TY - GEN
T1 - Channel Estimation in IRS-Assisted OTFS Communication via Residual Attention Network
AU - Singh, Shatakshi
AU - Trivedi, Aditya
AU - Saxena, Divya
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/12
Y1 - 2023/12
N2 - For intelligent reflecting surface (IRS) based communication, channel estimation methods have predominantly focused on low-mobility and static scenarios. However, in dynamic scenarios where mobility and channel variations take place, accurate channel estimation becomes a challenging task. To address this limitation, this paper proposes a novel approach for channel estimation in dynamic IRS-aided communication scenarios by leveraging the advantages of orthogonal time-frequency space (OTFS) modulation. The proposed approach converts the time-frequency domain channel representation into the delay-Doppler (DD) domain using OTFS modulation. By doing so, the channel estimation problem is transformed into estimating the DD channel, which is more suitable for dynamic scenarios. To estimate the DD channel, a residual attention-based channel estimation (RACE) model is proposed. The RACE model outperforms existing deep learning methods and conventional approaches. It achieves a lower normalized mean square error compared to other methods.
AB - For intelligent reflecting surface (IRS) based communication, channel estimation methods have predominantly focused on low-mobility and static scenarios. However, in dynamic scenarios where mobility and channel variations take place, accurate channel estimation becomes a challenging task. To address this limitation, this paper proposes a novel approach for channel estimation in dynamic IRS-aided communication scenarios by leveraging the advantages of orthogonal time-frequency space (OTFS) modulation. The proposed approach converts the time-frequency domain channel representation into the delay-Doppler (DD) domain using OTFS modulation. By doing so, the channel estimation problem is transformed into estimating the DD channel, which is more suitable for dynamic scenarios. To estimate the DD channel, a residual attention-based channel estimation (RACE) model is proposed. The RACE model outperforms existing deep learning methods and conventional approaches. It achieves a lower normalized mean square error compared to other methods.
KW - intelligent reflecting surface (IRS)
KW - orthogonal time-frequency space (OTFS)
KW - Residual attention channel estimation (RACE)
UR - https://www.scopus.com/pages/publications/85187782976
U2 - 10.1109/CICT59886.2023.10455192
DO - 10.1109/CICT59886.2023.10455192
M3 - Conference article published in proceeding or book
AN - SCOPUS:85187782976
T3 - 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023
SP - 1
EP - 5
BT - 2023 IEEE 7th Conference on Information and Communication Technology, CICT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Conference on Information and Communication Technology, CICT 2023
Y2 - 15 December 2023 through 17 December 2023
ER -