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IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 58, NO. 6, JUNE 2010
Performance—Complexity Comparison of Receivers
for a LTE MIMO-OFDM System
Johanna Ketonen, Student Member, IEEE, Markku Juntti, Senior Member, IEEE, and
Joseph R. Cavallaro, Senior Member, IEEE
Abstract—Implementation of receivers for spatial multiplexing
multiple-input multiple-output (MIMO) orthogonal-frequency-
division-multiplexing (OFDM) systems is considered. The linear
minimum mean-square error (LMMSE) and the X-best list
sphere detector (LSD) are compared to the iterative successive
interference cancellation (SIC) detector and the iterative A-best
LSD. The performance of the algorithms is evaluated in 3G
long-term evolution (LTE) system. The SIC algorithm is found to
perform worse than the K-best LSD when the MIMO channels
are highly correlated, while the performance difference diminishes
when the correlation decreases. The receivers are designed for
2×2 and 4×4 antenna systems and three different modulation
schemes. Complexity results for FPGA and ASIC implementations
are found. A modification to the А-best LSD which increases
its detection rate is introduced. The ASIC receivers are designed
to meet the decoding throughput requirements in LTE and the
X-best LSD is found to be the most complex receiver although
it gives the best reliable data transmission throughput. The SIC
receiver has the best performance-complexity tradeoff in the 2×2
system but in the 4 × 4 case, the X-best LSD is the most efficient.
A receiver architecture which could be reconfigured to using a
simple or a more complex detector as the channel conditions
change would achieve the best performance while consuming the
least amount of power in the receiver.
Index Terms—ASIC, FPGA, K-best, SIC, soft-output detector.
I. Introduction
MULTIPLE-INPUT multiple-output (MIMO) systems
offer an increase in capacity or diversity. Herein we
focus on the data transmission rate increase provided by
spatial multiplexing (SM). Orthogonal-frequency-division
multiplexing (OFDM) is a popular technique for wireless
high data rate transmission, because it enables efficient use
of the available bandwidth and a simple implementation. It
divides the frequency selective fading channel into parallel flat
Manuscript received October 15, 2009; accepted January 31, 2010. Date of
publication February 25, 2010; date of current version May 14, 2010. The as-
sociate editor coordinating the review of this manuscript and approving it for
publication was Prof. Jarmo Takala. This research has been supported in part
by Elektrobit, Nokia, Nokia Siemens Networks, Texas Instruments, Uninord,
the Finnish Funding Agency for Technology and Innovation (TEKES), Xilinx
and by the US National Science Foundation under Grants CCF-0541363, CNS-
0551692, CNS-0619767, EECS-0925942, and CNS-0923479. This paper was
presented in part at the Annual Asilomar Conference on Signals, Systems, and
Computers, Pacific Grove, CA, October 2008.
J. Ketonen and M. Juntti are with the Centre for Wireless Communications,
University of Oulu, Oulu FIN-90014, Finland (e-mail: (johanna.ketonen@ee.
oulu.fi; [email protected])
J. R. Cavallaro is with the Department of Electrical and Computer Engi-
neering, Rice University, Houston, TX 77005 USA (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TSP.2010.2044290
fading subchannels. The combination of MIMO and OFDM
is a promising wireless access scheme [1]. Timely examples
of MIMO-OFDM applications include the evolving third-
generation (3G) cellular systems known as long-term evo-
lution (LTE) and worldwide interoperability for microwave
access (WiMAX) system.
Transmission of independent data streams from different
antennae in SM-MIMO systems usually causes spatial mul-
tiplexing interference (SMI) or inter-antenna interference.
This calls for sophisticated receiver designs to cope with the
interference. The optimal detector would be the maximum a
posteriori probability (MAP) symbol detector providing soft
outputs or log-likelihood ratio (LLR) values to the forward
error control (FEC) decoder. Since the computational com-
plexity of both MAP and maximum likelihood (ML) detectors
depends exponentially on the number of spatial channels and
modulation symbol levels, several suboptimal solutions have
been proposed and studied.
Linear minimum mean-square error (LMMSE) or
zero-forcing (ZF) detection principles can be straightforwardly
applied in MIMO detection. However, the linear detectors
can suffer a significant performance loss in fading channels,
in particular with spatial correlation between the antenna
elements [2]. Ordered serial interference cancellation (OSIC)
was proposed already in the original papers considering the
Bell Laboratories layered space-time (BLAST) architecture
[3]-[5]. Therein, instead of jointly detecting signals from all
the antennas, the strongest signal can be detected first and its
interference can be cancelled from each received signal. In FEC
encoded systems, the detected symbols are decoded before
cancellation. The soft bit decisions from the turbo decoder are
used to calculate symbol expectations which are cancelled from
the remaining layers [6], [7].
Sphere detectors (SDs) calculate the ML solution by taking
into account only the lattice points that are inside a sphere of
a given radius [8], [9]. A list sphere detector (LSD) approxi-
mates the MAP detector and provides soft outputs for the FEC
decoder [10]. The breadth-first tree search based X-best LSD
algorithm is a modification of the X-best algorithm [11], [12].
The depth-first [13] and metric-first [14] sphere detectors have
a closer to optimal search strategy and achieve a lower bit error
rate than the breadth-first detector. However, the X-best LSD is
considered in this paper because it can be easily pipelined and
parallelized and provides a fixed detection rate. The breadth-first
X-best LSD can also be more easily implemented and provide
the high and constant detection rates required in the LTE.
Sphere detector implementations for mostly a 4 x 4 antenna
system and 16-quadrature amplitude modulation (QAM) have
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