Showing papers on "Adaptive beamformer published in 2005"

Robust adaptive beamforming

Abstract: Contributors. Preface. 1. Robust Minimum Variance Beamforming (R. Lorenz & S. Boyd). 2. Robust Adaptive beamforming Based on Worst-Case Performance Optimization (A. Gershman, et al.). 3. Robust Capon Beamforming (J. Li, et al.). 4. Diagonal Loading for Finite Sample Size Beamforming: An Asymptotic Approach (X. Mestre & M. Lagunas). 5. Mean-Squared Error Beamforming for Signal Estimation: A Competitive Approach (Y. Eldar & A. Nehorai). 6. Constant Modulus Beamforming (A. van der Veen & A. Leshem). 7. Robust Wideband Beamforming (E. Di Claudio & R. Parisi). Index.

Time-delay- and time-reversal-based robust capon beamformers for ultrasound imaging

Abstract: Currently, the nonadaptive delay-and-sum (DAS) beamformer is extensively used for ultrasound imaging, despite the fact that it has lower resolution and worse interference suppression capability than the adaptive standard Capon beamformer (SCB) if the steering vector corresponding to the signal of interest (SOI) is accurately known. The main problem which restricts the use of SCB, however, is that SCB lacks robustness against steering vector errors that are inevitable in practice. Whenever this happens, the performance of SCB may become worse than that of DAS. Therefore, a robust adaptive beamformer is desirable to maintain the robustness of DAS and adaptivity of SCB. In this paper we consider a recent promising robust Capon beamformer (RCB) for ultrasound imaging. We propose two ways of implementing RCB, one based on time delay and the other based on time reversal. RCB extends SCB by allowing the array steering vector to be within an uncertainty set. Hence, it restores the appeal of SCB including its high resolution and superb interference suppression capabilities, and also retains the attractiveness of DAS including its robustness against steering vector errors. The time-delay-based RCB can tolerate the misalignment of data samples and the time-reversal-based RCB can withstand the uncertainty of the Green's function. Both time-delay-based RCB and time-reversal-based RCB can be efficiently computed at a comparable cost to SCB. The excellent performances of the proposed robust adaptive beamforming approaches are demonstrated via a number of simulated and experimental examples.

Uniform circular and rectangular arrays for adaptive beamforming applications

Abstract: Numerous studies for smart antennas have already been conducted. However, these studies include mostly uniform linear arrays (ULAs) and uniform rectangular arrays (URAs) and not as much effort has been devoted to other configurations. In this letter, the performance of smart antennas with uniform circular arrays (UCAs) is examined. The primary motivation for this selection is the symmetry UCAs possess. This property provides UCAs a major advantage, the ability to scan a beam azimuthally through 360/spl deg/ with little change in either the beamwidth or the sidelobe level. Also, a comparison between UCAs and URAs in the context of adaptive beamforming is made in this letter.

Adaptive minimum bit-error rate beamforming

Abstract: An adaptive beamforming technique is proposed based on directly minimizing the bit-error rate (BER). It is demonstrated that this minimum BER (MBER) approach utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains in terms of a reduced BER over MMSE beamforming. A block-data adaptive implementation of the MBER beamforming solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is considered, and a stochastic gradient algorithm, referred to as the least bit error rate, is derived. The proposed adaptive MBER beamforming technique provides an extension to the existing work for adaptive MBER equalization and multiuser detection.

Medical ultrasound imaging using the fully adaptive beamformer

Abstract: Medical ultrasound beamforming is conventionally done using a classical delay-and-sum operation. This simplest beamforming suffers from drawbacks. Indeed, in phased array imaging, the beamformed radiofrequency signal is often polluted with off-axis energies. We investigate the use of an adaptive beamforming approach widely used in array processing, the fully adaptive beamformer, to reduce the bright off-axis energies contribution. We show that the fully adaptive beamformer cannot be applied to medical ultrasound as it was initially derived since the medical ultrasonic medium produces coherent or highly correlated signals and the algorithm fails to work within this context. Spatial smoothing preprocessing is introduced which allows the fully adaptive beamformer to operate. A complementary preprocessing that uses the received data obtained using consecutive transmission lines further improve the performances. Very promising results for the application of adaptive array processing techniques in medical ultrasound are obtained.

Multi-channel adaptive speech signal processing system with noise reduction

Abstract: An adaptive signal processing system eliminates noise from input signals while retaining desired signal content, such as speech. The resulting low noise output signal delivers improved clarity and intelligibility. The low noise output signal also improves the performance of subsequent signal processing systems, including speech recognition systems. An adaptive beamformer in the signal processing system consistently updates beamforming signal weights in response to changing microphone signal conditions. The adaptive weights emphasize the contribution of high energy microphone signals to the beamformed output signal. In addition, adaptive noise cancellation logic removes residual noise from the beamformed output signal based on a noise estimate derived from the microphone input signals.

Performance analysis of beamformers using generalized loading of the covariance matrix in the presence of random steering vector errors

Abstract: Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness. In this paper, we present a theoretical analysis of the signal-to-interference-plus-noise ratio (SINR) for the class of beamformers based on generalized (i.e., not necessarily diagonal) loading of the covariance matrix in the presence of random steering vector errors. A closed-form expression for the SINR is derived that is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any loading matrix. It provides insights into the influence of the loading matrix and can serve as a helpful guide to select it. Finally, the analysis enables us to predict the level of uncertainties up to which robust beamformers are effective and then depart from the optimal SINR.

Auxiliary antenna-assisted interference mitigation for radio astronomy arrays

Abstract: Interferometric image synthesis in radio astronomy is plagued by signal corruption from man-made sources. The very weak signals of interest can be overwhelmed by such interference. Recent work has proposed using array signal processing techniques of adaptive beamforming, adaptive filtering, and subspace projection to remove interference prior to image synthesis. In some practical scenarios, we have found poor cancellation performance when using such methods. Because signal-of-interest levels in radio astronomy (RA) are usually well below the noise, even interference to noise ratios (INR) less than unity can affect signal estimation. At these low INRs, it is difficult to estimate interference parameters or statistics with sufficient accuracy for high-performance adaptive cancellation or subspace projection. By adding a few (one to three) low-gain (relative to the primary telescope dishes) "auxiliary" antennas to an array, it is possible to overcome this problem. This paper will show that using such antennas (e.g. commercial-grade 3-m dishes) with an existing array can significantly improve interference rejection. New extensions to subspace projection spatial filtering methods are presented, along with analytical and simulated results for performance comparison.

Adaptive signal processing in medical ultrasound beamforming

Abstract: For over thirty years adaptive beamforming (AB) algorithms have been applied in RADAR and SONAR signal processing. Higher resolution and contrast is attainable using those algorithms at the price of an increased computational load. In this paper we consider four beamformers (BFs): Frost BF, Duvall BF, SSB, and SPOC. These algorithms are well know in the RADAR/SONAR literature. We have performed a series of simulations using ultrasound data to test the performance of those algorithms and compare them to the conventional, data independent, beamforming. Every algorithm was applied on single channel ultrasonic data that was generated using Field II. For a 32 element linear array operating at 5 MHz, beamplot results show that while the Duvall and SSB beamformers reduce sidelobes by roughly 20 dB, the sidelobes using the Frost algorithm rise by 23dB. The -6dB resolution is improved by 38%, 83%, and 43% in the case of Duvall, Frost, and SSB algorithms, respectively. In the case of SPOC, the beamplot shows a super-resolution peak with noise floor at -110 dB. Similar results were obtained for an array consisting of 64 elements.

Blind Source Separation of Convolutive Mixtures of Speech in Frequency Domain

Abstract: This paper overviews a total solution for frequency-domain blind source separation (BSS) of convolutive mixtures of audio signals, especially speech. Frequency-domain BSS performs independent component analysis (ICA) in each frequency bin, and this is more efficient than time-domain BSS. We describe a sophisticated total solution for frequency-domain BSS, including permutation, scaling, circularity, and complex activation function solutions. Experimental results of 2 × 2, 3 × 3, 4 × 4, 6 × 8, and 2 × 2 (moving sources), (#sources × #microphones) in a room are promising.

Robust adaptive beamforming based on the Kalman filter

Abstract: In this paper, we present a novel approach to implement the robust minimum variance distortionless response (MVDR) beamformer. This beamformer is based on worst-case performance optimization and has been shown to provide an excellent robustness against arbitrary but norm-bounded mismatches in the desired signal steering vector. However, the existing algorithms to solve this problem do not have direct computationally efficient online implementations. In this paper, we develop a new algorithm for the robust MVDR beamformer, which is based on the constrained Kalman filter and can be implemented online with a low computational cost. Our algorithm is shown to have a similar performance to that of the original second-order cone programming (SOCP)-based implementation of the robust MVDR beamformer. We also present two improved modifications of the proposed algorithm to additionally account for nonstationary environments. These modifications are based on model switching and hypothesis merging techniques that further improve the robustness of the beamformer against rapid (abrupt) environmental changes.

Calibration based beamforming, non-linear adaptive filtering, and multi-sensor headset

Abstract: A first set of signals from an array of one or more microphones, and a second signal from a reference microphone are used to calibrate a set of filter parameters such that the filter parameters minimize a difference between the second signal and a beamformer output signal that is based on the first set of signals. Once calibrated, the filter parameters are used to form a beamformer output signal that is filtered using a non-linear adaptive filter that is adapted based on portions of a signal that do not contain speech, as determined by a speech detection sensor.

Minimum variance adaptive beamforming applied to medical ultrasound imaging

Abstract: We have applied the minimum variance beam- former to medical ultrasound imaging and shown significant improvement in image quality compared to delay-and-sum. Reduced mainlobe width and suppression of sidelobes is demonstrated on both simulated and experimental RF data of closely spaced wire targets, resulting in increased resolution and contrast. The method has been applied to experimental RF data from a heart-phantom, demonstrating improved definition of the ventricular walls. We have evaluated the beamformers sensitivity to velocity errors and shown that reliable amplitude estimates are achieved if proper regular- ization is applied. I. INTRODUCTION Delay-and-sum (DAS) beamforming is the standard technique in medical ultrasound imaging. An image is formed by transmitting a narrow beam in a number of angles and dynamically delaying and summing the received signals from all channels. The large sidelobes of the DAS beamformer can be suppressed using aperture shading, resulting in increased contrast at the expense of resolution. In contrast to the predetermined shading in DAS, adaptive beamformers use the recorded wavefield to compute the aperture weights. By suppressing inter- fering signals from off-axis directions and allowing large sidelobes in directions where there is no received energy, the adaptive beamformers can increase resolution. The minimum variance (MV) adaptive beamformer (1) and subspace-based methods have mostly been studied in narrowband applications. Extensions to broadband imaging include preprocessing with focusing- and spa- tial resampling filters, allowing narrowband methods to be used on broadband data (2), (3). We have applied the MV beamformer to medical ultrasound imaging by prefocusing in the direction of the transmitted beam - as the delay-step in DAS - and replaced the summing with the MV method. Similar methods have been used by Mann and Walker (4), and Sasso and Cohen-Bacrie (5) in medical ultrasound imaging. The former use a con- strained adaptive beamformer on experimental data of a single point target and a cyst phantom demonstrating improved contrast and resolution, whereas the latter use an MV beamfomer on a simulated data-set, showing improved contrast in the final image. We demonstrate resolution improvement and sidelobe suppression on both simulated and experimental RF data of closely spaced wire targets, and show improvement in the image of a heart-phantom obtained from experimental RF data. We also evaluate robustness of the beamformer to errors in acoustic velocity, and show that reliable amplitude estimates are achieved by regularization.

Adaptive port-starboard beamforming of triplet sonar arrays

Abstract: For a low-frequency active sonar (LFAS) with a triplet receiver array, it is not clear in advance which signal processing techniques optimize its performance. Here, several advanced beamformers are analyzed theoretically, and the results are compared to experimental data obtained in sea trials. Triplet arrays are single line arrays with three hydrophones on a circular section of the array. The triplet structure provides the ability to solve the notorious port-starboard (PS) ambiguity problem of ordinary single-array receivers. More importantly, the PS rejection can be so strong that it allows to unmask targets in the presence of strong coastal reverberation or traffic noise. The theoretical and experimental performance of triplet array beamformers is determined in terms of two performance indicators: array gain and PS rejection. Results are obtained under several typical acoustic environments: sea noise, flow noise, coastal reverberation, and mixtures of these. A new algorithm for (beam space) adaptive triplet beamforming is implemented and tuned. Its results are compared to those of other triplet beamforming techniques (optimum and cardioid beamforming). These beamformers optimize for only one performance indicator, whereas in theory, the adaptive beamformer gives the best overall performance (in any given environment). The different beamformers are applied to data obtained with an LFAS at sea. Analysis shows that adaptive triplet beamforming outperforms conventional beamforming algorithms. Adaptive triplet beamforming provides strong PS rejection, allowing the unmasking of targets in the presence of strong directional reverberation (e.g., from a coast) and at the same time provides positive array gain in most environments.

Speech signal processing with combined noise reduction and echo compensation

Abstract: A speech signal processing system combines acoustic noise reduction and echo cancellation to enhance acoustic performance The speech signal processing system may be used in vehicles or other environments where noise-suppressed communication is desirable The system includes an adaptive beamforming signal processing unit, an adaptive echo compensating unit to reduce acoustic echoes, and an adaptation unit to combine noise reduction and adaptive echo compensating

Compact FPGA-based beamformer using oversampled 1-bit A/D converters

Abstract: A compact medical ultrasound beamformer architecture that uses oversampled 1-bit analog-to-digital (A/D) converters is presented. Sparse sample processing is used, as the echo signal for the image lines is reconstructed in 512 equidistant focal points along the line through its in-phase and quadrature components. That information is sufficient for presenting a B-mode image and creating a color flow map. The high sampling rate provides the necessary delay resolution for the focusing. The low channel data width (1-bit) makes it possible to construct a compact beamformer logic. The signal reconstruction is done using finite impulse response (FIR) filters, applied on selected bit sequences of the delta-sigma modulator output stream. The approach allows for a multichannel beamformer to fit in a single field programmable gate array (FPGA) device. A 32-channel beamformer is estimated to occupy 50% of the available logic resources in a commercially available midrange FPGA, and to be able to operate at 129 MHz. Simulation of the architecture at 140 MHz provides images with a dynamic range approaching 60 dB for an excitation frequency of 3 MHz.

Adaptive beamformer, sidelobe canceller, handsfree speech communication device

Abstract: The adaptive beamformer unit (191) comprises: a filtered sum beamformer (107) arranged to process input audio signals (u l, u2) from an array of respective microphones (101, 103), and arranged to yield as an output a first audio signal (z) predominantly corresponding to sound from a desired audio source (160) by filtering with a first adaptive filter (fl (-t)) a first one of the input audio signals (u l) and with a second adaptive filter (f2(-t)) a second one of the input audio signals (u2), the coefficients of the first filter (fl(-t)) and the second filter (f2(-t)) being adaptable with a first step size (al) and a second step size ((x2) respectively; noise measure derivation means (111) arranged to derive from the input audio signals (ul, u2) a first noise measure (xl) and a second noise measure (x2); and an updating unit (192) arranged to determine the first and second step size (al, (x2) with an equation comprising in a denominator the first noise measure (xl) for the first step size (al), respectively the second noise measure (x2) for the second step size (a2). This makes the beamformer relatively robust against the influence of correlated audio interference. The beamformer may also be incorporated in a sidelobe canceller topology yielding a more noise cleaned desired sound estimate, which can be used in a related, more advanced adaptive filter (fl (-t), f2(-t)) updating. Such a beamformer is typically useful for application in handsfree speech communication systems.

Robust adaptive beamforming using probability-constrained optimization

Abstract: Recently, robust minimum variance (MV) beamforming which optimizes the worst-case performance has been proposed in S.A. Vorobyov et al. (2003), R.G. Lorenz and S.P. Boyd (2005). The worst-case approach, however, might be overly conservative in practical applications. In this paper, we propose a more flexible approach that formulates the robust adaptive beamforming problem as a probability-constrained optimization problem with homogeneous quadratic cost function. Unlike the general probability-constrained problem which can be nonconvex and NP-hard, our problem can be reformulated as a convex nonlinear programming (NLP) problem, and efficiently solved using interior-point methods. Simulation results show an improved robustness of the proposed beamformer as compared to the existing state-of-the-art robust adaptive beamforming techniques

Robust adaptive beamforming using LMS algorithm with SMI initialization

Abstract: The least mean squares (LMS) algorithm is a simple adaptive beamforming algorithm that is well suited for continuous transmission systems. The LMS algorithm converges slowly when compared with other complicated algorithms, such as recursive least squares (RLS) (Shubair, R.M. and Merri, A., 2005). On the other hand, the sample matrix inversion (SMI) algorithm has a fast convergence behavior. However, because its speedy convergence is achieved through the use of matrix inversion, the SMI algorithm is computationally intensive. Moreover, the SMI algorithm has a block adaptive approach for which it is required that the signal environment does not undergo significant change during the course of block acquisition. The paper develops an algorithm for robust adaptive beamforming by combining the attributes of the LMS algorithm and SMI algorithm. This new algorithm uses the LMS algorithm, which is simple to implement and not computationally intensive, but with SMI initialization in order to ensure fast convergence. Numerical results verify the improved convergence, accuracy, and computational efficiency of the combined LMS/SMI algorithm.

A digital beamformer for high-frequency annular arrays

Abstract: Digital transmit and receive beamformers for a 45-MHz, 7-element annular array are described. The transmit beamformer produces 0- to 80-Vpp monocycle pulses with a timing error of less than /spl plusmn/125 ps. Up to four adjustable transmit focal zones can be selected. The dynamic receive beamformer uses a variable frequency sampling technique in which the frequency of analog-to-digital conversion on each channel is adjusted as the signals are received. The variable frequency clock signals required to trigger analog-to-digital conversion are obtained using a pair of high-frequency field-programmable gate arrays and a precision quartz oscillator. The gate arrays are also used to sum the digitized signals. A maximum receive beamformer timing error of less than /spl plusmn/900 ps was obtained on each channel. The performance of the combined transmit and receive beamformer was tested by imaging wire phantoms. Images of CD-1 mice were also generated. The system produced images with a dynamic range of 60 dB.

Power allocation for OFDM using adaptive beamforming over wireless networks

Abstract: The performance of a multiuser wireless network using orthogonal frequency-division modulation (OFDM), combined with power control and adaptive beamforming for uplink transmission is presented here. A network-wide adaptive power control algorithm is used to achieve the desired signal-to-interference-and-noise-ratio at each OFDM subcarrier and increase the power efficiency of the network. As a result, we can achieve a better overall error probability for a fixed total transmit power. With the assumption of fixed-modulation for all subcarriers, transmit powers and beamforming weight vectors at each subcarrier are updated jointly, using an iterative algorithm that converges to the optimal solution for the entire network. Unlike most of the loading algorithms, this approach considers fixed bit allocation and optimizes the power allocation and reduces the interference for the entire network, rather than a single transmitter. We also propose joint time-domain beamforming and power control to reduce the complexity resulting from the number of beamformers and fast Fourier transformed blocks. The proposed algorithm is also extended to COFDM and we show that it improves the performance of those systems.

Performance analysis of SMI adaptive beamforming arrays for smart antenna systems

Abstract: In this paper we have presented the theory and analyzed the performance of the SMI adaptive beamforming algorithm for smart antenna systems. The SMI algorithm is based on block-data weight adaptation. Simulations results obtained show that SMI algorithm is able to iteratively update the array weights to force deep nulls in the directions of the interferes, and achieve a maximum in the direction of the desired signal. The resulting beampattern is the same throughout all iterations since a solution for the optimal weights is independently computed for each data block. The SMI algorithm was found to converge almost immediately during the first data block with a small amount of residual error

Distributed Beamforming in Wireless Sensor Networks

Abstract: We present an adaptive distributed beamforming approach for sensor networks, wherein sensor nodes coordinate their transmissions to form a distributed antenna array that directs a beam toward an airborne relay (an unmanned aerial vehicle). Distributed beamforming using sensors is challenging since the number of nodes and their exact positions are unknown. A simulation model was implemented to study adaptive beamforming, and results are compared to theoretical results for random arrays. We show that the antenna main lobe remains stable in the presence of position errors and sensor failures, and thus can be steered in an adaptive manner as a UAV files past

SVD-based optimal filtering for noise reduction in dual microphone hearing aids: a real time implementation and perceptual evaluation

Abstract: In this paper, the first real-time implementation and perceptual evaluation of a singular value decomposition (SVD)-based optimal filtering technique for noise reduction in a dual microphone behind-the-ear (BTE) hearing aid is presented. This evaluation was carried out for a speech weighted noise and multitalker babble, for single and multiple jammer sound source scenarios. Two basic microphone configurations in the hearing aid were used. The SVD-based optimal filtering technique was compared against an adaptive beamformer, which is known to give significant improvements in speech intelligibility in noisy environment. The optimal filtering technique works without assumptions about a speaker position, unlike the two-stage adaptive beamformer. However this strategy needs a robust voice activity detector (VAD). A method to improve the performance of the VAD was presented and evaluated physically. By connecting the VAD to the output of the noise reduction algorithms, a good discrimination between the speech-and-noise periods and the noise-only periods of the signals was obtained. The perceptual experiments demonstrated that the SVD-based optimal filtering technique could perform as well as the adaptive beamformer in a single noise source scenario, i.e., the ideal scenario for the latter technique, and could outperform the adaptive beamformer in multiple noise source scenarios.

Improved adaptive beamforming using a hybrid LMS/SMI approach

Abstract: This paper proposes a hybrid algorithm for improved adaptive beamforming by combining the attributes of LMS and SMI algorithms. The proposed algorithm uses the LMS algorithm, which simple to implement and not computationally intensive, with SMI initialization in order to ensure fast convergence. Numerical results are presented to verify the improved convergence, accuracy, and computational efficiency of the proposed hybrid LMS/SMI algorithm.

Hearing aid and a method of processing input signals in a hearing aid

Abstract: A hearing aid comprises two microphones, directional processing means (Dir1, Dir2) for combining the respective audio signals to form a spatial signal, a beamformer (35) for controlling the directional processing means to provide adaptation of the spatial signal, and means (LFB1, LFB2) for boosting low frequencies of the spatial signal. A feedback estimator generates a feedback compensation signal (7a, 7b), which is combined with the boosted spatial signal. By applying feedback compensation only after directional processing and low frequency boosting, the device avoids interference by the feedback estimator with the function of the beamformer. The invention also provides a method of processing signals in a hearing aid.

Space-time Coordinated Distributed Sensing Algorithms for Resource Efficient Narrowband Target Localization and Tracking

Abstract: Distributed sensing has been used for enhancing signal to noise ratios for space-time localization and tracking of remote objects using phased array antennas, sonar, and radio signals The use of these technologies in identifying mobile targets in a field, emitting acoustic signals, using a network of low-cost narrow band acoustic micro-sensing devices randomly dispersed over the region of interest, presents unique challenges The effects of wind, turbulence, and temperature gradients and other environmental effects can decrease the signal to noise ratio by introducing random errors that cannot be removed through calibration This paper presents methods for dynamic distributed signal processing to detect, identify, and track targets in noisy environments with limited resources Specifically, it evaluates the noise tolerance of adaptive beamforming and compares it to other distributed sensing approaches Many source localization and direction-of-arrival (DOA) estimation methods based on beamforming using acoustic sensor array have been proposed We use the approximate maximum likelihood parameter estimation method to perform DOA estimation of the source in the frequency domain Generally, sensing radii are large and data from the nodes are transmitted over the network to a centralized location where beamforming is done These methods therefore depict low tolerance to environmental noise Knowledge based localized distributed processing methods have also been developed for distributed in-situ localization and target tracking in these environments These methods, due to their reliance only on local sensing, are not significantly affected by spatial perturbations and are robust in tracking targets in low SNR environments Specifically, Dynamic Space-time Clustering (DSTC)-based localization and tracking algorithm has demonstrated orders of magnitude improvement in noise tolerance with nominal impact on performance We also propose hybrid algorithms for energy efficient robust performance in very noisy environments This paper compares the performance of hybrid algorithms with sparse beamforming nodes supported by randomly dispersed DSTC nodes to that of beamforming and DSTC algorithms Hybrid algorithms achieve relative high accuracy in noisy environments with low energy consumption Sensor data from a field test in the Marine base at 29 Palms, CA, were analyzed for validating the results in this paper The results were compared to “ground truth” data obtained from GPS receivers on the vehicles

SINR estimation for power control in systems with transmission beamforming

Abstract: This paper takes a unified approach to the downlink transmission power control, while a transmission beamforming is applied in the base station. The proposed scheme is based on the estimation of signal-to-interference-and-noise-ratios (SINRs) by antenna array measurement and using this estimate in transmission power control. Hence power control algorithms needing SINR-levels can be applied instead of the simple relay power control. The SINR estimation technique does not require any additional measurements compared to a separate adaptive beamforming and power control, since the required measurements are needed for the adaptive beamforming update. The estimation is based on the knowledge of the level of caused interference to the multiple access links in the same cell, and the utilization of relay power control commands in SINR estimation.