Fast Channel Estimation for Massive Machine Type Communications

Page view(s)
65
Checked on Jan 22, 2025
Fast Channel Estimation for Massive Machine Type Communications
Title:
Fast Channel Estimation for Massive Machine Type Communications
Journal Title:
IEEE VTC-Fall-2022
DOI:
Publication Date:
29 September 2022
Citation:
Yonghong Zeng, Sumei Sun, Yuhong Wang, and Yugang Ma. "Fast Channel Estimation for Massive Machine Type Communication & IEEE VTC-Fall, 2022.
Abstract:
For massive machine type communications (mMTC), it is critical to squeeze the transmission overhead as packet length is usually short and power is limited. Reducing preamble/pilot length for channel estimation is thus very important. In this paper, we propose to use short preamble for channel estimation in generalized frequency division multiplexing (GFDM) communication. Based on the short preamble, we can estimate a short channel first. We then show that the conventional zero-padding method for extending short channel to long channel in GFDM is not accurate. A new efficient method to construct the long effective channel from the obtained short channel is proposed. The proposed new method can construct a long channel for GFDM equalization without knowing the time sync error. It is proved theoretically that the constructed channel is correct given that the length of cyclic prefix (CP) and cyclic suffix (CS) are longer than the original channel length plus the time sync error. Simulations at various situations are shown to verify the results.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE2020 AME industry alignment fund-pre-positioning (IAF-PP)
Grant Reference no. : A20F8a0044
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2577-2465
Files uploaded:

File Size Format Action
2022001958.pdf 166.40 KB PDF Open