Tu, Chung-LunChung-LunTuLin, Tse-YuanTse-YuanLinChiu, Kang-LunKang-LunChiuJou, Shyh-JyeShyh-JyeJouPEI YUN TSAI2024-09-182024-09-182022https://www.scopus.com/record/display.uri?eid=2-s2.0-85142485319&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/721436Austin, 27 May 2022 through 1 June 2022Channel estimation is a crucial issue for hybrid multiple-input multiple-output architecture of wideband wireless millimeter wave system. In this paper, we present a hardware implementation of channel estimation based on compressive sensing method. We exploit sparsity in channel space and take advantage of both the time domain and the frequency domain to effectively reduce the computational complexity. We then disassemble the sensing matrix into smaller dimensional matrices to further simply the sensing formula by utilizing the orthogonality of the DFT codebook. The sensing issue is solved by the generalized orthogonal matching pursuit with Cholesky decomposition techniques, which achieves a significant reduction in the number of computations. Finally, we evaluate the performance of the proposed method with the perfect channel state information, and hardware performance by fixed-point analysis and RTL design and synthesis results. © 2022 IEEE.channel estimationcompressed sensinggeneralized orthogonal matching pursuithybrid precodingmillimeter wave communicationsparse signal recoveryconference paper10.1109/ISCAS48785.2022.99378982-s2.0-85142485319