Advanced LSTM Channel Estimation Scheme in WAVE Communication Systems
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, in order to overcome the channel distortion due to doubly selective channel, a new deep learning-based channel estimation scheme is proposed. In the proposed scheme, two neural networks are combined to learn both the temporal and spectral characteristics of the channel. One is the AE (autoencoder) which is trained to track the channel characteristic in the time domain and the other is the LSTM (long short term memory) which is trained to track the channel characteristic in the frequency domain. Firstly, the AE is trained and then the LSTM is sequentially trained based on the AE output. After that, the trained neural network is used to the conventional DPA (data pilot aided) process to estimate the channel values in frequency domain. It is noted that the proposed LSTM network consists of fixed number of LSTM units, so it enables to track the temporal variation of the channel reliably, regardless of the position of the OFDM (orthogonal frequency division multiplexing) symbol in a frame. Therefore, the proposed scheme can enhance the PER (packet error rate) performance more by reducing the error propagation due to the DPA process. Through numerical results, it is confirmed that the proposed scheme shows the best PER performance of the conventional schemes in doubly selective channel environment.
Abstract
In this paper, in order to overcome the channel distortion due to doubly selective channel, a new deep learning-based channel estimation scheme is proposed. In the proposed scheme, two neural networks are combined to learn both the temporal and spectral characteristics of the channel. One is the AE (autoencoder) which is trained to track the channel characteristic in the time domain and the other is the LSTM (long short term memory) which is trained to track the channel characteristic in the frequency domain. Firstly, the AE is trained and then the LSTM is sequentially trained based on the AE output. After that, the trained neural network is used to the conventional DPA (data pilot aided) process to estimate the channel values in frequency domain. It is noted that the proposed LSTM network consists of fixed number of LSTM units, so it enables to track the temporal variation of the channel reliably, regardless of the position of the OFDM (orthogonal frequency division multiplexing) symbol in a frame. Therefore, the proposed scheme can enhance the PER (packet error rate) performance more by reducing the error propagation due to the DPA process. Through numerical results, it is confirmed that the proposed scheme shows the best PER performance of the conventional schemes in doubly selective channel environment.
참고문헌 (10)
- K. Zheng, "Reliable and Efficient Autonomous Driving : The Need for Heterogeneous Vehicular Networks", IEEE Communications Magazine, 53(12), pp.72~79 (2015)[DOI]
- A. Ghosh, "Enabling Seamless V2I Communications:Toward Developing Cooperative Automotive Applications in VANET Systems", IEEE Communications Magazine, 53(12), pp.80~86 (2015)[DOI]
- IEEE Std, "1609.0-2013, IEEE Guide for Wireless Access in Vehiclular Environments (WAVE)-Architecture", ~ (2014)
- J. A. Fernandez, "Performance of the 802.11p Physical Layer in Vehicle-to-Vehicle Environments", IEEE Trans. Veh. Tech, 61(1), pp.3~14 (2012)[DOI]
- Y. K. Kim, "Time and Frequency Domain Channel Estimation Scheme for IEEE 802.11p", Proc. IEEE ITSC’14, Oct (2014)
- S. Han, "A deep learning based channel estimation scheme for IEEE 802.11p systems", Proc. IEEE ICC’19 (2019)
- A. K. Gizzini, "Deep learning based channel estimation schemes for IEEE 802. 11p standard", IEEE Access, 8, pp.113751~113765 (2020)[DOI]
- A. K. Gizzini, "Joint TRFI and deep learning for vehicular channel estimation", Proc. IEEE Globecom Workshop’20 (2020)
- J. Pan, "Channel estimation based on deep learning in vehicle-toeverything environments", IEEE Comm. Lett, 25(6), pp.1891~1895 (2021)[DOI]
- Doc IEEE 80211, "2014"