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Smart antenna systems are used in a wide range of fixed and mobile communication applications. Generally smart antennas are made of antenna arrays and signal processors. The antenna array can have any kind of shape, from linear to circular or rectangular. For simplicity considerations in this paper we studied the linear antenna array. The main characteristic of a smart antenna system is the digital signal processing feature. The system processes the signals in order to obtain a desired radiation pattern. In this paper we will describe some of the most significant algorithms used for processing the signal received by every element of the array along with some of the advantages and disadvantages for each of them. The algorithms described are the LMS (Least Mean Squares), the RLS (Recursive Least Squares) and CMA (Constant Modulus Algorithm). [ABSTRACT FROM AUTHOR]

A distributed detection scheme where the sensors transmit with constant modulus signals over a Gaussian multiple access channel is considered. The deflection coefficient of the proposed scheme is shown to depend on the characteristic function of the sensing noise and the error exponent for the system is derived using large deviation theory. Optimization of the deflection coefficient and error exponent are considered with respect to a transmission phase parameter for a variety of sensing noise distributions including impulsive ones. The proposed scheme is also favorably compared with existing amplify-and-forward and detect-and-forward schemes. The effect of fading is shown to be detrimental to the detection performance through a reduction in the deflection coefficient depending on the fading statistics. Simulations corroborate that the deflection coefficient and error exponent can be effectively used to optimize the error probability for a wide variety of sensing noise distributions. [ABSTRACT FROM AUTHOR]

Abstract: A class of constant modulus algorithms (CMAs) subject to a conjugate symmetric constraint is proposed for blind beamforming based on the uniform linear array structure. The constraint is derived from the beamformer with an optimum output signal-to-interference-plus-noise ratio (SINR). The effect of the additional constraint is equivalent to adding a second step to the original adaptive algorithms. The proposed approach is general and can be applied to both the traditional CMA and its all kinds of variants, such as the linearly constrained CMA (LCCMA) and the least squares CMA (LSCMA) as two examples. With this constraint, the modified CMAs will always generate a weight vector in the desired form for each update and the number of adaptive variables is effectively reduced by half, leading to a much improved overall performance. [Copyright &y& Elsevier]

A distributed estimation scheme where the sensors transmit with constant modulus signals over a multiple access channel is considered. The proposed estimator is shown to be strongly consistent for any sensing noise distribution in the i.i.d. case both for a per-sensor power constraint, and a total power constraint. When the distributions of the sensing noise are not identical, a bound on their variances is shown to establish strong consistency. The estimator is shown to be asymptotically normal with a variance (AsV) that depends on the characteristic function of the sensing noise. Optimization of the AsV is considered with respect to a transmission phase parameter for a variety of noise distributions exhibiting differing levels of impulsive behavior. The robustness of the estimator in the sense of degrading gracefully when the sensing noise deviates from the Gaussian distribution to impulsive sensing noise distributions such as those with positive excess kurtosis, or those that do not have finite moments is shown. The proposed estimator is favorably compared with the amplify and forward scheme under an impulsive noise scenario. The effect of fading and phase error at the sensors are shown to not affect the consistency of the estimator, but to degrade the asymptotic variance by terms that depend on the fading and phase error distributions. Simulations corroborate our analytical results. [ABSTRACT FROM PUBLISHER]

Abstract: Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α-stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α-stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α-stable noise. [Copyright &y& Elsevier]

Abstract-The diagonal loading method is a simple and efficient method that improves the robustness of beamformers. However, determining an ideal diagonal loading factor (DLF) is not a trivial problem, one that still has not been adequately addressed. Although beamformers are widely used in radar, sonar, and many other applications, in this correspondence, we consider a beamformer used only for digital communication applications. In digital communications, user information is coded into a sequence with the constant modulus (CM) in the baseband. Obviously, if the beamformer can cancel interference sufficiently well, the outputs of the beamformer should satisfy the CM condition more precisely. Therefore, minimizing CM errors can act as a natural criterion for determining DLFs. In this correspondence, we present a cost function based on the criterion. We also propose a direct search algorithm for DLF determination by minimizing the cost function. Numerical experiments show the effectiveness of the pro. posed method. [ABSTRACT FROM AUTHOR]

Transmit beampattern design is a critically important task in many fields including defense and homeland security as well as biomedical applications. Flexible transmit beampattern designs can be achieved by exploiting the waveform diversity offered by an array of sensors that transmit probing signals chosen at will. Unlike a standard phased-array, which transmits scaled versions of a single waveform, a waveform diversity-based system offers the flexibility of choosing how the different probing signals are correlated with one another. Recently proposed techniques for waveform diversity-based transmit beampattern design have focused on the optimization of the covariance matrix R of the waveforms, as optimizing a performance metric directly with respect to the waveform matrix is a more complicated operation. Given an R, obtained in a previous optimization stage or simply pre-specified, the problem becomes that of determining a signal waveform matrix X whose covariance matrix is equal or close to R, and which also satisfies some practically motivated constraints (such as constant-modulus or low peak-to-average-power ratio constraints). We propose a cyclic optimization algorithm for the synthesis of such an X, which (approximately) realizes a given optimal covariance matrix R under various practical constraints. A numerical example is presented to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with the goodness of the Gaussian assumption when designing second-order blind estimation methods in the context of digital communications. The low- and high-signal-to-noise ratio (SNR) asymptotic performance of the maximum likelihood estimator—derived assuming Gaussian transmitted symbols—is compared with the performance of the optimal second-order estimator, which exploits the actual distribution of the discrete constellation. The asymptotic study concludes that the Gaussian assumption leads to the optimal second-order solution if the SNR is very low or if the symbols belong to a multilevel constellation such as quadrature-amplitude modulation (QAM) or amplitude-phase-shift keying (APSK). On the other hand, the Gaussian assumption can yield important losses at high SNR if the transmitted symbols are drawn from a constant modulus constellation such as phase-shift keying (PSK) or continuous-phase modulations (CPM). These conclusions are illustrated for the problem of direction-of-arrival (DOA) estimation of multiple digitally-modulated signals. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present new deterministic tensor-based techniques for the blind separation of a mixture of DS–CDMA signals received by an antenna array. First, we show that the blind receiver follows from a simultaneous matrix decomposition. We present a new, relaxed, bound on the number of users that can be allowed at the same time. We further derive two algorithms that jointly exploit the CDMA structure and the constant modulus property of the transmitted signals. [Copyright &y& Elsevier]

Abstract: The analytical constant modulus algorithm (ACMA) is an algebraic method for blind separation of co-channel users. Assuming constant modulus (CM) sources, we propose the linearly precoded ACMA (LPACMA) for blind separation of systems employing space-time codes (STCs) that do not preserve the CM property (non-CM STCs). The proposed algorithm requires fewer snapshots than STC-ACMA and resolves the permutation ambiguity inherent to blind algorithms whilst retaining the asymptotic convergence to the Wiener receiver. [Copyright &y& Elsevier]

Orthogonal frequency-division multiple access (OFDMA) gains increasing attention for broadband, high data rate wireless communications. In this paper, we develop a novel unitary precoded (UP) OFDMA scheme that is particularly appealing for the uplink, because it offers high bandwidth efficiency, and constant modulus transmissions for each user. Theoretical analysis of UP-OFDMA with channel coding shows the performance improvement introduced by unitary precoding. It provides useful guidelines for practical system designs, and also quantifies the performance of UP-OFDMA relative to the single-user bound. Simulations confirm that UP-OFDMA improves performance considerably relative to conventional OFDMA. [ABSTRACT FROM AUTHOR]

Constrained Cramér-Rao bounds are developed for convolutive multi-input multi-output (MIMO) channel and source estimation in additive Gaussian noise. Properties of the MIMO Fisher information matrix (FIM) are studied, and we develop the maximum rank of the unconstrained FIM and provide necessary conditions for the FIM to achieve full rank. Equality constraints on channel and signal parameters provide a means to study the potential value of side information, such as training symbols (semi-blind case), constant modulus (CM) sources, or known channels. Nonredundant constraints may be combined in an arbitrary fashion, so that side information may be different for different sources. The bounds are useful for evaluating the performance of SIMO and MIMO channel estimation and equalization algorithms. We present examples using the constant modulus blind equalization algorithm. The constrained bounds are also useful for evaluating the relative value of different types of side information, and we present examples comparing semi-blind, constant modulus, and known channel constraints. While the examples presented are primarily in the communications context, the CRB framework applies generally to convolutive source separation problems. [ABSTRACT FROM AUTHOR]

This paper describes a blind cochannel interference cancelling technique, indirect cochannel interference cancelling (ICIC), that cancels a constant envelope modulated interferer in cellular mobile radio communication channels. ICIC exploits the constant envelope property of the interfering signal to simplify the receiver structure. The basic concept of ICIC and the possible receiver structures are discussed. The performance of a ICIC receiver in practical channels as well as its sensitivities to the channel parameters are investigated by simulation. [ABSTRACT FROM AUTHOR]

The incorporation of directional sensitivity, provided by so-called adaptive antennas is useful in suppressing interfering signals that arise from spatially distinct mobile sources. The problem is that in a cellular radio environment where multipath exists, the standard adaptive antenna using reference signals may not properly lock on the desired signal. This is because the signal correlation matrix processed by the antenna may then be close to singular and standard algorithms fail. Also, most standard algorithms need to cooperate with the receiver for the spatial discrimination of signals. A smart antenna utilizing a blind algorithm is of interest since the antenna may not need to get any feedback from a receiver for the adjustment of weight coefficient for spatial processing and can stand alone to be plugged into any kind of receiver structure. In this paper, we address the convergence property of a Constant Modulus Algorithm which is a blind algorithm and, if employed, can provide no need for an antenna to cooperate with a receiver attached. By identifying a relationship between the weight coefficients and output signal amplitude, we also evaluate the performance of such a stand-alone antenna plus a CDMA matched filter reception. Our results show that for a three element CM array, the BER of a desired user with the other interfering users is much better than a conventional correlation receiver for a single user case since the array suppresses interferences and achieves array gain in SNR. [ABSTRACT FROM AUTHOR]

With sophisticated signal and information processing algorithms, air interfaces with space-time (ST) coding and multiple reception antennas substantially improve the reliability of wireless links. This paper proposes a new receiver algorithm for differential ST coded transmissions over the finite-impulse-response (FIR) rich multipath fading channels. The symbol detection introduced in this paper is a deterministic subspace-based approach in a multiple-input and multiple-output (MIMO) system framework. The receiver (i) operates in a blind fashion without estimating the channel or its inverse and (ii) is able to work with a small number of signal samples and hence can be applied in the quasistatic channels. The proposed scheme employs multiple antennas at both sides of the transceiver and exploits both the antenna diversity and the multiple constant modulus (MCM) characteristics of the signaling. The receiver is able to blindly mitigate the intersymbol interference (ISI) in a rich multipath propagation environment, and this has been verified through the extensive Monte Carlo simulations.

In this paper, an adaptive digital filter algorithm is presented to optimize the signal-to-noise ratio of an antenna array with multiuser signal inputs. These synchronous communication signals have constant envelope property and are transmitted through a Multiple-Input/Multiple-Output (MIMO) linear channel. This Multiuser Constant Modulus Algorithm (MU-CMA) using an antenna array can compensate for both Interuser Interference (IUI) and Intersymbol Interference (ISI). The method is derived by minimizing the cost function of the constant modulus signals as well as cross-correlations between them. The simulation result in this paper shows the performance of this algorithm. [ABSTRACT FROM AUTHOR]

• A low-complexity algorithm for array transmit beampattern synthesis under a constant modulus constraint is proposed based on the alternating direction method of multipliers framework. • By introducing auxiliary variables, the original problem is decomposed into several subproblems with closed-form solutions. • The convergence condition is derived and the convergent solution satisfies the Karush-Kuhn-Tucker conditions of the nonconvex primal problem. Transmit beampattern synthesis for antenna arrays is addressed in this paper. In pursuit of high radiation efficiency, most phased array radar systems utilize nonlinear power amplifiers, which leads to a constant modulus constraint for transmit weight vector optimization. Existing methods to solve this hard nonconvex problem (unit-modulus quadratic program) generally have high computational cost and large memory burden, which may increase dramatically as the array size increases. To that end, we propose a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework. By introducing auxiliary variables, the original problem is decomposed into several subproblems with closed-form solutions. The transmit weight vector and the phase response of the desired beampattern is updated alternately so that the cost function value can be monotonically reduced. With bounded penalty parameters, the proposed ADMM-based algorithm can guarantee to converge. We further prove that the converged solution satisfies the Karush-Kuhn-Tucker conditions of the nonconvex optimization problem. Numerical simulation results confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Waveform design has received considerable attention because it has numerous active sensing applications in sonar, radar, and communications. • Although existing solvers can produce constant modulus waveforms with low PSL, they lack the flexibility to control specified components of the waveform autocorrelation functions, i.e., local PSL And the se methods do not consider designing waveform with low PSL of autocorrelation in a spectrally crowded environment • The wave form autocorrelation can be expressed by using the FFT and IFFT. • Computationally efficient algorithms to design a constant modulus waveform with minimum local PSL under specified spectral and/or autocorrelation requirements. • Use PMM to tackle the equality constrained optimization problems, split the complicated problem into small problems, and tackle them individually. The problem of designing waveforms with specified aperiodic/periodic correlation and spectral properties has received much attention because of its wide applicability in sonar, radar, and communications. In particular, a waveform with low peak sidelobe level (PSL) of autocorrelation can improve the detection performance of a weak target masked by a nearby strong target in range compression radar systems, and that with adaptive spectrum nulls/notches can make the sensing systems not be interfered by other electromagnetic equipment. On the other hand, a constant modulus (CM) waveform can maximize the transmit power efficiency of the system. Whereas, it is difficult to design a CM waveform with minimum local PSL and specified autocorrelation and/or spectral properties due to numerous nonconvex inequality and equality constraints. To develop efficient algorithms for this problem, we first express the waveform autocorrelation in the frequency domain, then use the proximal method of multipliers to tackle the resultant nonconvex and nonsmooth constrained optimization. Numerical examples have shown that the proposed methods can produce the waveforms satisfying these challenging requirements. [ABSTRACT FROM AUTHOR]

In this correspondence, we reply to the comments in Gu ["Comments on `A blind signal separation method for multiuser communications'", IEEE Transactions on Signal Processing, vol. 55, no. 5, pp. 2355-2356, May 2007]. We explain that the global convergence analysis of the method in Castedo ["A blind signal separation method for multiuser communications", IEEE Transactions on Signal Processing, vol. 45, no. 5, pp. 1343-1348, May 1997] can be completed as an extension of the results that were already presented. Also, we recall that two of the authors put forward (Dapena and Castedo ["Stochastic gradient adaptive algorithms for blind source separation", Elsevier Signal Processing, vol. 75, pp. 11-27]) a similar method with more attractive properties whose convergence analysis is not affected by the counterexample provided in Gu et al [ABSTRACT FROM PUBLISHER]