Low-Complexity Semi-Blind Channel Estimation Algorithms for Vehicular Communications Using the IEEE 802.11p Standard

Awad, Moustafa M.; Seddik, Karim G.; Elezabi, Ayman

doi:10.1109/tits.2018.2834723

Cited by 18 publications

(8 citation statements)

References 29 publications

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“…Mehrabi [9] introduced decoded data bits into DPAS to suppress noise caused by demodulation, but the performance gain is still marginal at high SNR region. To further improve accuracy of CFR, Awad [8] transformed CFR into time domain and performed truncation operation, thus removing demodulation errors. However, because iterative accumulated noise is not eliminated completely, these schemes still suffer from error propagation especially in the rapid time-varying vehicular channels.…”

Section: Related Workmentioning

confidence: 99%

“…A lot of work has been proposed to track channel variations over the frame duration for vehicular communication under the IEEE 802.11p standard. The current method focuses on data pilot-aided successive (DPAS) whose key is to consider the demapped data signals as aided pilot [6][7][8][9]. The performance gain, however, is not evident especially at high signal-noise-ratio (SNR) region because of the error propagation caused by accumulated noise in the iterative process.…”

Section: Introductionmentioning

confidence: 99%

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A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

Zhu

Sheng

Fang

et al. 2020

J Wireless Com Network

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In vehicular communications using IEEE 802.11p, estimating channel frequency response (CFR) is a remarkably challenging task. The challenge for channel estimation (CE) lies in tracking variations of CFR due to the extremely fast time-varying characteristic of channel and low density pilot. To tackle such problem, inspired by image super-resolution (ISR) techniques, a deep learning-based temporal spectral channel network (TS-ChannelNet) is proposed. Following the process of ISR, an average decision-directed estimation with time truncation (ADD-TT) is first presented to extend pilot values into tentative CFR, thus tracking coarsely variations. Then, to make tentative CFR values accurate, a super resolution convolutional long short-term memory (SR-ConvLSTM) is utilized to track channel extreme variations by extracting sufficiently temporal spectral correlation of data symbols. Three representative vehicular environments are investigated to demonstrate the performance of our proposed TS-ChannelNet in terms of normalized mean square error (NMSE) and bit error rate (BER). The proposed method has an evident performance gain over existing methods, reaching about 84.5% improvements at some high signal-noise-ratio (SNR) regions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

Zhu

Sheng

Fang

et al. 2020

J Wireless Com Network

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“…Therefore, the accuracy of channel estimation has a significant effect on system performance, and numerous channel estimation schemes complying with the IEEE 802.11p standard have been proposed to adapt to fast-varying channels [16][17][18][19][20][21]. A good survey and review of other schemes can be found in [21].…”

Section: Introductionmentioning

confidence: 99%

“…The time-domain reliable test frequency domain interpolation (TRFI) scheme [17] improves the channel estimation performance using the time-frequency correlation properties, but it cannot achieve satisfactory performance at low SNRs. In [18], the decision-directed estimation with time-domain truncation (DD-TT) channel estimation scheme was proposed; however, performance degradation occurs at high SNRs. In [19], channel estimation using frequency self-organization was proposed to improve error performance.…”

Section: Introductionmentioning

confidence: 99%

Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems

Choi

Mun

Yook

2020

Sensors

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Wireless access in vehicular environments to support wireless communication between vehicles has been developed to provide road safety and infotainment services. In vehicular environments where the channel changes rapidly, channel estimation is very important in improving the reliability of wireless communication. Therefore, numerous channel estimation schemes have been proposed; however, none of the schemes proposed so far can perform well over the entire signal-to-noise ratio (SNR) region. In this paper, we propose a novel channel estimation scheme that selectively uses the better scheme between two channel estimation schemes on a symbol-by-symbol basis. The results show that the proposed scheme performs symbol-by-symbol selection of the better channel estimation scheme within a packet, and thus shows excellent performance over the entire SNR region in vehicular environments in terms of the bit error rate and packet error rate.

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“…To avoid these situations, vehicles use vehicle-to-vehicle (V2V) communications to exchange dynamic parameters and other security messages, e.g., collision warning, blind spot warning and emergency brake warning [19]. These safety-related messages need to be transmitted to nearby vehicles in a short period of time, which means that these messages have strict requirements on low latency and high packet delivery ratio [20][21][22]. Since all vehicles in the surrounding area should react in real time when an emergency occurs, broadcast is more practical than unicast [23].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of IEEE 802.11p for Continuous Backoff Freezing in IoV

Xia

Fan

et al. 2019

Electronics

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With the rapid development of cloud computing and big data, traditional Vehicular Ad hoc Networks (VANETs) are evolving into the Internet of Vehicles (IoV). As an important communication technology in IoV, IEEE 802.11p protocols have been studied by many experts and scholars. In IEEE 802.11p, a node’s backoff counter will be frozen when the channel is detected as busy. However, most studies did not consider the possibility of continuous backoff freezing when calculating delay. Thus, in this paper, we focus on the performance analysis of IEEE 802.11p for continuous backoff freezing. Specifically, we establish an analytical model to analyze the broadcast performance in the highway scene where vehicles can obtain traffic density from roadside units through Vehicle to Infrastructure (V2I) communications. We first calculate the relationship between vehicle density and the number of vehicles. Then, we derive the relationship between the number of vehicles and packet delay according to Markov chains. Next, we utilize the probability generating function (PGF) to transform traditional Markov chains into z domain under the situation of non-saturation. Finally, we employ the Mason formula to derive packet delay. As compared with the performance without considering the continuous backoff freezing, the simulation results have demonstrated that our analytical model is more reasonable.

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Low-Complexity Semi-Blind Channel Estimation Algorithms for Vehicular Communications Using the IEEE 802.11p Standard

Cited by 18 publications

References 29 publications

A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems

Performance Analysis of IEEE 802.11p for Continuous Backoff Freezing in IoV

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