A practical MMSE-ML detector for a MIMO SC-FDMA system - pdf 27

Link tải luận văn miễn phí cho ae Kết nối
Abstract—We propose a new minimum mean square erro
(MMSE) based maximum likelihood (ML) detector for a singl
carrier frequency division multiple access system. By utilizin
the distance between decision line and MMSE soft-output
the proposed detector reduces the resources required to check
superfluously accurate reliability of received symbol. The pro
posed detector provides almost same bit error rate with th
conventional MMSE-ML detector while requiring significantl
low computational complexity (below 10%) as SNR varies.
Index Terms—MIMO, SC-FDMA, MMSE-ML detection.
I. INTRODUCTION
I T is well known that multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing
(OFDM) is proper to serve high data rate for future mobil
communication systems. However, because of the large peak
to-average power ratio of OFDM, single carrier frequency
division multiple access(SC-FDMA) has been discussed as an
uplink transmission scheme in the third generation partnership
project(3GPP) long-term evolution(LTE) [1], [2].
There are various receivers to support MIMO SC-FDMA
system such as zero forcing(ZF), minimum mean squared er
ror(MMSE), and maximum likelihood(ML) receivers. Among
those, the linear frequency domain equalization(FDE), ZF
FDE or MMSE-FDE is often employed for SC-FDMA sys
tem because they have simple one-tap filters in frequency
domain [1]. On the other hand, ML receiver provides an
optimal bit error rate(BER) performance but suffers from high
computational complexity [3]. Therefore, we need to have
receiver which is an ML type for optimum performance and
has reasonable complexity.
In this letter, we propose a sub-optimal ML receiver with
moderate complexity for MIMO SC-FDMA system. Th
proposed algorithm considerably reduces the computationa
complexity of the conventional sub-optimal ML receiver in
[4] by using a pre-filter with a new parameter.


3du07f830nCGPZK
Music ♫

Copyright: Tài liệu đại học © DMCA.com Protection Status