Impact of Channel Estimation on The Performance of OFDM-IM With Barrage Jamming

Kaplan, Ahmet; Kurt, Güneş Karabulut; Altunbaş, İbrahim; Altun, Ufuk; Onaty, Furuzan Atay; Kucukyavuzy, Defne

doi:10.1109/siu49456.2020.9302463

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…It is important to note that since OFDM-IM owns in-active (idle) sub-carriers, conventional channel estimation schemes for 802.11p in OFDM systems is not workable. Consequently, several channel estimation methods have been proposed specifically for OFDM-IM systems [19]- [21]. For instance, in [19], a least squares (LS) channel estimation approach utilizing some of the inactive sub-carriers for pilot signals was introduced.…”

Section: A Introduction and Related Workmentioning

confidence: 99%

“…For instance, in [19], a least squares (LS) channel estimation approach utilizing some of the inactive sub-carriers for pilot signals was introduced. Additionally, channel estimation schemes that employ Zadoff-Chu sequences and the linear minimum mean squared error (LMMSE) algorithm were proposed in [20] and [21], respectively. However, these approaches have not adequately addressed the challenge of estimation in doublyselective channels.…”

Section: A Introduction and Related Workmentioning

confidence: 99%

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An Enhanced Channel Estimation Scheme in OFDM-IM Systems With Index Pilots for IEEE 802.11p Standard

Dong,

Zhang,

Shahrrava

et al. 2024

IEEE Access

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Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is emerging as a robust technology, especially valued in vehicular communication scenarios for its resilience. In such dynamic environments, reliable channel estimation becomes critical to maintain system performance due to the fast time-varying and double dispersion channel nature. Despite the harsh conditions, OFDM-IM enhances system performance by leveraging inactive sub-carriers to convey additional information. However, these idle sub-carriers complicate channel estimation compared to traditional OFDM systems. In response to these challenges, this paper introduces an enhanced channel estimation scheme tailored for the IEEE 802.11p standard, which supports cooperative intelligent transport systems (C-ITS). Our proposed method, the enhanced time-domain reliable test frequency-domain interpolation with index pilots (E-TRFI-IP), strategically places pilots and utilizes the positions of inactive sub-carriers to facilitate accurate detection and prevent performance degradation from inadequate channel tracking. Additionally, to further improve channel estimation performance, a comparison threshold methodology for reliable sub-carriers test is introduced by utilizing the structure of IM and joint interpolation method. Extensive simulation results demonstrate that E-TRFI-IP significantly surpasses existing channel estimation methods for both OFDM and OFDM-IM in terms of channel estimation performance. Notably, OFDM-IM systems employing the E-TRFI-IP estimation scheme also exhibit superior performance in bit error rate (BER) compared to those using conventional OFDM-IM and other channel estimation techniques.INDEX TERMS Channel estimation, Orthogonal frequency division multiplexing (OFDM), Index modulation (IM), Time domain reliable test frequency domain interpolation (TRFI), IEEE 802.11p standard, Vehicular communications.

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Section: A Introduction and Related Workmentioning

confidence: 99%

Section: A Introduction and Related Workmentioning

confidence: 99%

An Enhanced Channel Estimation Scheme in OFDM-IM Systems With Index Pilots for IEEE 802.11p Standard

Dong,

Zhang,

Shahrrava

et al. 2024

IEEE Access

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Deep learning‐based channel estimation for OFDM‐IM systems over Rayleigh fading channels

Adiguzel,

Develi

2024

Int J Communication

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SummaryDeep learning (DL)‐based channel estimation for orthogonal frequency division multiplexing with index modulation (OFDM‐IM) under Rayleigh fading channel conditions is presented in this paper. A deep neural network (DNN) is utilized to estimate the channel response in simulations. The proposed DNN is trained using the channel coefficient derived through the least squares (LS) method. Then channel estimation is conducted using the trained DNN. Within the DNN, the long short‐term memory (LSTM) layer is included as the hidden layer. Different scenarios are handled in simulations and the proposed DNN is compared with traditional channel estimation methods. The simulations demonstrate that the DL‐based channel estimation significantly surpasses the LS and minimum mean‐square error (MMSE) techniques.

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