Showing papers on "Adaptive beamformer published in 2010"

Wideband Beamforming: Concepts and Techniques

Abstract: This book provides an excellent reference for all professionals working in the area of array signal processing and its applications in wireless communications. Wideband beamforming has advanced with the increasing bandwidth in wireless communications and the development of ultra wideband (UWB) technology. In this book, the authors address the fundamentals and most recent developments in the field of wideband beamforming. The book provides a thorough coverage of the subject including major sub-areas such as sub-band adaptive beamforming, frequency invariant beamforming, blind wideband beamforming, beamforming without temporal processing, and beamforming for multi-path signals. Key Features: Unique book focusing on wideband beamforming Discusses a hot topic coinciding with the increasing bandwidth in wireless communications and the development of UWB technology Addresses the general concept of beamforming including fixed beamformers and adaptive beamformers Covers advanced topics including sub-band adaptive beamforming, frequency invariant beamforming, blind wideband beamforming, beamforming without temporal processing, and beamforming for multi-path signals Includes various design examples and corresponding complexity analyses This book provides a reference for engineers and researchers in wireless communications and signal processing fields. Postgraduate students studying signal processing will also find this book of interest.

Fully Automatic Computation of Diagonal Loading Levels for Robust Adaptive Beamforming

Abstract: The main drawback of the conventional diagonal loading (DL) approaches is that there is no clear guideline on how to choose the DL level reliably or how to select user parameters appropriately. An algorithm that can be used to compute the DL level completely automatically from the given data without the need of specifying any user parameter is considered. In this algorithm an enhanced covariance matrix estimate obtained via a shrinkage method, instead of the sample covariance matrix, is used in the standard Capon beamforming formulation. The performance of the resulting beamformer is illustrated via numerical examples, and it is compared with several other adaptive beamformers.

Novel Ring Resonator-Based Integrated Photonic Beamformer for Broadband Phased Array Receive Antennas—Part I: Design and Performance Analysis

Abstract: A novel optical beamformer concept is introduced that can be used for seamless control of the reception angle in broadband wireless receivers employing a large phased array antenna (PAA). The core of this beamformer is an optical beamforming network (OBFN), using ring resonator-based broadband delays, and coherent optical combining. The electro-optical conversion is performed by means of single-sideband suppressed carrier modulation, employing a common laser, Mach-Zehnder modulators, and a common optical sideband filter after the OBFN. The unmodulated laser signal is then re-injected in order to perform balanced coherent optical detection, for the opto-electrical conversion. This scheme minimizes the requirements on the complexity of the OBFN, and has potential for compact realization by means of full integration on chip. The impact of the optical beamformer concept on the performance of the full receiver system is analyzed, by modeling the combination of the PAA and the beamformer as an equivalent two-port RF system. The results are illustrated by a numerical example of a PAA receiver for satellite TV reception, showing that - when properly designed - the beamformer hardly affects the sensitivity of the receiver.

Eigenspace-based minimum variance beamforming applied to medical ultrasound imaging

Abstract: Recently, adaptive beamforming methods have been successfully applied to medical ultrasound imaging, resulting in significant improvement in image quality compared with non-adaptive delay-and-sum (DAS) beamformers. Most of the adaptive beamformers presented in the ultrasound imaging literature are based on the minimum variance (MV) beamformer which can significantly improve the imaging resolution, although their success in enhancing the contrast has not yet been satisfactory. It is desirable for the beamformer to improve the resolution and contrast at the same time. To this end, in this paper, we have applied the eigenspace-based MV (EIBMV) beamformer to medical ultrasound imaging and have shown a simultaneous improvement in imaging resolution and contrast. EIBMV beamformer utilizes the eigenstructure of the covariance matrix to enhance the performance of the MV beamformer. The weight vector of the EIBMV is found by projecting the MV weight vector onto a vector subspace constructed from the eigenstructure of the covariance matrix. Using EIBMV weights instead of the MV ones leads to reduced sidelobes and improved contrast, without compromising the high resolution of the MV beamformer. In addition, the proposed EIBMV beamformer presents a satisfactory robustness against data misalignment resulting from steering vector errors, outperforming the regularized MV beamformer.

Transmit Subaperturing for MIMO Radars With Co-Located Antennas

Abstract: We present a transmit subaperturing (TS) approach for multiple-input multiple-output (MIMO) radars with co-located antennas. The proposed scheme divides the transmit array elements into multiple groups, each group forms a directional beam and modulates a distinct waveform, and all beams are steerable and point to the same direction. The resulting system is referred to as a TS-MIMO radar. A TS-MIMO radar is a tunable system that offers a continuum of operating modes from the phased-array radar, which achieves the maximum directional gain but the least interference rejection ability, to the omnidirectional transmission based MIMO radar, which can handle the largest number of interference sources but offers no directional gain. Tuning of the TS-MIMO system can be easily made by changing the configuration of the transmit subapertures, which provides a direct tradeoff between the directional gain and interference rejection power of the system. The performance of the TS-MIMO radar is examined in terms of the output signal-to-interference-plus-noise ratio (SINR) of an adaptive beamformer in an interference and training limited environment, where we show analytically how the output SINR is affected by several key design parameters, including the size/number of the subapertures and the number of training signals. Our results are verified by computer simulation and comparisons are made among various operating modes of the proposed TS-MIMO system.

Direct-path suppression by spatial filtering in digital television terrestrial broadcasting-based passive radar

Abstract: The signal model of digital television terrestrial broadcasting (DTTB) is introduced and its ambiguity function is analysed here. In order to suppress the direct-path (and multipath) interference generated by single frequency network in DTTB-based passive radar, the corresponding spatial filtering methods are designed according to the feature of each channel. In the echo channel, the interference is suppressed by discarding the signal-subspace. The noise-subspace can be estimated from the power of the covariance matrix to avoid the eigen-decomposition and to eliminate the need to obtain the corresponding dimension. In the reference channel, the transmitting signal of a certain transmitter is regarded as the desired signal. The desired signal will be cancelled by conventional spatial filtering algorithms for the high signal-to-noise ratio case, which will also result in a distorted pattern and high side-lobes. To solve this problem, an improved general side-lobe canceller structure is proposed to realise the adaptive beamformer. To improve the robustness of the proposed methods and suppress the distributed clutter, the ‘broad null’ algorithm is combined with the beamformer. The simulation results show that the proposed methods are effective to suppress the interference in DTTB-based passive radar.

Collaborative Null-Steering Beamforming for Uniformly Distributed Wireless Sensor Networks

Abstract: Null-steering transmit beamformers aim to maximize the received signal power in the direction of the intended receiver while substantially reducing the power impinging on the unintended receivers located in other directions. The existing null-steering beamformers may not be directly applied in wireless sensor networks (WSNs) as they do not conform with the decentralized nature of WSNs and require every node to be aware of the locations of all other nodes in the network. This manuscript presents a novel collaborative null-steering beamformer that can be implemented in uniformly distributed WSNs in which each node is oblivious of other nodes' locations. The average beampattern expression of the proposed beamformer is derived and it is shown that the beampattern associated with any arbitrary realization of the nodes' locations converges with probability one to the so-obtained average beampattern as the number of collaborating nodes grows large. Properties of the average beampattern are analytically studied. In particular, it is proven that the average gain of the proposed beamformer is inversely proportional to the number of collaborating nodes in the directions of unintended receivers and further, if a mild condition is satisfied, it is approximately equal to that of the collaborative conventional beamformer in the directions with far angular distance from any unintended receiver. It is argued that if virtual unintended receivers are assumed at proper directions, then the proposed collaborative null-steering beamformer can form an average beampattern with sidelobe peaks substantially smaller than those of the average beampattern of the collaborative conventional beamformer. To substantiate this argument, the optimal direction of a virtual unintended receiver is obtained such that its associated collaborative null-steering beamformer forms an average beam-pattern with minimal largest side-lobe peak. Depending on the number of collaborating nodes, it is further shown that the largest average sidelobe peak of the latter beamformer is up to 6.6 (dB) less than that of the collaborative conventional beamformer.

Constrained adaptive filtering algorithms based on conjugate gradient techniques for beamforming

Abstract: This article proposes constrained adaptive algorithms based on the conjugate gradient (CG) method for adaptive beamforming. The proposed algorithms are derived for the implementation of the beamformer according to the minimum variance and constant modulus criteria subject to a constraint on the array response. A CG-based weight vector strategy is created for enforcing the constraint and computing the weight expressions. The devised algorithms avoid the covariance matrix inversion and exhibit fast convergence with low complexity. A complexity analysis compares the proposed algorithms with the existing ones. The convergence properties of the CCM criterion are studied, conditions for convexity are established and a convergence analysis for the proposed algorithms is derived. Simulation results are conducted for both stationary and non-stationary scenarios, showing the convergence and tracking ability of the proposed algorithms.

Optimal modal beamformer for sensor arrays

Abstract: A method of forming a beampattern in a beamformer of the type in which the beamformer receives input signals from a sensor array, decomposes the input signals into the spherical harmonics domain, applies weighting coefficients to the spherical harmonics and combines them to form an output signal, wherein the weighting coefficients are optimized for a given set of input parameters by convex optimization. Formulations are provided for forming second order cone programming constraints for multiple main lobe generation, uniform and non-uniform side lobe control, automatic null steering, robustness and white noise gain.

Detection Algorithms to Discriminate Between Radar Targets and ECM Signals

Abstract: We address adaptive detection of coherent signals backscattered by possible point-like targets or originated from electronic countermeasure (ECM) systems in presence of thermal noise, clutter, and possible noise-like interferers. In order to come up with a class of decision schemes capable of discriminating between targets and ECM signals, we resort to generalized likelihood ratio test (GLRT) implementations of a generalized Neyman-Pearson rule (i.e., for multiple hypotheses). The adaptive detectors rely on secondary data, free of signal components, but sharing the statistical characterization of the noise in the cell under test. The performance assessment focuses on an adaptive beamformer orthogonal rejection test (ABORT)-like detector; analytical expressions for the probability of false alarm, the probability of detection of the target, and the probability of blanking the ECM (coherent) signal are given. More remarkably, it guarantees the constant false alarm rate (CFAR) property. The performance assessment shows that it can outperform the adaptive sidelobe blanker (ASB) in presence of ECM systems.

Adaptive Reduced-Rank Constrained Constant Modulus Algorithms Based on Joint Iterative Optimization of Filters for Beamforming

Abstract: This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints. The proposed scheme consists of a bank of full-rank adaptive filters that forms the transformation matrix, and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. For each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation. The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving the performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. A detailed complexity study and a convexity analysis are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.

$M$ -DCSK-Based Chaotic Communications in MIMO Multipath Channels With No Channel State Information

Abstract: We consider chaotic digital communications in multiple-input-multiple-output (MIMO) wireless multipath fading channels. In particular, we focus on systems that employ M -ary differential chaos shift keying (M-DCSK). We consider two transceiver schemes, both of which require no channel state information at either the transmitter or the receiver. The first one employs a distinct chaotic sequence at each transmit antenna to spread the same data symbol and transmits omnidirectionally. At each receive antenna, the corresponding differential detection statistic is formed, and these statistics are then combined with equal gain for symbol detection. The second scheme employs a single chaotic spreading sequence and makes use of adaptive transmit and receive beamforming. The beamformers are updated by using a simple stochastic gradient method that is based on the received signal power and a finite-rate feedback strategy. Simulation results show that both schemes can effectively exploit the spatial diversity of the underlying MIMO system, and the adaptive beamforming scheme significantly outperforms the omnidirectional transmission.

Adaptive Constrained Constant Modulus Algorithm Based on Auxiliary Vector Filtering for Beamforming

Abstract: A constrained constant modulus (CCM) algorithm with the auxiliary vector filtering (AVF) technique is introduced for robust adaptive beamforming. The proposed scheme decomposes the adaptive filter into constrained (reference vector filters) and unconstrained (auxiliary vector filters) components. The weight vector is iterated by subtracting the scaling auxiliary vector from the reference vector, which are computed according to the CCM criterion. The proposed algorithm provides an iterative exchange of information between the scalar factor and the auxiliary vector, resulting in a faster convergence and an improved steady-state performance as compared with existing techniques with large filters. The convergence properties of the proposed algorithm are analyzed. Simulation results show that the proposed beamforming algorithm outperforms existing techniques and is robust against signature mismatch problems.

Robust Adaptive Beamforming in Partly Calibrated Sparse Sensor Arrays

Abstract: Two new approaches to adaptive beamforming in sparse subarray-based partly calibrated sensor arrays are developed. Each subarray is assumed to be well calibrated, so that the steering vectors of all subarrays are exactly known. However, the intersubarray gain and/or phase mismatches are known imperfectly or remain completely unknown. Our first approach is based on a worst-case beamformer design which, in contrast to the existing worst-case designs, exploits a specific structured ellipsoidal uncertainty model for the signal steering vector rather than the commonly used unstructured uncertainty models. Our second approach is based on estimating the unknown intersubarray parameters by maximizing the output power of the minimum variance beamformer subject to a proper constraint that helps to avoid trivial solution of the resulting optimization problem. Different modifications of the second approach are developed for the cases of gain-and-phase and phase-only intersubarray distortions.

A Robust Adaptive Beamformer Based on Worst-Case Semi-Definite Programming

Abstract: In this correspondence, a novel robust adaptive beamformer is proposed based on the worst-case semi-definite programming (SDP). A recent paper has reported that a beamformer robust against large steering direction error can be constructed by using linear constraints on magnitude response in SDP formulation. In practice, however, array system also suffers from many other array imperfections other than steering direction error. In order to make the adaptive beamformer robust against all kinds of array imperfections, the worst-case optimization technique is proposed to reformulate the beamformer by minimizing the array output power with respect to the worst-case array imperfections. The resultant beamformer has the mathematical form of a regularized SDP problem and possesses superior robustness against arbitrary array imperfections. Although the formulation of robust beamformer uses weighting matrix, with the help of spectral factorization approach, the weighting vector can be obtained so that the beamformer can be used for both signal power and waveform estimation. Simple implementation, flexible performance control, as well as significant signal-to-interference-plus-noise ratio (SINR) enhancement, support the practicability of the proposed method.

Sensitivity of beamformer source analysis to deficiencies in forward modeling.

Abstract: Beamforming approaches have recently been developed for the field of electroencephalography (EEG) and magnetoencephalography (MEG) source analysis and opened up new applications within various fields of neuroscience. While the number of beamformer applications thus increases fast-paced, fundamental methodological considerations, especially the dependence of beamformer performance on leadfield accuracy, is still quite unclear. In this article, we present a systematic study on the influence of improper volume conductor modeling on the source reconstruction performance of an EEG-data based synthetic aperture magnetometry (SAM) beamforming approach. A finite element model of a human head is derived from multimodal MR images and serves as a realistic volume conductor model. By means of a theoretical analysis followed by a series of computer simulations insight is gained into beamformer performance with respect to reconstruction errors in peak location, peak amplitude, and peak width resulting from geometry and anisotropy volume conductor misspecifications, sensor noise, and insufficient sensor coverage. We conclude that depending on source position, sensor coverage, and accuracy of the volume conductor model, localization errors up to several centimeters must be expected. As we could show that the beamformer tries to find the best fitting leadfield (least squares) with respect to its scanning space, this result can be generalized to other localization methods. More specific, amplitude, and width of the beamformer peaks significantly depend on the interaction between noise and accuracy of the volume conductor model. The beamformer can strongly profit from a high signal-to-noise ratio, but this requires a sufficiently realistic volume conductor model.

Adaptive ambient sound suppression and speech tracking

Abstract: A device for suppressing ambient sounds from speech received by a microphone array is provided. One embodiment of the device comprises a microphone array, a processor, an analog-to-digital converter, and memory comprising instructions stored therein that are executable by the processor. The instructions stored in the memory are configured to receive a plurality of digital sound signals, each digital sound signal based on an analog sound signal originating at the microphone array, receive a multi-channel speaker signal, generate a monophonic approximation signal of the multi-channel speaker signal, apply a linear acoustic echo canceller to suppress a first ambient sound portion of each digital sound signal, generate a combined directionally-adaptive sound signal from a combination of each digital sound signal by a combination of time-invariant and adaptive beamforming techniques, and apply one or more nonlinear noise suppression techniques to suppress a second ambient sound portion of the combined directionally-adaptive sound signal.

A Class of Constrained Adaptive Beamforming Algorithms Based on Uniform Linear Arrays

Abstract: A new class of adaptive beamforming algorithms is proposed based on a uniformly spaced linear array by constraining its weight vector to a specific conjugate symmetric form. The method is applied to the well-known reference signal based (RSB) beamformer and the linearly constrained minimum variance (LCMV) beamformer as two implementation examples. The effect of the additional constraint is equivalent to adding a second step in the derived adaptive algorithm. However, a difference arises for the RSB case since no direction-of-arrival (DOA) information of the desired signal is available, which leads to a two-stage structure for incorporating the imposed constraint. Compared to the traditional algorithms, the proposed ones can achieve a faster convergence speed and a higher steady state output signal-to-interference-plus-noise ratio, given the same stepsize.

System and method for adaptive beamforming training using fixed time window for heterogeneous antenna systems

Abstract: A system and method for performing a beamforming training between heterogeneous wireless devices in a wireless network is disclosed. A number of time slots in a fixed-time period are assigned for transmit and/or receive sector training. The number of time slots assigned for transmit and/or receive sector training is based on an antenna configuration of a wireless station.

SINR Analysis of the Subtraction-Based SMI Beamformer

Abstract: The sample matrix inversion (SMI) beamformer suffers from performance degradation due to the finite sample size effect and the arrival angle mismatch problem. A simple technique to provide robustness to the conventional SMI beamformer is to block the desired signal from the received data before calculating the beamformer's weight vector, which leads to the subtraction-based SMI (S-SMI) beamformer. In this correspondence, closed-form approximations of the expected value of the signal-to-interference-plus-noise ratio (SINR) for the S-SMI beamformer and the SMI beamformer are derived, where the effect of both finite sample size and arrival angle mismatch are considered. Simulations show that the derived approximations are close enough to represent the true values of the SINR, when the sample size is small and the arrival direction mismatch exists.

Adaptive beamforming for array imaging of plate structures using lamb waves

Abstract: Lamb waves are considered a promising tool for the monitoring of plate structures. Large areas of plate structures can be monitored using active arrays employing beamforming techniques. Dispersion and multiple propagating modes are issues that need to be addressed when working with Lamb waves. Previous work has mainly focused on standard delay-and-sum (DAS) beamforming while reducing the effects of multiple modes through frequency selectivity and transducer design. This paper presents a minimum variance distortionless response (MVDR) approach for Lamb waves using a uniform rectangular array (URA) and a single transmitter. Theoretically calculated dispersion curves are used to compensate for dispersion. The combination of the MVDR approach and the two-dimensional array improves the suppression of interfering Lamb modes. The proposed approach is evaluated on simulated and experimental data and compared with the standard DAS beamformer. It is shown that the MVDR algorithm performs better in terms of higher resolution and better side lobe and mode suppression capabilities. Known issues of the MVDR approach, such as signal cancellation in highly correlated environments and poor robustness, are addressed using methods that have proven effective for the purpose in other fields of active imaging.

Iterative Adaptive Kronecker MIMO Radar Beamformer: Description and Convergence Analysis

Abstract: We introduce an iterative procedure for design of adaptive KL-variate linear beamformers that are structured as the Kronecker product of K-variate (transmit) and L-variate (receive) beamformers. We focus on MIMO radar applications for scenarios where only joint transmit and receive adaptive beamforming can efficiently mitigate multi-mode propagated backscatter interference. This is because the direction-of-departure (DoD) on one interference mode, and the direction-of-arrival (DoA) on the other, coincide with those of a target, respectively. We introduce a Markov model for the adaptive iterative routine, specify its convergence condition, and derive final (stable) signal-to-interference-plus-noise ratio (SINR) performance characteristics. Simulation results demonstrate high accuracy of the analytical derivations. In addition, we demonstrate, that for the considered class of multiple-input multiple-output (MIMO) radar interference scenarios, the diagonally loaded sample matrix inversion (SMI) algorithm provides additional performance improvement and convergence rate for this iterative adaptive Kronecker beamformer.

Adaptive beamforming in interference networks via bi-directional training

Abstract: We study distributed algorithms for adjusting beamforming vectors and receiver filters in multiple-input multiple-output (MIMO) interference networks, with the assumption that each user uses a single beam and a linear filter at the receiver. In such a setting there have been several distributed algorithms studied for maximizing the sum-rate or sum-utility assuming perfect channel state information (CSI) at the transmitters and receivers. The focus of this paper is to study adaptive algorithms for time-varying channels, without assuming any CSI at the transmitters or receivers. Specifically, we consider an adaptive version of the recent Max-SINR algorithm for a time-division duplex system. This algorithm uses a period of bi-directional training followed by a block of data transmission. Training in the forward direction is sent using the current beam-formers and used to adapt the receive filters. Training in the reverse direction is sent using the current receive filters as beams and used to adapt the transmit beamformers. The adaptation of both receive filters and beamformers is done using a least-squares objective for the current block. In order to improve the performance when the training data is limited, we also consider using exponentially weighted data from previous blocks. Numerical results are presented that compare the performance of the algorithms in different settings.

Adaptive beamforming for 60 GHz radios: challenges and preliminary solutions

Abstract: Beamforming is a critical and natural solution component for 60 GHz radios due to the increased signal attenuation that occurs as a consequence of the very small wavelength (mm-length) at these frequencies. In this paper, we discuss two practical challenges for 60 GHz beamforming that are likely to arise in most real-world antenna implementations, namely the infeasibility of (a) explicit channel estimation, and (b) generating isotropic antenna patterns. To overcome these challenges, we design a novel iterative training algorithm for antenna weight vectors (AWVs) that uses relatively coarse-grained feedback to obtain the optimal transmit and receive AWVs. We also propose a simple, but effective technique to identify the best transmit and receive sectors that serve to initialize our AWV training algorithm - without these well-chosen initial AWVs, the convergence time of our algorithm can be significantly longer.

Robust Adaptive Beamformer Using Interpolation Technique for Conformal Antenna Array

Abstract: A novel robust adaptive beamforming method for conformal array is proposed. By using interpolation technique, the cylindrical conformal array with directional antenna elements is transformed to a virtual uniform linear array with omni-directional elements. This method can compensate the amplitude and mutual coupling errors as well as desired signal point errors of the conformal array e-ciently. It is a universal method and can be applied to other curved conformal arrays. After the transformation, most of the existing adaptive beamforming algorithms can be applied to conformal array directly. The e-ciency of the proposed scheme is assessed through numerical simulations.

Blind source separation based on time-frequency sparseness in the presence of spatial aliasing

Abstract: In this paper, we propose a novel method for blind source separation (BSS) based on time-frequency sparseness (TF) that can estimate the number of sources and time-frequency masks, even if the spatial aliasing problem exists Many previous approaches, such as degenerate unmixing estimation technique (DUET) or observation vector clustering (OVC), are limited to microphone arrays of small spatial extent to avoid spatial aliasing We develop an offline and an online algorithm that can both deal with spatial aliasing by directly comparing observed and model phase differences using a distance metric that incorporates the phase indeterminacy of 2p and considering all frequency bins simultaneously Separation is achieved using a linear blind beamformer approach, hence musical noise common to binary masking is avoided Furthermore, the offline algorithm can estimate the number of sources Both algorithms are evaluated in simulations and real-world scenarios and show good separation performance

Robust adaptive beamforming via estimating steering vector based on semidefinite relaxation

Abstract: Most of the known robust adaptive beamforming techniques can be unified under one framework This is to use minimum variance distortionless response principle for beamforming vector computation in tandem with sample covariance matrix estimation and steering vector estimation based on some information about steering vector prior Motivated by such unified framework, we develop a new robust adaptive beamforming method based on finding a more accurate estimate of the actual steering vector than the available prior The objective for finding such steering vector estimate is the maximization of the beamformer output power under the constraints that the estimate does not converge to an interference steering vector and does not change the norm of the prior The resulting optimization problem is a non-convex quadratically constrained quadratic programming problem, which is NP hard in general, but can be efficiently and exactly solved in our specific case Our simulation results demonstrate the superiority of the proposed method over other robust adaptive beamforming methods

Ground Based and Onboard Based Beamforming for Hybrid Terrestrial-Satellite Mobile System

Abstract: Adaptive beamforming in terrestrial networks is viable due to relaxed complexity constraints. In case of satellite payload, adaptive beamforming is currently not cost effective due to higher onboard complexity requirements, power consumption issues and associated costs. In our earlier proposed Hybrid Terrestrial-Satellite Mobile System (HTSMS) incorporating frequency reuse, Onboard Based Beam-Forming (OBBF) is employed to mitigate Co-Channel Interference (CCI) for a Orthogonal Frequency Division Multiple Access (OFDM) system. The less complex solution is to opt for Ground Based Beam-Forming (GBBF) saving valuable onboard resources. Despite the benefits of GBBF, high feeder link bandwidth is required to support uplink and downlink transmissions. Moreover with GBBF, the satellite payload complexity is a sensitive function of the feed signals transmitted through gateway uplinks and downlinks. Furthermore, performance of GBBF is highly sensitive to the gateway calibration system which must compensate instabilities induced due to payload/gateway component changes over temperature and life as well as propagation amplitude and phase dispersion effects. Hence the choice of beamforming not only depends on complexity but also on performance. In this paper we employ ground based and onboard based beamforming solutions for a Bit Interleaved Coded Modulation-OFDM (BICM-OFDM) in our proposed HTSMS. We also propose a semi static hybrid space/ground beamforming and show that it is a far less complex solution compared to onboard adaptive beamforming. We then investigate the applicability of onboard and ground based approaches and quantify their performance advantages for the HTSMS case in AWGN channel scenario.