Search

A serious shortcoming of current downlink power control methods is that their performance may be severely degraded when the downlink channel information is known imprecisely at the transmitter. In this paper, a computationally and implementationally simple centralized downlink power control method is proposed for cellular wireless communication systems using code division multiple access (CDMA) or space division multiple access (SDMA). Our method provides a substantially improved robustness against imperfect knowledge of the wireless channel by means of maintaining the required quality of service for the worst-case channel uncertainty. In the SDMA case, the proposed technique can be straightforwardly combined with any of the existing transmit beamforming methods. Simulation results validate substantial robustness improvements achieved by our approach.

The analysis of high signal-to-noise ratio (SNR) properties of the minimum output energy (MOE) receiver and the Capon channel estimation technique in the case of large direct-sequence code-division multiple-access (DS-CDMA) systems is discussed. Findings reveal that the signal-to-interference-plus-noise ratio (SINR) of the MOE receiver tends to infinity when the noise power tends to zero and the system load is less than one.

A new 2 X 2 full-rate full-diversity information-lossless space-time block code is presented. Results suggest the achievement of optimal diversity-multiplexing gain tradeoff.

A new joint blind channel and carrier frequency offset estimator is developed for orthogonally space-time block coded multiple-input multiple-output (MIMO) systems. A semiblind modification of this method is developed for estimating the channel subspace in a blind way.

A new blind subspace-based signature waveform estimation method is proposed for direct-sequence code division multiple-access (DS-CDMA) communications systems operating in the presence of unknown wide-sense stationary interference. The first-order perturbation theory is used for obtaining closed-form expressions for the mean-squared error of the estimated channel vector and it is shown that the temporally oversampled version of the proposed method can be applied to overloaded communications systems with lengthy channels.

Usually, centralized channel state information (CSI) is assumed to exploit the multiuser diversity with a smart transmission scheduler. However, such centralized CSI can be impractical for a broadband wireless communication system with a large number of mobile users (MUs). In this paper, we propose a decentralized method to exploit the multiuser diversity in a single cell scenario with orthogonal frequency-division multiplexing (OFDM) based downlink. The central part of our approach is the channel-aware ALOHA-based OFDM subcarrier assignment. According to it, each MU measures the channel at all OFDM subcarriers and tries to obtain proper ones by sending a service-request packet through the corresponding orthogonal uplink subchannel. This packet is sent when the measured channel-fading level exceeds a predetermined threshold [xi]. The base station processes these request packets with a collision-reception model, and assigns the corresponding subcarrier(s) to the MU whose request packet has been successfully received. Two implementation algorithms are developed, by solving the problem of optimization of [xi] under different system configurations. Computer simulations show that in comparison with the standard round-robin method, the proposed algorithms offer a substantial data-rate improvement, especially when the correlation property of the OFDM subcarriers is properly exploited. Index Terms--Access protocols, diversity methods, orthogonal frequency-division multiplexing (OFDM).

In the orthogonal frequency-division multiplexing (OFDM) scheme, some subcarriers may be subject to a deep fading. Adaptive techniques can be applied to mitigate this effect if the channel-state information (CSI) is available at the transmitter. In this paper, we study the performance of an OFDM-based communication system whose transmitter has only one bit of CSI per subcarrier, obtained through a low-rate feedback. Three adaptive approaches are considered to exploit such a CSI feedback: adaptive subcarrier selection; adaptive power allocation (APA); and adaptive modulation selection (AMS). Under the conditions of a constant raw data rate and perfect feedback channel, the performance of these approaches are analyzed and compared in terms of raw bit-error rate. It is shown that one-bit CSI feedback can greatly enhance the system performance. Moreover, imperfections of the feedback channel are considered, and their impact on the performance of these techniques is studied. It is shown that by exploiting the knowledge that the feedback channel is imperfect, the performance of the APA and AMS techniques can be substantially improved. Index Terms--Channel-state information (CSI), feedback channel, orthogonal frequency-division multiplexing (OFDM).

New semiblind methods are developed to jointly estimate the receive channel state information (CSI) of multiple multiantenna transmitters that use orthogonal space-time block codes (OSTBCs), which are based on the popular Capon and MUSIC spectral estimators. The analysis has shown that using the proposed Capon- and MUSIC-based semiblind channel estimators along with the coherent maximum-likelihood or the minimum-variance multiuser multiple-input and multiple-output (MIMO) receives results in a substantially better performance as compared with the case when the standard least-squares (LS) channel estimated are used.

A new blind subspace-based algorithm is proposed for signature waveform estimation in binary phase-shift-keying (BPSK)-modulated direct-sequence code-division multiple-access (DS-CDMA) systems. The theoretical analysis of the identifiability of the proposed technique has shown that it enjoys more relaxed identifiability conditions than the Liu and Cu (LX) algorithm.

The problem of direction-of-arrival (DOA) estimation in partly calibrated arrays composed of multiple subarrays with unknown interarray parameters and imperfectly known subarray orientations is addressed. Robust modifications of the rank reduction estimator (RARE) algorithm are found that substantially improve its DOA estimation performance in the case of subarray orientation errors and an appropriate Cramer-Rao bound is derived and the simulation results demonstrate substantial performance improvements achieved by the new technique.

A new approach to beam-space preprocessing with an improved robustness against out-of-sector interfering sources is developed. The techniques design the beam-space matrix filter based on proper tradeoffs between the in-sector (passband) source distortion and out-of-sector (stopband) source attenuation.

A more robust framework is proposed in which no statistical assumptions are made about the channel state information (CSI) mismatch or the channel where the goal is to design a transmitter that has the best performance under the worst-case CSI match. The design combines beamforming along the eigenvectors of the (deterministic) autocorrelation of the channel matrix perceived by the transmitter and power loading across those beams.

The performance of multiple-input multiple output channel estimation methods are studied using training sequences. The popular least squares (LS) and minimum mean-square-error (MMSE) approaches are considered and new scaled LS and relaxed MMSE techniques are proposed that require less knowledge of the channel second-order statistics and have better performance than the conventional channel estimators, therefore offering attractive tradeoffs, in terms of performance and prior knowledge of channel parameters.

The performance of orthogonal frequency division multiplexing (OFDM) communication systems can be improved by the new proposed linear block precoding technique, which is based on the channel mean cutoff rate and requires only the knowledge of the average relative channel multipath powers and delays at the transmitter. The proposed technique is highly effective as compared to the existing linear block procedures and can also be readily generalized to the multiple-input multiple-output (MIMO) case.

The channel matrix is estimated using specific properties of orthogonal space-time block codes (OSTBCs), which is then used in the maximum-likelihood (ML) receiver for the blind decoding of the OSTBCs. The results show that the proposed technique is applicable to any OSTBC and is better than the other coding schemes, with reference to the computational costs, scalar ambiguities, performance and the need of a pilot system.

Rank and determinant criteria as guidelines for designing space-time codes are reexamined and their fundamental limitations are explored. A more accurate characterization of the role of the rank of the code difference is then obtained using a tighter upper bound on the error probability.

New robust linear receiver techniques for joint space-time decoding and interference rejection in multi-access MIMO systems that use orthogonal space-time block codes and erroneous channel state information (CSI) are developed. The proposed receivers are based on worst-case performance optimization.

The problem of blind spatial signature estimation using the parallel factor (PARAFAC) analysis model is addressed in application to wireless communications. Identifiability issues and the relevant modified deterministic Cramer-Rao bound are studied and two blind spatial signature estimation algorithms are presented.

A new blind multiuser receiver that is robust against the effects of erroneously presumed desired user signature and short date length is proposed. The proposed method has a simple implementation with a computational complexity compatible with that of the fixed diagonal loading-based multiuser receiver.

Sparse sensor arrays composed of multiple widely separated subarrays are used to address the problem of maximum likelihood (ML) direction-of-arrival (DOA) estimation in unknown spatially correlated noise fields. The proposed approach concentrates the ML estimation problem with respect to all nuisance parameters.

In this correspondence, we prove an interesting property of orthogonal space-time block codes (OSTBCs). For flat block-fading channels, it is shown that the internal structure of the vector space of the input constellation remains invariant to the effects of both the OSTBC and the channel except for certain scaling factors. This property sheds light on the mechanism of maximum-likelihood (ML) decoding of OSTBCs and, in particular, provides an alternative explanation of why optimal decoding can be reduced to symbol-by-symbol decoding. New simple expressions for the ML decoder are obtained which clarify its intrinsic structure. Index Terms--Constellation space invariance, maximum-likelihood (ML) decoding, orthogonal space-time block codes (OSTBCs).

The problem of joint space-time decoding and multiaccess interference (MAI) rejection in multiuser multiple-input multiple-output (MIMO) wireless communication systems is addressed. Simulation results show that in multiaccess scenarios, the proposed techniques have substantially lower symbol error rates as compared with the matched filter (MF) receiver, which is equivalent to the maximum likelihood (ML) space-time decoder in the point-to-point MIMO communication case.

Exact expressions are obtained for the symbol-error probability of orthogonal space-time block codes at the output of the coherent maximum-likelihood decoder in the general case of arbitrary input signal constellation and code. Such expressions are derived for the cases of both deterministic (fixed) and random Rayleigh/Ricean fading channels, and both the two- and higher dimensional constellations. Index Terms--Exact probability of error, multidimensional constellations, orthogonal space-time block codes (OSTBCs).

Adaptive beamforming is used to enhance the detection of target echoes received by high frequency (HF) surface wave (HFSW) over-the-horizan (OTH) radars in the presence of spatially structured interference. External interference from natural and man-made sources typically masks the entire range-Doppler search space and is characterized by a spatial covariance matrix that is time-varying or nonstationary over the coherent processing interval (CPI). Adaptive beamformers that update the spatial filtering weight vector within the CPI are likely to suppress such interference most effectively, but the intra-CPI antenna pattern fluctuations result in temporal decorrelation of the clutter which severely degrades subclutter visibility after Doppler processing. A robust adaptive beamformer that effectively suppresses spatially nonstationary interference without degrading subclutter visibility is proposed here. The proposed algorithm is computationally efficient and suitable for practical implementation. Its operational performance is evaluated using experimental data recorded by the IIuka HFSW OTH radar, located near Darwin in far north Australia.

A new algorithm for parametric localization of multiple incoherently distributed sources is presented. This algorithm is based on an approximation of the array covariance matrix using central and noncentral moments of the source angular power densities.

A generalized Capon spatial spectrum estimator for localization of multiple incoherently distributed sources in sensor arrays is developed. The proposed generalized Capon technique estimates the source central angles and angular spreads by means of a two-dimensional (2-D) parameter search.

An innovative approach is proposed for robust adaptive beamforming in the presence of unknown arbitrary-type mismatches of the signal array response.

A new adaptive beamformer with strengthened robustness against an arbitrary unknown signal steering mismatch is described and discussed.

The direction-finding problem is examined in partly calibrated arrays involving subarrays that are displaced by unknown vector translations. The subarrays are calibrated and identically oriented, but they are not necessarily identical

A windowed local polynomial approximation (LPA) of time-varying directions of arrival is used to develop an LPA-beamformer for localization of rapidly moving sources. Closed-form expressions are obtained for the bias and variance of the LPA-beamformer in the moving source case and the problem of the optimal window choice is discussed. Index Terms--Array processing, beamforming, moving sources.

The problem of estimating parameters of multiple wideband polynomial-phase signal (PPS) sources in sensor arrays is discussed. A maximum likelihood (ML) direction of arrival (DOA) estimator is described, and the exact Cramer-Rao bound (CRB) is derived for the general case of multiple constant-amplitude polynomial-phase sources.

The direction finding problem is considered in the imperfect spatial coherence case.

Research is presented concerning the elimination of all the distortion parameters from wavefront amplitude distorted in an arbitrary manner. The accuracy of the estimator is discussed.

Basic adaptive beamforming techniques have been compared using shallow sea sonar data recorded in a towed horizontal array situation with moving interfering sources related to shipping noise. Perfomance of adaptive array algorithms undergoes strong degradation when they are involved with moving interfering sources. Results improve significantly when several robust algorithms related to traditional adaptive beamforming schemes are used.

A novel pseudo-noise resampling method is proposed to mitigate the impact of outliers in Unitary ESPRIT. The method does not require any additional data in that it exploits exactly the same data snapshots as the failed estimate itself. It uses synthetically generated data as in the bootstrap technique instead of unavailable additional data. The technique uses the main idea of the estimator bank approach. Another major difference is that the standard estimator bank approach requires a priori knowledge of source localization sectors, while the new technique is free of such a restriction.

A beamspace root modification of the pseudorandom joint estimation strategy (PR-JES) is advanced to exploit the superior performance and reduced computational complexity of beamspace and root implementations of eigenstructure techniques. PR-JES generates an eigenstructure-based estimator bank for specific samples of covariance or data matrix. PR-JES then removes outliers by combining the results of parallel underlying estimators. The results of simulation studies are presented.