Blind Beamforming Research Articles

Blind Beamforming for Coverage Enhancement With Intelligent Reflecting Surface

Blind Beamforming for Coverage Enhancement With Intelligent Reflecting Surface

Blind Adaptive Beamforming for a Global Navigation Satellite System Array Receiver Based on Direction Lock Loop

The adaptive beamforming algorithm can realize interference suppression and navigation signal enhancement, and has been widely used. However, achieving high-precision real-time estimation of the direction of arrival (DOA) parameters of navigation signals in strong-interference scenarios with low complexity is still a challenge. In this paper, a blind adaptive beamforming algorithm for a Global Navigation Satellite System (GNSS) array receiver based on direction lock loop is proposed without using the prior information of the DOA parameter. The direction lock loop is used for DOA tracking and estimation after interference suppression, which uses the spatial correlation of the array beam pattern to construct a closed direction-tracking loop. The DOA estimation value is adjusted in real time based on the loop errors. A blind beamformer is constructed using the DOA estimation results to provide gain by forming a beam in the satellite direction. This method improves the accuracy and dynamic adaptability of DOA estimation while significantly reducing the computational complexity. The theoretical analysis and simulation results verify the effectiveness of the proposed algorithm.

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.

Blind beamforming-based strong interference suppression in underwater acoustic direct-sequence code-division multiple-access systems.

In an underwater acoustic direct-sequence code-division multiple-access (DS-CDMA) system, the performance of the conventional receiver is severely limited by the strong multiple-access interferences, which is due to the near-far effect. In this paper, a time-frequency-time with eigendecomposition-based blind beamformer (TFT-EBB) method is proposed for a distant user to suppress strong interference from a nearby user in DS-CDMA underwater acoustic communications. When the interference level is moderately higher than that of the desired signal, the TFT-EBB method can estimate the composite steering vector of all multipath components of the weak desired user signal via the eigendecomposition of the array covariance matrix at each frequency bin. Then the proposed energy detection-based RAKE receiver is cascaded to achieve multipath diversity, in which it requires less prior information compared with the conventional RAKE receiver. The proposed method is evaluated by using both simulated and field experimental data, which verifies its effectiveness.

Magnetic Blind Beamforming for Battery-Free Wireless Sensor Networks

It is challenging to maintain a large number of wireless sensors in inaccessible media, such as underground and intra-body. Tiny battery-free sensors are desirable since they create negligible impacts on the environment and do not require battery replacement. Battery-free sensors can harvest energy from wireless signals to support their normal operation. However, they cannot cooperate with the charger due to the lack of energy in the initial stages. This paper proposes the Magnetic Blind Beamforming (MagBB) using tri-axis coils to power up battery-free wireless sensors. Battery-free sensors with randomly orientated coils may experience significant misalignment losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils with arbitrary orientations can receive sufficient voltages for charging. It does not require any information about the battery-free sensor’s coil orientation or location. A proof-of-concept prototype is developed. The numerical simulations and experimental results show that MagBB can efficiently improve the reliability of wireless charging. Sensor coils with arbitrary orientation and location within the operation range can harvest sufficient energy. This technology has the potential of powering ultra-dense wireless sensors with low communication overhead.

Analytical Blind Beamforming for a Multi-Antenna UAV Base-Station Receiver in Millimeter-Wave Bands.

Over the last decade, unmanned aerial vehicles (UAVs) with antenna arrays have usually been employed for the enhancement of wireless communication in millimeter-wave bands. They are commonly used as aerial base stations and relay platforms in order to serve multiple users. Many beamforming methods for improving communication quality based on channel estimation have been proposed. However, these methods can be resource-intensive due to the complexity of channel estimation in practice. Thus, in this paper, we formulate an MIMO blind beamforming problem at the receivers for UAV-assisted communications in which channel estimation is omitted in order to save communication resources. We introduce one analytical method, which is called the analytical constant modulus algorithm (ACMA), in order to perform blind beamforming at the UAV base station; this relies only on data received by the antenna. The feature of the constant modulus (CM) is employed to restrict the target user signals. Algebraic operations, such as singular value decomposition (SVD), are applied to separate the user signal space from other interferences. The number of users in the region served by the UAV can be detected by exploring information in the measured data. We seek solutions that are expressible as one Kronecker product structure in the signal space; then, the beamformers that correspond to each user can be successfully estimated. The simulation results show that, by using this analytically derived blind method, the system can achieve good signal recovery accuracy, a reasonable system sum rate, and acceptable complexity.

Instantaneous, Zero-Feedback Fading Mitigation With Simple Backscatter Radio Tags

This work offers a novel, backscatter radio-based, blind beamforming technique that assists the reception of signals in multipath conditions. The technique is based on simple, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">assistive</i> backscattering tags, implemented without any means of signal amplification. These tags can <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">blindly</i> (i.e., without feedback) modify the phase of the associated wireless channels, leading to constructive addition of the multipath components (i.e., alignment). Such alignment events offer distributed beamforming gain. Analysis is performed and an expression for the probability of alignment is offered, along with an expression for the achievable gain, corroborated by simulations and experimental results. It is shown that increased number of assistive tags offers higher beamforming gain, at the expense of increased delay. The effectiveness of the proposed beamforming technique is highlighted with experiments and measurements on the radio frequency identification (RFID) Gen2 standard. Commercial RFIDs that initially could not be interrogated due to insufficient impinged power, were successfully interrogated hundreds of times/minute with the assistance of the beamforming tags. It was experimentally shown that the proposed beamforming tags can offer power gains in the range of 0.4 – 3.7 dB. While the technique was tested on commercial RFIDs, it is not limited to a specific communication standard.

A Practical Spectrum Sharing Scheme for Cognitive Radio Networks: Design and Experiments

Spectrum shortage is a fundamental problem in wireless networks and this problem becomes increasingly acute with the rapid proliferation of wireless devices. To address this problem, spectrum sharing in the context of cognitive radio networks (CRNs) has been considered a promising solution. In this paper, we propose a practical spectrum sharing scheme for a small CRN that comprises a pair of primary users and a pair of secondary users by leveraging the multiple-input and multiple-output (MIMO) technology. In our scheme, we assume that the secondary users take full responsibility for cross-network interference cancellation (IC). We also assume that the secondary users have no knowledge about the primary network, including its signal waveform, frame structure, and network protocol. The key components of our proposed scheme are two MIMO-based interference management techniques: blind beamforming (BBF) and blind interference cancellation (BIC). We have built a prototype of our scheme on a wireless testbed and demonstrated that the prototyped secondary network can coexist with commercial Wi-Fi devices (primary users). Experimental results further show that, for a secondary device with two or three antennas, BBF and BIC achieve an average of 25dB and 33dB IC capability in an office environment, respectively.

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.

Performance Optimization of a Multi-Source, Multi-Sensor Beamforming Wireless Powered Communication Network With Backscatter

This paper formulates and solves a multi-objective joint optimization problem where both the sum-throughput and fairness of a radio frequency (RF) energy harvesting wireless powered communication network (WPCN) are maximized using multiple beamforming hybrid access points (H-APs) and backscatter communication-enabled combination sensors. The paper proposes the multi-source, multi-sensor blind adaptive beamforming with combination sensors (MS2-BABF/combo) protocol. The protocol is analyzed to determine its performance with metrics, including WPCN sum-throughput, fairness in the achievable rates by sensors, sum-throughput and fairness tradeoff, and sensor dropout rate. Numerical results show that the MS2-BABF/combo protocol delivered up to approximately 296% increase in sum-throughput compared to the reference time-switching (TS) RF energy harvesting protocol in a dynamic environment, achieved a dropout rate of 0% instead of 28.5% by the reference TS protocol at 10mW H-AP transmission power, and increased Jain’s fairness index from J = 0.76 to up to 0.90 and 0.80 with and without sensors operating in backscatter mode, respectively. The findings are significant for future widespread adoption of WPCN systems by increasing its performance thus the versatility of applicable scenarios in the real world.

Development of blind algorithm with automatic gain control

In this paper, the concept of blind algorithm with automatic gain control (AGC) is introduced in adaptive antenna system for signal optimization with an aim to estimate the desired response in adaptive fashion. Blind algorithm with AGC is a hybrid two-stage adaptive filtering algorithm; sequentially combining constant modulus algorithm (CMA) and Bessel least mean square (BLMS) algorithm. Blind Bessel beamformer with AGC does not require external reference signal to update its weight vectors and step size for convergence but updates itself from own reference signal obtained from the output of CMA. Similarly, step size is obtained from the correlation matrix which is the product of the signals induced in array elements of antenna. BLMS is the modified version of LMS algorithm; based on the non-uniform step size exploiting the asymptotic decay property of Bessel function of the first kind. The output of CMA provides input and reference signals for BLMS that makes it blind. The contributions of this paper include the development of novel blind theory concept and presentation of an AGC method in order to make the Bessel beamformer blind which can update itself electronically through the correlation matrix depending on the signal array vector with the aim to make the signal power constant.

Constant modulus algorithms via low-rank approximation

Abstract We present a novel convex-optimization-based approach to the solutions of a family of problems involving constant modulus signals. The family of problems includes the constant modulus and the constrained constant modulus, as well as the modified constant modulus and the constrained modified constant modulus. These solutions are shown to constitute semidefinite programs (SDP), thus enabling efficient interior-point methods with polynomial time complexity. The performance of the proposed solutions, demonstrated in several simulated experiments for the task of blind beamforming, is shown to be superior to existing methods.

Blind adaptive constrained constant modulus algorithms based on unscented Kalman filter for beamforming

This work proposes constrained constant modulus unscented Kalman filter (CCM-UKF) algorithm and its low-complexity version called reduced-rank constrained constant modulus unscented Kalman filter (RR-CCM-UKF) algorithm for blind adaptive beamforming. In the generalized sidelobe canceller (GSC) structure, the proposed algorithms are devised according to the CCM criterion. Firstly, the cost function of the constrained optimization problem is transformed to suit the Kalman filter-style state space model. Then, the optimum weight vector of the beamformer can be estimated by using the recursive formulas of UKF. In addition, the a priori parameters of UKF (system and measurement noises) are processed adaptively in the implementation. Simulation results demonstrate that the proposed algorithms outperform the existing methods in terms of convergence speeds, output signal-tointerference- plus-noise ratios (SINRs), mean-square deviations (MSDs) and robustness against steering mismatch.

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.

Robust Cross-SCORE Algorithm Against Bit Transitions for GPS Interference Suppression

In the blind beamforming technique utilizing self-coherence properties of GPS signals, the covariance matrices estimated from sample data, including the transition between two adjacent bits, suffer from performance degradation. In this letter, we propose the estimation of the covariance matrices using the sample blocks, which are free from the bit transitions under different times of arrival of multiple GPS signals. It combines with the robust cross-SCORE algorithm to suppress the GPS interference. It is shown that an efficient combination of two algorithms is capable of providing high gains to multiple GPS signals simultaneously, while suppressing strong interference.

An Eigendecomposition-Based Approach to Blind Beamforming in a Multipath Environment

This letter devises a new approach to blind beamforming for multipath coherent signal reception. It is shown that in the scenarios where the interferences are moderately stronger than the signal, namely, the interference-to-signal ratio is mildly high, the composite steering vector (CSV) of multipath coherent signals can be estimated via the eigendecomposition of a reduced covariance matrix, which subtracts the components of interferences and noise from the array covariance matrix. The proposed eigendecomposition-based blind beamformer (EBB) is thereby obtained by substituting the estimate of CSV into the minimum variance distortionless response beamformer. It is also shown that the proposed EBB weight vector is actually equal to the principal eigenvector of the reduced covariance matrix. The performance of the proposed EBB is demonstrated by numerical simulations.

Blind Beamforming for Conformal Arrays

Blind beamforming methods are employed when the geometry of the array and the positions of its antennas are unknown. The existing blind beamforming methods can only tackle with linear and planar arrays. Such methods cannot be simply extended to conformal arrays. In this letter, a novel method for blind beamforming for an arbitrarily shaped conformal array is proposed. By calculating the singular value decomposition (SVD) of the cumulant matrix of the received data, the manifold matrix of the array is first extracted. This matrix determines the direction of the sources and is employed in the linearly constrained minimum variance (LCMV) formulation to obtain the beamforming weights. The individual pattern of each antenna is also incorporated into the LCMV formulation. Our simulation results show that the proposed method can be greatly employed in conformal array blind beamforming and locate directional sources solely based on the received data.

Widely linear UKF constant modulus algorithm for blind adaptive beamforming

Based on a uniform linear array, a new widely linear unscented Kalman filter-based constant modulus algorithm (WL-UKF-CMA) for blind adaptive beamforming is proposed. The new algorithm is designed according to the constant modulus criterion and takes full advantage of the noncircular property of the signal of interest (SOI), significantly increasing the output signal-to-interference-plus-noise ratio (SINR), enhancing the convergence speed and decreasing the steady-state misadjustment. Since it requires no known training data, the proposed algorithm saves a large amount of the available spectrum. Theoretical analysis and simulation results are presented to demonstrate its superiority over the conventional linear least mean square-based CMA (L-LMS-CMA), the conventional linear recursive least square-based CMA (L-RLS-CMA), WL-LMS-CMA, WL-RLS-CMA and L-UKF-CMA.