TY - GEN
T1 - Deep Learning based Channel Estimation for Full-Duplex Backscatter Communication Systems
AU - Jung, Chae Yoon
AU - Kang, Jae Mo
AU - Kim, Dong In
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - A novel deep learning (DL) based channel estimation method is proposed for full-duplex backscatter communication systems to realize the wireless-powered sensor networks (WPSN) for internet of things (IoT). We aim to minimize the power consumption at a sensor node by reflecting the supplied power signal from an access point (AP), which is called backscatter communication. Moreover, by adopting the frequency-shifted modulation technique during backscatter transmission, full-duplex communication is performed between the AP and the sensor node. However, this incurs a problem that the uplink and downlink channels are cascaded, which results in degrading the performance of beamforming. In order to overcome this problem, we propose a novel channel estimation method that extracts separate uplink and downlink channels from the cascaded channels. We formulate the problem for joint channel estimation and pilot optimization, and then design the DL based channel estimator, which is composed of feedforward neural network(FNN) and convolutional neural network(CNN), for compensating nonlinearity and non-convexity. Finally, we analyze the performance of the proposed DL based channel estimator compared to the conventional channel estimator.
AB - A novel deep learning (DL) based channel estimation method is proposed for full-duplex backscatter communication systems to realize the wireless-powered sensor networks (WPSN) for internet of things (IoT). We aim to minimize the power consumption at a sensor node by reflecting the supplied power signal from an access point (AP), which is called backscatter communication. Moreover, by adopting the frequency-shifted modulation technique during backscatter transmission, full-duplex communication is performed between the AP and the sensor node. However, this incurs a problem that the uplink and downlink channels are cascaded, which results in degrading the performance of beamforming. In order to overcome this problem, we propose a novel channel estimation method that extracts separate uplink and downlink channels from the cascaded channels. We formulate the problem for joint channel estimation and pilot optimization, and then design the DL based channel estimator, which is composed of feedforward neural network(FNN) and convolutional neural network(CNN), for compensating nonlinearity and non-convexity. Finally, we analyze the performance of the proposed DL based channel estimator compared to the conventional channel estimator.
KW - Beamforming
KW - channel estimation (CE)
KW - deep learning (DL)
KW - full-duplex backscatter communication
KW - internet of things (IoT)
KW - wireless-powered sensor networks (WPSN)
UR - http://www.scopus.com/inward/record.url?scp=85151980592&partnerID=8YFLogxK
U2 - 10.1109/ICAIIC57133.2023.10066967
DO - 10.1109/ICAIIC57133.2023.10066967
M3 - Conference contribution
AN - SCOPUS:85151980592
T3 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
SP - 347
EP - 352
BT - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2023
Y2 - 20 February 2023 through 23 February 2023
ER -