Joint Timing Offset and Delay Spread Estimation for OFDM Symbol Synchronization over Multipath Fading Channels

Sasithong, Pruk; Wuttisittikulkij, Lunchakorn; Vanichchanunt, Pisit

doi:10.1109/ecti-con47248.2019.8955428

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Authors of [5] proposed to use Manhattan distance as the symbol-by-symbol correlator to estimate the RMSDS. The maximum likelihood principle is adopted to jointly estimate the symbol-time offset and the delay spread of the typical multipath channels in literature [6]. The above mentioned methods involves either new architecture or high computational complexity, and brings difficulties for the practical implementation.…”

Section: Introductionmentioning

confidence: 99%

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

Tan

Chen

Sun

et al. 2023

J. Phys.: Conf. Ser.

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With the rapid increase in the number of sensors in Power Internet of Things (PIOT), the data volume surges and efficient communications are needed. Precise estimation of delay spread is critical to improve the communication performance. In this paper, we propose a statistical learning (SL) based root mean square delay spread (RMSDS) estimation method, which consists of offline training and online estimation. During the offline training process, the instantaneous delay spread (IDS) for each data frame is firstly computed based on the known pilots. The acquired IDS is a random variable and its statistical parameter is related to the actual RMSDS, which is preset and known during offline training. Then, the functional relationship between the statistical parameter and the preset RMSDS is acquired through the SL algorithm. The acquired functional relationship can be used in the online estimation process to accurately estimate the RMSDS. Simulation results indicate that the proposed method can accurately estimate the RMSDS and greatly improve the bit error rate performance.

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Section: Introductionmentioning

confidence: 99%

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

Tan

Chen

Sun

et al. 2023

J. Phys.: Conf. Ser.

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“…Generally, using data‐aided approaches leads to increased estimation accuracy, occupying bandwidth, and sending additional information. The reference by Sasithong and others [20] is an example of synchronization without data aids. Using CPes, the time offset and delay spread in multipath channels are jointly estimated based on the ML algorithm.…”

Section: Introductionmentioning

confidence: 99%

Joint synchronization and parameter estimation in OFDM signaling

Karami

Bahramgiri

2022

ETRI Journal

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Challenges in cognitive radio and tactical communications include recognizing anonymously received signals and estimating parameters in a blind or semiblind manner. In this paper, we examine this issue for orthogonal frequency division multiplexing (OFDM) signaling. There are several parameters in OFDM signaling, and the blind receiver must extract and consider the synchronization issue. We assume that the blind receiver is aware of modulation type, OFDM, and not aware of chip duration and the length of cyclic prefix. First, we present new criteria based on kurtosis to estimate these parameters and compare their performance at different levels of additive white Gaussian noise with methods based on correlation, kurtosis, maximum likelihood, and matched filter. Then, we perform synchronization and estimate the start time based on these criteria and several new criteria in two steps: fine and coarse synchronization. Finally, in a more practical setup, we present the idea of jointly estimating the mentioned parameters and the signal start time as coarse synchronization. We compare different criteria and show that one of the proposed criteria has the highest efficiency.

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Joint Timing Offset and Delay Spread Estimation for OFDM Symbol Synchronization over Multipath Fading Channels

Cited by 2 publications

References 7 publications

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

Statistical Learning Based Wireless Channel Parameter Estimation for Power Internet of Things

Joint synchronization and parameter estimation in OFDM signaling

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