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1- Performance of a Novel Adaptive Multistage Full Parallel

Interference Canceller for CDMA Systems

Among these proposed schemes, the parallel interference cancellation (PIC) scheme simultaneously

removes interference from each users received signal [5]. Compared with the serial IC (SIC) scheme,

since the PIC is performed in parallel for all users, the time delay required in the PIC to finish theprocedure is at most a few bits in time. However, with heavy system loads, the conventional multistage

PIC scheme, which attempts to completely cancel interference caused by all other users, suffersperformance degradation due to poor cancellations, which are brought about by the relatively high error

rate of bit decisions in the preceding stages. Hence, when the interference estimate is poor (as in the

earlier stages of IC), the partial cancellation proposed by Divsalar et al. [7], who chose not to cancel theentire amount of estimated multiuser interference, is better than complete PIC. The constant weight PPIC(CW-PPIC) scheme in [7] is suitable for perfect power control environments. However, its performance

obviously suffers degradations in nearfar scenarios. Partial cancellation weights (PCWs) in a PPIC

scheme can be updated using the least mean square (LMS) algorithm [9], and PCWs may be negativevalues when bit-decision errors occur in the previous stage. The PPIC scheme using the LMS algorithm

has been found to be superior to the conventional PIC (CPIC) and the CW-PPIC [9] schemes. However,

due to slow convergence, the adaptation weights at the last chip, which are fed back as the PCW of thePPIC, results in an excess MSE in the PIC. Therefore, in this paper, instead of the PCW, the adaptation

weight at the last chip in a bit interval is used to judge the correctness of bit decisions in a preceding

stage. In addition to what was presented in a previous work [14], in this paper, we demonstrate the rewardof using the adaptation PCWs of LMS-based PPIC schemesand show the performance of a multistage adaptive full PIC (AFPIC) multiuser detector with a proposed

bit-inversion (BI) procedure over frequency-selective fading channels.

2- Achieving Near-Optimal Detection Using Adaptive Joint Combination of MLD

and MMSE-SIC over Spatially Correlated MIMO Channels

Abstractwe develop an efficient hard-detector for multiple-input multiple-output (MIMO) channels,

which adaptively combines maximum-likelihood detection (MLD) and minimum-mean-square-error

(MMSE) with a successive interference canceller (SIC) together. Unlike the conventional joint

combination scheme which may suffer from considerable degradation in bit-error-rate(BER)

performance over correlated channels and where only onedata stream is detected by MLD, our

proposed scheme adaptively controls the number of data streams to be detected by MLD basedon the

analytical characterization of reliability for the detection.Simulation results illustrate that near-optimal

BER performancecan be obtained at much lower computational complexity by theproposed method as

compared to existing techniques, regardlessof the spatial correlation of the MIMO channels.

I. INTRODUCTION

Multiple-input multiple-output (MIMO) is considered as akey technology to improve the system capacity

and bandwidth efciency in wireless communications. In order to minimizethe bit-error-rate (BER), the

optimal detector is understood tobe achieved by a maximum-likelihood detector (MLD), e.g.,[1].

However, the required computational complexity of MLD grows exponentially with the number of

transmit antennas and the constellation size. To alleviate this, suboptimal detectionalgorithms such as

zero-forcing (ZF), minimum-mean-square-error (MMSE) and MMSE in combination with a successive

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interference canceller (SIC) [2], termed MMSE-SIC, havebeen developed, but they tend to suffer from a

considerableloss in the BER performance due to the problem of noise enhancement or error

propagation.To devise efcient MIMO detectors, recent efforts attempted to combine MLD with MMSE-

SIC [3], [4]. In these combination schemes (CSs), some streams are detected by MMSE-SIC,while others

are detected by MLD. Alternative approachessuch as the gradient-based search (GBS) [5] and

sphereprojection (SP) [6] have also been proposed. Although thesesuboptimal algorithms work well in

uncorrelated MIMO channels, this is not the case if the channel is spatially correlated,which arises from

insufcient spacing between antennas orscattering with a small angular spread. An efcient high-

performance MIMO detector for spatially correlated MIMOchannels is however not understood and

needs to be sought.In this paper, we devise a joint CS (integrating MLD withMMSE-SIC) which decides

the number of data streams to bedetected by MLD based on an analytical characterization ofthe

reliability for detection. Simulation results indicate that theproposed algorithm achieves near-optimal

BER performance atmuch reduced complexity over correlated MIMO channels.In the sequel, vectors are

represented in lowercase boldfaceletters while matrices are written in uppercase boldface letters.CMN

denotes the set of M N matrices with complexentries, while Im is an mm identity matrix. The

superscripts()T and () represent the transpose and hermitian operations,respectively. E[] denotes the

expectation, while Dec[] is thecomponent-wise quantization and A denotes an estimate of

A. takes the statistical average of an input random variable and returns

the Frobenius norm of a vector. 2(Ndof ) meansthe chi-square distribution with Ndof degrees of

freedom.

3- Adaptive Two-Stage GSC-Based PIC Detection for Time-Varying MIMO

ChannelsAbstractAdaptive parallel interference cancellation (PIC) has been recently proposed for the signal detection in multipleinput multiple-output (MIMO) systems. However, it suffers from error propagation when operated in time-varying

channels. In this letter, an adaptive two-stage PIC with the minimum variance (MV) criterion is proposed to solve theproblem. Adaptation with the MV criterion is realized with a decision feedback generalized sidelobe canceller (DFGSC).In the first-stage cancellation, a special structure involving dual DFGSCs is developed. All adaptation operations areimplemented with the least-mean-square (LMS) algorithm. Simulations show that the proposed adaptive PIC detectioncan significantly outperform the conventional adaptive PIC detection in time-varying MIMO channel environments.

I. INTRODUCTION

IN recent years, much attention is paid to the development of multiple-input multiple-output (MIMO) systems.With multiple antennas at both the transmitter and the receiver, the spectral efficiency of a communication systemcan be increased dramatically [1]. A successive interference cancellation (SIC) approach, known as the vertical Bell

Laboratories layered space-time (V-BLAST) system, is commonly used to achieve a substantial portion of the

Shannon capacity for MIMO channels [2]. However, the V-BLAST algorithm requires high computational

complexity, and the ordering operation inherent in the SIC structure often increases the processing delay and

restricts the use of adaptive realization. Generally, there is still no efficient way for the V-BLAST system to work in

time-varying channel environments. Lately, parallel interference cancellation (PIC) detection schemes wereproposed for the signal detection in MIMO systems [3]-[6]. In contrast to SIC, PIC detects different data symbols

from different transmit antennas in parallel and it is generally implemented with a multistage structure. It has the

advantages of low computational complexity and low processing delay. Since PIC does not require the ordering

operation, it is more adequate for adaptive implementations. The conventional adaptive PIC can provide satisfactory

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performance when the channel is static or varies only slightly, but its performance can be significantly degraded in

ordinary changing environments. This is due to the error propagation effect inherent in the multistage PIC structure.

In this letter, we propose a new adaptive PIC scheme to improve the MIMO detection performance, especially in

time-varying environments. Our emphasis is on the two-stage PIC throughout the letter. The optimization is basedon the minimum variance (MV) criterion [7]. The MV detector can be realized adaptively with the generalized side

lobe cancellation (GSC) structure [8]. However, the conventional adaptive GSC suffers from the problems of slowconvergence and lack of robustness. Recently, a decision feedback generalized side lobe canceller (DFGSC) has

been proposed and the problems inherent in GSC are solved successfully [9]. Here, we extend the use of the

DFGSC. We employ a dual-DFGSC structure and propose an adaptive DFGSC-based PIC for the MIMO signal

detection. This can effectively outperform the conventional adaptive PIC under time-varying channels. All

adaptations are based on the simple yet efficient least-mean-square (LMS) algorithm. This will keep the overall

computational complexity at a low level and make the proposed scheme feasible for real-world applications.

Convergence analysis in time-varying environments is also provided. Simulation results confirm that the proposed

adaptive PIC detection can perform significantly better than the conventional adaptive PIC detection in changing

channel environments

4- Link-Adaptive MIMO Systems With Ordered SIC Receiver Using Stream-

Ordering Algorithms in Multiuser Environments

The growing demand for multimedia services in wireless communications has developed methods toincrease system capacity and reliability. In multiple-inputmultiple-output (MIMO) systems, capacity

increase is brought about by using the spatial multiplexing mode, which offers capacity proportional to

the number of parallel transmit streams that can be created (i.e., the minimum number of transmit andreceive antennas) [1][4].

There are MIMO receivers for spatial multiplexing schemes, such as the maximum-likelihood (ML)receiver, the linear receiver, the successive interference cancellation (SIC) receiver, etc. [1]. The ML

receiver is an optimal receiver, but it is difficult to implement due to high complexity arising fromexhaustive searches over all candidate vector symbols. On the other hand, linear receivers, such as

zeroforcing (ZF) or minimum mean square error (MMSE) receivers, have low decoding complexity, but

detection performance decreases in proportion to the number of transmit antennas. Therefore, there has been a study on a practical nonlinear receiver, namely, the ordered SIC (OSIC) receiver, whichsuccessively decodes data streams through

nulling and canceling [4]. Although the OSIC receiver requires higher complexity than the linear receiver,it outperforms linear receivers, and its performance is closer to the performance of the ML receiver due to

the selection diversity arising from canceling the detected signal out of the received signal [1]. The typicalsystem of the OSIC receiver is V-BLAST using a forwardorderingscheme [4], [5].

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6- Design of Adaptive Multi-Branch SIC Receivers for MIMO Spatial Multiplexing

Systems

AbstractIn this paper we propose an adaptive successive interference cancellation (SIC) strategy for multiple-inputmultiple-output (MIMO) spatial multiplexing systems based on multiple interference cancellation branches. The

proposed detection structure employs SICs on several parallel branches which are equipped with different orderingpatterns so that each branch produces a vector of estimate symbols by exploiting a certain ordering pattern. The noveldetector, therefore, achieves higher detection diversity by selecting the branch which yields the estimates with bestperformance according to the selection rule. We consider two selection rules for the proposed detector, namely, maximumlikelihood (ML), minimum mean square error (MMSE) criteria. An efficient adaptive receiver is developed to update thefilter weight vectors and estimate the channel using the recursive least squares (RLS) algorithm. The simulation results

reveal that our scheme successfully mitigates the error propagation and approaches the performance of the optimal MLdetector, while requiring a significantly lower complexity than the ML and sphere decoder detectors.

I. INTRODUCTIONThe deployment of multiple transmit and receive antennas has been recognized to significantly improves wireless

link performance in communication systems. The degrees of freedom afforded by the multiple antennas can offerdramatic multiplexing [1][5] and diversity gains [6], [7]. The multiplexing gains enable high spectral efficiencies,

whereas the diversity gains make the links more reliable and allow low error rates over wireless fading channels.

For a spatial multiplexing system, in order to separate individual data streams, the designer may resort to severaldetection techniques, which are similar to multiuser detection methods [8]. The optimal maximum likelihood (ML)

performance can be approached using the sphere decoding (SD) algorithm [9], [10]. However, the complexity of this

algorithm can be polynomial or exponential depending on the signal-to-noise ratio (SNR) and the signal

constellation. The complexity of the SD is typically very high for low to moderate SNR values, whereas it decreases

for high SNR values. However, due to coding schemes, it is typical for detectors to operate at low to moderate SNR

values. This renders the application of the SD complex and has motivated the development of various alternative

low complexity strategies. A promising transmission system, called diagonal Bell Laboratories Layered Space- Time(D-BLAST) proposed by Foschini [3], is the first BLAST architecture. Owing to the large computational complexity

required for the scheme, a simplified version, called the Vertical BLAST (V-BLAST) has been proposed in [4], [5].The V-BLAST works as an ordered SIC and is equivalent to the generalized decision feedback equalizer (GDFE)

[11]. A number of other strategies were also investigated to achieve the capacity gain of MIMO system including the

linear and the decision feedback (DF) detector [12] [13] and the parallel interference cancellation (PIC) [14]. In this

work, we propose a novel SIC strategy for MIMO spatial multiplexing systems based on multiple processing

branches. An adaptive MB-SIC MIMO receiver structure based on the RLS algorithm is developed. The proposed

detection structure is equipped with SICs on several parallel branches which employ different ordering patterns.

Namely, each branch produces a symbol estimate vector by exploiting a certain ordering pattern. Thus, there is a

group of symbol estimate vectors at the end of the multi-branch (MB) structure. Based on different application

requirements, different criteria, such as the ML, the MMSE criteria, can be used as selection rules to select thebranch with the best performance. We also propose alternative ordering schemes with different tradeoffs between

performance and complexity. The simulation results reveal that our scheme successfully mitigates the errorpropagation and approaches the performance of the optimal ML detector.

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5- A Multiuser Detection for MC-CDMA System Based on Particle Swarm

Optimization Algorithm with Hopfield Neural Network

Multicarrier code division multiple access (MC-CDMA is considered as a candidate for the future wireless

transmission which combines CDMA and OFDM [1]. In MC-CDMA system, the information symbols are first

spread by spreading codes, followed by OFDM modulation such that each chip of spreading code is modulated byone subcarrier. The symbols from different users are subjected to independent fading at different subcarriers, which

destroy the orthogonality of spreading codes [2] . At receiver, the technology of MUD is used to estimate theinformation symbols of different users. MUD for CDMA can be applied to MC-CDMA, because the output signal

model of MC-CDMA is similar to that of CDMA. Suboptimal detection schemes have been proposed because of the

exponential growth of the computational complexity of the optimal multiuser detection OMD algorithm with the

number of active users [3]. Typical suboptimal detection scheme such as minimum mean square error (MMSE) MUD

has some disadvantages, although it eliminates MAI to some extent. With the further research on smart algorithms,

many scholars use them to solve problems of MUD for CDMA. PSO simulates the social behavior of a flock ofmigrating birds [4] , and it provides faster rate of convergence than genetic algorithm (GA) at a cost of reducing the

global convergence. In this paper, we apply HNN to PSO to solve problems of MUD through translating

maximizing the objective function of the PSO into minimizing the energy function of the HNN. Simulation results

show that performance of the proposed MUD scheme has faster rate of convergence and lower computational

complexity than MUD based on PSO.

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Improved Parallel Interference Cancellation for CDMA

Another popular approach is to employ interference cancellation, i.e., to attempt removal of the

multiuser interference from each users received signal before making data decisions.In principle, the IC

schemes considered in the literature fall into two categories, namely, serial (successive) and parallel

cancellation. With regard to the former, Viterbi [6] (see alsoDent [7] and Patel and Holtzman [19])

suggested coordinated processing of the received signal with a successive cancellation scheme in which

the interference caused by the remainingusers is removed from each user in succession. One

disadvantage of this scheme is the fact that a specic geometric powerdistribution must be assigned to

the users in order that eachsee the same signal power to background plus interferencenoise ratio. This

comes about because of the fact that withsuccessive cancellation the rst user to be processed sees

allof the interference from the remaining users, whereaseach user downstream sees less and less

interference as thecancellation progresses. Another disadvantage of this schemehas to do with the

required delay necessary to fully accomplishthe IC for all users in the system. Since the IC

proceedsserially, a delay on the order of computation stages isrequired to complete the job.

Nevertheless, Viterbi showed thatthe successive IC scheme could approach channel capacity forthe

aggregate Gaussian noise channel. As such, the schemedoes not become multiuser interference limited.

Parallel processing of multiuser interference simultaneouslyremoves from each user the interference

produced by theremaining users accessing the channel. In this way, each userin the system receives

equal treatment insofar as the attemptis made to cancel his or her multiple-user interference.

Ascompared with the serial processing scheme, since the ICis performed in parallel for all users, the

delay required tocomplete the operation is at most a few bit times. The earlypapers that dealt with

parallel IC recognized the desire to arriveat a structure that could be motivated by the ML approach.In

particular, a multistage iterative approach was suggestedby Varanasi and Aazhang [8], [9] which at a

given stageestimated a given users bit under the assumption that the exactknowledge of the otherusers bits in the same transmissioninterval needed to compute the multiuser interference could

bereplaced by estimates of these bits from the previous stage. Itwas indeed this basic idea which led to

the multistage iterativeschemes subsequently proposed by Yoon, Kohno, and Imai[11][13] and Kawabe

et al. [14]. What was common to all ofthese schemes was the fact that at each stage of the iteration,an

attempt was made for each user to completely cancel theinterference caused by all the other users.As

we shall see inthis paper, this is not necessarily the best philosophy. Rather,when the interference

estimate is poor (as in the early stagesof interference cancellation), it is preferable not to cancelthe

entire amount of estimated multiuser interference.Asthe IC operation progresses, the estimates of the

multiuserinterference improve and, thus, in the later stages of theiterative scheme, it becomes desirable

to increase the weightof the interference being removed. The motivation behind thisapproach can also

be derived from ML considerations as wasdone for the total IC approach previously considered.With the

above discussion in mind, this paper presents anew parallel interference cancellation scheme that

signicantlyreduces the degrading effect of multiuser interference but witha complexity linear in the

number of users and with improvedperformance over the previously considered parallel and

serialprocessing techniques. When compared with classical CDMAwithout IC, the improvement in

performance is dramatic atthe expense of a practically feasible increase in complexity.Although our

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scheme (as well as the other schemes mentioned)is suitable to the case of a nonuniform power

distributionas well as a uniform power distribution among the users, inthis paper we shall primarily

focus on the latter. In addition,although ours and the other parallel schemes are applicableto

asynchronous transmission with no increase of complexity,we shall assume here that all users have

synchronous datastreams. This case results in worst case performance, i.e.,if the data transition instants

of the various users are notaligned, then on the average they have less of an interferingeffect on one

another. This happens since the subchip intervalsproduced by the multiplication of two sequences

spreadsinto a wider spectrum and their integral is on the averagelower. Note, that in the asynchronous

time/nonuniform powercase the estimation of more parameters is needed, namely,the delays of the

users and their powers. This parameterestimation problem is common to most of the above techniques.

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Comments on Partial Parallel Interference Cancellation for CDMA

AbstractIn this letter we comment on partial parallel interference cancellation as discussed in the

above paper by Divsalaret al. The aforementioned work showed that by multiplying symbol estimates by

a factor less than unity in early stages of cancellation, the performance of parallel cancellation can be

improved relative to full (brute force) cancellation. In this letter we analyze the improvement of

parallel cancellation when using partial cancellation, and provide additional insight into the gains.

Specically, we show that the decision statistic is biased when linear (soft) estimates of the symbol or

channel are used for cancellation. Partial cancellation improves performance in this case by reducing the

decision statistic bias.

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RECENT work has shown that the performance of parallel interference cancellation in a CDMA system

employing BPSK modulation can be improved by performing partial cancellation [1][3]. Partial

cancellation involves multiplying the symbol estimate of each user by a factor less than unity before

attempting cancellation. In this letter we expand uponthe results in [1], and show that when linear

estimators areused to create symbol and/or channel gain estimates, a biasis introduced in the decision

statistic of the next stage. This bias can dominate performance. Partial cancellation reducesthis bias and

thus improves performance.In early cancellation stages, the reliability of symbol decisions is worse than

at later stages. Since cancelling signalswith incorrect symbol estimates will add interference ratherthan

remove it, an intuitive approach is to cancel a fractionof the estimated interference if a symbol estimate

is thoughtto be unreliable, thus reducing the effect of symbol errors.This is termed partial cancellation.

The simplest approach isto multiply all symbol estimates by a constant factor lessthan unity. Since

reliability of data and channel estimateswill in general improve at later stages of cancellation, thefactor

at each stage should approach one at the nal stage.A more sophisticated approach is to use variable

factorsbased on value of the correlator output. A more thoroughdiscussion of optimizing the partial

cancellation factor can befound in [4].

Results in [1] concentrated on the simple approach usingconstant factors at each stage. Two symbol

estimators wereconsidereda simple hard limit of the matched lter outputs,and a linear estimate. The

linear estimate simply used thecorrelator output itself (i.e., soft outputs) as the symbol estimate. While

perfect channel knowledge was assumed in [1],the second estimate can be viewed as a joint estimate of

thechannel and symbol assuming perfect carrier/phase recovery.This is a common method of estimating

the channel when nopilot or training sequences are available.Results [1, Figs. 6 and 7] show that partial

cancellationprovides performance improvements over full cancellation.However, it can be seen that the

improvement is dependenton the type of symbol estimate used. When a linear estimateis used [1, Fig.

6], the brute force canceller performedvery poorly and partial cancellation improved

performancedramatically. However, when hard decisions were used forsymbol estimates [1, Fig. 7], theperformance gains were muchmore modest.

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G. Adaptive MultiuserDetectionIn all the multiuser receiver schemes discussed earlier, the required parametersexcept for the transmitted data

estimateswere assumed to be known at the receiver. To remove this constraint while reducing the complexity,

adaptive receiver structures have been proposed [84]. An excellent summary of these adaptive receivers has been

provided by Woodward and Vucetic [24]. Several adaptive algorithms have been introduced for approximating the performance of the MMSE receivers, such as the least mean squares (LMS) [51] algorithm, the recursive leastsquares (RLS) algorithm [51], and the Kalman filter [51]. Xie et al. [85] showed that the adaptive MMSE approach

could be applied to multiuser receiver structures with a concomitant reduction in complexity. In the adaptive

receivers employed for asynchronous transmission by Rapajic and Vucetic [84], training sequences were invoked in

order to obtain the estimates of the parameters required. Lim et al. [86] introduced a multiuser receiver for an

asynchronous flat-fading channel based on the Kalman filter, which compared favorably with the finite impulseresponse MMSE detector. An adaptive decision feedback-based JD scheme was investigated by

Seite and Tardivel [87], where the LMS algorithm was used to update the filter coefficients in order to minimize the

MSE of the data estimates. New adaptive filter architectures for downlink DS-CDMA receivers were suggested by

Spangenberg et al. [88], where an adaptive algorithm was employed in order to estimate the CIR, and this estimated

CIR was then used by a channel equalizer. The output of the channel equalizer was finally processed by a fixed

multiuser detector in order to provide the data estimates of the desired user.

H. BlindDetectionThe novel class of multiuser detectors, referred to as blind detectors, does not require explicit knowledge of the

spreading codes and CIRs of the multiuser interferers. These detectors do not require the transmission of training

sequences or parameter estimates for their operation. Instead, the parameters are estimated blindly according to

certain criteria, hence the term blind detection. RAKE-type blind receivers have been proposed, for example, byPovey et al. [89] for fast-fading mobile channels, where decision-directed CIR estimators were used for estimating

the multipath components and the output of the RAKE fingers was combined employing various signal combining

methods. Liu and Li [90] also proposed a RAKE-type receiver for frequency- selective fading channels. In [90], a

weighting factor was utilized for each RAKE finger, which was calculated.

I. HybridandNovel MultiuserReceiversSeveral hybrid multiuser receiver structures have also been proposed recently [111][114]. Bar-Ness [111]

advocated the hybrid multiuser detector that consisted of a decorrelator for detecting asynchronous users, followed

by a data combiner maximizing the SNR, an adaptive canceller, and another data combiner. The decorrelator matrixwas adaptively determined. A novel multiuser CDMA receiver based on genetic algorithms (GAs) was considered

by Yen et al. [112], where the transmitted symbols and the channel parameters of all the users were jointly

estimated. The ML receiver of synchronous CDMA systems exhibits a computational complexity that is

exponentially increasing with the number of users, since at each signaling instant, the corresponding data bit of all

users has to be determined. Hence, the employment of ML detection invoking an exhaustive search is not a practical

approach. GAs have been widely used for solving complex optimization problems in engineering, since theytypically constitute an attractive compromise in performance versus complexity terms. Using the approach of [112],

GAs can be invoked in order to jointly estimate the users channel parameters as well as the transmitted bit vector of

all the users at the current signaling instant with the aid of a bank of matched filters at the receiver. It was shown in

[112] that GA-based multiuser detectors can approach the single-user BER performance at a significantly lower

complexity than

that of the optimum ML multiuser detector without the employment of training sequences for channel estimation.

The essence of this GA-based technique [112] is that the search space for the most likely data vector of all theusers at a given signaling instant was limited to a certain population of vectors, and the candidate vectors were

updated at each iteration according to certain probabilistic genetic operations, known as reproduction, crossover,

ormutation. Commencing with a population of tentative decisions concerning the vector of all the users received

bits at the current signaling instant, the best data vectors were selected as parent vectors according to a certain

fitness criterionwhich can be also considered to be a cost functionbased on the likelihood function [112] inorder to generate the offspring for the next generation of data vector estimates. The aim is that the offspring

should exhibit a better fitness or cost-function contribution than the parents, since then the algorithm will converge.

The offspring of data vector estimates were generated by employing a so-called uniform crossover process, where

the bits between two parent or candidate data vectors were exchanged according to a random crossover mask and a

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certain exchange probability. Finally, the so-called mutation was performed, where the value of a bit in the data

vector was flipped according to a certain mutation probability. To prevent the loss of high-fitness parent

sequences during the process of evolution of the estimated user data vectors, the highest merit estimated user data

vector that was initially excluded from the pool of parent vectors in creating a new generation of candidate datavectors was then used to replace the lowest merit offspring. Neural network-type multiuser equalizers have also

been proposed as CDMA receivers [115], [116]. Specifically, Tanner and Cruickshank proposed a nonlinear receiverthat exploited neural-network structures and employed pattern recognition techniques for data detection [115].This work [115] was extended to a reduced complexity neural-network receiver for the downlink scenario [116].

The advantage of the neural network-based receivers is that they are capable of learning the optimum partitioning

rules in the signal constellation space, even when the received interference-contaminated constellation points are

linearly nonseparable. In this scenario, linear receivers would exhibit a residual BER even in the absence of channel

noise. Other novel techniques employed for mitigating the multipath fading effects inflicted on multiple users

include joint transmitter-receiver optimization proposed by Jang et al. [113] and Vojcic and Jang [114]. In these

schemes, transmitter precoding was carried out, such that the mean squared errors of the signals at all the receivers

were minimized. This required the knowledge of the CIRs of all the users, and the assumption was made that the

channel fading was sufficiently slow, such that CIR prediction could be employed reliably by the transmitter.Recently, there has been significant interest in iterative detection schemes, where channel coding was exploited in

conjunction with multiuser detection in order to obtain a high BER performance. The spreading of the data and theconvolutional channel coding was viewed as a serially concatenated code structure, where the CDMA channel was

viewed as the inner code and the single user convolutional codes constituted the outer codes. After processing the

received signal in a bank of matched filters (MFs), the MF outputs were processed using a so-called turbo-styleiterative decoding (aka TEQ) [3], [117] process. In this process, a multiuser decoder was used to produce bit

confidence measures, which were used as soft inputs of the single-user channel decoders. These single-user decoders

then provided similar confidence metrics, which were fed back to the multiuser detector. This iterative process

continued, until no further performance improvement was recorded.

Giallorenzi and Wilson [118] presented the ML solution for the asynchronous CDMA channel, where the user

data was encoded with the aid of convolutional codes. Near-single-user performance was achieved for the two-user

case in conjunction with fixed length spreading codes. The decoder was implemented using the Viterbi channeldecoding algorithm, where the number of states increased exponentially with the product of the number of users andthe constraint length of the convolutional codes. Later, a suboptimal modification of this technique was proposed[68],where the MAI was canceled via multistage cancellation and the soft outputs of the Viterbi algorithm were

supplied to each stage of the multistage canceller for improving the performance. Following this, several iterative

multiuser detection schemes employing channel-coded signals have been presented [119][124]. For example,

Alexanderet al. [121], [123] proposed the multiuser maximum a posteriori(MAP) detectors for the decoding of theinner CDMA channel code and invoked single-user MAP decoders for the outer convolutional code. A reduced

complexity solution employing the -algorithm [35] was also suggested, which resulted in a complexity that

increased linearlyrather than exponentially, as in [118]with the number of users [122]. Wang and Poor [124]

employed a soft-output multiuser detector for the inner channel code, which combined soft IC and instantaneous

linear MMSE filtering, in order to reduce the complexity. These iterative receiver structures showed considerablepromise, and near-single-user performance was achieved at high SNRs. Having considered the family of various

CDMA detectors, let us nowturn our attention to a suite of adaptive-rate CDMA schemes.

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CHAPTER 4

PIC SCHEME BASED ON ADAPTIVE MMSE DETECTOR

The capacity and performance of DS-CDMA systems is limited by the MAI and the near-

far problem. Many multiuser detection schemes were proposed to mitigate theseproblems. Among them, PIC is one of the promising detectors. In recent years, PIC has

drawn a lot of interests, and studies on PIC for DS-CDMA systems have gone so far as an

experimental evaluation phase. One of the most advanced work can be seen in [36].

PIC has low complexity and the potential to combat the near-far problem, since it is

designed to cancel interference. However, its performance is dependent on the accuracy

of the data estimates. In the conventional PIC (CPIC) [16], MFs are used for data

estimation, which are sensitive to near-far problem. Therefore, the potential of

PIC is limited. In addition, CPIC requires the information of all users involved in the

received signal for complete interference cancellation. Consequently, in multi-cell

environment, it cannot suppress the interference from other cells (inter-cellinterference) since the base station contains the information of users only in its own

cell.

On the other hand, adaptive MMMSE detector [9,13] is shown to have much improved

performance over the conventional detector. Also the adaptive nature of the detector

allows it to learn the required information and adjust itself to the prevailing

interference and noise environment. As a result, it can suppress the interference from

the other cells (inter-cell interference) without the exact knowledge of the interferers.

Taking into account the attributes of PIC and adaptive MMSE detectors, a new

multiuser detector is presented in this chapter. It exploits the advantages of the two

detectors by combining a simple blind adaptive MMSE (BAMMSE) detector with the

PIC. In the proposed adaptive PIC (APIC) scheme, BAMMSE detectors are used for

data estimation in each stage instead of MFs. The remainder of the chapter is organized

as follows. The system model is described in the next section. Because the APIC

scheme is related to the MF, MMSE, CPIC detectors, the theoretical performances of

these three fundamental multiuser detectors are analyzed in Section 4.2. The APIC

scheme is discussed in detail in Section 4.3. This section also includes the performance

analysis in multi-cell environment. In Section 4.4, the simulation results of this scheme

are presented along with the theoretical results for perfect power control case, near-far

channels and multi-cell environment. Finally, the last section summarizes the chapter

with some concluding remarks.