This paper presents the delay spread characteristics based on the measurements of the wideband multiple-input multiple-output radio channels in outdoor environments. Measurement data were collected by a channel testbed which consists of four transmitters and eight receivers, and operates at a carrier frequency of 1.8 GHz with a bandwidth of 2.5 MHz. The four transmitting antennas and eight receiving antennas can work simultaneously. The experiments were originally conducted within the J. J. Pickle Research Campus, University of Texas at Austin. Based on the measurement data in the non-line-of-sight environments, the channel features such as multipath intensity profiles, root-mean-square delay spreads and their distributions were examined. It was found that the empirical probability density function of the rms delay spread in the non-line-of-sight MIMO channels roughly follows Gaussian distribution.

A new approach to adaptive downlink beamforming to combat fast Rayleigh fading is presented. In this approach, the antennas at the base transceiver station form transmit beam patterns according to the prediction of downlink channels. The channel prediction is a linear prediction based on the autoregressive model, which is downsampled to extend the memory span given fixed model order. For a wideband code-division multiple-access downlink, pre-RAKE transmission is employed to achieve the multipath diversity gain. In particular, we combine pseudoinverse directions of arrival beamforming with pre-RAKE transmission to alleviate self-interference. The beamforming weights are adjusted within a downlink frame to compensate the predicted fading. We give measures of the prediction and beamforming performance and evaluate the impact of prediction errors on the downlink. Ray tracing simulations in a three-dimensional urban physical model show that the predictive downlink beamforming outperforms the conventional beamforming over Rayleigh-fading channels.

We analyze the application of the vector channel synthesis and prediction filter introduced by Arredondo et al.. Ray-tracing simulations show that predictions of the downlink channel can result in improvements in signal-to-interference ratio in excess of 10 dB in nonline-of-sight scenarios, compared with beamforming without predictions.

In this paper, we investigate diversity combining schemes for OFDM wireless LAN systems with antenna array. Current standards were designed for diversity selection instead of combining. It is well known that the combining results in a higher receiver gain than the selection. However, the combining requires significantly more complexity than the selection. This paper is focused on implementing a low-complexity diversity combining scheme, which reduces the complexity in vector channel estimation. We demonstrate the performance of the proposed combining scheme via computer simulations.

Many researchers have done significant work to reduce fast fading in single channel wireless systems using prediction. We introduce a novel synthesis and prediction filter at the smart antenna base station to predict the vector channel in time-division duplex systems. We show the advantage of modeling each component of the vector channel with the same coefficients over modeling each channel separately. Experimental results from measurements taken in downtown Austin show that prediction of the vector channel is feasible, even as far as ten steps ahead. Ray tracing simulations of downtown Austin, assuming noiseless line-of-sight and nonline-of-sight channels, show that these predictions enhance downlink beamforming resulting in improvements in downlink received power in excess of 10 dB in nonline-of-sight scenarios compared to beamforming without predictions. Furthermore, this improvement allows an increase in the duplex interval by more than three times, assuming constant mobile velocity.

Researchers have studied the behavior of the vector channel in order to study the quality of beamforming methods and to increase downlink signal power and signal-to-interference ratio. Jeng (see IEEE Trans. Antennas Propagat., vol.46, p.749-57, 1998) showed that knowledge beyond simply directions of arrival of the vector channel at one frequency may not be useful at another. We propose that if the ratio between two frequencies, f/sub 1/ and f/sub 2/, is close to one, then the autoregressive (AR) coefficients which model the behavior of the vector channel at f/sub 1/ are essentially the same AR coefficients as those which model the vector channel at f/sub 2/. This implies that accurate predictions of the vector channel at f/sub 2/ can be performed knowing only the channel at f/sub 1/, as well as enough knowledge of the channel at f/sub 2/ in order to provide initial conditions. Ray-tracing simulations of downtown Austin, TX, show that this hypothesis holds for f/sub 1/ = 1.85 GHz and f/sub 2/ = 1.93 GHz, which correspond to uplink and downlink frequencies, respectively, in US personal communications systems.

This paper investigates the benefit of mutual coupling compensation via a method of moments (MoM) approach in a uniform circular antenna array operating at 1.8 GHz. This mutual coupling compensation technique is applied to a direction of arrival (DOA) study of up to two cochannel mobile users. Field measurements and computer simulations are examined to explore the assumptions of the technique and verify its effect when using the Bartlett and MUSIC DOA algorithms. Computer simulations considering the application of the technique to down-link beamforming are also included. Experimental results show that the mutual coupling compensation technique improves up-link DOA algorithm performance primarily by reducing unwanted sidelobe levels. This reduction in sidelobe levels aids in down-link beamforming weight design. Specifically, simulation results show that use of the compensation technique allows DOA-based down-link beamforming algorithms to perform similarly to spatial signature-based algorithms. All field measurements were made using the smart antenna testbed at the University of Texas at Austin.

Most recent studies of transmit diversity and space-time coding have focused on random channels which feature rich scattering around both transmitters and receivers. In this paper, we consider a highly structured vector channel in a block fading scenario. Specular propagation and local scattering are assumed at the base-station and mobile station respectively. The channel knowledge required for the transmit design includes: a subspace formed by direction vectors of specular propagation and length of the fading coherent interval. The relatively stationary subspace information reflecting the spatial power spectral distribution is exploited to derive a code design criterion. The optimal design turns out to be a joint coding and beamforming scheme and resistance to fading within the subspace is provided by such code.

Transmit diversity and space-time coding have received a lot of attention in previous years. We propose a direction-time transmit scheme for a fast block fading channel which exploits the parametric structure of the time-varying vector channel. Specular propagation and local scattering are assumed at the base-station and mobile station respectively. The partial channel knowledge needed for the transmit design includes: the number of specular paths, path direction vectors and length of the coherent interval. Transmission matrix codewords are formed according to this partial channel information such that multiple channels are induced and symbols are spread over different transmission directions. Diversity gain and blind channel estimation are achieved simultaneously at the mobile station through equalization.

We propose an improved fast algorithm for spatial signature estimation in array antenna systems. We provide an analysis for a fast estimation algorithm, which is not fully exploited in the previous work of Kim and Xu (see Proc. of IEEE Vehicular Technology Conference, 1997). Based on the analysis, we derive an improved algorithm which yields more accurate estimates with tolerable increment of computational complexity. The improvement is accomplished by compensation of noise power. Two different estimation methods for the noise power are presented. We investigate the performance of the proposed method via computer simulation.

Joint detection with the minimum-mean square error (MMSE) criterion has been explored in previous works. Yang, Liu, Xu and Kim (see Asilomar Conference on Signals, Systems and Computers, Asilomar, CA, 1999) discussed joint detection with cyclic reduction, which has been proved to be faster than the popular Cholesky decomposition based algorithm under many scenarios. In this paper, a faster version of cyclic reduction which exploits displacement structures is presented. The proposed fast algorithm is useful when the channel length is long. Simulation results are given for a wireless communication setup.

In mobile communications, smart antenna systems that utilize an antenna array and perform advanced signal processing techniques can achieve greater channel capacity and improve link quality by selective reception/transmission at the base station. However, development of adaptive signal processing algorithms for smart antenna system applications require the accurate knowledge about the multichannel propagation characteristics. A few experimental results on the spatial signature variation of a uniform linear array (ULA) at 900 MHz have been reported. This paper presents the experimental results on the channel propagation characteristic variations of a 1.8 GHz smart antenna system using a uniform circular array (UCA) in moving mobile scenarios. The results indicate the stability of direction-of-arrival (DOAs) of multipath components in all scenarios, and the instability of the spatial signatures in scenarios with strong multipaths.

The problem of blindly estimating multiple digital co-channel communication signals using an antenna array is studied in the presence of multipath fading. We develop a fast sequential-estimation algorithm for separating multi-user signals based on the geometric observation made by Kansen and Xu (see Proc. ICASSP'96, p.1085-8, Atlanta, GA, 1996). When the signals are constrained to a finite alphabet, it is possible to visualize the geometric properties of the problem, which can be exploited to sequentially extract the digital co-channel communication signals. We present simulation results comparing the speed and BER performance with different methods.

Smart antenna systems have the potential to substantially increase the range of base stations and boost the SINR of signals. In this paper, we study several criteria in downlink weighting vector design which is key to exploit the full potential of smart antenna systems, and give the optimal power assignment when the orientations of the weighting vectors are known. Simulation results have shown significant improvement offered by the proposed optimal power assignment method. In particular, we can equalize each user's downlink performance by significantly reducing the output power. Since the power amplifiers at the base station are the most expensive subsystems, this approach can lead to significant cost reduction for a base station.

The signal-to-noise ratio (SNR) and signal bandwidth have been viewed as the dominant factors determining the channel capacity. In wireless communications, the channel capacity can be increased for a given SNR and a given spectral region, by exploiting the spatial diversity provided by the use of multiple antennas and transceivers at a base station. We calculate the channel capacity enhancement of a so-called space-division-multiple-access (SDMA) system and investigate its dependence with respect to different decoding schemes, terminal positions, and receiver numbers. Inner and outer capacity boundaries for joint decoding and independent decoding are presented, along with physical explanations as to how these boundaries can be achieved. We show that exploitation of the spatial diversity not only increases the overall achievable rates of both joint and independent decoding, but also closes the gap between their corresponding capacity regions, thus bringing the performance of the low-cost independent decoding scheme close to that of the optimal joint decoding. Practical issues of optimum projection and power control are also addressed.

When sequentially separating a linear combination of co-channel digital signals, it is necessary at each step to test the validity of the currently estimated signal prior to proceeding to extract the next one. We describe a procedure for use with sequential algorithms which uses a deflation-based approach combined with a simple test statistic. The deflation step removes the contributions of the currently identified signals. The simple test statistic takes into account the error terms introduced into the data by the deflation. The method has been successfully applied in an existing sequential estimation algorithms.

Antenna arrays can be employed in mobile communications to increase channel capacity as well as communication quality via spatially selective reception/transmission at base stations. In most wireless communications systems, directions of arrival of multipath signals need to be found for spatial selective transmission. Unfortunately, due to the coherent nature of multipath signals, it is quite difficult to find their directions of arrival. In this paper, we will present a subspace smoothing algorithm for finding the directions of arrival of multipath signals based on the mobile terminal signals received at different time instances. More importantly, we will present our experimental results to demonstrate that the spatial diversity is present for slight movements of a mobile terminal and that the subspace smoothing approach is effective in real wireless scenarios. All of the experiments were performed using the smart antenna testbed at the University of Texas at Austin.

In a multi-transmitter broadcast system, the weight vector for each message signal can provide an additional degree of freedom for signal enhancement and interference suppression by taking advantage of the spatial diversity among the users. The design of optimal weight vectors that maximize the overall channel capacity is an open problem. Under certain power constraints, the channel capacity R is a highly nonlinear function of the M-dimensional weight vectors {w/sub i/}, where M is the number of transmitters. Hence, a closed-form algebraic solution that maximizes R over {w/sub i/} does not seem to be tractable. We decouple the weight vectors in R to simplify the optimization problem to a search for the maxima of a smooth multidimensional function. Based on this decoupling, we derive and evaluate two algorithms for computing weight vectors for the two-user and three-user cases: orthogonal and optimal. We also propose a near-optimum algorithm for the two-user case. The optimal algorithm requires an iterative search.

We propose a fast sequential-estimation algorithm for separating multiple cochannel digital signals using an antenna array with unknown array response. We present simulation results comparing speed and bit error rate (BER) performance with an existing fast joint-estimation algorithm, the iterative least squares with projection (ILSP) algorithm. The new algorithm proves to be faster with only a modest degradation in BER. In addition, convergence speed is very robust with respect to additive noise.

Space-division multiple-access (SDMA) schemes have been proposed to increase the capacity of wireless communication systems by simultaneously transmitting and receiving multiple co-channel signals through different spatial channels. We address the uplink (remote users to the base station antenna array) blind channel and sequence identification problem for an SDMA system. We show that multiuser blind identification can be accomplished by exploiting the spatial and temporal diversities of an antenna array system. In particular, a recursive estimation algorithm is developed to recover multiple signals from intersymbol interference (ISI) and co-channel interference (CCI) by taking advantage of a special structure of the array output and the finite-alphabet property of digital communication signals. The implementation of the proposed approach in practical applications is discussed, and field experiments have been conducted to demonstrate the effectiveness of the proposed algorithm.

The relationship between the blind digital co-channel communications problem and the problem of finding a separating hyperplane in IR/sup d/ is explored. A simple model for the blind digital co-channel communications problem is X=AS, where X is a known IR/sup d/spl times/N/ matrix. A is an unknown full-rank IR/sup d/spl times/d/ matrix, and S is an unknown full-rank d/spl times/N matrix with elements drawn from a multiple-amplitude-shift-keying (M-ASK) alphabet S. An algorithm with proof is given that solves for A and S up to a simple matrix factor. The algorithm is extended to the complex quadrature-amplitude modulation (QAM) alphabet. The key step in this algorithm involves finding a separating hyperplane parallel to one of the hyperplanes defining the received signal vectors. The geometric interpretation of this relationship is discussed. Examples with noisy data are presented and refinements to the algorithm are discussed.

Due to the rapid growth of wireless cellular communications, an important problem in cellular radio today is increasing the frequency re-use factor of the cells. One way to achieve this increase is through smart antenna arrays at the cell base sites. We examine a space division multiple access (SDMA) problem arising from the demodulation of multiple co-channel digital signals arriving at an antenna array. In the absence of noise, the problem can be completely described by the equation X=AS, where A is an unknown array response matrix and S is the unknown digital signal matrix. We show that the matrix A is completely described by the facets of the convex hull of X. We then give an algorithm that solves exactly for A and S (up to a simple matrix factor T). We conclude with a description of how the algorithm can be extended to solve the noisy problem X=AS+V.

Multipath induced interchip-interference (ICI) alters waveforms of transmitted signals and presents a major obstacle to direct-sequence (DS) code-division-multiple-access (CDMA) communications. Since practical systems often employ aperiodic pseudorandom (PN) spreading sequences, source separation/equalization based on users signature waveforms may be prohibitive. We consider antenna array CDMA systems with aperiodic PN sequences and present a blind method to estimate the filter coefficients in a standard 2D RAKE receiver. A simple cost function is formulated based on which we derive a least-squares equalizer to effectively suppress both ICI and multiuser interference (MUI) by exploiting the channel diversities.

By exploiting the spatial diversity among different signals, anti-jamming communications can be accomplishing using antenna arrays. In this paper, we discuss the self-recovery problem for antenna array wireless systems after the communication link is broken due to abrupt environmental changes. In particular, we derive a novel approach that can extract the desired signal from heavy interference without using a priori spatial information of the desired signal or jamming sources. We show how this new method can be modified to handle intersymbol interference and demonstrate its effectiveness using computer simulations. Results from field experiments show that the new approach can reliably reject interference or jamming sources and recover the desired signal under 0 dB C/I.

Synchronous code-division multiple-access (CDMA) techniques possess intrinsic protection against co-channel interference due to orthogonal codes employed and thus, offers higher capacity than existing frequency-division multiple-access (FDMA) or time-division multiple-access (TDMA) systems. In the presence of multipath, however, each signal is subject to frequency-selective fading and the orthogonality condition does not necessarily hold leading to increased cross correlation. In these scenarios, multiuser detection need to be performed to suppress interference and recover the message symbols. To implement such a technique, explicit knowledge of the (nonorthogonal) signature waveforms of all users is required. We propose a blind estimation scheme that provides closed-form estimates of the signature waveforms by exploiting the structure information of the data output. In particular, we show that the subspace of the data matrix contains sufficient information for unique determination of the signature waveforms. Based on this observation, a multiple signal classification (MUSIC)-like algorithm is derived. Performance analysis of the new approach is also presented.

This paper presents some preliminary results of experimental studies of space-division-multiple-access (SDMA) systems for wireless communications to expand capacity, increase coverage, and improve quality. Although SDMA schemes have been studied by a number of researchers, most of these studies are based on theoretical analyses and computer simulations. Very few real RF or microwave experiments have been conducted to validate the feasibility of various signal processing algorithms, such as direction finding and signal copy techniques. Also, no extensive experiments have been conducted to study the channel propagation associated with multiple antennas. The purpose of this paper is to present our preliminary experimental results using our recently developed antenna array testbed. We will also discuss the implications of these results on various array signal processing algorithms.

In this paper, we study two of the fundamental operations of a TDD smart antenna system, namely, the uplink channel and sequence identification and downlink selective transmission. For uplink, our focus is on the development of a blind estimation algorithm which is capable of resolving a multiuser system without the use of training sequence or any input statistics. For downlink, we propose a spatial channel pre-equalization scheme which simultaneously eliminates the intersymbol interference (ISI) and the co-channel interference (CCI) for all users using FIR filters. Both algorithms were validated by RF experiments using the smart antenna testbed developed in the University of Texas at Austin.

Most wireless communication systems use different carriers for uplinks and downlinks, hence the downlink beamforming can only be performed based on the directions-of-arrival (DOA) information of the uplink signals. In this paper, we propose an effective downlink transmission scheme for TDMA mobile communication systems via the integration of direction finding and blind signal estimation techniques. With an M-element antenna array, our new scheme can estimate up to 2M/sup 2//3 DOAs of direct path and multipath signals, while a conventional DOA estimation algorithm can resolve no more than M DOAs. RF experiments show that by incorporating these additional DOA estimates, much improved interference suppression was obtained.

A spatial signature is a response vector of an antenna array to a mobile unit at a certain location. The mobile subscribers at different locations exhibit different spatial signatures. The exploitation of spatial diversity (or difference of spatial signatures) is the basic idea of the so-called space-division-multiple-access (SDMA) scheme, which can be used to significantly increase the channel capacity and quality of a wireless communication system. Although the SDMA schemes have been studied by a number of researchers, most of these studies are based on theoretical analysis and computer simulations with ideal assumptions. Not much experimental study has been conducted on spatial signature variation due to nonideal perturbations in a real wireless communication environment. The purpose of this paper is to present our preliminary experimental results of spatial signature variation using our recently developed antenna array testbed. The results to be presented include the spatial signature variation with time and frequency and in several typical scenarios: (1) the mobile unit is stationary, so are its surroundings; (2) the mobile unit is stationary, but the surroundings vary; (3) the mobile unit moves in several different directions.

Synchronous code-division multiple-access (CDMA) techniques possess intrinsic protection against co-channel interference due to orthogonal codes employed and thus, offers higher capacity than existing frequency-division multiple-access (FDMA) or time-division multiple-access (TDMA) systems. In the presence of multipath, however, each signal is subject to frequency-selective fading and the orthogonality condition does not necessarily hold leading to increased cross correlation. In these scenarios, multiuser detection need to be performed to suppress interference and recover the message symbols. To implement such a technique, explicit knowledge of the (nonorthogonal) signature waveforms of all users is required. We propose a blind estimation scheme that provides closed-form estimates of the signature waveforms by exploiting the structure information of the data output. In particular, we show that the subspace of the data matrix contains sufficient information for unique determination of the signature waveforms. Based on this observation, a multiple signal classification (MUSIC)-like algorithm is derived. Performance analysis of the new approach is also presented.

We present an iterative constant modulus algorithm using the least squares method with projection to blindly separate and estimate multiple co-channel frequency modulated (FM) signals in an antenna array system. The constant modulus property of FM signals is exploited to simultaneously determine the spatial signature and the waveform for each signal. Furthermore, the spatial response of the array and the propagation environment do not need to be known prior to using this algorithm, making it particularly useful in mobile communications. Computer simulations are presented to verify its promising performance.

In this communication, necessary and sufficient conditions are presented for the unique blind identification of possibly nonminimum phase channels driven by cyclostationary processes. Using a frequency domain formulation, it is first shown that a channel can be identified by the second-order statistics of the observation if and only if the channel transfer function does not have special uniformly spaced zeros. This condition leads to several necessary and sufficient conditions on the observation spectra and the channel impulse response. Based on the frequency-domain formulation, a new identification algorithm is proposed.>

Conventional blind channel identification algorithms are based on channel outputs and the knowledge of the probabilistic model of a channel input. In some practical applications, however, the input statistical model may not be known, or there may not be sufficient data to obtain accurate enough estimates of certain statistics. We consider the system input to be an unknown deterministic signal and study the problem of blind identification of a channel which can be decomposed into multichannel FIR systems driven by a input signal. A new deterministic blind identification algorithm based solely on the system outputs are proposed. Necessary and sufficient identifiability conditions concerning the channel and the deterministic input signal are also presented.>

Tong et al. (see IEEE Trans. on Information Theory, March 1994) has proposed a parametric approach for blind equalization based solely on the second-order statistics. It first demonstrates the possibility of achieving satisfactory blind equalization by using a few hundred data samples in some typical wireless communication scenarios. Nevertheless, the algorithm is still far from optimal and is quite computationally intensive. In this paper, we use a different approach and derive a new algorithm which is less complicated as the original one. Simulation studies also show that some improvement of the new approach over its alternative in some scenarios.>

A blind symbol estimation technique for digital communication is developed by exploiting a special data structure of the oversampled channel output. The proposed method achieves direct symbol estimation without determining the channel characteristics. Moreover, if the transmitting symbols belong to a finite set of alphabets, the new approach can be extended to handle multiple sources.>

A new blind channel identification and equalization method is proposed that exploits the cyclostationarity of oversampled communication signals to achieve identification and equalization of possibly nonminimum phase (multipath) channels without using training signals. Unlike most adaptive blind equalization methods for which the convergence properties are often problematic, the channel estimation algorithm proposed here is asymptotically ex-set. Moreover, since it is based on second-order statistics, the new approach may achieve equalization with fewer symbols than most techniques based only on higher-order statistics. Simulations have demonstrated promising performance of the proposed algorithm for the blind equalization of a three-ray multipath channel.>

Forward/backward (FB) averaging is employed when possible in sensor array processing to improve parameter estimation accuracy by exploiting a centro-symmetry of the signal subspace. While FB averaging effectively doubles the number of data samples, it also introduces intersample correlation. This correlation complicates not only the performance analysis, but the detection of the number of sources as well. It is shown how existing detection procedures such as sequential hypothesis (or likelihood ratio) tests, minimum description length criterion (MDL), and AIC (Akaike information criterion) can be modified when FB averaging is used.>

A fast signal-subspace decomposition (FSD) algorithm is presented for sample covariance matrices, which only needs O(M/sup 2/d) flops, where d(>

Most high-resolution algorithms for sensor array processing require an eigendecomposition which is difficult to implement in parallel and costs at least O(M/sup 3/) multiplications for an M*M matrix, corresponding to M sensors. Recently, S.D. Silverstein et al, (1991) presented a subband parallel scheme, which reduced the computation time from O(M/sup 3/) to O(L/sup 3/), where L(>