Systolic-array based regularized QR-decomposition for IEEE 802.11n compliant soft-MMSE detection

This paper considers the implementation of a soft-output MMSE detector for packet-based MIMO-OFDM transmission. The paper focuses on channel-matrix preprocessing realized with a QR decomposition which needs to be carried out under tight latency constraints. We discuss how the preprocessing algorithm should be selected to meet the specific requirements of the soft-output MMSE detector. Additionally we develop a pipelined systolic-array architecture that is particularly well suited to be combined with low-latency pipelined FFTs. We have implemented the QR decomposition method proposed for an IEEE 802.11n transceiver with 4 spatial streams. In a 0.13 µm 1P8M CMOS technology the corresponding circuit is capable to process 110 complex-valued 4×4-dimensional channel matrices for soft-output MMSE detection per second and gate equivalent and achieves a sustained throughput of 20 million decompositions per second, which is sufficient to meet the stringent latency requirements of IEEE 802.11n.

[1]  Yin-Tsung Hwang,et al.  A low complexity complex QR factorization design for signal detection in MIMO OFDM systems , 2008, 2008 IEEE International Symposium on Circuits and Systems.

[2]  Chih-Hung Lin,et al.  Iterative QR Decomposition Architecture Using the Modified Gram-Schmidt Algorithm for MIMO Systems , 2010, IEEE Trans. Circuits Syst. I Regul. Pap..

[3]  H. T. Kung.  Why systolic architectures? , 1982, Computer.

[4]  Babak Daneshrad,et al.  An efficient FPGA based MIMO-MMSE detector , 2007, 2007 15th European Signal Processing Conference.

[5]  Rohit U. Nabar,et al.  Introduction to Space-Time Wireless Communications , 2003 .

[6]  Andreas Peter Burg,et al.  VLSI Implementation of a High-Speed Iterative Sorted MMSE QR Decomposition , 2007, 2007 IEEE International Symposium on Circuits and Systems.

[7]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[8]  Jarmo Takala,et al.  Complex-valued QR decomposition implementation for MIMO receivers , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[9]  Chih-Hung Lin,et al.  Iterative $QR$ Decomposition Architecture Using the Modified Gram–Schmidt Algorithm for MIMO Systems , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[10]  Poras T. Balsara,et al.  VLSI Architecture for Matrix Inversion using Modified Gram-Schmidt based QR Decomposition , 2007, 20th International Conference on VLSI Design held jointly with 6th International Conference on Embedded Systems (VLSID'07).

[11]  P. Glenn Gulak,et al.  A low-complexity high-speed QR decomposition implementation for MIMO receivers , 2009, 2009 IEEE International Symposium on Circuits and Systems.