Channel Estimation and Tracking Algorithms for Harsh Vehicle to Vehicle Environments

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

doi:10.1109/vtcfall.2015.7390864

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…Meanwhile, the author also proposes a new algorithm of Gradient Rake-Matching Pursuit (GRMP) algorithm to reduce complexity and improve system robustness. Three channel estimation and tracking algorithms, Finite Alphabet with Time Truncation (FA-TT), Minimum Distance with Time Truncation (MD-TT), and Decision Directed with Time Truncation (DD-TT) are reposted in [21]. Those algorithms obtain very high performance in low mobile environments as well as fast varying channels, which meet the requirements of improving system robustness.…”

Section: The Robust Communications In V2vmentioning

confidence: 99%

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Yong

Shen

Sun

et al. 2019

J Wireless Com Network

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Cyber-physical systems (CPSs) are characterized by integrating computation, communication, and physical system. In typical CPS application scenarios, vehicle-to-vehicle (V2V) and Industry Internet of Things (IIoT), due to doubly selective fading and non-stationary channel characteristics, the robust and reliable end-to-end communication is extremely important. Channel estimation is a major signal processing technology to ensure robust and reliable communication. However, the existing channel estimation methods for V2V and IIoT cannot effectively reduce intercarrier interference (ICI) and lower the computation complexity, thus leading to poor robustness. Aiming at this challenge, according to the channel characteristics of V2V and IIoT, we design two channel estimation methods based on the Bayesian filter to promote the robustness and reliability of end-to-end communication. For the channels with doubly selective fading and non-stationary characteristics of V2V and IIoT scenarios, in the one hand, basis extended model (BEM) is used to further reduce the complexity of the channel estimation algorithm under the premise that ICI can be eliminated in the channel estimation. On the other hand, aiming at the non-stationary channel, a channel estimation and interpolation method based on extended Kalman filter (EKF) and unscented Kalman filter (UKF) Bayesian filters to jointly estimate the channel impulse response (CIR) and time-varying time domain autocorrelation coefficient is adopted. Through the MATLAB simulation, the robustness and reliability of end-to-end communication for V2V and IIoT are promoted by the proposed algorithms.

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Section: The Robust Communications In V2vmentioning

confidence: 99%

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Yong

Shen

Sun

et al. 2019

J Wireless Com Network

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“…To cope with these shortcomings, numerous works have been conducted on the applications of mmWave massive MIMO time-varying channels (Kabaoglu, 2009;Fernandez et al, 2010;Bourdoux et al, 2011;Gu et al, 2014;Awad et al, 2015;Choi et al, 2016;Gao et al, 2017a;Ma et al, 2018;Wu et al, 2019). Although the complexity of transceivers is reduced by hybrid analog/digital structures, the high cost of hardware prevents the transceivers from exploiting one dedicated radio frequency (RF) chain for each equipped antenna (Awad et al, 2015;Ma et al, 2018;Wu et al, 2019;Brighente et al, 2020), and hybrid beamforming schemes require more pilot overhead for fast time-varying channels (Garcia et al, 2016;Kong et al, 2017). Although many channel tracking schemes have been researched in the applications of mmWave V2I communication (Bourdoux et al, 2011;Gu et al, 2014;Awad et al, 2015;Gao et al, 2017a;Mehrabi et al, 2020), however, those schemes are suitable only for the terahertz frequency band and cannot be applied to fast timevarying mmWave channels.…”

Section: Introductionmentioning

confidence: 99%

“…Although the complexity of transceivers is reduced by hybrid analog/digital structures, the high cost of hardware prevents the transceivers from exploiting one dedicated radio frequency (RF) chain for each equipped antenna (Awad et al, 2015;Ma et al, 2018;Wu et al, 2019;Brighente et al, 2020), and hybrid beamforming schemes require more pilot overhead for fast time-varying channels (Garcia et al, 2016;Kong et al, 2017). Although many channel tracking schemes have been researched in the applications of mmWave V2I communication (Bourdoux et al, 2011;Gu et al, 2014;Awad et al, 2015;Gao et al, 2017a;Mehrabi et al, 2020), however, those schemes are suitable only for the terahertz frequency band and cannot be applied to fast timevarying mmWave channels. Apart from that, works studied by Awad et al (2015) and Zhang et al (2016) have modeled slowly time-varying channels in adjacent time slots using a one-order Markov process, and the classical Kalman filter was used to track the time-varying channels.…”

Section: Introductionmentioning

confidence: 99%

“…Although many channel tracking schemes have been researched in the applications of mmWave V2I communication (Bourdoux et al, 2011;Gu et al, 2014;Awad et al, 2015;Gao et al, 2017a;Mehrabi et al, 2020), however, those schemes are suitable only for the terahertz frequency band and cannot be applied to fast timevarying mmWave channels. Apart from that, works studied by Awad et al (2015) and Zhang et al (2016) have modeled slowly time-varying channels in adjacent time slots using a one-order Markov process, and the classical Kalman filter was used to track the time-varying channels. However, those schemes are adopted only at microwave frequencies, and the mmWave beamspace channels with special sparse structure cannot be modeled by the one-order Markov process.…”

Section: Introductionmentioning

confidence: 99%

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Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

Zhao

Zou

Sun

2021

Front Inform Technol Electron Eng

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Millimeter wave (mmWave) has been claimed as the viable solution for high-bandwidth vehicular communications in 5G and beyond. To realize applications in future vehicular communications, it is important to take a robust mmWave vehicular network into consideration. However, one challenge in such a network is that mmWave should provide an ultra-fast and high-rate data exchange among vehicles or vehicle-to-infrastructure (V2I). Moreover, traditional real-time channel estimation strategies are unavailable because vehicle mobility leads to a fast variation mmWave channel. To overcome these issues, a channel estimation approach for mmWave V2I communications is proposed in this paper. Specifically, by considering a fast-moving vehicle secnario, a corresponding mathematical model for a fast time-varying channel is first established. Then, the temporal variation rule between the base station and each mobile user and the determined direction-of-arrival are used to predict the time-varying channel prior information (PI). Finally, by exploiting the PI and the characteristics of the channel, the time-varying channel is estimated. The simulation results show that the scheme in this paper outperforms traditional ones in both normalized mean square error and sum-rate performance in the mmWave time-varying vehicular system.

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“…To address this issue, a number of research works focus on channel estimation of mmWave massive MIMO V2X communications in static or slowly time-variant cases [5], [11], [13], [17], [18]. In [7], [19], [20], the authors pointed out that mmWave massive MIMO communication is the only feasible way to achieve high-bandwidth connected vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Novel NE-DFT Channel Estimation Scheme for Millimeter-Wave Massive MIMO Vehicular Communications

Zhao¹,

Zou²

2020

IEEE Access

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Having the ability to provide an ultrafast and high-rate data exchange, millimeterwave (mmWave) massive MIMO has been viewed as one of the technologies with the most potential for vehicular cellular systems in next-generation wireless communications. To alleviate the adverse influence of huge path losses, beamforming techniques are always introduced in various mmWave systems to provide sufficient channel gains. However, it is important to note that the traditional channel estimation algorithms may no longer be available in vehicular cellular networks due to the rapid movements of pedestrians and vehicles. Under this condition, this paper proposes a noise elimination-based discrete Fourier transform (DFT) channel estimation strategy, namely, the NE-DFT channel estimation strategy, for mmWave vehicular communications. Specifically, we first use the iterative cancellation method to initially estimate all path parameters. Then, to further improve the estimate accuracy, we set a decision threshold to determine the authenticity of the estimated paths. Furthermore, the energy distribution of each path in the channel is analyzed, and an additional estimation scheme is designed that enables a more accurate estimation of the previously estimated paths, which uses the comparison value between the total channel matrix energy and the actual signal matrix energy as an auxiliary judgment to successively select the path with the minimum comparison value until a sufficient number of real paths are selected. Finally, the channel matrix is reconstructed using the estimated channel parameters. Simulation results verify that the proposed NE-DFT channel estimation scheme can achieve much better NMSE performance than the conventional scheme, even in comparison with the time-variant channel. INDEX TERMS Massive MIMO, millimeter-wave (mmWave), vehicular communication, channel estimation.

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Channel Estimation and Tracking Algorithms for Harsh Vehicle to Vehicle Environments

Cited by 9 publications

References 6 publications

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

EKF/UKF-based channel estimation for robust and reliable communications in V2V and IIoT

Prior information based channel estimation for millimeter-wave massive MIMO vehicular communications in 5G and beyond

A Novel NE-DFT Channel Estimation Scheme for Millimeter-Wave Massive MIMO Vehicular Communications

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