|
Academic Open Internet Journal |
Volume 13, 2004 |
A SELECTIVE SURVEY ON MULTIUSER DETECTION TECHNIQUES
IN CDMA SYSTEMS
A. Rajeswari
Electronics and Communication Engineering Department,
Coimbatore Institute
of Technology,
Dr.K.Gunavathi
Electronics and Communication Engineering Department,
Dr. A. Shanmugam
Electronics and Communication Engineering Department,
Bannari Amman Institute of Technology,
ABSTRACT
Due to the
demand for cellular wireless services, recent interests are in techniques,
which can improve the capacity of CDMA systems. One such technique is multiuser detection. The purpose of this article is to
present a selected survey of the published literature in the area of multiuser detection techniques. The topics include
discussion on multiple access interference cancellation techniques both linear
and nonlinear. Also a very brief note on complexity of the multiuser
detectors is also presented.
Indexing Terms: Code division multiple access, Multi user detection, Multiple Access Interference.
Code
Division Multiple Access (CDMA) systems are very attractive for mobile
communication applications because of their efficient use of the channel and
their allow ness for nonscheduled user transmissions. Direct sequence (DS) CDMA
is a method, which enables the users to share the same RF channel to transmit
data simultaneously. The DS CDMA transmitter multiplies each user’s signal by a
distinct code waveform. The detector
receives a signal composed of the sum of all users’ signals, which overlap in
time and frequency. The detector receives a signal composed of the sum of all
users’ signals, which overlap in time and frequency. In a conventional DS CDMA
system, a particular users’ signal is detected by correlating the entire
received signal with users’ code waveform. We will first review some important
features of CDMA systems needed for discussion to follow.
DS CDMA systems are generally divided into synchronous and
asynchronous systems. In synchronous systems, all users are synchronized at the
chip level, i.e. the delays for all users are set to zero. In asynchronous
systems, the delays are different for different users. An example of
synchronous systems is TDD-mode and the FDD downlink in WCDMA. An asynchronous
system is the FDD uplink in WCDMA.
Another important property in CDMA systems concerns the
spreading codes, which can be either long or short. A short spreading sequence
has a periodicity same as the symbol time whereas a long sequence is a pseudo
random sequence. In short codes, the cross correlations remains unchanged over
time. In a long code, the cross correlation changes from symbol to symbol. But
the received signal in short code system is cyclo
stationary, which is exploited, in several signal-processing algorithms.
Another
classification of the DS CDMA systems is that the receiver can be a linear or
non-linear receiver. A linear receiver is one in which the soft decision is a
linear function of the receiver. A non-linear receiver has a non-linear
relation between the received signals. Furthermore, the DS-CDMA receivers are
divided into single user and multiuser detectors. A
single user receiver detects the data of one user at a time whereas a multiuser receiver jointly detects several users’
information. Single user and multiuser receivers are
also sometimes called as decentralized and centralized receivers respectively. In its simplest form, the single-user detector is a matched
filter to the desired signal. Other users’ signals are treated as noise (self
noise). This self-noise limits the systems capacity and can jam out all
communications in the presence of a strong near by signal (the near-far
problem). The capacity is optimized when all users enter the base station at
the same power level forcing the use of power control circuits in the terminal
transmitters. Multiuser detection on the other hand
demodulates all the signals at once and in so doing removes the self-noise
components. This detection is therefore more near far tolerant, signal
processing intensive and still a research item, and they promise to give a
large-scale increase in capacity. They are more likely to be applied when the
high bit rate options becomes available in 3G systems. Multiuser
detection seeks to remove this MAI limitation by the use of appropriate signal
processing.
The uplink is asynchronous and it uses long spreading codes.
In the base station all the users data need to be demodulated. Multiuser detection is most suitable in the base station
due to complexity and other reasons.
REVIEW
DS CDMA systems are very sensitive to the near far effect.
To overcome the above drawbacks, there are five basic techniques to suppress
MAI in CDMA systems [1].
1.
Channel Coding techniques
2. Power
control
3. Smart antennas
4.
Interference Cancellation and multi users detection techniques
5. Any combination of the above
In [1], an overview of the most common
strategies of multiuser detection can be found.
Extensive references to relevant research work are found in the book of Verdu [2], where each chapter is ended with bibliographical
notes. In 1986 Verdu proposed the novel idea that detection of CDMA
signals should exploit the structure inherent in the MAI and not just treating
it as noise [2]. With this notion, the conventional matched filter is no longer
the optimal detector, and there is a class of multiuser
detectors, which are able to reduce MAI and hence, lead to better performance.
As a result, capacity of the system also increases. The interference suppression
capability is however limited due to the non-zero cross correlation between the
users. Even if the number of users is
few, there may be a near far problem when the user near the receiver overpowers
those who are far away.
In this paper, we concentrate on the fourth technique,
namely interference cancellation and joint detection techniques. For future
multimedia wireless cellular services, it is likely that the downlink will be
the bottleneck, and hence, improvement in the uplink does not necessarily
increase the overall system capacity. On the other hand, multiuser
detection for synchronous CDMA is simpler in theory. This is the motivation
behind the work done in this paper.
The simplest scheme to demodulate CDMA signal is to
use the conventional matched filter detector, which is optimal in AWGN noise
but is sub-optimal because the MAI does not necessarily resemble white Gaussian
noise [2]. The received signal is passed through a bank of matched filters
attached in Rake configuration that coherently demodulates and despreads each of the received paths [3]. The problem of
this receiver arises from the fact that, even if the powers of all users are
equal, some cross correlation among signals might still have high values due to
different path delays. Therefore even by adjusting the power level using fast
power control and selecting codes with low cross correlations, the performance
of the matched filter receiver is limited and so is the capacity since, to
maintain acceptable interference limits, the number of users have to be
reduced. P.Rapajic and B.S.Vucetic
[4] presented a receiver structure in which the bank of matched filters is
replaced by an adaptive fractionally spaced LMS filter [5]. Prior to data
transmission it is assumed that the receiver has been trained by a known
training sequence, while an adaptive algorithm constantly adjusts it during
data transmission. In [6] a bank of LMS filters replaces the user’s bank of
matched filters attached to the RAKE. In this case channel estimates are also
required making the existence of training sequences again necessary.
LINEAR
Detectors
The Optimal detector
In [6] it
is stated that the optimum detection problem may be solved using Viterbi algorithm.
It is also asserted that optimal detection in the case of an
asynchronous system requires knowledge of the entire transmitted sequence of
each user. Among the DS-CDMA detectors utilizing knowledge of the interferers
the first is the detector proposed by Schneider in [7]. Here it is shown that
optimal performance in synchronous situations requires estimates of all users
for the bit period under consideration. The optimal multiuser
detector for CDMA systems using Viterbi’s algorithm
and assuming a perfect knowledge of the channel, is proposed by Verdu in the mid of 1980’s in [8-10]. The optimal detector developed in [9] by Verdu presents the optimal solution for the asynchronous
case. It is shown that complexity per binary decision is O(2K-1)
where K is the number of simultaneous users. For a large value of K, the
practical implementation is very complex. This sparked intense research in DS
CDMA systems. Although Schnieder predates Verdu in this area of multiuser
detection, Verdu is seen to be the first to start
research in this area. The important difference between Schneider’s and Verdu’s work is the simpler metric used in the latter.
The decorrelating
detector suppresses the interference by finding a linear transformation such
that the transmitted symbols for all users are completely recovered in the
absence of noise. Although Schneider [6] studied decorrelator
type receivers and erroneously claimed they were optimal with respect to bit
error probability, this detector is attributed to Lupas
and Verdu [11,12]. This is a simple scheme, which is
optimal when the received signal can be fed into a bank of matched filters,
each matched to the signature sequence of a different user.
The only source of interference is the background
noise resulting in noise enhancement and poor performance in noise-limited
environment. The decorrelating receiver has several
similarities with the zero forcing equalizer. The asynchronous decorrelating detector has two steps [12]. First the MAI is
removed while introducing the intersymbol
interference (ISI). Second, zero forcing equalizer removes the ISI.
Other equalizers such as MLSE equalizer can also be
used [13]. Sliding window approximations of the decorrelator
for asynchronous systems are discussed in [14-16]. Here the basic idea is to
apply the decorrelating principle on a window and
neglect the edge effects. Once a symbol has been detected, the window is
advanced to the next position. If the
sliding window size equals the symbol duration, the one shot decorrelator is got. In [15-16], a so called isolation bit
insertion decorrelator has been discussed which
inserts known bits between blocks of information bits in order to break up the
full decorrelating problem into smaller ones.
One way of reducing the complexity of a decorrelating detector is to use a FIR filter, found by
truncating the optimal decorrelating IIR filter. This
concept is discussed in [17,18]. A quasi-synchronous approach has been
discussed in [19]. Here, by ensuring that users are received synchronously,
interference can be rejected without exact knowledge of the interfering user’s
delays by projecting the received vector onto a line orthogonal to all time
shifts in a small region of the interfering users’ spreading codes.
Adaptive versions of the decorrelating
detector are discussed in [20-23]. One concluding remark on the decorrelator is the resemblance with adaptive antennas,
where a similar technique is known as null steering [24]. Non-linear receivers
employing decision feedback are proposed in [25]. Here the first user is
demodulated by its decorrelating detector whereas
other users subtract a linear combination of previous decisions from the
whitened matched filter outputs. The decorrelating
DFE detector is error-free when there is no noise.
A multiuser detector that
is closely related to the decorrelating detector is
the linear minimum mean square error detector. The multi user detection problem
is converted into a linear estimation problem. The goal is to minimize the
mean-square-error (MSE) between the user k’s
bit and the output of the kth linear
transformation. The MMSE detector approaches the conventional matched filter as
noise tends to infinity. As the
signal-to-noise goes to infinity, the linear MMSE detector converges to the decorrelator. The important references to MMSE detectors
are [26-27]. The probability of error analysis is discussed in [28] which has
some discussions on the Gaussian approximation. The advantages of the adaptive
MMSE receiver are that it requires no knowledge of the interference parameters
and also it can completely suppress the narrow band interference while it
adapts to the actual interference. The adaptive MMSE using the LMS algorithm is
discussed in [27,29-31]. The performance of the adaptive MMSE receivers in
fading channels is discussed in [32].
INTERFERENCE
CANCELLATION TECHNIQUES
Interference cancellation
detectors are based on the idea of canceling the MAI. There are basically two
types: successive interference cancellation and parallel interference
cancellation. Hybrids of these two are also possible.
Successive interference cancellation (SIC) is based
on that if a decision has been made about an interferer’s bit, then that
interfering signal can be recreated at the receiver and subtracted from the
received waveform. If the decision was correct, this would cancel the
interfering signal otherwise, it will double the contribution of the
interferer. After subtraction, the receiver treats the system as if there are
one fewer user and the same process can be repeated with another interferer,
until all but one user have been detected. When making a decision about the kth user, it is
assumed that the decisions of users k+1,
…, K are correct and the presence of users
1, …, k-1 are neglected. This
has two advantages.
1.Cancels the strongest signal that causes the
largest interference to other users.
2.Canceling the strongest signal has the minimum MAI
since the strongest signal is excluded from its own MAI.
Viterbi
suggested the process of received signal by a successive interference
cancellation scheme [33]. Later Dent et al. Proposed a SIC approach applied in
the frequency domain according to which first users are detected and then their
bins set to zero [34]. Patel and Holtzman extensively analyzed
the SIC scheme [35] [36]. In [37] an SIC scheme is presented for non-coherent
systems wherein the decoder is used for ranking the users. Cho
and Lee [38] presented a combination of SIC and MMSE detector which gave a great
improvement in the performance on the system.
Parallel interference cancellation (PIC) is an
iterative technique: the nth stage of
the detector uses decisions of the (n-1)
th stage to cancel MAI present in the received
signal, with the belief that reliability of the decisions will improve as it
propagates through the stages. There is a class of multistage detectors, which
use a bank of conventional matched filters as their first stage. Successive
cancellation is used for all users in the subsequent stages. As the number of
stages approaches infinity, the decisions of the detector do not converge to
the optimum ones.
Hui and Letaief [41] presented an
improved multistage PIC scheme. Here sift decisions are applied when they
exceed the threshold and hard decisions are applied at the next stage of
cancellation to the output of the matched filter as long as their value is well
below the threshold. Sanada and Wang [42] presented a
PIC in error convolutional coded system, which is
best, suited to data communication system because of the delay in the decoding
process. Weighted cancellation for IC schemes are discussed in [43]. Further
improved versions of PIC are discussed in [44]. DSP implementation of partial
IC is discussed in [45] and a linear PIC structure that converges to MMSE
solution is presented in the same paper. Finally Rasmussen et al. [46]
described PIC using matrix algebra. Here a linear matrix filter is applied
directly to the received chip matched signal vector. Hybrid IC schemes are
proposed in [47] where a group-wise IC has been proposed where the K received
signals are divided into groups, canceling the signals of a particular group
simultaneously instead of one signal at a time. The merit of this scheme is
that considerably delay and hardware reduction is got over conventional SIC and
PIC.
The decision-feedback (DFE) detector is another type
of non-linear detector. It combines many of the features from SIC and PIC. The
common references are [25,26,37]. Basically these detectors are multiuser decision feedback equalizers with two matrix
filters: a feedback filter and a forward filter. The outputs from the matched
filter bank are sorted in descending order of magnitude and the feedback filter
is chosen such that SIC is obtained. If the feedback filter is strictly
triangular matrix with zeros along the diagonal it would treat the MAI.
Table 1 gives a comparison of
different multiuser detection algorithms with respect
to signature of different users, signature of interferers, timing of
interferers, relative amplitudes and training sequences. It is seen that different
algorithms for multiuser detection require different
knowledge about the user of interest and the interfering users.
Table 1 Assumed knowledge for multiuser
detection algorithms
|
Multiuser Detector |
Signature of desired user |
Signature of interferers |
Timing of desired User |
Timing of interferers |
Relative amplitudes |
Training sequence |
|
Matched filter |
Y |
N |
Y |
Na |
Y |
N |
|
Optimum multiuser |
Y |
Y |
Y |
Y |
Y |
N |
|
Optimum linear receiver |
Y |
Y |
Y |
Y |
Y |
N |
|
Decorrelator |
Y |
Y |
Y |
Y |
N |
N |
|
SIC & Multistage |
Y |
Yb |
Y |
Y |
Y |
N |
|
Adaptive MMSE |
N |
N |
Yc |
N |
N |
Y |
a Strict power control required for adequate
performance
b Adequate performance with information about most
powerful interferers only
Symbol timing only may be
adequate
Y- Yes, N – No
COMPUTATIONAL
COMPLEXITY OF THE MULTIUSER DETECORS
The computational complexity of the detection is very
important for both simulation and implementation. Multiuser
detectors with high complex structures require very high-speed processors for
implementation. The computational complexity per bit decision is given for the
different multiuser detectors in terms of number of
users K, the frame length Nb, the
number of paths tracked i.e. Rake fingers L, the spreading factor N, the number
of samples per chip Ns, and
the number of stages for the multistage receiver s [48]. Table 2 gives the
computational complexity of the different multiuser
detectors.
In terms of complexity, it is shown
in [48] that the decorrelator is extremely sensitive
to the amount of the update required for the matrix inversion. Between the two cancellation schemes,
successive interference cancellation is overall less computationally intensive,
but the parallel scheme is more flexible, allowing slower processors in
parallel to perform the computation. These approaches can provide significant
computational savings at the cost of memory storage requirements. The exact
amount of savings will depend on the rate of update required for the
correlation matrix.
Figure 1 illustrates the analytical performance of
the multiuser receivers for the first user in AWGN
with perfect power control [49]. As expected, Conventional MF receiver has the
worst performance. It can be seen that the single user bound and the decorrleating decision-feedback detector share the same
curve. This is because user 1 is the weakest user and therefore, the analytical
performance of decision-feedback coincides with the single user bound. The
probability of bit error of multistage parallel interference canceller is
evaluated stage at s=3. It has better performance than linear detectors. The
difference between them at Pb =10-4
is about 1dB. It is also seen that MMSE receiver generally performs better than
decorrelator especially when signal-to-noise ratio is
low. However, for high signal-to-noise ratios, it can only attain a very small
performance improvement over the decorrelator. In
fact, the main advantage of the MMSE detector over decorrelator
is the ease with which it can be implemented adaptively
Conclusion
This article attempts to consolidate
the published literature in the area of multiuser
detection techniques in CDMA systems. Multi
user detection is signal processing intensive and they promise to give a
large-scale increase in capacity of present 3G and future generation wireless
communication systems. However, the topics of adaptive multiuser
detectors, neural network based and genetic algorithms based multiuser detection techniques are not presented in this
article. A number of issues such as interference cancellation combined with
smart antennas, which can potentially lead to further limitations, but could
also produce useful complexity reduction, investigation of the impact of multiuser detection algorithms on other CDMA features, such
as multicode or smart antennas should be studied that
are as important themselves.
Table 2 Computational complexity of multiuser detectors
|
Multiuser Detector |
Computational Complexity Per bit decision |
|
Decorrelator |
|
|
Parallel Interference
Cancellation |
|
|
Successive interference
Cancellation |
|
|
Decision feedback detector |
|
Figure 1. Analytical comparison of different multiuser detectors in AWGN channel
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