Iterative turbo beamforming for OFDM based hybrid terrestrial-satellite mobile system

In the context of orthogonal frequency division multiplexing (OFDM)-based systems, pilot-based beamforming (BF) exhibits a high degree of sensitivity to the pilot sub-carriers. Increasing the number of reference pilots significantly improves BF performance as well as system performance. However, this increase comes at the cost of data throughput, which inevitably shrinks due to transmission of additional pilots. Hence an approach where reference signals available to the BF process can be increased without transmitting additional pilots can exhibit superior system performance without compromising throughput. Thus, the authors present a novel three-stage iterative turbo beamforming (ITBF) algorithm for an OFDM-based hybrid terrestrial-satellite mobile system, which utilises both pilots and data to perform interference mitigation. Data sub-carriers are utilised as virtual reference signals in the BF process. Results show that when compared to non-iterative conventional BF, the proposed ITBF exhibits bit error rate gain of up to 2.5 dB with only one iteration

Improving LMS/NLMS-Based Beamforming Using Shirvani-Akbari Array

Abstract ULA is the most common geometry exp lo ited in array signal processing. In the beamforming operation, employing the ULA leads to obtaining narrower beamwidth with respect to other geometries in similar element numbers. Recently, Shirvani and Akbari proposed a new array by adding two elements to the ULA in top and bottom of the array axis, named as SAA. Th is new array offers a considerable imp rovement in DOA estimation performance in detection and resolution of signal sources placed at angles close to the array endfires. In this article, the performance of the proposed SAA is investigated especially in beamforming and co mpared with ULA. LMS and NLMS algorith ms that are popular adaptive beamforming methods are used for evaluation and co mparing the performance of SAA and ULA. Considering array factor, mean square erro r and bit error rate metrics, simu lation results show improved convergence speed and higher data transmission accuracy in different signal source locations, boresight angles as well as endfire ones, for SAA with respect to ULA

GPS Anti-Jamming Technique Using Smart Antenna Systems

This paper presents a global positioning system (GPS) anti-jamming technique using a smart antenna system. In anti-jamming systems, adaptive array antennas are used to estimate the direction of signals arriving at the antenna and spatially filter the desired signal from the unwanted signals by adaptively controlling the direction of the maximum radiated beam.  In this study, the uniform linear array was used for the smart antenna configuration. The work compared the performance of non-blind adaptive algorithms with blind algorithms for adaptive beamforming. Non-blind adaptive algorithm using least mean square (LMS) algorithm and blind algorithm using constant modulus algorithm (CMA) was studied and implemented for adaptive beamforming while estimation of signal parameters via rotational invariance technique (ESPRIT) and multiple signal classification (MUSIC) algorithms were implemented for the direction of arrival (DOA) estimation. The effect of varying the number of elements in the antenna array and the required spacing between them was also investigated. Results of comparison carried out using numerical analysis showed that both algorithms performed well for the DOA estimation, with MUSIC algorithm producing a better direction of arrival spectrum with little or no minor peaks. For the beamforming, both LMS and CMA produced maximum radiation in the direction of the desired signal. LMS placed deeper nulls in the directions of interference with faster convergence and fewer errors as compared with CMA that presented errors and was able to suppress the interference to a minimal extent. It was also shown that as the number of elements in the array increases, a more directive beam and DOA spectrum is produced

MVDR broadband beamforming using polynomial matrix techniques

This thesis addresses the formulation of and solution to broadband minimum variance distortionless response (MVDR) beamforming. Two approaches to this problem are considered, namely, generalised sidelobe canceller (GSC) and Capon beamformers. These are examined based on a novel technique which relies on polynomial matrix formulations. The new scheme is based on the second order statistics of the array sensor measurements in order to estimate a space-time covariance matrix. The beamforming problem can be formulated based on this space-time covariance matrix. Akin to the narrowband problem, where an optimum solution can be derived from the eigenvalue decomposition (EVD) of a constant covariance matrix, this utility is here extended to the broadband case. The decoupling of the space-time covariance matrix in this case is provided by means of a polynomial matrix EVD. The proposed approach is initially exploited to design a GSC beamformer for a uniform linear array, and then extended to the constrained MVDR, or Capon, beamformer and also the GSC with an arbitrary array structure. The uniqueness of the designed GSC comes from utilising the polynomial matrix technique, and its ability to steer the array beam towards an off-broadside direction without the pre-steering stage that is associated with conventional approaches to broadband beamformers. To solve the broadband beamforming problem, this thesis addresses a number of additional tools. A first one is the accurate construction of both the steering vectors based on fractional delay filters, which are required for the broadband constraint formulation of a beamformer, as for the construction of the quiescent beamformer. In the GSC case, we also discuss how a block matrix can be obtained, and introduce a novel paraunitary matrix completion algorithm. For the Capon beamformer, the polynomial extension requires the inversion of a polynomial matrix, for which a residue-based method is proposed that offers better accuracy compared to previously utilised approaches. These proposed polynomial matrix techniques are evaluated in a number of simulations. The results show that the polynomial broadband beamformer (PBBF) steersthe main beam towards the direction of the signal of interest (SoI) and protects the signal over the specified bandwidth, and at the same time suppresses unwanted signals by placing nulls in their directions. In addition to that, the PBBF is compared to the standard time domain broadband beamformer in terms of their mean square error performance, beam-pattern, and computation complexity. This comparison shows that the PBBF can offer a significant reduction in computation complexity compared to its standard counterpart. Overall, the main benefits of this approach include beam steering towards an arbitrary look direction with no need for pre-steering step, and a potentially significant reduction in computational complexity due to the decoupling of dependencies of the quiescent beamformer, blocking matrix, and the adaptive filter compared to a standard broadband beamformer implementation.This thesis addresses the formulation of and solution to broadband minimum variance distortionless response (MVDR) beamforming. Two approaches to this problem are considered, namely, generalised sidelobe canceller (GSC) and Capon beamformers. These are examined based on a novel technique which relies on polynomial matrix formulations. The new scheme is based on the second order statistics of the array sensor measurements in order to estimate a space-time covariance matrix. The beamforming problem can be formulated based on this space-time covariance matrix. Akin to the narrowband problem, where an optimum solution can be derived from the eigenvalue decomposition (EVD) of a constant covariance matrix, this utility is here extended to the broadband case. The decoupling of the space-time covariance matrix in this case is provided by means of a polynomial matrix EVD. The proposed approach is initially exploited to design a GSC beamformer for a uniform linear array, and then extended to the constrained MVDR, or Capon, beamformer and also the GSC with an arbitrary array structure. The uniqueness of the designed GSC comes from utilising the polynomial matrix technique, and its ability to steer the array beam towards an off-broadside direction without the pre-steering stage that is associated with conventional approaches to broadband beamformers. To solve the broadband beamforming problem, this thesis addresses a number of additional tools. A first one is the accurate construction of both the steering vectors based on fractional delay filters, which are required for the broadband constraint formulation of a beamformer, as for the construction of the quiescent beamformer. In the GSC case, we also discuss how a block matrix can be obtained, and introduce a novel paraunitary matrix completion algorithm. For the Capon beamformer, the polynomial extension requires the inversion of a polynomial matrix, for which a residue-based method is proposed that offers better accuracy compared to previously utilised approaches. These proposed polynomial matrix techniques are evaluated in a number of simulations. The results show that the polynomial broadband beamformer (PBBF) steersthe main beam towards the direction of the signal of interest (SoI) and protects the signal over the specified bandwidth, and at the same time suppresses unwanted signals by placing nulls in their directions. In addition to that, the PBBF is compared to the standard time domain broadband beamformer in terms of their mean square error performance, beam-pattern, and computation complexity. This comparison shows that the PBBF can offer a significant reduction in computation complexity compared to its standard counterpart. Overall, the main benefits of this approach include beam steering towards an arbitrary look direction with no need for pre-steering step, and a potentially significant reduction in computational complexity due to the decoupling of dependencies of the quiescent beamformer, blocking matrix, and the adaptive filter compared to a standard broadband beamformer implementation

Developing an Enhanced Adaptive Antenna Beamforming Algorithm for Telecommunication Applications

As a key enabler for advanced wireless communication technologies, smart antennas have become an intense field of study. Smart antennas use adaptive beamforming algorithms which allow the antenna system to search for specific signals even in a background of noise and interference. Beamforming is a signal processing technique used to shape the antenna array pattern according to prescribed criteria. In this thesis, a comparative study is presented for various adaptive antenna beamforming algorithms. Least mean square (LMS), normalized least mean square (NLMS), recursive least square (RLS), and sample matrix inversion (SMI) algorithms are studied and analyzed. The study also considers some possible adaptive filter combinations and variations, such as: LMS with SMI weights initialization, and combined NLMS filters with a variable mixing parameter. Furthermore, a new adaptive variable step-size normalized least mean square (VSS-NLMS) algorithm is proposed. Sparse adaptive algorithms, are also studied and analyzed, and two-channel estimations sparse algorithms are applied to an adaptive beamformer, namely: proportionate normalized least-mean-square (PNLMS), and lp norm PNLMS (LP-PNLMS) algorithms. Moreover, a variable step size has been applied to both of these algorithms for improved performance. These algorithms are simulated for antenna arrays with different geometries and sizes, and results are discussed in terms of their convergence speed, max side lobe level (SLL), null depths, steady-state error, and sensitivity to noise. Simulation results confirm the superiority of the proposed VSS-NLMS algorithms over the standard NLMS without the need of using combined filters. Results also show an improved performance for the sparse algorithms after applying the proposed variable step size

Adaptive array antenna design for wireless communication systems

Adaptive array antennas use has been limited to non-commercial applications due to their high cost and hardware complexity. The implementation cost of adaptive array antennas can be kept to a minimum by using cost effective antennas, reducing the number of elements in the array and implementing efficient beamforming techniques. This thesis presents techniques for the design of adaptive array antennas which will enable their cost effective implementation in wireless communication systems. The techniques are investigated from three perspectives, namely, reconfigurable antenna design, wide scan array design and single-port beamforming technique. A novel single-feed polarisation reconfigurable antenna design is proposed in the first stage of this study. Different polarisation states, namely, linear polarisation (LP), left-hand circular polarisation (LHCP) and right-hand circular polarisation (RHCP), are achieved by perturbing the shape of the main radiating structure of the antenna. The proposed antenna exhibits good axial ratio (< 3 dB at 2.4 GHz) and has high radiation efficiency in both polarisation modes (91.5 % - LHCP and 86.9 % - RHCP). With a compact single feeding structure, the antenna is suitable for implementation in wireless communication devices. The second stage of the study presents the design procedure of wide scan adaptive array antennas with reduced number of elements. Adaptive array antennas with limited number of elements have limited scanning range, reduced angular scanning resolution and high sidelobe levels. To date, design synthesis of adaptive array antennas has been targeted on arrays with a large number of elements. This thesis presents a comprehensive analysis of adaptive array antennas with less than 10 elements. Different array configurations are analysed and various array design parameters such as number of elements, separation between elements and orientation of the elements are analysed in terms of their 3 dB scan range. The proposed array, the 3-faceted array, achieves a scanning range up to ±70°, which is higher than ±56° obtained from the Uniform Linear Array. The faceted arrays are then evaluated in the context of adaptive beamforming properties. It was shown that the 3-faceted array is suitable for adaptive array applications in wireless communication systems as it achieves the highest directivity compared to other faceted structures. The 3-faceted array is then synthesised for low sidelobe level. Phase correction together with amplitude tapering technique is applied to the 3-faceted array. The use of conventional and tuneable windowing techniques on the 3- faceted array is also analysed. The final stage of the study investigates beamforming techniques for the adaptive array antenna. In the first part, beamforming algorithms using different performance criteria, which include maximum signal-to noise-ratio (SINR), minimum (mean-square Error) MSE and power minimisation, are evaluated. In the second part, single-port beamforming techniques are explored. In previous single-port beamforming methods, the spatial information of the signals is not fully recovered and this limits the use of conventional adaptive beamforming algorithms. In this thesis, a novel signal estimation technique using pseudo-inverse function for single-port beamforming is proposed. The proposed polarisation reconfigurable antenna, the 3-faceted array antenna and the single-port beamforming technique achieve the required performance, which suggests the potential of adaptive array antennas to be deployed commercially, especially in wireless communication industry

A study Of beamforming techniques and their blind approach

Beamforming is a technique in which an array of antennas is exploited to achieve maximum reception in a specified direction by estimating the signal arrival from a desired direction (in the presence of noise) while signals of the same frequency from other directions are rejected. This is achieved by varying the weights of each of the sensors (antennas) used in the array. It basically uses the idea that, though the signals emanating from different transmitters occupy the same frequency channel, they still arrive from different directions. This spatial separation is exploited to separate the desired signal from the interfering signals. In adaptive beamforming the optimum weights are iteratively computed using complex algorithms based upon different criteria

Adaptive antenna array beamforming using a concatenation of recursive least square and least mean square algorithms

In recent years, adaptive or smart antennas have become a key component for various wireless applications, such as radar, sonar and cellular mobile communications including worldwide interoperability for microwave access (WiMAX). They lead to an increase in the detection range of radar and sonar systems, and the capacity of mobile radio communication systems. These antennas are used as spatial filters for receiving the desired signals coming from a specific direction or directions, while minimizing the reception of unwanted signals emanating from other directions.Because of its simplicity and robustness, the LMS algorithm has become one of the most popular adaptive signal processing techniques adopted in many applications, including antenna array beamforming. Over the last three decades, several improvements have been proposed to speed up the convergence of the LMS algorithm. These include the normalized-LMS (NLMS), variable-length LMS algorithm, transform domain algorithms, and more recently the constrained-stability LMS (CSLMS) algorithm and modified robust variable step size LMS (MRVSS) algorithm. Yet another approach for attempting to speed up the convergence of the LMS algorithm without having to sacrifice too much of its error floor performance, is through the use of a variable step size LMS (VSSLMS) algorithm. All the published VSSLMS algorithms make use of an initial large adaptation step size to speed up the convergence. Upon approaching the steady state, smaller step sizes are then introduced to decrease the level of adjustment, hence maintaining a lower error floor. This convergence improvement of the LMS algorithm increases its complexity from 2N in the case of LMS algorithm to 9N in the case of the MRVSS algorithm, where N is the number of array elements.An alternative to the LMS algorithm is the RLS algorithm. Although higher complexity is required for the RLS algorithm compared to the LMS algorithm, it can achieve faster convergence, thus, better performance compared to the LMS algorithm. There are also improvements that have been made to the RLS algorithm families to enhance tracking ability as well as stability. Examples are, the adaptive forgetting factor RLS algorithm (AFF-RLS), variable forgetting factor RLS (VFFRLS) and the extended recursive least squares (EX-KRLS) algorithm. The multiplication complexity of VFFRLS, AFF-RLS and EX-KRLS algorithms are 2.5N2 + 3N + 20 , 9N2 + 7N , and 15N3 + 7N2 + 2N + 4 respectively, while the RLS algorithm requires 2.5N2 + 3N .All the above well known algorithms require an accurate reference signal for their proper operation. In some cases, several additional operating parameters should be specified. For example, MRVSS needs twelve predefined parameters. As a result, its performance highly depends on the input signal.In this study, two adaptive beamforming algorithms have been proposed. They are called recursive least square - least mean square (RLMS) algorithm, and least mean square - least mean square (LLMS) algorithm. These algorithms have been proposed for meeting future beamforming requirements, such as very high convergence rate, robust to noise and flexible modes of operation. The RLMS algorithm makes use of two individual algorithm stages, based on the RLS and LMS algorithms, connected in tandem via an array image vector. On the other hand, the LLMS algorithm is a simpler version of the RLMS algorithm. It makes use of two LMS algorithm stages instead of the RLS – LMS combination as used in the RLMS algorithm.Unlike other adaptive beamforming algorithms, for both of these algorithms, the error signal of the second algorithm stage is fed back and combined with the error signal of the first algorithm stage to form an overall error signal for use update the tap weights of the first algorithm stage.Upon convergence, usually after few iterations, the proposed algorithms can be switched to the self-referencing mode. In this mode, the entire algorithm outputs are swapped, replacing their reference signals. In moving target applications, the array image vector, F, should also be updated to the new position. This scenario is also studied for both proposed algorithms. A simple and effective method for calculate the required array image vector is also proposed. Moreover, since the RLMS and the LLMS algorithms employ the array image vector in their operation, they can be used to generate fixed beams by pre-setting the values of the array image vector to the specified direction.The convergence of RLMS and LLMS algorithms is analyzed for two different operation modes; namely with external reference or self-referencing. Array image vector calculations, ranges of step sizes values for stable operation, fixed beam generation, and fixed-point arithmetic have also been studied in this thesis. All of these analyses have been confirmed by computer simulations for different signal conditions. Computer simulation results show that both proposed algorithms are superior in convergence performances to the algorithms, such as the CSLMS, MRVSS, LMS, VFFRLS and RLS algorithms, and are quite insensitive to variations in input SNR and the actual step size values used. Furthermore, RLMS and LLMS algorithms remain stable even when their reference signals are corrupted by additive white Gaussian noise (AWGN). In addition, they are robust when operating in the presence of Rayleigh fading. Finally, the fidelity of the signal at the output of the proposed algorithms beamformers is demonstrated by means of the resultant values of error vector magnitude (EVM), and scatter plots. It is also shown that, the implementation of an eight element uniform linear array using the proposed algorithms with a wordlength of nine bits is sufficient to achieve performance close to that provided by full precision

Increasing the Capacity of Wireless Networks Using Beamforming

Wireless mobile communications are growing in an exponential manner, especially in terms of the number of users. Also, the demand for high Quality of Service (QoS) has become essential. Nowadays, subscribers are using more applications such as the internet, video conferencing, and high quality TV. These applications require high data rates. The Space Division Multiple Access (SDMA) is the key element that can enable reusing of the same channels among different users in the same cell to meet this demand. For the application of SDMA in an efficient way, it is required to identify the users’ positions and directions in the cell. The Direction of Arrival (DOA) algorithms can estimate the incident angles of all the received signals impinging on the array antenna. These algorithms give the DOAs of all relevant signals of the user sources and interference sources. However, they are not capable of distinguishing and identifying which one is the direction of the desired user. In this thesis, we have proposed to use a Reference Signal (RFS) known by the transmitter and the receiver to identify which one of the estimated DOAs is the DOA of the desired user in the cell. Using a RFS and applying the correlation concept, we can distinguish the desired signal from the others. Moreover, we have considered the Affine Projection Algorithm (APA) to enhance the accuracy of the estimated direction and to form a beam towards the desired user and nulls towards the interferers. Our simulation results assure that, in the presence of the RFS, the DOA algorithms can identify the direction of the desired user with high accuracy and resolution. We have investigated this concept on different DOA algorithms such as MUltiple Signal Classification (MUSIC), ROOT MUSIC, and Estimate the direction of arrival of Signals Parameters via Rotational Invariance Technique (ESPRIT) algorithms. Moreover , we have introduced an approach for using the smart antennas (SA) to exploit the space diversity for the next generations of mobile communication systems. We have applied a combination of the MUSIC and the Least Mean Squares (LMS) algorithms. We have proposed the MUSIC algorithm for finding the directions of the users in the cell. In addition, we have considered the LMS algorithm for enhancing the accuracy of the DOA, performing the beam generation process, and keeping track of the users in the cell. Furthermore, we have proposed a scheduling algorithm that performs the scheduling in terms of the generated beams. The space diversity, together with the time and frequency diversities of LTE (Long Term Evolution) results in a large capacity increase in the next generations of wireless mobile communication systems. Simulation results show that the proposed algorithm called MUltiple Signal Classification and Least Mean Squares (MLMS), has the capability to converge and completely follow the desired user signal with a very high resolution. The convergence and the accurate tracking of the desired signal user take place after 13 iterations while in the traditional LMS, the convergence needs 85 iterations to take place. This means an 84.7% improvement over the traditional LMS algorithm for the same number of calculations in each iteration. In contrast to the traditional LMS algorithm, the proposed algorithm can work in the presence of high level of interference. Furthermore, the proposed scheduling scheme based on beamforming shows a gain of 15% in the total aggregated throughput for each 10o decrease in the beam size. The proposed model provides an optimum, complete, and practical design for the next generations of the mobile communication systems. In this model, we have proposed a mechanism to find the direction of each user in the cell, enhance the accuracy of the obtained DOAs, and perform scheduling based on the generated beams. In addition, we have presented an approach for Frequency Reuse (FR) based on beamforming for 5G. We have implemented a synthesizer in order to smartly form the desired beam shape and make the nulls deeper. We have taken the advantage of the SAs, beamforming capabilities, and the radiation pattern (RP) synthesizing techniques to build up a FR scheme for 5G. Also, we have developed a formula for calculating the Signal to Interference and Noise Ratio (SINR) in terms of the desired and the interferers directions. The objective is to maintain the SINR at the minimum acceptable levels required by the LTE while reducing the beam sizes, and hence increase the FR factor. The simulation results show that with a Uniform Linear Antenna (ULA) of 11 elements, we can achieve the desirable SINR levels using beams of 100 width, which improves the FR factor from 1 to 18, and subsequently increases the number of mobile users