27, 29], where the maximum CFO did not exceed 5% of the true CF value.…”

Section: Numerical Resultsmentioning

confidence: 52%

“…Finally, the computational complexity of the LSCE‐MCD, according to the analysis carried out in [29] for the greedy implementation, results to be

O false(M^{2} false)

.…”

Section: Proposed Joint Multi‐cycle Detector/estimatormentioning

confidence: 99%

Multi‐cycle spectrum sensing for OFDM signals under cyclic frequency offsets in cognitive vehicular networks

Tani¹,

Chiti²,

Fantacci³

et al. 2020

IET Communications

View full text Add to dashboard Cite

“…However, while performing uplink transmission in OFDMA, various clients convey signals to base station. Hence, CFO estimation (Chopra et al, 2017) turns out to be more challenging because various users have dissimilar CFOs. Several optimization algorithms are considered to optimize the parameters and obtain better results (Shankar and Jaisankar, 2016;Singh et al, 2018;Shareef and Rao, 2018).…”

Section: Ijpcc 161mentioning

confidence: 99%

Self-improved grey wolf optimization for estimating carrier frequency offset in SCM-OFDM systems

Rashmi

Sarvagya

2020

IJPCC

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Purpose The purpose of this paper is to demonstrate a proficiency for accomplishing optimal CFO and keep down the error among the received and transmitted signal. Orthogonal frequency-division multiplexing (OFDM) is considered as an attractive modulation scheme that could be adopted in wireless communication systems owing to its reliability in opposition to multipath interruptions under different subchannels. Carrier frequency offset (CFO) establishes inter-carrier interference that devastates the orthogonality between the subcarriers and fluctuates the preferred signal and minimizes the effectual signal-to-noise ratio (SNR). This results in corrupted system performance. For sustaining the subcarriers’ orthogonality, timing errors and CFOs have to be approximated and sufficiently compensated for. Single carrier modulation (SCM) is a major feature for efficient OFDM system. Design/methodology/approach This paper introduces a novel superposition coded modulation-orthogonal frequency-division multiplexing (SCM-OFDM) system with optimal CFO estimation using advanced optimization algorithm. The effectiveness of SCM-OFDM is validated by correlating the transmitted and received signal. Hence, the primary objective of the current research work is to reduce the error among the transmitted and received signal. The received signal involves CFO, which has to be tuned properly to get the signal as closest as possible with transmitted signal. The optimization or tuning of CFO is done by improved grey wolf optimization (GWO) called GWO with self-adaptiveness (GWO-SA). Further, it carries the performance comparison of proposed model with state-of-the-art models with the analysis on bit error rate (BER) and mean square error (MSE), thus validating the system’s performance. Findings From the analysis, BER of the proposed and conventional schemes for CFO at 0.25 was determined, where the adopted scheme at 10th SNR was 99.6 per cent better than maximum likelihood, 99.6 per cent better than least mean square (LMS), 99.3 per cent better than particle swarm optimization (PSO), 75 per cent better than genetic algorithm (GA) and 25 per cent better than GWO algorithms. Moreover, MSE at 1st SNR, the proposed GWO-SA scheme, is 4.62 per cent better than LMS, 60.1 per cent better than PSO, 37.82 better than GA and 67.85 per cent better than GWO algorithms. Hence, it is confirmed that the performance of SCM-OFDM system with GWO-SA-based CFO estimation outperformed the state-of-the-art techniques. Originality/value This paper presents a technique for attaining optimal CFO and to minimize the error among the received and transmitted signal. This is the first work that uses GWO-SA for attaining optimal CFO.

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Detection and cycle frequency estimation

Napolitano

2020

Cyclostationary Processes and Time Series

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Spectrum sensing for OFDM signals using pilot induced cyclostationarity in the presence of cyclic frequency offset

Cited by 8 publications

References 25 publications

Multi‐cycle spectrum sensing for OFDM signals under cyclic frequency offsets in cognitive vehicular networks

Multi‐cycle spectrum sensing for OFDM signals under cyclic frequency offsets in cognitive vehicular networks

Self-improved grey wolf optimization for estimating carrier frequency offset in SCM-OFDM systems

Detection and cycle frequency estimation

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