Pilot Design Strategies for Block Sparse Channel Estimation in OFDM Systems

Vimala, P.; Yamuna, G.

doi:10.17485/ijst/2017/v10i24/110694

Cited by 5 publications

(2 citation statements)

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“…In this paper, NMSE is used as a measurement indicator to evaluate the estimation performance for SNR values in a certain interval. The expression of NMSE is defined as follows [10]: (27) where H real is the real channel frequency response and H est is the estimated channel frequency response. In Fig.…”

Section: B Estimation Performance On Ber and Nmsementioning

confidence: 99%

“…These complicated UWA environment places higher requirements and greater challenges to achieve high-efficiency and reliable transmissions for wireless communication. More recently, the orthogonal frequency division multiplexing (OFDM) technique has been adopted to UWA communications, due to its excellent performance in resisting inter symbol interference (ISI) and reducing multipath fading effect [8]- [10]. As a multicarrier system, OFDM divides…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

et al. 2019

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Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications. INDEX TERMS Deep neural networks, OFDM systems, channel estimation, underwater acoustic communication.

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Section: B Estimation Performance On Ber and Nmsementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%