Improved coding gain of clipped OFDM signal using avalanche effect of AES block cipher

El-Ganiny, M. Youssef; ElAttar, Hussein M.; Dahab, Mohamed A. Aboul; Elgarf, Talaat A.

doi:10.1109/pacrim.2017.8121910

Cited by 4 publications

(8 citation statements)

References 2 publications

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“…However, clipping could cause a significant increase in BER of the decoded signal at the RX. To address this problem, authors in [18,19] introduced a channel codingbased technique to mitigate the rough clipping noise. However, this solution reduces the system SE and increases the system hardware complexity.…”

Section: Related Workmentioning

confidence: 99%

“…Besides sparsity feature exploitation, prior-knowledge of the amplitudes of the activated/inactivated subcarriers are exploited by the proposed scheme to perform a low complex CS-based detection at RX. Unlike [18,19], due to robustness and reliability of the proposed scheme, there is no need for channel coding for mitigating the clipping distortion and the channel noise As will be indicated in the simulation Section, EE and humble hardware complexity of the proposed OFDM-SIM scheme make it a promising candidate for IoT systems.…”

Section: Related Workmentioning

confidence: 99%

“…Our simulation parameters are similar to that used in [19]. The simulation is running on = 256 under AWGN and slow Rayleigh fading channels.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…The simulation is running on = 256 under AWGN and slow Rayleigh fading channels. To reduce the clipping effect, the coded OFDM in [19] relies on two stages of convolutional channel coding with rate 1/2, that correspond to a net channel coding rate of 1/4. Both BPSK and QPSK are applied on subcarrier modulation.…”

Section: A Simulation Setupmentioning

confidence: 99%

“…Many works have been proposed in literature to mitigate clipping noise at the RX to improve the BER performance. For example, channel coding rate is adapted in [18,19] to cope with the rough clipping noise, but this solution associated with low SE and high system hardware complexity.…”

Section: Introductionmentioning

confidence: 99%

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Sparse Index OFDM Modulation for IoT Communications

et al. 2020

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One of the greatest challenges facing the physical layer design of the internet of things (IoT) resides in the imposed constraint of very low power consumption. Recently, new modulation scheme termed OFDM with sparse index modulation (OFDM-SIM) has been introduced as an energy efficient multicarrier scheme (MCS). Although of its high energy efficiency (EE) and spectral efficiency (SE), OFDM-SIM cannot fulfill the IoT energy requirements owing to its high PAPR. In this regard, an enhanced OFDM-SIM is proposed in this paper as an energy efficient MCS for IoT communications. In particular, a novel clippingcompressive sensing (CS) based PAPR reduction technique for OFDM-SIM is proposed. In the transmitter (TX) side, considering the complexity constraints for IoT devices, the simple and low complex clipping method is exploited to deal with the PAPR issue. On the receiver (RX) side, a robust CS signal recovery scheme is proposed to deal with tough resulting clipping noise. Unlike high complex conventional CS-based schemes, the proposed scheme exploits the inherent sparsity of the received enhanced OFDM-SIM signal rather than clipping noise sparsity to achieve a low complex CS signal detection. Moreover, in this paper, the information-theoretic limits on sparsity recovery are exploited to derive an upper bound measure of the bit error rate (BER). The simulation results demonstrate the superiority of the proposed scheme, as it significantly enhances the overall system performance in terms of EE and PAPR reduction compared to the conventional clipped coded-OFDM. INDEX TERMS IoT; OFDM-Sparse index modulation (OFDM-SIM); PAPR; Clipping; Compressive sensing (CS).

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Our simulation parameters are similar to that used in [19]. The simulation is running on = 256 under AWGN and slow Rayleigh fading channels.…”

Section: A Simulation Setupmentioning

confidence: 99%

Section: A Simulation Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%