Abstract—Spatial modulation (SM) is a recently developed
transmission technique that usesmultiple antennas. The basic idea
is to map a block of information bits to two information carrying
units: 1) a symbol that was chosen from a constellation diagram
and 2) a unique transmit antenna number that was chosen from
a set of transmit antennas. The use of the transmit antenna num-
ber as an information-bearing unit increases the overall spectral
efficiency by the base-two logarithm of the number of transmit
antennas. At the receiver, a maximum receive ratio combining
algorithm is used to retrieve the transmitted block of information
bits. Here, we apply SM to orthogonal frequency division multi-
plexing (OFDM) transmission.We develop an analytical approach
for symbol error ratio (SER) analysis of the SM algorithm in
independent identically distributed (i.i.d.) Rayleigh channels. The
analytical and simulation results closely match. The performance
and the receiver complexity of the SM–OFDM technique are
compared to those of the vertical Bell Labs layered space–time
(V-BLAST–OFDM) and Alamouti–OFDM algorithms. V-BLAST
uses minimummean square error (MMSE) detection with ordered
successive interference cancellation. The combined effect of spatial
correlation, mutual antenna coupling, and Rician fading on both
coded and uncoded systems are presented. It is shown that, for the
same spectral efficiency, SM results in a reduction of around 90%
in receiver complexity as compared to V-BLAST and nearly the
same receiver complexity as Alamouti. In addition, we show that
SM achieves better performance in all studied channel conditions,
as compared with other techniques. It is also shown to efficiently
work for any configuration of transmit and receive antennas, even
for the case of fewer receive antennas than transmit antennas.
Index Terms—Interchannel interference (ICI), multiple-input–
multiple-output (MIMO), orthogonal frequency division multi-
plexing (OFDM), receiver complexity, space–time coding (STC)
coded modulation, spatial modulation (SM), vertical Bell Labs
layered space–time (V-BLAST).
Manuscript received June 12, 2006; revised February 20, 2007, June 29,
2007, September 4, 2007, and September 18, 2007. This work was supported
by the Samsung Advanced Institute of Technology, Suwon, Korea. The review
of this paper was coordinated by Prof. E. Bonek.
R. Y. Mesleh is with the School of Electrical Engineering and Computer Sci-
ence, Jacobs University, D-28759 Bremen, Germany (e-mail: r.mesleh@jacobs-
university.de).
H. Haas is with the Institute for Digital Communications, University of
Edinburgh, EH9 3JL Edinburgh, U.K., and also with Jacobs University,
D-28759 Bremen, Germany (e-mail: [email protected]).
S. Sinanovi´ c is with the Institute for Digital Communications, University of
Edinburgh, EH9 3JL Edinburgh, U.K. (e-mail: [email protected]).
C. W. Ahn was with Gwangju Institute of Science and Technology,
Gwangju 500-712, Korea. He is now with the Department of Computer En-
gineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: cwan@
evolution.re.kr).
S. Yun is with the Telecommunication R&D Center, Samsung Electronics
Company, Ltd., Suwon 442-600, Korea (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available online
at
Digital Object Identifier 10.1109/TVT.2008.912136
I. INTRODUCTION
THE NEED for high data rate and high spectral effi-
ciency are the key elements that drive research in future
wireless communication systems [53]. Adaptive coding and
modulation, iterative (turbo) decoding algorithms, space–time
coding (STC), multiple antennas and multiple-input–multiple-
output (MIMO) systems, multicarrier modulation, and ultra
wideband radio are examples of enabling technologies for next-
generation wireless communication. Among the set of existing
technologies, MIMO orthogonal frequency division multiplex-
ing (MIMO–OFDM) with adaptive coding and modulation is
a promising candidate for future wireless systems. A MIMO
system boosts spectral efficiency by using multiple antennas
to simultaneously transmit data to the receiver [1]–[4]. OFDM
converts a frequency-selective channel into a parallel collection
of frequency flat-fading subchannels, in which the available
bandwidth is very efficiently used [5]. The OFDM technique
has been adopted in several wireless standards such as digital
audio and video broadcasting, the IEEE 802.11a standard [6],
the IEEE 802.16a metropolitan area network standard, and the
local area network standard [7].
There are three main categories of MIMO techniques. The
first category improves power efficiency by maximizing spatial
diversity, e.g., using delay diversity [8], [9]. In such systems
(e.g., STC), the capacity improvement results from diversity
gain, which reduces the bit error probability for the same spec-
tral efficiency. However, the maximum spectral efficiency of
full-diversity STC systems is one symbol per symbol duration
for any number of transmit antennas [9]. These systems can
be designed to achieve full diversity gain with very low re-
ceiver complexity. In addition, STCs are well known to combat
channel imperfections that exist in real-time implementations
of MIMO systems [10], [11]. The second category of MIMO
techniques exploits knowledge of the channel at the transmitter.
It decomposes the channel matrix by using singular value de-
composition and uses the resulting unitary matrices as prefilters
and postfilters at the transmitter and receiver, respectively, to
achieve capacity gain [12], [13]. In this paper, channel in-
formation is assumed to be known only at the receiver, with
no channel information at the transmitter. Therefore, these
techniques are not implemented here, but possible scenarios
will briefly be discussed in future works.
The third type ofMIMO technique uses a layered space–time
approach to transmit multiple independent data streams over
the antennas to increase capacity. A well-known technique
is the Bell Labs layered space–time (BLAST) architecture
[3]. The BLAST scheme demultiplexes a user data stream
into a number of substreams that are equal to the number of
transmission antennas. Two types of BLAST realizations have
Link download for Ketnooi:
transmission technique that usesmultiple antennas. The basic idea
is to map a block of information bits to two information carrying
units: 1) a symbol that was chosen from a constellation diagram
and 2) a unique transmit antenna number that was chosen from
a set of transmit antennas. The use of the transmit antenna num-
ber as an information-bearing unit increases the overall spectral
efficiency by the base-two logarithm of the number of transmit
antennas. At the receiver, a maximum receive ratio combining
algorithm is used to retrieve the transmitted block of information
bits. Here, we apply SM to orthogonal frequency division multi-
plexing (OFDM) transmission.We develop an analytical approach
for symbol error ratio (SER) analysis of the SM algorithm in
independent identically distributed (i.i.d.) Rayleigh channels. The
analytical and simulation results closely match. The performance
and the receiver complexity of the SM–OFDM technique are
compared to those of the vertical Bell Labs layered space–time
(V-BLAST–OFDM) and Alamouti–OFDM algorithms. V-BLAST
uses minimummean square error (MMSE) detection with ordered
successive interference cancellation. The combined effect of spatial
correlation, mutual antenna coupling, and Rician fading on both
coded and uncoded systems are presented. It is shown that, for the
same spectral efficiency, SM results in a reduction of around 90%
in receiver complexity as compared to V-BLAST and nearly the
same receiver complexity as Alamouti. In addition, we show that
SM achieves better performance in all studied channel conditions,
as compared with other techniques. It is also shown to efficiently
work for any configuration of transmit and receive antennas, even
for the case of fewer receive antennas than transmit antennas.
Index Terms—Interchannel interference (ICI), multiple-input–
multiple-output (MIMO), orthogonal frequency division multi-
plexing (OFDM), receiver complexity, space–time coding (STC)
coded modulation, spatial modulation (SM), vertical Bell Labs
layered space–time (V-BLAST).
Manuscript received June 12, 2006; revised February 20, 2007, June 29,
2007, September 4, 2007, and September 18, 2007. This work was supported
by the Samsung Advanced Institute of Technology, Suwon, Korea. The review
of this paper was coordinated by Prof. E. Bonek.
R. Y. Mesleh is with the School of Electrical Engineering and Computer Sci-
ence, Jacobs University, D-28759 Bremen, Germany (e-mail: r.mesleh@jacobs-
university.de).
H. Haas is with the Institute for Digital Communications, University of
Edinburgh, EH9 3JL Edinburgh, U.K., and also with Jacobs University,
D-28759 Bremen, Germany (e-mail: [email protected]).
S. Sinanovi´ c is with the Institute for Digital Communications, University of
Edinburgh, EH9 3JL Edinburgh, U.K. (e-mail: [email protected]).
C. W. Ahn was with Gwangju Institute of Science and Technology,
Gwangju 500-712, Korea. He is now with the Department of Computer En-
gineering, Sungkyunkwan University, Suwon 440-746, Korea (e-mail: cwan@
evolution.re.kr).
S. Yun is with the Telecommunication R&D Center, Samsung Electronics
Company, Ltd., Suwon 442-600, Korea (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available online
at
You must be registered for see links
.Digital Object Identifier 10.1109/TVT.2008.912136
I. INTRODUCTION
THE NEED for high data rate and high spectral effi-
ciency are the key elements that drive research in future
wireless communication systems [53]. Adaptive coding and
modulation, iterative (turbo) decoding algorithms, space–time
coding (STC), multiple antennas and multiple-input–multiple-
output (MIMO) systems, multicarrier modulation, and ultra
wideband radio are examples of enabling technologies for next-
generation wireless communication. Among the set of existing
technologies, MIMO orthogonal frequency division multiplex-
ing (MIMO–OFDM) with adaptive coding and modulation is
a promising candidate for future wireless systems. A MIMO
system boosts spectral efficiency by using multiple antennas
to simultaneously transmit data to the receiver [1]–[4]. OFDM
converts a frequency-selective channel into a parallel collection
of frequency flat-fading subchannels, in which the available
bandwidth is very efficiently used [5]. The OFDM technique
has been adopted in several wireless standards such as digital
audio and video broadcasting, the IEEE 802.11a standard [6],
the IEEE 802.16a metropolitan area network standard, and the
local area network standard [7].
There are three main categories of MIMO techniques. The
first category improves power efficiency by maximizing spatial
diversity, e.g., using delay diversity [8], [9]. In such systems
(e.g., STC), the capacity improvement results from diversity
gain, which reduces the bit error probability for the same spec-
tral efficiency. However, the maximum spectral efficiency of
full-diversity STC systems is one symbol per symbol duration
for any number of transmit antennas [9]. These systems can
be designed to achieve full diversity gain with very low re-
ceiver complexity. In addition, STCs are well known to combat
channel imperfections that exist in real-time implementations
of MIMO systems [10], [11]. The second category of MIMO
techniques exploits knowledge of the channel at the transmitter.
It decomposes the channel matrix by using singular value de-
composition and uses the resulting unitary matrices as prefilters
and postfilters at the transmitter and receiver, respectively, to
achieve capacity gain [12], [13]. In this paper, channel in-
formation is assumed to be known only at the receiver, with
no channel information at the transmitter. Therefore, these
techniques are not implemented here, but possible scenarios
will briefly be discussed in future works.
The third type ofMIMO technique uses a layered space–time
approach to transmit multiple independent data streams over
the antennas to increase capacity. A well-known technique
is the Bell Labs layered space–time (BLAST) architecture
[3]. The BLAST scheme demultiplexes a user data stream
into a number of substreams that are equal to the number of
transmission antennas. Two types of BLAST realizations have
Link download for Ketnooi:
You must be registered for see links