Blind Beamforming Algorithm Research Articles

Locating Everyday Objects Using NFC Textiles

This paper builds a Near-Field Communication (NFC) based localization system that allows ordinary surfaces to locate surrounding objects with high accuracy in the near-field. While there is rich prior work on device-free localization using far-field wireless technologies, the near-field is less explored. Prior work in this space operates at extremely small ranges (a few centimeters), leading to designs that sense close proximity rather than location. We propose TextileSense, a near-field beamforming system that can track everyday objects made of conductive materials (for example, a human hand) even if they are a few tens of centimeters away. We use multiple flexible NFC coil antennas embedded in ordinary and irregularly shaped surfaces we interact with in smart environments---furniture, carpets, and so forth. We design and fabricate specialized textile coils woven into the fabric of the furniture and easily hidden by acrylic paint. We then develop a near-field blind beam-forming algorithm to efficiently detect surrounding objects, and use a data-driven approach to further infer their location. A detailed experimental evaluation of TextileSense shows an average accuracy of 3.5 cm in tracking the location of objects of interest within a few tens of centimeters from the furniture.

Configuring Intelligent Reflecting Surface With Performance Guarantees: Blind Beamforming

This work gives a blind beamforming strategy for intelligent reflecting surface (IRS), aiming to boost the received signal-to-noise ratio (SNR) by coordinating phase shifts across reflective elements in the absence of channel information. While the existing methods of IRS beamforming typically first estimate channels and then optimize phase shifts, we propose a conditional sample mean based statistical approach that explores the wireless environment via random sampling without performing any channel estimation. Remarkably, the new method just requires a polynomial number of random samples to yield an SNR boost that is quadratic in the number of reflective elements, whereas the standard random-max sampling algorithm can only achieve a linear boost under the same condition. Moreover, we gain additional insight into blind beamforming by interpreting it as a least squares problem. Field tests demonstrate the significant advantages of the proposed blind beamforming algorithm over the benchmark algorithms in enhancing wireless transmission.

Kaiser Window Based Blind Beamformers for Radar Application

Blind beamforming algorithms are the first choice for radar applications as they won’t use any training signal. But the major downside of constant modulus algorithm (CMA) is very low convergence rate. To overcome this problem and to make this algorithm more suitable for real-time applications, we firstly improved the convergence rate of CMA by making its step size adaptive. Then, we applied Kaiser window to improved CMA and conventional least-square CMA (LS-CMA) to suppress the sidelobe levels. These proposed algorithms are named Kaiser window-constant modulus algorithm and Kaiser window least square-constant modulus algorithm, respectively. Computer simulations validate the effectiveness of the proposed beamformers.

Blind beamforming using fractional Fourier transform domain cyclostationarity

A new scheme of blind beamforming algorithm is proposed in this paper. The beamformer efficiently filters out the compact desired chirp signal and significantly suppress the cyclically uncorrelated nonstationary interference and stationary (colored) Gaussian noise by identifying and utilizing the optimum fractional Fourier transform (FrFT) domain second-order cyclostationarity. This greatly improves the beamformer output signal-to-interference plus noise ratio (SINR) by effectively reducing the optimum FrFT domain noncyclic signal-colored noise cross-terms in the presence of finite data length de-correlation operation. Simulation results show that the new method works well under a wide range of signal-to-noise ratio and signal-to-interference ratio.

Efficient Blind Beamforming Algorithms for Phased Array and MIMO RADAR

In this paper, we devised two new adaptive blind beamformers based on well-known constant modulus algorithm (CMA) and least square CMA (LS-CMA). First, we improved the convergence rate of CMA and then applied Hanning window to both CMA and LS-CMA to reduce side-lobe level (SLL). These new algorithms are Hanning window CMA (HW-CMA) and Hanning window LS-CMA (HW-LS-CMA),- Simulation results show the significant improvement in the convergence rate and drastic reduction in the SLL. The proposed HW-CMA converges within less than 10 iterations and the SLLs of both algorithms are reduced below −44 dB with the improvement of 33 dB as compared to classical CMA. Hence the proposed beamformers can be used in advanced communication applications.

Blind separation of partially overlapping data packets

The paper discusses the separation of partially overlapping data packets by an antenna array in narrowband communication systems. This problem occurs in asynchronous communication systems and several transponder systems such as Radio Frequency Identification (RFID) for wireless tags, Automatic Identification System (AIS) for ships, and Secondary Surveillance Radar (SSR) and Automatic Dependent Surveillance—Broadcast (ADS—B) for aircraft. Partially overlapping data packages also occur as inter-cell interference in mutually unsynchronized communication systems. Arbitrary arrival times of the overlapping packets cause nonstationary scenarios and makes it difficult to identify the signals using standard blind beamforming techniques. After selecting an observation interval, we propose subspace-based algorithms to suppress partially present (interfering) packets, as a preprocessing step for existing blind beamforming algorithms that assume stationary (fully overlapping) sources. The proposed algorithms are based on a subspace intersection, computed using a generalized singular value decomposition (GSVD) or a generalized eigenvalue decomposition (GEVD). In the second part of the paper, the algorithm is refined using a recently developed subspace estimation tool, the Signed URV algorithm, which is closely related to the GSVD but can be computed non-iteratively and allows for efficient subspace tracking. Simulation results show that the proposed algorithms significantly improve the performance of classical algorithms designed for block stationary scenarios in cases where asynchronous co-channel interference is present. An example on experimental data from the AIS ship transponder system confirms the effectiveness of the proposed algorithms in a real application.

A blind beamforming algorithm for multitarget signals based on time-frequency analysis

The existing blind beamforming methods are effective only under the condition that the source signals have some special statistical or structural characteristics. Additionally, the structure of cascade model is complicated and the stability of parallel model is poor when dealing with multi-target signals. To address these problems, a novel blind beamforming algorithm for multi-target signals based on time-frequency (TF) analysis is proposed in this paper. The received array signals are first transformed into time-frequency domain by using quadratic time-frequency distributions (TFDs). Then, the single-source auto-term TF points which show energy concentration at a single signal are extracted through three operations:(i) removing noise points by setting a reasonable threshold, (ii) separating auto-term TF points from cross-term points, and (iii) selecting the single-source auto-term TF points from the auto-term ones. Moreover, these single-source auto-term TF points are classified by the principal eigenvector of their spatial time-frequency distribution matrixes. For each class of TF points, the uncertain set of signal steering vector is given, whose radius is defined as the ultimate range between the center and the elements in the class. Within the uncertain set, an optimization algorithm is provided to get the optimal estimation of the signal steering vector. Finally, the blind beamforming for multi-target signals is achieved based on the Capon method, which can enhance the desired signals and suppress the noise and interference signals. In addition, the influence of parameters selection, the clustering method of unknown source number, and the computational complexity of the proposed algorithm are analyzed. The proposed algorithm can achieve parallel output of multi-target signals under the condition that the array manifold and the direction of arrival (DOA) are unknown. Also, the complex iterative solving processing may be avoided and special limitations on signal characteristics are unnecessary. As a result, it has wide applicability and superior stability compared with the existing blind beamforming methods. Simulations illustrate that the proposed algorithm can well separate multi-target signals which are TF-nondisjoint to a certain extent. It can achieve a higher output signal to interference plus noise ratio (SINR) compared with the constant modulus algorithm (CMA), the independent component analysis (ICA) algorithm, and the joint approximate diagolization of eigenmald (JADE) algorithm. Furthermore, the output performance of the proposed algorithm is close to the optimal Capon beamformer.

Performance Analysis of Blind Beamforming Algorithms in Adaptive Antenna Array in Rayleigh Fading Channel Model

In this paper, we analyze the performance of adaptive blind algorithms – i.e. Kaiser Constant Modulus Algorithm (KCMA), Hamming CMA (HAMCMA) – with CMA in a wireless cellular communication system using digital modulation technique. These blind algorithms are used in digital signal processor of adaptive antenna to make it smart and change weights of the antenna array system dynamically. The simulation results revealed that KCMA and HAMCMA provide minimum mean square error (MSE) with 1.247 dB and 1.077 dB antenna gain enhancement, 75% reduction in bit error rate (BER) respectively over that of CMA. Therefore, KCMA and HAMCMA algorithms give a cost effective solution for a communication system.

Blind Separation of Coherent Signals Based on Toeplitz Matrix Reconstruction

A new blind beamforming algorithm for separating and estimating coherent signals arriving at an antenna array is proposed in this paper. This algorithm is implemented through arranging the received data’s correlation function of each array element and reference array element (the first array element), to form the Hermitian Toeplitz matrix first. Then through the singular value decomposition of the matrix the signal subspace and noise subspace can be get, in order to achieve the direction vector of coherent sources to beamform, which can separate the independent signals from different directions effectively without knowing the transcendent knowledge of the signals. The new approach can estimate the coherent signals’ direction-of-arrival(DOA) on the basis of separation, and has good performance under the low SNR. The simulation results show that the proposed method is effective．

Sidelobe Suppression for Blind Adaptive Beamforming with Sparse Constraint

Blind adaptive beamforming has been widely used in radio communication systems for long-distance and high-speed transmission. The high sidelobe level of the beam pattern using the classic blind beamforming algorithm known as spectral self-coherent restoral method may lead to severe degradations under the conditions of unexpected interferences or high noise power. In this letter, a novel blind beamforming algorithm based on the sparse beam pattern constraint for all possible interferences and sidelobes is proposed to solve the problem of sidelobe and interference suppression.

Efficient Robust Blind Beamforming Algorithm

Because blind adaptive beamforming algorithms do not depend on any reference signal, they have found numerous important applications in signal processing. However, the conventional constrained constant modulus algorithm (CMA) may suffer significant performance degradation in the presence of the slight mismatches between the actual and assumed signal steering vectors. In this paper, to combat the mismatches, a novel robust constrained CMA is proposed for implementing double constraints with recursive method updating, which is based on explicit modeling of uncertainties in the desired signal array response. The proposed robust constrained CMA provides an improved robustness against the signal steering vector mismatches, enhances the array system performance under random perturbations in sensor parameters and makes the mean output array SINR consistently close to the optimal one. The performance of the proposed algorithm is compared with that of linear constrained CMA algorithm by computer simulations, the results of which demonstrate a marked improvement in the robustness against signal steering vector mismatches.

LSCMA Blind Adaptive Algorithm with Comparison of Optimization Capabilities for Smart Antenna Systems

In the present paper convergence characteristics of blind beam-forming algorithm Least Square Constant Modulus Algorithm (LSCMA) are analyzed. This algorithm is used with Smart antenna systems (SAS) for improving the performance of wireless communication system by optimizing the radiation pattern according to the signal environment. This can increase the coverage and capacity of a system in multipath channels by overcoming the interference and noise. The data rate can be enhanced by mitigating fading due to cancellation of multipath components. In this paper optimization capabilities of LSCMA are considered by changing of parameters. The results reveal improvement in gain and reduction in side-lobe level. The reference signal is basically a training sequence used to train the adaptive antenna array or a desired signal based upon an a priori knowledge of nature of the arriving signals. In case where a reference signal is not available one must opt to a group of optimization techniques that are blind to the exact content of the incoming signals. In this paper the comparison of optimization capabilities of LSCMA blind adaptive BFA with reference to number of antenna elements and their spacing is presented. Section II presents functioning of ABFA and the mathematical description of LSCMA. In section III simulation results using MATLAB 7.0 with reference to number of antenna elements, inter-element distance and angle of arrival (AOA) are obtained to find the optimized values. The array factor plots and LSCMA output for the same values of above parameters are presented.

A New Combination of RAKE Receiver and Adaptive Antenna Array Beamformer for Multiuser Detection in WCDMA Systems

The aim of this paper is to combine smart antenna beamforming and RAKE receiver in wideband code division multiple access (WCDMA). The proposed method combines spatial diversity as well as temporal diversity to improve the performance and overcome both interferences and multipath fading. This investigation has focused on one of the new proposed blind beamforming algorithms. It is based on constrained constant modulus (CCM) algorithm which is used for deriving a recursive-least-squares (RLS-) type optimization algorithm. We illustrate the comparison of bit error rate (BER) of the proposed receiver with simple correlator and also 1D-RAKE receiver in multiuser detection (MUD) WCDMA. The simulation results show that the proposed 2D-RAKE receiver offers lower BER rather than conventional ones, that is, it is an effective solution for decreasing the effect of interference and increasing the capacity, in a joint state.

Blind Beamforming Schemes in SC-FDMA Systems With Insufficient Cyclic Prefix and Carrier Frequency Offset

In cyclic prefix (CP)-based transmission systems, if the time duration of the cyclic extension is shorter than the channel impulse response duration, system performance is degraded due to the introduction of intersymbol interference (ISI) and intercarrier interference (ICI). In this paper, we propose a blind beamforming algorithm for single-carrier frequency-division multiple-access (SC-FDMA) systems under insufficient CP sceIn cyclic prefix (CP)-based transmission systems, if the time duration of the cyclic extension is shorter than the channel impulse response duration, system performance is degraded due to the introduction of intersymbol interference (ISI) and intercarrier interference (ICI). In this paper, we propose a blind beamforming algorithm for single-carrier frequency-division multiple-access (SC-FDMA) systems under insufficient CP scenarios. In addition, we also consider the effects of carrier frequency offsets (CFOs) in the processing schemes. The simulation results are presented by employing an SC-FDMA uplink signal model to illustrate the efficacy of the proposed algorithms.narios. In addition, we also consider the effects of carrier frequency offsets (CFOs) in the processing schemes. The simulation results are presented by employing an SC-FDMA uplink signal model to illustrate the efficacy of the proposed algorithms.

Multiple Blind Beamforming Based on LSCMA

Blind beamforming plays an important role in multiple-input multiple-output (MIMO) Systems, radar, cognitive radio, and system identification. In this paper, we propose a new algorithm for multiple blind beamforming algorithm based on the least square constant modulus algorithm (LSCMA). The new method consists of the following three parts: (a) beamforming of one signal with LSCMA. (b) direction-of-arrival (DOA) estimation of the remaining signals by rooting the weight vector polynomial. (c) beamforming of the remaining signals with linear constraints minimum variance (LCMV) method. After the convergence of LSCMA, one signal is captured and the arrival angles of the remaining signals can be obtained by rooting the weight vector polynomial. Therefore, beamforming can be quickly established for the remaining signals using LCMV method. Simultaneously the DOA of the signals can also be obtained. Simulation results show the performance of the presented method.

A Block-by-Block Blind Post-FFT Multistage Beamforming Algorithm for Multiuser OFDM Systems Based on Subcarrier Averaging

Chi et al. proposed a computationally efficient fast kurtosis maximization algorithm for blind equalization of multiple-input multiple-output linear time-invariant systems. This algorithm is also an iterative batch processing algorithm and has been applied to blind source separation. This paper considers blind beamforming of multiuser orthogonal frequency division multiplexing (OFDM) systems. Assuming that the channel is static within one OFDM block, a blind post-FFT multistage beamforming algorithm (MSBFA) based on subcarrier averaging is proposed. The algorithm basically comprises:( i) source (path signal) extraction using a hybrid beamforming algorithm composed of a Fourier beamformer and a kurtosis maximization beamformer, (ii) time delay estimation and compensation, (iii) classification (path-to-user association) and blind maximum ratio combining (of path signals). The designed beamformer is exactly the same for all the subcarriers, effectively utilizes multipath diversity for performance gain, and works well even in an environment with spatially correlated sources. Some simulation results are presented to demonstrate the effectiveness of the proposed MSBFA.

DETERMINISTIC BLIND BEAMFORMING FOR ELECTROMAGNETIC VECTOR SENSOR ARRAY

Deterministic blind beamforming algorithms try to separate superpositions of source signals impinging on electromagnetic vector sensor array by using deterministic properties of the signals. This paper links electromagnetic-vector-sensor-array beamforming problem to the parallel factor (PARAFAC) model, which is an analysis tool rooted in psychometrics and chemometrics. Exploiting this link, it derives a deterministic blind beamforming algorithm. The blind beamforming algorithm doesn't require DOA (direction of arrival) information and polarization information. The simulation results reveal that the performance of the blind beamforming algorithm for electromagnetic vector sensor array is close to nonblind MMSE method, and this algorithm works well in array error condition.

Fast Adaptive Blind Beamforming Algorithm for Antenna Array in CDMA Systems

In this paper, the maximum signal-to-interference-plus-noise ratio (MSINR) beamforming problem in antenna-array CDMA systems is considered. In this paper, a modified MSINR criterion presented in a previous paper is interpreted as an unconstrained scalar cost function. By applying recursive least squares (RLS) to minimize the cost function, a novel blind adaptive beamforming algorithm to estimate the beamforming vector, which optimally combines the desired signal contributions from different antenna elements while suppressing noise and interference, is derived. Neither the knowledge of the channel conditions (fading coefficients, signature sequences and timing of interferers, statistics of other noises, etc.) nor training sequence is required. Compared with previously published adaptive beamforming algorithms based on the stochastic-gradient method, it has faster convergence and better tracking capability in the time-varying environment. Simulation results in various signal environments are presented to show the performance of the proposed algorithm.

A novel blind adaptive space-time receiver for multi-code DS-CDMA

Abstract-The capacity of conventional wireless communications systems based upon Direct-Sequence Code Division Multiple Access (DS-CDMA) schemes, is mainly limited by Multiple Access Interference (MAI). Therefore, methods aimed at reducing MAI are mandatory solutions to increase the capacity of such systems. Smart Antennas are a promising technology for improving the performance of high capacity mobile communications systems, because they are expected to be able to increase the Signal to Interference plus Noise power Ratio (SINR) of the received signals by dynamically adapting the equivalent array beam pattern in order to track the movements of the users within the cell. In this paper we propose a novel blind adaptive beamforming algorithm tailored for the up-link of multi-code multi-rate DS-CDMA wireless communications systems. Our proposal is based upon a modified Constant Modulus (CM) criterion, whose fundamental feature is the capability to overcome the main drawback of the classic CM algorithm, which lies in its difficulty of ensuring the convergence toward the desired user. The proposed algorithm exploits user-specific information intrinsically known at the receiver, which is the code uniquely associated to each user, without requiring the knowledge of the spatiotemporal propagation channel nor any specific training sequence. Within this paper we present a broadband solution, designed to temporally re-align uncorrelated clusters of multipaths, as well as spatially recombine correlated multipaths belonging to the same cluster. Computer simulations show that our proposed technique can effectively exploit path diversity and multipath correlation, also in the presence of Doppler effect and time-variable propagation conditions.

Neural blind beamformer for cyclostationary signals

A blind beamforming algorithm based on a neural network is presented according to the characteristic of cyclostationary signals. This method transforms the question of estimating beamformer weight vectors into the one of computing the SVD of the cross correlation matrix of array input signals and their frequency shift signals. A cross correlation neural network is introduced to compute the SVD of the cross correlation matrix so as to reduce the computational complexity and carry out the blind beamforming more efficiently. The improved cross-coupled Hebbian learning rule presented can make the weights of the neural network converge much fast. Therefore, it is more promising in the practical use. This method can restrain noise and interference. Simulation proves its correctness.