RESEARCH ON MULTI-ANTENNA RECEIVERS
AND MIMO SYSTEMS 1993-2002
Signals and Systems, Uppsala University
C Tidestav and
In wireless mobile radio communication, there is an endless quest for
increased capacity and improved quality.
Within this area, we have during the last years studied ways
to utilize antennas with multiple elements in an intelligent way.
Multiple Input Multiple Output (MIMO) Systems
for Wireless Communications
In communication theory,
MIMO refers to radio links with multiple antennas at the
transmitter and the receiver side. Given
multiple antennas, the spatial dimension can be exploited
to improve the
performance of the wireless link. The performance is
often measured as the
average bit rate (bit/s) the wireless link can provide or
as the average bit
error rate (BER). Which one has most importance
depends on the application.
Given a MIMO channel,
duplex method and a
transmission bandwidth, the system can be
Flat or frequency selective fading
With full, limited or without transmitter channel
state information (CSI)
Where full CSI
means the knowledge of the
complete MIMO channel transfer function.
In a TDD system with a duplex time
less than the coherence time of the channel,
full CSI is available at the
transmitter, since then, the channel is reciprocal.
In FDD systems, there
commonly exists a feedback channel from the
receiver to the transmitter that
provides the transmitter with some partial CSI.
This could be information of
which subgroup of antennas to be used or which
eigenmode of the channel that
is strongest. It is also possible to achieve a highly
robust wireless link
without any CSI at the transmitter, by using
transmit diversity. Diversity
can be achieved through so called space-time codes,
like the Alamouti code
for two transmit antennas and high bit
rates is achieved by spatial
multiplexing systems, such as the pioneer
system from Bell Labs abbreviated
If a broadband wireless connection is desired,
the symbol rate must be increased further
which at some point will lead to a
frequency selective channel. Then, there
are two ways to go, either we employ
pre- or post-equalization of the channel
or we divide the channel into many
narrowband flat fading sub-channels,
a technique utilized by OFDM, and
transmit our data on these sub streams,
without the need for channel equalization.
Hence, it is always possible to convert a
frequency selective channel to many
flat fading channels using OFDM and
apply the developed flat fading MIMO
signalling techniques to each of these sub-channels.
Our research on MIMO systems has so far considered
flat fading MIMO channels, with or without
CSI at the transmitter. Some
results from our research is shown below
MIMO with full CSI at the transmitter
When full CSI is available at the transmitter,
it is possible to transmit data on the MIMO
channel eigenmodes. A MIMO system
with N transmit antennas and M receive
antennas has min(N,M) eigenmodes. The
gain of these eigenmodes is proportional
to the singular values of the MIMO
channel, so they have disparate power.
We propose to use adaptive modulation
techniques to transmit over these eigenmodes,
to maximize the bit rate or
minimize the BER of the transmission.
The figure above shows the data throughput
(bit/(sHz)) as a function of total transmit
power for a 4 times 4 MIMO system
in a Rayleigh fading channel.
The blue curve is the Shannon limit and the
dashed curve the throughput of the
BER is fixed at the target 10^(-5). In
practice, the data rates chosen for the
sub-channels can not be arbitrarily
chosen, they must belong to the
discrete set of modulation types
(BPSK,QPSK,16QAM,…) so the dotted
curve shows the throughput when adopting
the discrete rates. The figure below
show the rate per subchannel
corresponding to the figure above.
We see that the subchannel with the lowest
gain is only used when the total power
(SNR) is larger than 26 dB.
in correlated channels with no CSI at the transmitter
When CSI is not available at the transmitter,
transmit diversity at a low implementation
complexity can be achieved with
orthogonal space-time block codes (STBC).
Multiple antennas at a portable
device imply that the antenna spacing has
to be small. This implies that the
signals that enter the different antennas
will be correlated and the
performance will degrade. An important
parameter in the model for the
scattering channel is the angle spread D
of the received signals. With small
antenna element spacing, the mutual
coupling can be significant, and in our model
the electromagnetic coupling has been
taken into account.
The figure above shows the outage capacity at
10% probability, which roughly is the bit
rate per Hz of bandwidth that can
be transmitted over the 2 times 2 MIMO
Rayleigh fading channel 90% of the
time when varying the antenna element
separation (assuming dipoles).
The i.i.d. Rayleigh fading channel is
also shown as a reference, and we see clearly
how the correlation (introduced
by the angle spread D) reduce the capacity
of the channel. When mutual
coupling is taken into account, the curves
oscillate, due to the oscillating
behaviour of the mutual impedance
between two adjacent dipoles.
It is interesting to note that when the
signals are highly correlated (D=6 degrees),
the mutual coupling actually improves
the outage capacity for small antenna element spacing,
by decorrelation of the signals.
Algorithms for Combined Spatial and Temporal Equalization
The received baseband signal in a
TDMA cellular system
is corrupted by noise, by intersymbol interference due to
multipath propagation and by co-channel interference from
If only one antenna element is available at the receiver
one can use filtering of the received time series
in order to estimate the transmitted sequence,
i.e. temporal equalization. If several antenna elements are available,
it becomes possible to perform spatial filtering by
forming beams in the direction of a desired signal.
Noise and interference and also delayed signals
which would result in intersymbol interference,
can then be suppressed if they arrive from other directions.
The beamforming concept can be combined with temporal
equalization, resulting in spatio-temporal equalization.
It then becomes possible to make effective use of
the energy in delayed signals arriving from several directions,
while suppressing the signals from co-channel interferers.
Spatio-temporal equalization can be performed by generalizing
multiple-input-single-output (MISO) DFE.
One can also use a MLSE Viterbi detector.
A MISO DFE will, by necessity, contain a larger number of
adjustable parameters than a SISO DFE.
This leads to two potential problems.
These two key issues have been investigated, for different
filter structures and different adjustment schemes.
The structure of one promising algorithm,
the Multiple Independent Beam Decision Feedback
Equalizer (MIB-DFE), is illustrated below.
- The adjustment of many filter parameters, based on
short training sequences, is sensitive to noise. Misadjustment
may lead to poor performance.
- The computational complexity of the algorithm will increase.
When multiple antenna elements are present, we may investigate
the still harder task of detecting several users simultaneously,
on the same frequency band. Our research in this direction
is described on our page on
The use of arrays of antenna elements is practical at the base station,
but much less practical at the mobile.
The investigated techniques are therefore primarily applicable in the
transmission from the mobiles to the base stations.
Channel reuse within cell
In a TDMA system, the combination of a certain time
slot and a certain frequency is called a channel.
In a cellular system, every channel is used by multiple
base stations. However, due to interference from one
base station to another, not all channels are used in all
cells. Instead, channels are reused at a certain interval.
The interval with which the channels are reused is called
the reuse distance. In a GSM system, the reuse
distance is between 9 and 30.
In the future, the spatial dimension must be exploited
more thoroughly. The first step is of course to decrease
the reuse distance. This is the primary objective for the
algorithms described above. If every channel could be
used in each cell, the system capacity would rise by a factor
equal to the reuse distance in the current system. But if an
even larger increase in capacity is desired, we have to lower
the reuse distance below one or, in other words,
perform channel reuse within cell.
The algorithms previously described could actually be
used as tools for such a scheme. For such an application,
one of the users in the cell is considered as "desired",
whereas the remaining users are considered as "interference".
The signals from the other users will in this case be
considered as nothing but colored noise, which can be
significantly suppressed by using combined temporal and
Another option would be to consider the signals as signals
rather than as colored noise. We could in this case use the
property that all signals are digital, i.e., they can only assume
a finite number of values. The model would in this case
be a multiple input-multiple output (MIMO) channel
model. As input we would use the transmitted symbols
from all users, and as output the sampled output from the
antenna. The situation is depicted to the right for a case
with two users and two antennas.
The problem of interference suppression has then
become a problem of multiuser detection. We can
then use a multiuser detector to detect both signals
simultaneously. The problem is very similar to the
problem encountered in a DS-CDMA system.
To solve this multiuser detection problem, it is possible to
use generalisations of any equalizer used to mitigate
intersymbol interference, i.e. a linear equalizer, a decision
feedback equalizer or the Viterbi algorithm.
The generalisation is relatively straightforward.
The derivation is based on a multivariable channel model of the form
Here, d is a vector of input symbol and y is a vector of
channel outputs. The matrix coefficients B
constitute the channel and are the basis for the equalizer design.
We have derived a multivariable DFE to solve the
multiuser detection problem. This multivariable DFE
is described in greater detail in the section
Research on multiuser
detectors for CDMA based cellular systems.
To address the performance of this multiuser detector,
we have performed simulations in a scenario which
resembles a GSM system with two users and two
antennas in each cell. As were the case for the CDMA
system, it is of interest to investigate the performance
when the received powers of the users are very different.
In this case, the transmit powers of the users are either
equal or differ by 20 dB.
The structure of the transmission is similar to the
structure in GSM: the data is transmitted in bursts
of length 148 with a training sequence of length 26 in
the middle. Parameter estimation was performed using
ordinary LS. We simulated the system for signal to noise
ratios between 5 and 20 dB. We compared the performance
to the performance of a corresponding system where one
user and one antenna were removed.
As can be seen, the system performs better with two
users and two antennas than with one antenna and
one user. This algorithm will also be applied to
measurements from an antenna array. These measurements
are supplied by Ericsson.
PhD Thesis by Mattias Wennström, 2002. on MIMO systems
PhD Thesis by Claes Tidestav, 1999, on MIMO DFE's
PhD Thesis by Erik Lindskog, 1999, on space-time methods.
Licentiate Thesis by Erik Lindskog, 1995.
Licentiate Thesis by Stefano Bigi, 1995, which considers
the design of SISO IIR DFE's based on short data records.
(IEEE SP 2001), on multivariable DFE's for multiuser detection.
(IEEE Com'99), on enabling channel reuse within a TDMA cell.
(ICC'02), on reduced rank channel estimation in CDMA systems.
on the optimality and performance of transmit and
receive space diversity in MIMO channels.
(VTC'99), on enabling channel reuse within a TDMA cell.
(VTC'99), on reduced rank channel estimation.
(ICASSP'99), on the structure and design of
MIMO Decision Feedback equalizers.
(EUSIPCO'98), on exploiting pulse shaping informantion.
(IEEE ICUPC'98), on "Bootstrap equalization"
(IEEE VTC'97), on the implementation of Viterbi detectors
in antenna array receivers.
(IEEE ICUPC'96), on using direction-of-arrival parametrization.
with the sample matrix inversion method for a GSM
signal with sampling offset (IEEE PIMRC'95)
Combined spatial and temporal equalization
using an adaptive antenna array
and a decision feedback equalization scheme (IEEE ICASSP'95).
for multipath environments in mobile radio applications
spatio-temporal equalization and adaptive interference cancellation
for multipath environments in mobile radio applications.
(IEEE PIMRC'95), on the MIMO DFE as a multiuser detector.
(Asilomar'95), on utilizing adjacent TDMA frames.
Master's Thesis by Claes Tidestav, 1993,
addressing the impact of antenna correlation.