Estimation of UFMC Time-Varying Fading Channel Using Adaptive Filters

Jamoos, Ali; Hussein, Moataz

doi:10.1109/icpet.2018.00014

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…As a result, not used CP, and it is easier to implement than FBMC. Because a prototype filter known as the Chebyshev was utilized, it resulted in improved spectral efficiency and less spectral leakage [67]. Moreover, ICI, ISI, and inter-block Interference (IBI) are embedded into the UFMC, even if there is just a minor offset [68].…”

Section: ) Orthogonal Multiple Access (Oma)mentioning

confidence: 99%

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Alhaj,

Jamlos,

Manap

et al. 2024

IEEE Access

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The fifth-generation (5G) wireless communications have been deployed in many countries with the following features: wireless networks at 20 Gbps as peak data rate, a latency of 1 ms, reliability of 99.999%, maximum mobility of 500 km/h, a bandwidth of 1 GHz, and a capacity of 10 6 up to Mbps/m 2 . Nonetheless, the rapid growth of applications, such as extended/virtual reality (XR/VR), online gaming, telemedicine, cloud computing, smart cities, the Internet of Everything (IoE), and others, demand lower latency, higher data rates, ubiquitous coverage, and better reliability. These higher requirements are the main problems that have challenged 5G while concurrently encouraging researchers and practitioners to introduce viable solutions. In this review paper, the sixth-generation (6G) technology could solve the 5G limitations, achieve higher requirements, and support future applications. The integration of multiple access techniques, terahertz (THz), visible light communications (VLC), ultra-massive multiple-input multiple-output (um-MIMO), hybrid networks, cell-free massive MIMO, and artificial intelligence (AI)/machine learning (ML) have been proposed for 6G. The main contributions of this paper are a comprehensive review of the 6G vision, KPIs (key performance indicators), and advanced potential technologies proposed with operation principles. Besides, this paper reviewed multiple access and modulation techniques, concentrating on Filter-Bank Multicarrier (FBMC) as a potential technology for 6G. This paper ends by discussing potential applications with challenges and lessons identified from prior studies to pave the path for future research.

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Section: ) Orthogonal Multiple Access (Oma)mentioning

confidence: 99%

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Alhaj,

Jamlos,

Manap

et al. 2024

IEEE Access

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“…But the right explanation, due to Ghandour-Haidar et al [28] in 2012, after the preliminary work of Barbieri et al [29] in 2009, is that the CM criterion with low order p is not able to follow the dynamics of the fading, since the dynamic MSE is approximately constant with respect to the Doppler frequency (see eq. (22) or fig. B.1 in [28]), whereas it should be able to decrease for low Doppler (the channel is theoretically easier to estimate).…”

Section: Challenge Of Slow Fading Channel Estimation With An Ar(p)-kamentioning

confidence: 99%

“…In conclusion, disappointing performance is obtained with low-order AR(p)-KF tuned with the CM criterion for the slow fading scenario, which is the most common one for practical applications, and solutions have to be found. It should be noted that in spite of this important warning highlighted from 2012, many authors still continue to use the KF with an AR (p = 1 or 2) model tuned with the CM criterion for Clarke channel estimation [19][20][21][22][23][24] with low to moderate Doppler frequencies. It appears thus important to continue to communicate on this point, and recommend alternative solutions.…”

Section: Challenge Of Slow Fading Channel Estimation With An Ar(p)-kamentioning

confidence: 99%

“…This criterion is known as the correlation matching (CM) criterion [9,[11][12][13] 1 and the solution is obtained by solving the Yule-Walker equations [14]. Over the past two decades, an extensive literature on Rayleigh Channel estimation was based on an AR(p)-KF tuned using the CM criterion [12,[15][16][17][18]11] and still continues nowadays [19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Second-order autoregressive model-based Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation

Husseini¹,

Simon²,

Ros³

2019

Digital Signal Processing

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Second-order autoregressive modelbased Kalman filter for the estimation of a slow fading channel described by the Clarke model: Optimal tuning and interpretation. Digital Signal Processing, Elsevier, 2019, 90, pp. AbstractThis paper treats the estimation of a flat fading Rayleigh channel with Jakes' Doppler spectrum model and slow fading variations. A common method is to use a Kalman filter (KF) based on an auto-regressive model of order p (AR(p)). The parameters of the AR model can be simply tuned by using the correlation matching (CM) criterion. However, the major drawback of this method is that high orders are required to approach the Bayesian Cramer-Rao lower bound. The choice of p together with the tuning of the model parameters is thus critical, and a tradeoff must be found between the numerical complexity and the performance. The reasonable tradeoff arising from setting p = 2 has received much attention in the literature. However, the methods proposed for tuning the model parameters are either based on an extensive grid-search analysis or experimental results, which limits their ap-$ plicability. A general solution for any scenario is simply missing for p = 2 and this paper aims at filling this gap. We propose using a Minimization of Asymptotic Variance (MAV) criterion, for which a general closed-form formula has been derived for the optimal tuning of the model and the mean square error. This provides deeper insight into the behaviour of the KF with respect to the channel state (Doppler frequency and signal to noise ratio).Moreover, the paper interprets the proposed solution, especially the dependence of the shape of the optimal AR(2) spectrum on the channel state.Analytic and numerical comparisons with first-and second-order algorithms in the literature are also performed. Simulation results show that the proposed AR(2)-MAV model performs better than the literature and similarly to AR(p)-CM models with p ≥ 15.

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