The authors propose a suboptimal algorithm for adaptive subcarrier, bit and power allocation for orthogonal frequency division multiple access-based spectrum-sharing networks. This problem in its original form is non-convex and may be solved using greedy algorithms or integer linear programming (ILP) techniques. However, the computational complexity of the latter techniques is quite high, while the suboptimal greedy algorithms are not very well suited for spectrum-sharing networks because of multiple constraints on the transmitted power, interference leakage and individual user data rate. Therefore the authors propose a novel recursion-based linear optimisation framework that provides a solution that is very close to the optimal one and that has the ability to perform adaptive subcarrier, bit and power allocation for multiple users in the presence of multiple individual user constraints. Owing to the convexity of the proposed algorithm at each recursion, its overall complexity is substantially lower than that of the ILP-based solution.

For two-way wireless relay networks (TWRNs), the simultaneous bidirectional transmission has been shown to outperform other strategies using decode-and-forward (DF) distributed space-time coding (DSTC), thanks to its high spectral efficiency. However, it has a rather high relay decoding complexity and cannot use the direct link between the communicating terminals. In this letter, we propose a simple DSTC transmission scheme for TWRNs that avoids the latter disadvantages at the same symbol rate and with a performance advantage at high powers. Our strategy allows the communicating terminals to use the direct link between them to achieve a higher diversity gain. An extension of the proposed strategy to the differential case is also discussed.

We generalize the concept of multiuser peer-to-peer (MUP2P) relay networks to that of a multi-group multicasting (MGM) relay network where each source may broadcast its message to a group of multiple users. State-of-the-art beamforming methods, which have been proposed for MUP2P relay networks, are shown to be straightforwardly extendable to such MGM networks. These methods aim to minimize the total transmitted relay power subject to receiver quality-of-service (QoS) constraints using convex approximations of the underlying non-convex problem. Due to the increase in number of receivers, these approximations may become inaccurate in the MGM case leading to severe performance degradation and problem infeasibility. To avoid this drawback, we propose an iterative method where the aforementioned convex approximations are successively improved. Our technique overcomes the difficulties emerging in the MGM and MUP2P relay networks for large numbers of users and outperforms the state-of-the-art methods developed for MUP2P relay networks.

In this paper, we develop a new approach to the robust beamforming for general-rank signal models. Our method is based on the worst-case performance optimization using a semi-definite constraint on the mismatched signal covariance matrix. The resulting robust adaptive beamforming problem is solved using iterative semi-definite programming (SDP) with a guarantee of convergence. The performance improvement of the proposed approach over the current robust adaptive beamforming techniques developed for the general-rank signal environments is confirmed by simulation results.

In this paper, a new direction-of-arrival (DOA) estimation technique applicable to partly-calibrated arrays (PCAs) composed of arbitrary subarrays with unknown subarray displacements is developed. The new method is not restricted to any specific array geometry and allows joint estimation of the DOAs and calibration of the entire sensor array. Computer simulations show that the proposed approach substantially outperforms the known DOA estimation methods applicable to such PCAs.

In cooperative networks, users share their resources to establish reliable connections between each other. If two users want to communicate through a cooperative network, different transmission schemes are possible in which other users serve as relays. In this work, we compare different relaying schemes on the basis of their maximal capacity. We assume that the channel state information is available and the relays use the amplify-and-forward protocol. An optimal technique for one-directional transmissions forms the basis for capacity maximization of the bi-directional four-phase scheme. The analysis for the asymptotic behavior of the one-directional scheme also provides simple relations for the maximal sum-capacity of the bidirectional two-phase scheme. For the third bi-directional scheme with three-phases, upper bounds on the maximal capacity are obtained.

In this paper, a low-complexity suboptimal decoder for coherent and non-coherent quasi-orthogonal space time block codes with three and four transmit antennas is proposed. Our decoder enjoys a nearly linear complexity and approximately the same performance as the optimal maximum-likelihood (ML) decoder. Simulations show the advantages of the proposed decoder with respect to several other popular approaches to the coherent and non-coherent decoding.

In this paper, distributed beamforming and power control are considered for amplify-and-forward wireless relay networks with single antenna nodes. The optimal relay weights are designed by maximizing the mutual information in the case of perfect channel state information, individual node power constraints and the existence of a direct link between the source and destination nodes. In our approach, the source node transmits up to two precoded independent information streams as opposed to several previously proposed techniques in which a single symbol is always transmitted. Our solution to the relay power control given the power allocation at the source node is obtained analytically with linear complexity. Simulations show that the proposed method outperforms the previously proposed techniques in terms of mutual information, outage probability and bit error rate.

A new approach to distributed cooperative beamforming in two-way half-duplex relay networks with frequency selective channels is proposed. In our scheme, two transceivers simultaneously transmit the signals they wish to exchange to the relays. The relay received signals are the sum of the channel-convoluted versions of the transmitted signals. Each relay retransmits a filtered version of its received signal to both transceivers. The proposed distributed beamforming approach is based on minimizing the total relay transmitted power subject to the quality-of-service requirements for both transceivers. We show that this problem is convex, and thus, it can be efficiently solved as a second-order cone program. Simulation results demonstrate that the transmitted power can be significantly reduced and the problem feasibility can be substantially improved by using such a filter-and-forward relaying strategy instead of the traditional amplify-and-forward relaying approach.

A cooperative beamforming and power control scheme for amplify-and-forward wireless relay networks with single-antenna nodes is developed. Assuming perfect channel state information (CSI) and the existence of the direct link between the source and destination nodes, we design the optimal beamforming and power control weights that maximize the mutual information. In contrast to previous approaches to this problem, we consider the transmission of two independent information signals that can have different powers. The solution is obtained by considering the relay power control problem independently of the power allocation to the information signals. This results in an analytical solution that is then exploited in an iterative algorithm to obtain the final optimal beamforming weights. Our numerical results show that the proposed technique achieves a higher mutual information than previously proposed distributed beamforming and power control techniques.

We address the problem of worst-case robust downlink beamforming for a multi-antenna secondary network (SN) in a cognitive radio framework. An important issue is the interference leaked to the primary users (PUs) resulting from the transmission between the SN base station and the secondary users (SUs). Our aim is to provide the SUs with a minimum acceptable quality-of-service (QoS), while keeping the interference to the PUs below a given threshold. Previous solutions for this scenario involve several coarse approximations. Here we avoid these approximations and obtain an exact reformulation of the worst-case problem using Lagrange duality. Finally, we use semidefinite relaxation (SDR) to convert the resulting problem to a convex form. Computer simulations show that the SDR step does not involve any approximation as the resulting solution is always rank-one.

Two new approaches to adaptive beamforming in sparse subarray-based sensor arrays are proposed. Each subarray is assumed to be well calibrated but the intersubarray gain and/or phase mismatches are assumed to remain unknown or imperfectly known. Our first approach is based on a worst-case beamformer design that, unlike the existing worst-case designs, exploits a structured ellipsoidal uncertainty model for the signal steering vector. Our second approach exploits the idea of estimating the signal steering vector by maximizing the output power of the minimum variance beamformer. Several modifications of our second approach are developed for the cases of gain-and-phase and phase-only intersubarray distortions.

In this paper, a new direction-of-arrival (DOA) estimation technique for partly calibrated arrays with pairwise calibrated sensors is proposed. Our approach is shown to substantially outperform several popular DOA estimation methods applicable to such partly calibrated arrays. As a byproduct, our algorithm can also blindly estimate the source spatial signatures.

Robust adaptive beamforming based on worst-case performance optimization is known to provide a substantially improved robustness against signal self-nulling as compared to the traditional adaptive beamforming techniques. The worst-case performance optimization based beamformers of and can be alternatively obtained by solving the one-dimensional (1D) covariance fitting problem. In this paper, we show that the robustness of this approach can be significantly improved by extending it to multi-dimensional (MD) covariance fitting.

Two closed-form blind channel estimators for orthogonally coded multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems are proposed. The key idea of our approaches is to estimate the channel parameters in the time domain instead of doing this in the frequency domain. Our first approach is based on the maximum likelihood (ML) technique with the relaxed finite alphabet constraint, while the second approach uses the generalized Capon estimator to obtain the channel parameters. The proposed techniques amount to solving eigenvector problems.

Distributed beamforming is a powerful approach to reliable communications in relay networks. In this paper, a novel multiple peer-to-peer beamforming technique is proposed for relay networks with multiple source-destination pairs and frequency selective source-to-relay and relay-to-destination channels. Our technique uses a filter-and-forward relaying strategy to compensate for channel frequency selectivity and mitigate inter-symbol interference. Simulation results clearly demonstrate that the proposed technique substantially outperforms the existing amplify-and-forward multiple peer-to-peer beamforming techniques in frequency selective fading environments.

We study the fundamental capacity limits of the underlay cognitive broadcast (BC) channel under average transmit power and average interference power constraints. In a fading environment, the sum capacity is found from a constrained water-filling solution given in. This solution is investigated for different average transmit power budgets and average interference thresholds, and its specific regions are characterized. Further, capacity expressions are derived for the Rayleigh fading case. In a system with K users and Rayleigh fading channels, it is shown that the capacity scales like log(log(K)) for large K. This result indicates that the same multiuser diversity gain can be achieved in a cognitive BC system as in the conventional BC system without spectrum sharing.

We propose a novel signal to interference and noise (SINR) balancing technique for a downlink cognitive radio network (CRN) wherein multiple cognitive users (also referred to as secondary users (SUs)) coexist and share the licensed spectrum with the primary users (PUs) using the underlay approach. The proposed beamforming technique maximizes the worst SU SINR while ensuring that the interference leakage to PUs is below specific thresholds. Due to the additional interference constraints imposed by PUs, the principle of uplink-downlink duality used in the conventional downlink beamformer design cannot be directly applied anymore. To circumvent this problem, using an algebraic manipulation on the interference constraints, we propose a novel SINR balancing technique for CRNs based on uplink-downlink iterative design techniques. Simulation results illustrate the convergence and the optimality of the proposed beamformer design.

In this paper, the downlink opportunistic scheduling approach is studied in a multiuser environment with single-antenna transmitter and users. Exact bit error rate (BER) expressions are derived under the assumptions of full and quantized channel state information (CSI) at the transmitter. These expressions are then used to optimize the channel state feedback parameters. Moreover, asymptotic BERs are investigated in the limiting cases of a high signal-to-noise ratio (SNR) and a large number of users K. It is shown that both under the full and quantized CSI assumptions, the achieved BER is proportional to SNR-Kand K-SNRin these two asymptotic cases, respectively. This means that the diversity order is equal to K, whereas the multiuser diversity gain is equal to SNR. In the case when the CSI feedback is quantized, the impact of feedback errors on the achieved BER is studied. It is shown that the opportunistic scheduling can greatly improve the BER performance even if the feedback is quite low-rate and erroneous.

The problem of transmit beamforming for single-group multicasting is considered, where the objective is to transmit common information to a (large) number of users. The transmitter is assumed to have accurate downlink channel state information (CSI) for all users, and the objective is to design the beamformer weights to minimize the total transmitted power subject to meeting the quality-of-service (QoS) constraints of all users. This is an NP-hard problem that has recently drawn considerable interest (e.g., in the context of UMTS-LTE / E-MBMS). Several channel orthogonalization-based methods are proposed to solve this problem in an approximate way. Our techniques are shown to offer an improved performance-to-complexity tradeoff as compared to the original semidefinite relaxation (SDR) based multicasting technique.

A computationally efficient distributed beamforming technique for multi-user relay networks is developed. The channel state information is assumed to be known at the relays or destinations, and the total relay transmitted power is minimized subject to the destination quality-of-service constraints. It is shown that this problem can be approximately converted to a convex second-order cone programming form. As a result, the proposed network beamforming technique offers a substantially reduced computational complexity than earlier state-of-the-art techniques that are based on semidefinite relaxation.

A half-duplex distributed beamforming technique for relay networks with frequency selective fading channels is developed. The network relays use the filter-and-forward (FF) strategy to compensate for the transmitter-to-relay and relay-to-destination channels using finite impulse response (FIR) filters. With the channel state information (CSI) being available at the receiver, the transmit relay power is minimized subject to the destination quality-of-service (QoS) constraint. This distributed beamforming problem is shown to have a closed-form solution. Simulation results demonstrate substantial improvements in terms of the relay transmitted power and feasibility of the destination QoS constraint as compared to amplify-and-forward (AF) distributed beamforming techniques.

The use of cooperative schemes in wireless networks has recently attracted much attention in scenarios where application of multiple-antenna systems is impractical. In such scenarios, the requirement of having full channel state information (CSI) at the receiver side can be relaxed by using differential distributed (DD) transmission schemes. However, in the DD schemes proposed so far, the decoding complexity as well as the delay requirements increase with the number of relays. In this paper, we propose a low-rate feedback-based DD approach (with one-bit feedback per relay) that enjoys full diversity, linear maximum likelihood (ML) decoding complexity, and unrestrictive delay requirements. In addition, the proposed feedback scheme does not require any CSI knowledge at the receiver, and its implementation is simple. Computer simulations demonstrate substantial performance improvements of the proposed techniques as compared to several popular cooperative transmission schemes.

The problem of multiuser downlink beamforming is studied under the assumption that the transmitter has erroneous covariance-based channel state information (CSI). The goal is to minimize the transmit power under the worst-case quality-of-service (QoS) constraints. Previous convex optimization-based solutions to this problem involve several coarse approximations of the original problem. In our proposed solution, such coarse approximations are avoided and an exact representation of the worst-case solution is obtained using Lagrange duality. The so-obtained problem is then converted to a convex form using semidefinite relaxation (SDR). Computer simulations show that the SDR step does not involve any approximation as the resulting solution is always rank-one. Simulation results demonstrate substantial performance improvements over earlier worst-case optimization-based downlink beamforming techniques.

We investigate multiple-input multiple-output (MIMO) eigenmode transmission using statistical channel state information at the transmitter. We consider a general jointly correlated MIMO channel model, which does not require separable spatial correlations at the transmitter and receiver. For this model, we first derive a closed-form tight upper bound for the ergodic capacity, which reveals a simple and interesting relationship in terms of the matrix permanent of the eigenmode channel coupling matrix and embraces many existing results in the literature as special cases. Based on this closed-form and tractable upper bound expression, we then employ convex optimization techniques to develop low-complexity power allocation solutions involving only the channel statistics. Necessary and sufficient optimality conditions are derived, from which we develop an iterative water-filling algorithm with guaranteed convergence. Simulations demonstrate the tightness of the capacity upper bound and the near-optimal performance of the proposed low-complexity transmitter optimization approach.

The performance of multiple-antenna communication systems is known to critically depend on the amount of channel state information (CSI) available at the transmitter. In the low-rate CSI feedback case, an important problem is what kind of information should be submitted to the transmitter in each feedback cycle and what is the optimal transmission strategy in this case. In this paper, we address this problem in the multiple-input single-output (MISO) case by analytically comparing the bit error rate (BER) performance of different low-rate feedback based transmitter strategies involving various combinations of transmit antenna selection, Alamouti's spacetime coding, and adaptive power allocation.

In this paper, we develop an improved approach to the worst- case robust adaptive beamforming for general-rank signal models by means of taking into account the positive semi-definite constraint for the mismatched signal covariance matrix. The resulting robust adaptive beamforming problem is solved in an iterative way using semi-definite programming (SDP) at each iteration. Simulation results show that the proposed technique achieves a substantially improved performance as compared to the current robust adaptive beamforming techniques developed for the general-rank signal environments.

Orthogonal space-time block codes (OSTBCs) represent an attractive choice of space-time coding scheme because of their simple maximum-likelihood (ML) decoding and full diversity property. However, the code orthogonality property limits their achievable transmission rate. In this paper, new high-rate block codes are proposed that are referred to as orthogonal structure based STBCs. To obtain these codes, the proposed design adds extra-symbols to the OSTBC matrix using different reasonable strategies. Because of the internal OSTBC structure of the proposed designs, the ML decoder can be implemented in a fast way. Simulations validate an improved performance-to-complexity tradeoff of the proposed codes as compared to several other popular choices of space-time codes.

Two computationally efficient high-resolution methods are proposed for direction-of-arrival (DOA) estimation in arbitrary nonuniform sensor arrays. Our first algorithm is based on the fact that the spectral MUSIC function is periodic in angle. Expanding this function using Fourier series, we reformulate the DOA estimation problem as an equivalent polynomial rooting problem. Our second approach applies the inverse Fourier transform to the so-obtained root-MUSIC polynomial to compute the null-spectrum without any polynomial rooting, using a simple line search. The proposed techniques are shown to offer substantially improved performance-to- complexity tradeoffs as compared to the existing root-MUSIC-type methods applicable to non-uniform arrays.

Covariance matrix tapering (CMT) is a popular approach to improve the robustness of adaptive beamformers against moving or wideband interferers. In this paper, we develop a computationally efficient online implementation of the CMT technique based on a low-rank approximation of the taper matrix and the recursive least squares (RLS) algorithm. It is demonstrated that the performance of the proposed low-rank CMT approach is very close to that of the conventional CMT technique.

A new blind channel estimation technique for multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed. It estimates the channel parameters in the time domain jointly for all subcarriers instead of doing this in the frequency domain independently for each subcarrier. This results in a substantially improved parsimony of the channel parameterization along with the ability to use coherent processing across the subcarriers. It is shown that using semi-definite relaxation (SDR), our channel estimation problem can be transferred to a convex form and then solved efficiently using modern convex optimization tools.

A novel blind subspace-based channel estimation technique is developed for direct-sequence code division multiple-access (DS-CDMA) systems operating in unknown wide-sense stationary noise environments. Unlike the existing blind algorithms designed for unknown noise environments, the proposed technique is applicable to any symbol constellation and does not require any auxiliary antennas at the receiver side. The proposed technique is based on the generalized correlation decomposition (GCD) that is used to obtain more accurate estimates of the noise subspace and the user-of-interest channel vector. Simulation results show that when the optimal GCD weighting matrices are used, the estimation performance is substantially improved as compared to the conventional singular value decomposition (SVD)-based blind channel estimation techniques.

A new algorithm for estimating the parameters of multiple wideband polynomial-phase signals (PPSs) in sensor arrays is developed. The spatial high-order instantaneous moments (SHIMs) are first defined using a nonlinear transformation of the array snapshot vectors. Then, the properties of SHIMs of multiple wideband PPSs are employed to obtain recursive estimates of the PPS frequency parameters. The time-frequency properties of SHIMs are then exploited for estimating the source directions-of-arrival (DOAs) using spatial time-frequency distributions (STFDs). The proposed algorithm is shown to have an improved performance compared to the chirp beamformer technique. Since our algorithm is based on multiple one-dimensional searches, it also offers a much simpler implementation avoiding multi-dimensional search, which is needed for other algorithms.

A new 2timesx2 full-rate full-diversity space-time block code (STBC) is proposed that satisfies the non-vanishing determinant property and offers a reduced computational complexity as compared to the other existing full-rate codes. The performance of our new STBC is shown to be comparable to that of the best full-rate STBCs known so far. This performance is achieved at the decoding complexity which is substantially lower than that of the standard sphere decoder.

Estimator bank-based direction finding techniques make use of a number of parallel randomly weighted MUSIC direction-of-arrival (DOA) estimates and obtain the final estimate by keeping only the "successful" candidates while sorting out the outlying estimates. In this paper, we develop a powerful approach to detect the outliers in the estimator bank-based direction finders using the likelihood ratio quality assessment. Computer simulations show substantial improvements of the proposed approach as compared to the earlier techniques used to sort out the outliers in the estimator bank-based direction finding methods.

The problem of joint channel and carrier frequency offset (CFO) estimation is addressed in the context of multiple-input multiple-output (MIMO) communications using orthogonal space-time-block codes (OSTBCs). A new semiblind method is proposed to jointly estimate the channel matrix and the CFO parameter. Our method blindly estimates the CFO parameter along with a low-dimensional subspace where the channel is located, and then uses a few training blocks to extract the channel parameters from this subspace.

In this paper, an interesting relationship between the worst-case optimization-based and probability-constrained approaches to the robust adaptive beamformer design is found both in the cases of Gaussian and non-Gaussian steering vector mismatch. The established relationship demonstrates that the probabilistic beamformer design may be approximately interpreted in terms of the worst-case design, and quantifies the parameters of the latter design in terms of the beamformer outage probability.

High signal-to-noise ratio (SNR) performance of the blind minimum output energy (MOE) receiver and the Capon channel estimation technique are analyzed in the large code division multiple access (CDMA) network where both the spreading factor and number of users go to infinity with the same rate. Upper and lower bounds on the signal-to-interference-plus-noise ratio (SINR), efficiency and asymptotic efficiency of the MOE receiver are derived and compared with those for the optimum minimum mean squared error (MMSE) receiver.

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

A new approach to multi-antenna downlink beam- forming is proposed that provides an improved robustness against uncertainty in the downlink channel covariance matrices caused by errors between the actual and estimated channel values. The proposed method uses the knowledge of the statistical distribution of such a covariance uncertainty to minimize the total downlink transmit power under the constraint that the outage probability does not exceed a certain threshold value. Although our approach initially leads to a non-convex optimization problem, it can be reformulated in a convex form using the semidefinite relaxation technique. The resulting convex optimization problem can be solved efficiently using the well-established interior point methods. Computer simulations verify performance improvements of the proposed technique as compared to the robust transmit beamforming method based on the worst-case performance optimization with judicious selection of the upper bounds on channel covariance errors.

In this paper, we develop a state-space approach to the blind multiuser detection problem with robustness against mismatches in the desired user signature and the time-varying number of users in the channel. The solution is obtained adaptively using a second-order extended Kalman filter (EKF) and requires only O(L2) operations per iteration, where L is the dimension of the subspace containing the signatures of all the users. We also present a state-space approach to the decision directed multiuser detection problem and an algorithm for switching between robust blind and decision directed detection. The proposed switching algorithm is based on using the normalized innovation square (NIS) of the blind detector to test for its convergence and the NIS of the decision directed detector to detect nonstationarities. Thus, it combines the advantages of both these detection schemes and can achieve an output signal- to-interference-plus-noise ratio (SINR) comparable to that of the minimum mean square error (MMSE) detector without any training, even in the presence of mismatches in the desired user signature. Therefore, it is well suited to practical nonstationary environments where users repeatedly enter and leave the system making the cost of retraining un affordable.

Two related problems of the design of robust adaptive beamformers and multiuser multiple-input multiple-output (MIMO) receivers are considered. A popular recent solution to these problems is based on the worst-case performance optimization. Unfortunately, in practical applications the actual worst case occurs with a very low probability and, as a result, the worst-case based designs may be overly conservative. As a less conservative alternative to the worst-case designs, the so-called probabilistically constrained designs are introduced. The latter approach guarantees that the distortionless response constraint is satisfied for a mismatched array response with a certain selected probability. Improved flexibility and performance of the robust probabilistically constrained designs with respect to the worst-case designs are illustrated via simulations.

A new blind subspace-based channel and signature waveform estimation technique is proposed for DS-CDMA communication systems operating in the presence of unknown wide-sense stationary interference. Unlike the existing algorithms, our technique requires single receive antenna and is applicable to the general case of arbitrary transmitted symbol constellations. Necessary and sufficient conditions for identifiability of the proposed technique are derived. Closed-form expressions for the mean-squared error (MSE) of the estimated channel are obtained and verified by means of simulations

Traditional multiuser receiver algorithms developed for multiple-input-multiple-output (MIMO) wireless systems are based on the assumption that the channel state information (CSI) is precisely known at the receiver. However, in practical situations, the exact CSI may be unavailable because of channel estimation errors and/or outdated training. In this paper, we address the problem of robustness of multiuser MIMO receivers against imperfect CSI and propose a new linear technique that guarantees the robustness against CSI errors with a certain selected probability. The proposed receivers are formulated as probabilistically constrained stochastic optimization problems. Provided that the CSI mismatch is Gaussian, each of these problems is shown to be convex and to have a unique solution. The fact that the CSI mismatch is Gaussian also enables to convert the original stochastic problems to a more tractable deterministic form and to solve them using the second-order cone programming approach. Numerical simulations illustrate an improved robustness of the proposed receivers against CSI errors and validate their better flexibility as compared with the robust multiuser MIMO receivers based on the worst case designs.

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

The exact probabilities of error in the case of multimedia multicast transmission in multiple-input multiple-output (MIMO) mobile wireless networks using orthogonal space-time block codes (OSTBC) are analyzed. The cases of nonuniform 4-PSK and 8-PSK constellations are considered. Comparisons of the derived expressions with numerical probabilities of error demonstrate the validity of our analysis.

We address the problem of estimating the frequencies and damping factors of a multidimensional signal which consists of several damped complex exponentials. Such a problem is of interest in several applications, such as nuclear magnetic resonance spectroscopy, where the 2-dimensional (2D) frequencies and damping factors are used to determine the structure of proteins. We herein propose a new algorithm which exploits the multiple-invariance structure that exists in the data model. Unlike search-based parameter estimation techniques, such as D-MUSIC of Y. Li et al. (1998), which have been developed for multidimensional harmonic retrieval of damped exponentials, our algorithm uses polynomial rooting to obtain the parameters of interest efficiently in a search-free fashion.

In this paper, we improve the performance of robust linear receivers for multi-user multiple-input multiple-output (MIMO) wireless systems by exploiting the inherent structure of orthogonal space-time block codes (OSTBC). This particular structure results in a worst-case optimization problem with structured uncertainty set. Exploiting this structure, an improved robust linear receiver with a combination of fixed diagonal loading and adaptive non-diagonal loading of the data covariance matrix is obtained.

We consider the problem of simultaneous estimation of the channel state information (CSI) of several transmitters that use orthogonal space-time block codes to communicate with a single receiver. Based on the generalizations of the Capon and MUSIC techniques, we propose two novel algorithms to estimate multi-user MIMO channels. These algorithms estimate the subspace spanned by the user channels blindly and use only a few training blocks to extract the users' CSI from this subspace.

A new subspace-based blind algorithm for signature waveform estimation in direct-sequence code division multiple-access (DS-CDMA) systems is proposed. Our technique represents a generalization of the popular technique by H. Liu and G. Xu (see IEEE Trans. Communications, vol.44, p.1346-54, 1996) and additionally exploits the non-circularity of the transmitted signals and the circularity of noise to increase the dimension of the observation space twice while keeping the dimension of the signal subspace unchanged. This leads to a substantially improved performance of the proposed algorithm and enables it to be applied to scenarios with larger numbers of active users and lengthier user channels as compared to the original algorithm by Liu and Xu.

Transmit beamforming is a powerful approach to enhance the performance of wireless communication systems employing multiple antennas at the transmitter. Using channel covariance information, transmit beamforming has been pursued based on various criteria. A major drawback of existing techniques is that most of them require nearly perfect knowledge of the channel covariance matrix at the transmitter, which is not necessarily possible in practice. In this paper, we propose a more robust framework in which we take this issue into account and design a robust transmit beamformer to have the best performance under the worst-case mismatch. We show that, for small mismatches, eigen-beamforming along the eigenvectors of the presumed channel correlation matrix offers the best worst-case performance. We also combine eigen-beamforming with robust power loading that is achieved by spatial water-filling-type strategy in which the water level is determined in a simple form.

A new spectral search-based direction-of-arrival (DOA) estimation method is proposed that extends the idea of the conventional ESPRIT DOA estimator to a much more general class of array geometries than assumed by the conventional ESPRIT technique. A computationally efficient polynomial rooting-based search-free implementation of the proposed algorithm is also developed.

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.

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 that is obtained through a low rate feedback. Three adaptive approaches are considered to exploit such a CSI feedback: adaptive subcarrier selection, adaptive power allocation and adaptive modulation selection. Under the condition of constant raw data rate, the performance of these approaches is analyzed and compared in terms of raw bit error rate (BER). We have found that one-bit CSI feedback can greatly enhance the system performance. Among the three approaches, the adaptive subcarrier selection approach is found to have the lowest BER when the feedback is perfect.

We study the problem of direction-of-arrival (DOA) estimation using partly calibrated arrays composed of multiple subarrays with unknown inter-subarray parameters and imperfectly known subarray orientations. The recently developed spectral and root variants of the rank reduction estimator (RARE) can handle scenarios where no calibration between subarrays is available but, unfortunately, they are very sensitive to subarray orientation errors. Therefore conventional RARE can be applied to such partly calibrated arrays only if all subarray misorientations are negligibly small. In this paper, we develop a new modification of RARE which improves its robustness against subarray misorientations. The performance of the proposed robust RARE algorithm is demonstrated to be close to the stochastic Cramer-Rao bound (CRB) of the considered estimation problem.

The problem of joint space-time decoding and interference rejection in multiple-access MIMO wireless communication systems is considered in the case of erroneous or limited channel state information (CSI) at the receiver. Linear beamforming-type techniques that have an improved robustness in such an imperfect CSI case are proposed.

A new computationally simple approach to blind decoding of orthogonal space-time block codes (STBC) is proposed. Our approach estimates the channel matrix in a closed form and uses this estimate in the maximum likelihood (ML) receiver to decode the symbols. It exploits specific properties of the orthogonal STBC and is free of major drawbacks of other blind space-time decoding schemes.

Parallel factor (PARAFAC) analysis is an extension of low-rank matrix decomposition to higher-way arrays. It decomposes a given array in a sum of multilinear terms. PARAFAC analysis generalizes and unifies common array processing models (like joint diagonalization and ESPRIT); it has found numerous applications from blind multiuser detection and multi-dimensional harmonic retrieval to clustering and nuclear magnetic resonance. The prevailing fitting algorithm in all these applications is based on alternating least squares (ALS) optimization, which is matched to Gaussian noise. In many cases, however, measurement errors are far from being Gaussian. We develop an iterative algorithm for least absolute error fitting of general multilinear models, based on efficient interior point methods for linear programming (LP). We also benchmark its performance in Laplacian, Cauchy, and Gaussian noise environments, versus the respective CRBs and the commonly used ALS algorithm.

We propose a new blind multiuser receiver which is robust against the effects of erroneously presumed desired user signature and short data length. Our approach is based on the explicit modeling of possible mismatches in the mean-square error cost function and worst-case performance optimization. We show that this approach leads to a multiuser receiver which uses the data covariance matrix with an adaptive diagonal loading. Simulation results show performance improvements achieved by our approach relative to existing techniques.

Channel estimation in multiple-input multiple-output (MIMO) wireless communication systems plays a key role in the performance of space-time decoders that depends on the accuracy of the channel knowledge. In this paper, we study the performance of MIMO channel estimation using training sequences. The least squares (LS), minimum-mean-square error (MMSE), and a new scaled LS (SLS) approaches to the channel estimation are studied and the optimal choice of training signals is investigated for each of these techniques.

The problem of joint space-time decoding and multiple-access interference (MAI) rejection in multiple-access multiple-input multiple-output (MIMO) wireless communication systems is addressed. We assume that both the receiver and multiple transmitters are equipped with multiple antennas and that space-time block codes are used to send the data simultaneously from each transmitter to the receiver. A new linear minimum variance (MV) receiver structure is developed to decode the data sent from the transmitter-of-interest and to reject MAI, self-interference, and noise. Simulation results show that in multiple-access MIMO scenarios, the proposed receivers have a substantially lower symbol error rate as compared to the matched filter (MF) receiver which is equivalent to the maximum likelihood (ML) space-time decoder in the point-to-point MIMO communications case.

A new computationally simple approach to blind decoding of orthogonal space-time block codes is proposed. Our approach estimates the channel matrix in a closed form and uses this estimate in the maximum likelihood (ML) receiver to decode the symbols. It exploits specific properties of the orthogonal space-time block codes (STBCs) and is free of most of the shortcomings of other blind space-time decoding schemes.

Adaptive beamforming methods degrade in the presence of both signal steering vector errors and interference nonstationarity. We develop a new approach to adaptive beamforming that is jointly robust against these two phenomena. Our beamformer is based on the optimization of the worst case performance. A computationally efficient convex optimization-based algorithm is proposed to compute the beamformer weights. Computer simulations demonstrate that our beamformer has an improved robustness as compared to other popular robust beamforming algorithms.

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.

The performance of blind multiuser detection methods is known to degrade severely in the presence of even small mismatches between the actual and the presumed desired user signatures. Such mismatches may occur in practical situations due to an imperfect knowledge of the channel impulse response. We propose a new robust approach to blind multiuser detection in the presence of unknown arbitrary-type mismatches of the desired user signature. Two different formulations of a robust multiuser receiver are considered. The proposed formulations are based on the explicit modeling of uncertainties in the covariance matrix of the desired user signature and/or data covariance matrix and optimization of the worst-case performance. Simple closed-form solutions to the considered robust multiuser detection problems are derived. The proposed methods have a computational complexity comparable to that of the traditional blind multiuser detection algorithms, and, at the same time, offer an improved robustness and faster convergence rates.

We prove a new interesting property of space-time block codes (STBCs) that are based on generalized orthogonal designs. For flat block-fading channels, it is shown that the internal structure of the vector space of the input constellation remains invariant to the combined effect of the STBC and the channel, except for a scaling factor. The established constellation space invariance property is entirely due to the specific structure of the STBCs based on the generalized orthogonal designs. Using this property, we obtain simple exact expressions for the error probability of the maximum likelihood (ML) decoder in the general case when the channel, STBC, and input signal constellations are arbitrary. Such expressions are obtained in both the cases when the channel realization is deterministic (fixed) and random. In the latter case, simple expressions are derived for the average error probability.

A new robust power adjustment method is proposed for transmit beamforming in cellular communication systems that use antenna arrays at base stations (BSs). Our method provides an improved robustness against imperfect knowledge of the wireless channel by means of maintaining the required quality of service (QoS) for the worst-case channel uncertainty.

The performance of blind multiuser detection methods is known to degrade in the presence of mismatches between the actual and the presumed desired user signatures. Such mismatches may occur in practical situations due to an imperfect knowledge of the channel impulse response. We propose a new robust approach to blind multiuser detection in the presence of unknown arbitrary-type mismatches of the desired user signature. The formulations of our robust multiuser receivers are based on the explicit modeling of uncertainties in the covariance matrix of the desired user signature/data covariance matrix and optimization of the worst-case performance. The proposed methods have a computational complexity comparable to that of the traditional blind multiuser detection algorithms, while offering an improved robustness and faster convergence rates.

Multiple antenna wireless communication systems have recently attracted significant attention due to their higher capacity as compared to the systems that employ a single antenna. For systems with a large number of antennas, there is a strong motivation to develop techniques with reduced hardware and computational costs. An efficient approach to achieve this goal is the optimal antenna subset selection. We propose a fast antenna selection algorithm for wireless multiple-input multiple-output (MIMO) systems. Our algorithm achieves almost the same outage capacity as the optimal selection technique while having a lower computational complexity than existing nearly optimal antenna selection methods.

The performance of adaptive beamforming methods may degrade in the presence of even slight mismatches between the actual and presumed array responses to the desired signal. This paper addresses the problem of robust adaptive beamforming in the presence of unknown arbitrary (yet norm-bounded) mismatches of such type as well as interference-plus-noise covariance matrix mismatch. Our approach is developed for the case of an arbitrary dimension of the signal subspace and, therefore, it can be applied to both rank-one and higher-rank signal models. The proposed beamformer is based on the optimization of the worst-case signal-to-interference-plus-noise ratio (SINR). The obtained closed-form solution combines two different types of diagonal loading (DL) applied to the signal and data covariance matrices. An efficient on-line implementation of our beamformer is developed. Simulations validate substantial performance improvements relative to other popular adaptive beamforming techniques.

Adaptive beamforming methods are known to degrade in the presence of both signal steering vector errors and interference nonstationarity. In this paper, we develop a new approach to adaptive beamforming which is jointly robust against these two phenomena. Our approach is based on the optimization of the worst-case beamforming performance. A computationally efficient convex optimization based algorithm is proposed to compute the beamformer weights. Computer simulations compare the performance of our algorithm with other robust adaptive beamforming techniques.

In this paper blind multiuser detection of Direct Sequence Code Division Multiple Access (DS-CDMA) signals over time-varying time-dispersive channels is considered. A number of methods for multiuser detection over time-dispersive channels have been proposed in previous research. It is shown in this paper that in a time-varying channel these methods will not perform satisfactorily and an adaptive multiuser detector for time-varying channels based on the Interacting Multiple Models estimator is proposed. It is shown by simulations that the proposed method outperforms the existing ones in a time-varying channel.

A new approach to adaptive beamforming with sidelobe control is developed. The proposed beamformer represents a modification of the popular minimum variance distortionless response (MVDR) beamformer. It minimizes the array output power while maintaining the distortionless response in the direction of the desired signal and a sidelobe level that is strictly guaranteed to be lower than some given (prescribed) threshold value. The resulting modified MVDR problem is shown to be convex, and its second-order cone (SOC) formulation is obtained that facilitates a computationally efficient way to implement our beamformer using the interior point method.

We study the uniqueness of the signal Direction Of Arrival (DOA) estimates obtained using the RAnk Reduction Estimator (RARE) [I] in partly calibrated subarray-based sensor arrays. A new identifiability condition is derived for such class of arrays which guarantees that the array manifold is unambiguous. The equivalence of the MUSIC solution for the signal DOA's (obtained in the fully calibrated array case) and the RARE solution (obtained in the case of partly calibrated array of the same configuration) is proved and the uniqueness of the RARE DOA estimates is established.

The problem of direction finding in partly calibrated arrays of an arbitrary geometry is addressed. We assume that an array is composed of multiple calibrated subarrays and consider the cases of unknown (or known with some error) inter-subarray displacements, imperfect synchronization of subarrays in time, as well as unknown channel mismatches between subarrays. Recently, the so-called RAnk Reduction Estimator (RARE) has been proposed as a particular solution to the problem of direction finding in partly calibrated arrays with unknown inter-subarray displacements. However, the application of RARE is restricted by the special case of arrays composed of identically oriented linear subarrays with interelement spacings which are integer multiples of a certain shortest baseline. In this paper, we propose a more general subspacebased approach which further develops the ideas of RARE and is applicable to partly calibrated arrays composed of subarrays of arbitrary geometry. The algorithms proposed enable simple extensions to the two-dimensional (2D) Direction-Of-Arrival (DOA) estimation case.

If the desired signal is present in training snapshots, the adaptive array performance is known to be quite sensitive even to slight mismatches between the presumed and actual signal steering vectors. Such mismatches can occur as a result of environmental nonstationarities, look direction errors, imperfect array calibration or distorted antenna shape, as well as distortions caused by medium inhomogeneities, near-far mismatch, source spreading, and local scattering. The similar type of performance degradation can occur when the signal steering vector is known exactly but the training sample size is small. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an arbitrary unknown signal steering vector mismatch. Our approach is based on the optimization of worst-case performance using Second-Order Cone (SOC) programming. The adaptive beamformer proposed is shown to have a substantially improved robustness as compared to existing algorithms and enjoy simple implementation.

We study Friedlander's (1993) array interpolation technique, whose main shortcoming in multisource scenarios is that it does not provide sufficient robustness against sources arriving outside specified interpolation sectors. In this letter, we develop a new robust interpolation approach by minimizing the interpolation error inside the sectors of interest while setting multiple "stopband" constraints outside these sectors to prevent performance degradation effects caused by out-of-sector sources. Computationally efficient convex formulations of the robust interpolation matrix design problem using second-order cone programming are derived.

We consider the direction-of-arrival (DOA) problem for a wavefront whose amplitude and phase vary randomly along the array aperture. This phenomenon can for instance originate from propagation through an inhomogeneous medium. A simple and accurate DOA estimator is derived in the case of an uniform linear array of sensors. The estimator is based upon a reduced statistic obtained from the sub-diagonals of the covariance matrix of the array output. It only entails computing the Fourier transform of an (m-1)-length sequence where m is the number of array sensors. A theoretical expression for the asymptotic variance of the estimator is derived. Numerical simulations validate the theoretical results and show that the estimator has an accuracy very close to the Cramer-Rao bound.

The stochastic Cramer-Rao bound (CRB) plays an important role in array processing because several high-resolution direction-of-arrival (DOA) estimation methods are known to achieve! this bound asymptotically In this paper, we study the stochastic CRB on DOA estimation accuracy in the general case of arbitrary unknown noise field parametrized by a vector of unknowns. We derive explicit closed-form expressions for the CRB and examine its properties theoretically and by representative numerical examples.

Environmental parameter estimation for a shallow ocean is addressed by using wideband shipping noise as a source of acoustic energy. Unknown locations of the broadband acoustic sources are estimated simultaneously with the ocean depth using the approximate conditional maximum likelihood estimator (CMLE). This procedure is tested via computer simulations and applied to the experimental hydrophone towed array data.

We consider the direction finding problem in time-varying arrays composed of identically oriented subarrays displaced by unknown vector translations. A new eigenstructure-based estimator is proposed for such a class of partly calibrated sensor arrays.

The stochastic Cramer-Rao bound (CRB) for direction estimation in array processing applications was indirectly derived some ten years ago as the (asymptotic) covariance matrix of the maximum likelihood (ML) estimator. Attempts to obtain the stochastic CRB directly via the CRB theory fell short of providing a simple derivation and consequently, no direct derivation of this useful performance bound was available in the open literature. we correct this situation by providing a textbook-like direct derivation of the stochastic CRB.

The previously developed concept of narrowband spatial time-frequency distributions (STFDs) is extended to the wideband case. A new STFD-based root-MUSIC estimator is proposed. This estimator exploits a short-window spatial pseudo Wigner-Ville distribution (SPWVD) to enable application of the subspace-based approach. To combine all relevant SPWVD points, the proposed technique employs an extended coherent signal-subspace (CSS) principle involving coherent averaging over a pre-selected set of time-frequency points rather than the conventional frequency-only averaging procedure.

A real-valued (unitary) formulation of the popular root-MUSIC direction-of-arrival (DOA) estimation technique is considered. This unitary root-MUSIC algorithm is shown to reduce the computational complexity in the eigenanalysis stage of root-MUSIC, because it exploits the eigendecomposition of a real-valued covariance matrix. Theoretical, numerical, and experimental results are presented showing that, additionally, unitary root-MUSIC has improved threshold and asymptotic performances relative to conventional root-MUSIC. It can be then recommended that the former technique should always be preferred to the conventional root-MUSIC algorithm.

The recently developed concept of narrowband spatial time-frequency distributions (STFDs) is extended to the wide-band case. A new STFD-based wideband root-MUSIC estimator is proposed. This technique employs an extended coherent signal-subspace (CSS) principle involving coherent averaging over a pre-selected set of time-frequency points rather than the conventional frequency-only averaging procedure.

A windowed local polynomial approximation (LPA) of time-varying directions-of-arrival (DOA) is exploited to derive a new form of beamformer for source localization and tracking in nonstationary environments. The relationship between our LPA-beamformer and conventional beamformer is discussed.

The performances of high resolution array processing methods are known to degrade in random inhomogeneous media. Such a degradation is caused by random amplitude and phase variations of source wavefronts. In this letter, the popular covariance matching approach to direction finding in the presence of multiplicative noise is extended to the multiple source case. Using a few unrestrictive physics-based assumptions on the environment, we derive a generic multiplicative noise data model. Based on this model, new direction finding techniques are proposed.

We propose a new MODE-based direction of arrival (DOA) estimation algorithm with an improved SNR threshold as compared to the conventional MODE technique. Our algorithm preserves all good properties of MODE, such as asymptotic efficiency, excellent performance in scenarios with coherent sources, as well as a reasonable computational cost. Similarly to root-MODE, the proposed method does not require any global multidimensional optimization since it is based on a combination of polynomial rooting and a simple combinatorial search. Our technique is referred to as MODEX (MODE with EXtra roots) because it makes use of a certain polynomial with a larger degree than that of the conventional MODE-polynomial. The source DOAs are estimated via checking a certain (enlarged) number of candidate DOAs using either the stochastic or the deterministic maximum likelihood function. To reduce the computational cost of MODEX, a priori information about source localization sectors can be exploited.

A new pseudo-noise resampling technique is proposed to mitigate the effect of outliers in Unitary ESPRIT. This scheme improves the performance of Unitary ESPRIT in unreliable situations, where the so-called reliability test has a failure. For this purpose, we exploit a pseudo-noise resampling of a failed Unitary ESPRIT estimator with a censored selection of "successful" resamplings recovering the non-failed outputs of the reliability test.

After reviewing the main existing methods for determining the maximum-likelihood (ML) estimates of the direction-of-arrival (DOA) parameters in array signal processing applications, we introduce a new conceptually simple and computationally effective approach that consists of maximizing the likelihood function (LF) over a set of points derived from the data. We show that the data-supported grid search of the LF provides a performance similar to that achieved by a genetic algorithm, but at a significantly lower computational cost. We use an ESPRIT-like algorithm to obtain the grid points with support in the data, although our approach is not limited to this choice.

A beamspace root modification of pseudorandom joint estimation strategy (PR-JES) is developed. The essence of PR-JES is to generate the eigenstructure-based estimator bank for given sample covariance or data matrix. Combining the results of "parallel" underlying estimators, PR-JES removes the outliers and improves the threshold performance. In the case of a non-uniform array, the interpolated array approach is used to enable the application of root underlying estimators. Simulations and results of real ultrasonic data processing show that the proposed beamspace root implementation significantly outperforms spectral elementspace PR-JES and achieves a performance similar or better than that of the stochastic ML method.

The 2-D elementspace and beamspace extensions of Friedlander's (1993) wideband interpolated root-MUSIC technique are applied to azimuth-velocity source location using real seismic data from the GERESS array (Germany). We demonstrate that the 2-D interpolated root-MUSIC is able to estimate the parameters of a typical seismic source with a good accuracy. The use of the interpolated root-MUSIC and its beamspace modification is motivated by the significant reduction of processing time allowing on-line implementation.

The performance of high-resolution direction finding methods degrades in several practical situations where the wavefronts have imperfect spatial coherence. The original solution to this problem was proposed by Paulraj and Kailath (1988), but their technique requires a priori knowledge of the matrix characterizing the loss of wavefront coherence along the array aperture. A novel solution to this problem is proposed, which does not require a priori knowledge of the spatial coherence matrix. Our technique is based on the multidimensional minimization of the appropriate concentrated cost function using the genetic algorithm (GA).

A new pseudorandom spatial spectrum resampling scheme for improving the threshold performance of eigenstructure direction of arrival (DOA) estimators is proposed. Our approach is based on combining additional information arising when several underlying DOA estimators are available simultaneously, e.g., are calculated in parallel manner using given estimate of covariance matrix. Pseudorandomly generated weighted MUSIC estimators are exploited as appropriate underlying spatial spectral functions. Simulations show that our approach significantly improves the DOA estimation signal-to-noise ratio (SNR) threshold relative to the conventional and beamspace MUSIC estimators, and performs in the threshold region almost as well as the stochastic maximum likelihood (ML) estimator.

Given an n-element linear array with the fixed positions x/sub 1/ and x/sub n/ of the leftmost and rightmost array sensors, it is shown that the stochastic Cramer-Rao bound (CRB) and MUSIC performance depend on positions of the remaining n-2 sensors within the interval [x/sub 1/, x/sub n/]. The asymptotic performance of the interpolated array approach shows similar dependence. The most favorable geometries are unrealizable for q

A simple approximate maximum likelihood (AML) estimator is derived for estimating a power of a single signal with rank-one spatial covariance matrix known a priori except for scaling. The noise are assumed to have different and unknown powers in each array sensor. The variance of the introduced AML estimator is compared with the exact Cramer-Rao bound (CRB) of this estimation problem analytically and by computer simulations. It is shown analytically that the AML estimator achieves the CRB in the majority of practically important cases. Computer simulations have been performed showing that the estimation errors of the AML estimator are very close to the CRB for a wide SNR range.

The application of sensor array processing methods for estimation and localization of wavefield sources is well-known. We extend the sensor array processing approach to estimating the parameters of the fields of a nonwave nature (the so-called nonwave fields). Considering the static and diffusion fields as typical examples of nonwave field, we derive the Cramer-Rao bounds of source parameter estimation errors. These theoretical results are completed by the experimental results of localization of diffusion sources in distilled water by a chemical sensor array, showing potentially high performance of sensor array approach. A modified version of the well-known CLEAN deconvolution algorithm has been used for experimental data processing. The nonwave field sensor array processing can find various applications such as localization of pollution sources and another types of admixtures, detection of metallic masses and wandering currents, etc.

We consider the popular spatial smoothing technique and show via the covariance matrix eigenvalue analysis that the simple suboptimal rule for choosing of the subarray size exists in a practically important situation of two coherent equipower closely spaced sources. This rule has been derived by maximizing the distance between the signal subspace and the noise subspace eigenvalues of spatially smoothed covariance matrix and it does not require any a priori information about the signal source parameters. >

The problem of weak moving signal detection and tracking in the presence of strong interference is investigated using the real data of experiment in the Baltic Sea (September 1990) with an underwater horizontal receiving array of 64 hydrophones. The authors employ three very simple adaptive beam-forming algorithms for signal detection and tracking. The signal-to-interference power ratio (SIR) threshold of signal detection was obtained by a special technique, which allows to examine the interference suppression algorithms with change the SIR in consecutive order. The results of real data processing show that the SIR threshold is about -30 dB.>