The high-density deployment of access points (APs) and their serious mutual interference have made both frequency acquisition and data detection even more difficult in wireless local area network (WLAN). In light of this, this paper presents an antenna-array-assisted algorithm to solve above problems in a multiuser multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) interference network. The algorithm begins with the estimation of the channel parameters, including the frequency offsets, delays, and angle selectivity. To make a good use of the array signal characteristics, these parameters are estimated in an frequency-angle-frequency (FAF) tree structure, in which two frequency estimations and one angle estimation are employed alternatively. One special feature in the FAF tree structure is that temporal filtering or spatial beamforming is invoked between the parameter estimations to decompose the signals so as to enhance the estimation accuracy. Thereafter, based on these parameter estimates, a data detection procedure is developed to mitigate both multiple access interference (MAI) and co-channel interference (CCI). Simulations show that the proposed algorithm can provide satisfactory performance even in networks with MAIs and CCIs sharing the same frequency band.

A low-complexity and accurate receive-signal strength (RSS)-based algorithm is proposed to estimate a target location. To achieve the aim of low complexity, a local linearization technique based on the local linearity in the surface of power decay profile (PDP), established by training logarithmic RSS measurements and collected from each individual access point (AP), was devised. To achieve a near-optimum solution, the stochastic properties of measurement errors and the reliability of the measurement data are introduced into the factor graph framework. Numerical experiments show that the proposed algorithm not only achieves a near maximum likelihood (ML) solution based on training RSS measurements, but also enjoys low complexity.

To apply the promising idea of iterative information processing to the problem of joint channel estimation and data decoding, one may utilize the soft information of the coded bits to help refine the channel estimate, e.g., with Wiener filtering. However, the performance of such an iterative approach depends on not only the construction of the iteratively refined soft channel, but also the structure of the adopted error-correction code. Through extrinsic information transfer chart analysis, we investigate the impact of different soft channel construction schemes on multicarrier systems possessing turbo coding/modulation structures: (1) one with bit-interleaved coded modulation signals, and (2) the other with low-density parity- check code encoded modulation signals. It is shown that the former benefits significantly from iterative information processing regardless of channel construction scheme and the performance of the systems with different schemes converges. On the contrary, the same observation cannot be applied to the latter, a capacity- approaching system.

The look-up-table based digital adaptive predistortion (DAPD-LUT) is a low-cost and popular power amplifier (PA) linearization scheme. Most existing DAPD-LUT schemes adopt uniform spacing in indexing its LUT entries, completely regardless of the system state information (SSI): 1) the PA characteristics and 2) the input signal statistics, and are therefore sub-optimum. Some other existing DAPD-LUT schemes assume either one or both the SSI are perfectly known, and make use of this informations to design a fixed LUT index spacing. Without prior knowledge of the SSI, we propose a low-complexity iterative procedure to update LUT spacing for a DAPD-LUT scheme, which means the proposed scheme is capable of adapting itself to optimize the DAPD-LUT scheme for PA with all kinds of characteristic processing signals.

To extend network coverage and to possibly increase data packet throughput, the future wireless cellular networks could adopt relay nodes for multi-hop data transmission. This letter proposes a novel distributed scheduling algorithm for downlink relay networks. Soft-information indicating the probability of activating each network link is exchanged iteratively among neighboring network nodes to determine an efficient schedule. To ensure collision-free simultaneous data transmissions, collision-avoiding local constraint rules are enforced at each network node. To increase resource utility, the soft-information is weighted according to the urgency of data transmission across each link, which also helps maintain throughput fairness among network users.

For antenna-array-based multiple-input multiple- output orthogonal-frequency-division-multiplexing (MIMO-OFDM) wireless systems, gain in channel throughput reduced through sufficient feedback of the channel state information (CSI) is significant, particularly when the number of transmit antennas is larger than the number of receive antennas. In this letter, we demonstrate that, in such scenarios, (1) the CSI of each OFDM sub-carrier can be parameterized into a short bit stream by a proposed low-complexity QR decomposition on the corresponding MIMO channel matrix, (2) the overall CSI can be reliably represented by a proposed parameter interpolation on the above bit streams of only a fraction of sub-carriers, and (3) a MIMO-OFDM system with a low-rate CSI feedback parameterized above can provide a channel throughput comparable to the channel capacity.

With extrinsic information transfer (EXIT) chart code design tool, people can more easily design a capacity-approaching low-density parity-check (LDPC) codes for antenna-array-based multiple-input multiple-output (MIMO) systems. However, extremely time-consuming Monte-Carlo simulations are required to evaluate the statistics used in the EXIT charts, which makes capacity-approaching LDPC code design almost impossible for large-scale MIMO systems. By making use of large-system performance analysis technique, we propose a time-efficient code design approach which eliminates the necessity of time-consuming Monte Carlo simulations and then makes capacity-approaching LDPC code design possible for large-scale MIMO systems. Numerical experiments show that the so-constructed LDPC codes attain comparable bit error rate performance with existing well-designed LDPC codes. In addition, we demonstrate that redesigning LDPC code is very helpful only when the ratio of number of transmit and that of receive antennas of a MIMO system varies.

This paper presents an antenna-array-assisted approach to jointly estimate nominal directions of arrival (DOAs) and frequency offsets, and detect data in uplink multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless networks. To achieve this, two MUltiple SIgnal Classification (MUSIC)-based algorithms are first addressed to jointly estimate the nominal DOAs and frequency offsets of the incoming rays. The first algorithm is an extension of the classic two-dimensional (2-D) MUSIC, which, however, calls for enormous amount of computations. To alleviate the computational overhead, the second algorithm estimates the nominal DOAs and frequency offsets in a space-frequency-space (SFS) tree structure, in which two S-MUSICs and one F-MUSIC are invoked alternatively to estimate the nominal DOAs and the frequency offsets, respectively. In between every other MUSIC, a spatial beamforming process and a temporal filtering process are employed to decouple the uplink signals from different transmitters, thus enhancing the estimation accuracy. Thereafter, based on the estimated nominal DOAs and frequency offsets, a data detection procedure is also addressed. Simulations show that both algorithms can provide satisfactory performance while the SFS MUSIC calls for substantially lower computational complexity.

In this paper, we analyze the bit-error-rate (BER) performance of the optimum multiuser detection (MUD) with channel mismatch in multicarrier code-division-multiple-access (MC-CDMA) systems. The BER performance of the optimum MUD without channel mismatch in MC-CDMA systems has been recently derived using the replica method. However, it is left unjustified, since the replica method is not a rigorous approach. In addition, it is NP-hard to implement an optimum MUD algorithm. To justify the BER performance and to make the optimum MUD feasible, based on Pearl's belief propagation (BP) scheme, we put together a low-complexity iterative MUD algorithm for MC-CDMA systems. Furthermore, channel mismatch is introduced into the BP-based MUD algorithm to make the scenario general. With channel mismatch, the analytical results of the BP-based MUD algorithm conform perfectly to, and the simulation results of the BP-based MUD algorithm conform very closely to the BER performance of the optimum MUD derived using the replica method, which is a nontrivial extension of the existing replica approach mentioned above. Without channel mismatch, the problem becomes a special case of our contribution.

With a unified approach, this paper investigates the asymptotic performance or, equivalently, the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations. Using the replica method originally developed for statistical physics, we provide analytical solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint decoding or various suboptimum spatial equalizers followed by a bank of temporal decoders. Important physical meanings revealed though the analytical solutions to those more practical combinations, such as how the input-output mutual information is affected by the channel spatial correlations, are highlighted along our derivation. Moreover, we provide a novel waterfilling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications

A low-complexity high-accuracy algorithm is proposed to estimate the location of a target MS based on network-side time-of-arrival (TOA) measurements. Under a factor graph framework, the proposed algorithm first constructs a graphical model for the mobile position location problem by dividing the problem into many mutually-interactive local constraints. Each local constraint is enforced by a separate local processing unit. Efficient exchange of soft-information among local processing units in the mobile switching center (MSC) then iteratively purifies the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable solution accuracy, the maximum likelihood (ML) solution accuracy

Asymptotic Analysis of MIMO Wireless Systems With Spatial Correlation at the Receiver With a unified approach, this paper investigates the asymptotic performance, or equivalently, the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations. Using the replica method originally developed for statistical physics, we provide analytical solutions to the input–output mutual information of MIMO systems at the transmit side, with arbitrary inputs, and at the receive side, with either the optimum space–time joint decoding, or various suboptimum spatial equalizers followed by a bank of temporal decoders. Important physical meanings revealed though the analytical solutions to those more practical combinations, such as how the input–output mutual information is affected by the channel spatial correlations, are highlighted along our derivation. Moreover, we provide a novel waterfilling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter.

Exploiting the Fourier basis structure both in the space and the time domains, we develop a low-complexity multiuser space-time coding scheme, multiuser (MU) angle-frequency coding scheme (MU-AFCS), to properly schedule the data streams of each user with respect to its corresponding angle-frequency channel structure for downlink wireless systems. With the proposed approach, a large amount of space resource left over by one user, in clustered multipath wireless channels, can be easily identified and used by the others without serious signal collision in the space domain. In doing so, low channel capacity resulting from poor channel structures in systems, allowing only single-user transmission at a time, can be greatly boosted. The key advantage of this approach is that only limited feedback of channel state information to the transmitter is required while multiuser macro-diversity is sufficiently exploited. In addition, the complexity of the proposed approach is much lower than that of the existing ones serving similar purposes. Through theoretical analyses and computer simulations, we demonstrate that the MU-AFCS can significantly increase the channel capacity as compared to the traditional orthogonal resource division MU multiple-input multiple-output (MIMO) systems.

Abstract In this paper, we present a blind and robust algorithm to jointly estimate the directions of arrival (DOAs) and propagation delays in DS/CDMA systems. The rationale of the proposed approach is a hybrid of one‐dimensional (1‐D)multiplesignalclassification (MUSIC) algorithms and constrained spatial/temporal filtering processes. As previously addressed TST MUSIC, two temporal (T)‐MUSIC and one spatial (S)‐MUSIC are employed alternatively in the proposed algorithm to estimate the group delays and the DOAs respectively. However, by utilizing the structure of the data model, no training sequences are required. Furthermore, a constrained temporal filtering process and a constrained spatial beamforming process, which minimize the filtered noise power under a set of judiciously chosen linear constraints, are now employed after the T‐MUSIC and S‐MUSIC respectively. These filtering processes aim to effectively partition the incoming rays and to be robust against the propagation errors in the tree‐structured estimation scheme so that the overall performance can be enhanced. Also, the incoming rays are grouped, isolated and estimated, and the pairing of the estimated delays and DOAs is automatically achieved without extra computational overhead. Furnished simulations show that the new approach can exhibit satisfactory performance with low complexity compared with previous works. Copyright © 2005 John Wiley & Sons, Ltd.

A novel I/Q (in-phase/quadrature) imbalance compensation scheme is proposed for quadrature-signal-mixing radio-frequency transceivers, employing either the direct-conversion architecture or the low-intermediate-frequency architecture. An adaptive DSP (digital signal processing) algorithm is developed for the proposed scheme, to on-line compensate the I/Q imbalance in both the transmitter and the receiver simultaneously, which is impossible for the other existing I/Q imbalance compensation schemes so far. In addition, the proposed scheme requires little extra calibration hardware, suffers no hidden errors introduced by the calibration circuitry, and is thus highly suitable for implementation in practical applications.

This paper presents a novel self-organizing distributed algorithm for finding a broadcasting schedule in a packet radio network via only local collaborative interactions among neighboring network stations. Inspired by the huge success of the low density parity check (LDPC) codes in the field of error control coding, we transform the broadcast scheduling problem (BSP) into an LDPC-like problem through a factor graph. In the proposed algorithm, the constraint rules of the BSP are divided into many simple local rules, each of which is enforced by a local processing unit in the factor graph. The soft-information, describing the probability that each station will transmit a data packet, is then efficiently exchanged among the local processing units by using the sum-product algorithm to iteratively optimize the broadcasting schedule. Simulation results indicate that the proposed algorithm performs better than the other existing central-processing algorithms in terms of the channel utilization and the average packet delay. This is true especially when the network scenario is very complex. Furthermore, the proposed algorithm is both low in complexity and completely distributed, which makes it suitable for implementation in practical network applications.

Evolution of cellular systems in urban environments results in microcell and macrocell overlaying architectures with heavy traffic loading seen around hot spots. To achieve high spectrum efficiency, serious traffic blocking problems usually emerge in such an architecture if the network resource is not well managed. Based on a normal graph framework, we propose a fully-distributed dynamic channel assignment algorithm for hierarchical cellular systems using antenna arrays. Utilizing the available mobile station location information, the proposed algorithm defines soft-information to reflect the local traffic distribution. The soft-information is then iteratively exchanged among mobile stations and base stations to increase system capacity significantly.

We use the replica method originally developed in statistical physics to investigate the asymptotic sum-rate of a Gaussian antenna-array-based multiple-input multiple-output (MIMO) multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. The asymptotic solution is not only rigorously valid for systems with large array sizes, but it also produces highly accurate ergodic results for systems with only a few antenna elements at each transmitter and receiver. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.

This paper presents a tree-structured subspace-based algorithm for joint estimation of the directions of arrival (DOAs) and frequencies of arrival (FOAs) in wireless communication systems. The proposed approach is a hybrid of one-dimensional (1-D) subspace-based algorithms and spatial/temporal filtering processes, both of which are invoked alternatively to enhance the estimation accuracy. Two temporal and one spatial 1-D subspace-based algorithms are employed alternatively to estimate the FOAs and the DOAs, respectively. Between these subspace-based algorithms, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which minimize the filtered noise power under a set of linear constraints. These filtering processes aim to effectively partition the incoming rays and to be robust against the propagation errors in the tree-structured estimation scheme so that the overall performance can be enhanced. Furthermore, the estimated FOAs and DOAs are automatically paired without extra computational overhead. Furnished simulations show that the new approach can provide comparable performance with reduced complexity compared with previous works.

Recent advances in information theory show that employing multiple antennas at both sides of a wireless channel promises enormous capacity potential. With perfect knowledge of channel state information (CSI) at the transmitter, eigen-beamforming is the optimal coding scheme to exploit this potential. However, in nonstationary wireless environments, high complexity on multiple-input-multiple-output (MIMO) channel tracking and large amounts of CSI feedback render such an approach impractical. By exploiting the wireless multipath channel structure characterized by the path delays and the path directions-of-departure/arrival, we propose a new space-time transmit scheme which employs a structure-based water-filling algorithm.

We use the replica method originally developed in statistical physics to investigate the asymptotic sum-rate of a Gaussian antenna-array-based multiple-input multiple-output (MIMO) multiple-access wireless channel having spatial correlations at both the transmitters and the receiver. The asymptotic solution is not only rigorously valid for systems with large array sizes, hut it also produces highly accurate ergodic results for systems with only a few antenna elements at each transmitter and receiver. This otters the asymptotic solution important practical values in analyzing and designing a MIMO multiple access system that makes best use of the wireless channel structure. Furthermore, with the asymptotic solution, we provide an efficient iterative water-filling algorithm to determine the optimum transmit signal covariance matrices when only the slow-varying channel spatial covariance information is available.

In a unified approach, this paper investigates the asymptotic performance, or equivalently the large-system properties, of various point-to-point systems with antenna-array-based multiple-input multiple-output (MIMO) channels having spatial correlations at both the transmitter and the receiver. Using the replica method originally developed for statistical physics, we provide closed-form solutions to the input-output mutual information of MIMO systems: 1) at the transmit side, with either Gaussian inputs or various nonGaussian arbitrary inputs, and 2) at the receive side, with either the optimum space-time joint-decoding, or various suboptimum spatial equalizers followed by a bank of temporal decoders. Among all these combinations, in Part I, we focus on the performance analysis of the MIMO systems with the optimum space-time joint-decoding, i.e., the information-theoretic optimum decoding. Based on the framework developed in this paper, some known performance results are recovered and some new performance results are discovered. Furthermore, we provide a novel water-filling algorithm to determine the data-rate-maximizing transmit signal covariance matrix when only the channel long-term spatial correlations are available at the transmitter. In Part II, we focus on the performance analysis of the MIMO systems with various suboptimum spatial equalizers, each followed by a bank of temporal decoders.

For pt.I, see ibid., p.3504-8 (2004). In Part I of this paper, we analyzed the asymptotic mutual information of the joint-decoding MIMO systems. Here in Part II, we focus on the performance analysis of the MIMO systems with various separate decoding schemes, i.e., with various suboptimum spatial equalizers followed by a bank of temporal decoders. Among the suboptimum spatial equalizers, under the proposed framework, we cover both 1) linear spatial equalizers such as linear MMSE spatial equalizer, linear zero-forcing spatial equalizer, and matched-filter equalizer, and 2) nonlinear spatial equalizers such as individually optimal spatial equalizer and marginal optimal spatial equalizer. Closed-form solutions are derived for all the possible combinations of 1) various transmit-side arbitrary input distributions and 2) the receive-side decoding schemes listed above. Important physical meanings revealed though the closed-form solutions to those more practical combinations, as how the input-output mutual information is affected by the transmit-side and the receive-side channel spatial correlations, are highlighted along our derivation.

In a wireless system with multipath multiple-input multiple-output (MIMO) channels using antenna arrays, the delay spread of the multipaths results in intersymbol interference (ISI) and channel frequency selectivity. In contrast with the channel frequency selectivity, spatial gains at the multipath angles also naturally result in channel angle selectivity. Based on the channel selectivity structures characterized by the path delays and the path directions-of-departure/arrival, we seek new insights into the matching of space-time codes and multipath MIMO channels in their angle-frequency (AF) structures.

Multiple-access interference is one of the major impediments in the reverse link of code-division multiple-access (CDMA) systems, due to the synergy of the users' spreading codes, transmission delays, and the channel characteristics. A space-time dynamic signature-assignment (DSA) algorithm was briefly described by the authors in a previous paper. In this paper, we further elaborate on the space-time DSA approach and its receiver structure for the reverse link of direct-sequence (DS)-CDMA systems using multiple antennas at the receiver. The space-time DSA dynamically assigns the users' spreading codes and transmission delays, i.e., to assign the user signatures, in order to minimize mutual crosscorrelations. In assigning the signatures, the DSA adopts a low-complexity iterative algorithm which utilizes channel information and aims to maximize the signal-to-interference-plus-noise ratio of the poorest performing user at the base station. Analytic results as well as further simulation results are provided to support our arguments.

Making use of the network side time-of-arrival (TOA) measurements, we propose a low-complexity high-accuracy algorithm to estimate the location of a target mobile station (MS). Under a factor graph framework, the proposed algorithm efficiently exchange soft information among local processing units in the mobile switching center (MSC) to iteratively purify the estimate of the MS location. Numerical results show that the proposed algorithm not only enjoys advantages of low complexity, suitable for integrated-circuit implementation, but it is also able to achieve performance very close to the optimum achievable bound, the maximum likelihood (ML) bound.

This paper presents an improved tree-structured multiple signal classification (MUSIC) algorithm to jointly estimate the directions of arrival (DOA) and propagation delays in a CDMA system. The proposed algorithm makes use of one spatial (S)-MUSIC and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOA, respectively. In contrast to the previous version, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which try to minimize the filtered output power under a set of judicious chosen linear constraints. Such a constrained filtering approach can effectively partition the incoming rays and suppress the propagation error in the tree-structured estimation scheme, thus in turn enhancing the overall performance. Furthermore, the pairing of the estimated DOA and delays is also automatically determined. Compared with previous works, the new approach calls for low computational complexity but exhibits superior performance, as shown in the furnished simulations.

Systems that employ multiple antennas in both the transmitter and the receiver of a wireless system have been shown to promise extraordinary spectral efficiency. With full channel knowledge at the transmitter and receiver, Raleigh and Cioffi (1998) proposed a spatio-temporal coding scheme, discrete matrix multitone (DMMT), to achieve asymptotically optimum multiple-input-multiple-output (MIMO) channel capacity. The DMMT can be regarded as an extension of the discrete multitone for a digital subscriber lines (DSL) system to the MIMO wireless application. However, the DMMT is basically impracticable in nonstationary wireless environments due to its high-computational complexity. Exploring second-order statistics, we develop an efficient adaptive blind coding scheme for a high-capacity time-division duplexing (TDD) system with slow time-varying frequency-selective MIMO channels. With this method, neither a training sequence nor feedback of channel information is required in the proposed blind approach. Besides, the computational complexity of the proposed scheme is significantly lower than that of the coding scheme described by Raleigh and Cioffi. Simulation results show that the proposed architecture works efficiently in indoor wireless local area network applications.

In this paper, we propose a novel low complexity minimum mean-squared based multiuser detection (MMSE MUD), which employs partial but essential information of the interference, in a multichannel TDMA system. The new approach begins with the determination of the effective channel length of the interference based on information theoretic criteria. We then truncate the channel taps according to the taps' power using the thus obtained effective channel length. The computational complexity of the resulting MUD is thereby reduced. Analytic studies are also carried out to provide more insight into the proposed approach. The furnished simulations show that the new MUD offers close bit-error-rate (BER) performance to the full complexity one but with substantially reduced complexity.

We use the parametric channel identification algorithm proposed by Chen, Kim and Liang (see IEEE Transactions on Vehicular Technology, p.1923-35, 1999) (to adaptively track the fast-fading channels for the multi-channel MLSE equalizer. The channel tracking scheme per-survivor processing recursive least square (PSP RLS) is considered. An analytic lower bound for the multi-channel MLSE equalizer in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms are presented, and then these results are compared with the analytic BER lower bound and with the conventional MLSE equalizers directly tracking the FIR channel taps. The proposed algorithm is shown to perform significantly better than the conventional adaptive MLSE algorithm.

In wireless communication systems with specular multipath channels, the channel structure can be used to improve the system performance.] However, the channel structure turns obscure for diffused multipath channels. We analyze the diffused multipath channels and develop a low-complexity algorithm to determine the optimal dimension of the approximated channel subspace as well as the optimal basis for the subspace, in the sense of least-square error of the channel estimates. Simulation results show that (1) the system performance is significantly improved by exploring the structure of the diffused multipath channels, (2) the performance improvement is even more significant in a severe environment where the signal to noise ratio is low.

A multiple signal classification (MUSIC)-based approach, time-space-time MUSIC (TST-MUSIC), is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless channel. The MUSIC for the DOA and the propagation delay estimation are referred to as the S-MUSIC and the T-MUSIC, respectively. Using the space-time characteristics of the multiray channel, the proposed algorithm in a tree structure combines the temporal filtering techniques and the spatial beamforming techniques with one S-MUSIC and two T-MUSICs. The incoming rays are thus grouped, isolated, and estimated.

The parametric channel estimator is used in the parametric maximum-likelihood sequential estimation algorithms used to equalize the multipath wireless channels. We examine the performance of the parametric channel estimators against additive white Gaussian noise and the nonparametric channel model. We first show that the parametric channel estimators eliminate all the noise outside the signal subspace, and thus significantly reduce the performance degradation caused by the noise. Next, we show that the conventional channel estimators are special cases of the parametric channel estimator subject to some deteriorating constraints regardless of the channel model.

We use the parametric channel identification algorithm proposed by Chen and Paulraj (see Proc. IEEE Vehicular Technology Conf., p.710-14, 1997) and by Chen, Kim and Liang (see IEEE Trans. Veh. Technol., p.1923-35, 1999) to adaptively track the fast-fading channels for the multichannel maximum likelihood sequence estimation (MLSE) equalizer using multiple antennas. Several commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. An analytic lower bound for the multichannel MLSE equalizer with no channel mismatch in the time-varying specular multipath Rayleigh-fading channels is derived. Simulation results that illustrate the performance of the proposed algorithms working with various channel tracking schemes are presented, and then these results are compared with the analytic bit error rate (BER) lower bound and with the conventional MLSE equalizers directly tracking the finite impulse response (FIR) channel tap coefficients. We found that the proposed algorithm always performs better than the conventional adaptive MLSE algorithm, no matter what channel tracking scheme is used. However, which is the best tracking scheme to use depends on the scenario of the system.

In order to gain insights on equalization design in wireless mobile communication systems, we compare the performance of several multi-channel MLSE equalizers which adaptively track fast-fading channels. Commonly-used channel tracking schemes, decision-directed recursive least square (DD/RLS), per-survivor processing recursive least square (PSP/RLS) and other reduced-complexity MLSE algorithms are considered. Simulation results that illustrate the performance of the equalizers working with various channel tracking schemes are presented.

The parametric MLSE algorithms used to equalize the multipath wireless channels have been proposed by Chen and Arogyaswami (see ICASSP'97, Munich, Germany, 1997 and VTC'97, Phoenix, 1997). In this paper, we examine the robustness of these algorithms against performance degradation factors, such as additive Gaussian noise, and parameter estimation errors. We show that, in the specular multipath channels with a small number of paths, the parametric MLSE algorithms, compared with the MLSE algorithms with the conventional FIR channel estimation, always perform better.

To guarantee low correlations among user signatures in the reverse-link at the base station, we propose an adaptive algorithm to jointly update the user signatures. Making use of the wireless channel information, we iteratively reassign the spreading signature of the users with the minimum SINR (signal to interference-plus-noise ratio). As shown by simulation results, the proposed algorithm is likely to provide signatures which are orthogonal among users if the number of users is small, then multi-user detection is no longer required. When the number of users is large, the proposed algorithm still tries to maintain equal distances among user signatures, and thus improves the condition of the channel matrix and brings about an increase of capacity by up to 3-4 times, as compared to the same receivers with random codes.

We propose a novel algorithm for the maximum-likelihood sequence estimation (MLSE) equalizer for the Global System for Mobile Communications (GSM) system. Specifically, we use a parametric model for the channel, along with a modified phase pulse-shaping function of Gaussian minimum shift keying (GMSK) modulation to obtain the modified Viterbi equalizer which we refer to as the parametric channel-Viterbi equalizer (PC-VE). In contrast to the conventional Viterbi equalizer with a finite impulse response (FIR) channel description, the PC-VE avoids the linear approximation error. The PC-VE also has a lower computational complexity if the number of the propagation paths is less than the number of the FIR channel taps multiplied by the number of antennas. The proposed algorithm is applicable to both single and multiantenna receivers. An analytical expression for the BER as a function of the SNR, path delays, and path DOAs has been derived. Some simulation results that illustrate the performance of the proposed algorithm are presented.

In this paper, we address the problem of receiving a digitally modulated signal in the presence of another identically modulated co-channel interference (CCI) signal. We propose a joint MLSE algorithm that uses a dynamic channel description (J-MLSE/DCD). This algorithm deals with CCI more effectively while keeping the overall complexity low. Our approach is to adaptively truncate the channels of both the signal and the interference according to their individual power, thereby greatly reducing the complexity. Analytic BER lower bounds for the J-MLSE algorithms in the time-varying specular multipath Rayleigh-fading channels are derived. Simulation results for the GMSK signals used in the GSM system are presented.

In this paper, we address the problem of receiving a digitally modulated signal in the presence of another identically modulated cochannel interfering (CCI) signal. We propose a joint MLSE algorithm that uses a dynamic channel description (J-MLSE/DCD). This algorithm deals with CCI more effectively while keeping the overall complexity low. In the proposed algorithm, we adaptively truncate the channels of both the desired signal and the CCI according to their individual power, thereby greatly reducing the complexity. We also describe the channel by using only a small number of channel parameters to reduce the channel mismatch before the Viterbi algorithm is applied. Analytic bit-error rate (BER) lower bounds for the J-MLSE algorithms in the time-varying specular multipath Rayleigh-fading channels are derived. Simulation results of the GMSK modulated signals used in the GSM system with path delay spread up to three symbols are presented. We find that the proposed algorithm performs significantly better than the spatial-whitened MLSE and is comparable to the high-complexity joint MLSE.

In this paper, we propose a novel algorithm for the MLSE equalizer for the GSM system. Specifically, we use a parametric model for the channel, to obtain a modified Viterbi equalizer which we refer to as the parametric channel-Viterbi equalizer (PC-VE). In contrast to the conventional Viterbi equalizer with a FIR channel description, the PC-VE avoids the linear approximation error and has a lower computational complexity. The proposed algorithm is applicable to both single and multi-antenna receivers. Some simulation results that illustrate the performance of the proposed algorithm are presented.