Browsing by Author "Tsai, Yu-Cheng"

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A Flexible Geometric Mean Decomposition Processor for MIMO Communication Systems
(2017-02)
Tsai, Yu-Cheng
;
Chen, Chiao-En
;
Yang, Chia-Hsiang
;
電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
This paper presents a geometric-mean-decomposition (GMD) processor for multiple-input multiple-output (MIMO) communication systems. The proposed GMD pro cessor has a flexible architecture that supports channel matrices of arbitrary dimensions. A complex-valued bi-diagonalization is proposed at the preprocessing stage, which substantially reduces the overall hardware complexity. The proposed GMD processor naturally supports an economic channel state information (CSI) feedback mechanism, rendering it highly suitable for frequency-division- duplexing (FDD) systems. To illustrate the performance of the proposed architecture, we demonstrate an efficient implementation supporting 2 to 8 spatial streams in a 90-nm CMOS technology. The latency constraint of 16 mu s specified in the 802.11ac standard is adopted for this hardware realization. The chip integrates 437.7 K gates in 1.830 x 1.829 mm(2) and dissipates 125.5 mW at 66.67 MHz from a 1 V supply. Compared with the existing GMD implementations, this work supports GMD for matrices of larger dimensions with comparable silicon area, despite the added spatial information feedback capability. Index Terms-CORDIC, geometric mean decomposition
An Iterative Geometric Mean Decomposition Algorithm for MIMO Communications Systems
(2015-01-01)
Chen, Chiao-En
;
Tsai, Yu-Cheng
;
Yang, Chia-Hsiang
;
電機工程學系
;
Department of Electrical and Computer Engineering
This paper presents an iterative geometric mean decomposition (IGMD) algorithm for multiple-input-multiple-output (MIMO) wireless communications. In contrast to the conventional geometric mean decomposition (GMD) algorithm, the proposed IGMD does not require the explicit Kth root computation in the preprocessing stage but depends on a carefully constructed iterative procedure that generates the GMD in its limit. We prove analytically that the proposed IGMD is guaranteed to converge to the exact GMD under certain sufficient conditions, and propose three different constructions achieving this condition. Both numerical simulations and complexity analysis of the proposed IGMD have been conducted and compared with the conventional GMD. Simulation results show that our new IGMD algorithm effectively reduces the complexity overhead and hence is more advantageous for low-complexity implementations.