Joint Estimation of Carrier Frequency Offset and Channel Impulse Response for Linear Periodic Channels

Shaked, Roee; Shlezinger, Nir; Dabora, Ron

doi:10.1109/tcomm.2017.2741943

Cited by 17 publications

(5 citation statements)

References 34 publications

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Section: A Related Workmentioning

confidence: 99%

“…To improve the overall performance of the OFDM systems, joint optimization of CFO with other parameters was proposed in [11,12]. In [11], a framework of pilot-aided joint estimation of CFO along with the Channel Impulse Response (CIR) for linear periodic channels was studied. A Joint Maximum Likelihood Estimator (JMLE) that guarantees higher spectral efficiency and lower computational complexity was proposed.…”

Section: A Related Workmentioning

confidence: 99%

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Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Hussien¹,

Abdelmoaty²,

Elsaadany³

et al. 2023

IEEE Access

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The Beyond fifth Generation (B5G) communication systems imposed several challenges on radio designers. For example, a machine is required to set up a call at a low Signal-to-Noise Ratio (SNR), as low as -10 dB, in the extended coverage mode. Moreover, only one receive antenna will be available, and virtually no frequency diversity. Such requirements present major challenges to maintaining timing and frequency synchronization. Carrier Frequency Offset (CFO) estimation is at the heart of these challenges. Different ways have been proposed for CFO estimation such as maximum likelihood based on a cyclic prefix. Nevertheless, these methods remain limited in various ways. At the same time, Machine Learning (ML) techniques showed outstanding performance in several wireless communication problems. In this work, we propose an ML-based approach for CFO estimation in OFDM systems. Specifically, we propose a Gradient-Boosting Machine (GBM)-based solution to predict the CFO given the received Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). Furthermore, we make our dataset available for public access to encourage other researchers to pursue this promising direction. We compare our results with different baseline models (i.e., artificial neural networks and support vector machines). The experimental results show that our model outperforms other baseline models due to its ensemble nature which enables ensemble models to obtain a better generalization behavior.INDEX TERMS 5G, carrier frequency offset, gradient-boosting, machine learning, new radio.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Hussien¹,

Abdelmoaty²,

Elsaadany³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The channel estimation problem has been largely studied in PLC. However, many published works employ signals that do not conform to the existing standards [20], [21], deal with narrowband PLC systems [22]- [24] or outdoor broadband ones [21], [25]. Channels in both scenarios exhibit notable differences with respect to indoor ones.…”

Section: Introductionmentioning

confidence: 99%

Channel estimation for OFDM-based indoor broadband power line communication systems

Cortes¹,

Cañete²,

Díez³

2023

J. Commun. Netw.

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“…Using data symbols and a feedback loop enables the adjustment of the phase of the received data symbols, ensuring accurate data transmission and reception between devices. The accuracy of the delicate synchronization process is essential in networks with high data rates and high-order modulation schemes [21], [22].…”

Section: Introductionmentioning

confidence: 99%

HaLow Wi-Fi performance in multiusers and channels environment with MATLAB Simulink

Faydhe,

Ibrahim,

Kamal

2023

Int. j. commun. netw. inf. secur.

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HaLow Wi-Fi (IEEE 802.11ah) wireless networking standard. As opposed to 2.4 GHz and 5 GHz-based conventional Wi-Fi networks, it leverages 900 MHz frequencies license-exempt for enabling networks Wi-Fi with a longer range. Lower energy usage makes it possible to build extensive networks of sensors or stations that work together to communicate signals, which is another advantage. In this paper IEEE 802.11ah Wi-Fi system design and implemented using MATLAB Simulink and tested under multiusers and channels environment in terms of Spectrum analyzer and constellation Diagram where 4 users, 2 MHz and 4 MHz channels bandwidth used to perfume the test also power of coarse synchronization, fine synchronization and initial channel estimation, to make Wi-Fi networks with a greater range possible were illustrated in space time stream.

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Joint Estimation of Carrier Frequency Offset and Channel Impulse Response for Linear Periodic Channels

Cited by 17 publications

References 34 publications

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Carrier Frequency Offset Estimation in 5G NR: Introducing Gradient Boosting Machines

Channel estimation for OFDM-based indoor broadband power line communication systems

HaLow Wi-Fi performance in multiusers and channels environment with MATLAB Simulink

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