A combined Kalman filter and constant modulus algorithm beamformer for fast-fading channels

Beamformers which use only the constant modulus algorithm (CMA) are unable to track properly time-variant signals in fast-fading channels. The Kalman kilter (KF), however, has significant advantage in time-varying channels but needs a training sequence to operate. A combined CMA and KF algorithm is therefore proposed in order to utilise the advantages of both algorithms. The associated stepsize of the combination is also varied in accordance with the magnitude of the output. Simulations are presented to demonstrate the potential of this new approac

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

Adaptive beamforming and switching in smart antenna systems

The ever increasing requirement for providing large bandwidth and seamless data access to commuters has prompted new challenges to wireless solution providers. The communication channel characteristics between mobile clients and base station change rapidly with the increasing traveling speed of vehicles. Smart antenna systems with adaptive beamforming and switching technology is the key component to tackle the challenges. As a spatial filter, beamformer has long been widely used in wireless communication, radar, acoustics, medical imaging systems to enhance the received signal from a particular looking direction while suppressing noise and interference from other directions. The adaptive beamforming algorithm provides the capability to track the varying nature of the communication channel characteristics. However, the conventional adaptive beamformer assumes that the Direction of Arrival (DOA) of the signal of interest changes slowly, although the interference direction could be changed dynamically. The proliferation of High Speed Rail (HSR) and seamless wireless communication between infrastructure ( roadside, trackside equipment) and the vehicles (train, car, boat etc.) brings a unique challenge for adaptive beamforming due to its rapid change of DOA. For a HSR train with 250km/h, the DOA change speed can be up to 4⁰ per millisecond. To address these unique challenges, faster algorithms to calculate the beamforming weight based on the rapid-changing DOA are needed. In this dissertation, two strategies are adopted to address the challenges. The first one is to improve the weight calculation speed. The second strategy is to improve the speed of DOA estimation for the impinging signal by leveraging on the predefined constrained route for the transportation market. Based on these concepts, various algorithms in beampattern generation and adaptive weight control are evaluated and investigated in this thesis. The well known Generalized Sidelobe Cancellation (GSC) architecture is adopted in this dissertation. But it faces serious signal cancellation problem when the estimated DOA deviates from the actual DOA which is severe in high mobility scenarios as in the transportation market. Algorithms to improve various parts of the GSC are proposed in this dissertation. Firstly, a Cyclic Variable Step Size (CVSS) algorithm for adjusting the Least Mean Square (LMS) step size with simplicity for implementation is proposed and evaluated. Secondly, a Kalman filter based solution to fuse different sensor information for a faster estimation and tracking of the DOA is investigated and proposed. Thirdly, to address the DOA mismatch issue caused by the rapid DOA change, a fast blocking matrix generation algorithm named Simplifized Zero Placement Algorithm (SZPA) is proposed to mitigate the signal cancellation in GSC. Fourthly, to make the beam pattern robust against DOA mismatch, a fast algorithm for the generation of at beam pattern named Zero Placement Flat Top (ZPFT) for the fixed beamforming path in GSC is proposed. Finally, to evaluate the effectiveness and performance of the beamforming algorithms, wireless channel simulation is needed. One of the challenging aspects for wireless simulation is the coupling between Probability Density Function (PDF) and Power Spectral Density (PSD) for a random variable. In this regard, a simplified solution to simulate Non Gaussian wireless channel is proposed, proved and evaluated for the effectiveness of the algorithm. With the above optimizations, the controlled simulation shows that the at top beampattern can be generated 380 times faster than iterative optimization method and blocking matrix can be generated 9 times faster than normal SVD method while the same overall optimum state performance can be achieved

UNDERWATER COMMUNICATIONS WITH ACOUSTIC STEGANOGRAPHY: RECOVERY ANALYSIS AND MODELING

In the modern warfare environment, communication is a cornerstone of combat competence. However, the increasing threat of communications-denied environments highlights the need for communications systems with low probability of intercept and detection. This is doubly true in the subsurface environment, where communications and sonar systems can reveal the tactical location of platforms and capabilities, subverting their covert mission set. A steganographic communication scheme that leverages existing technologies and unexpected data carriers is a feasible means of increasing assurance of communications, even in denied environments. This research works toward a covert communication system by determining and comparing novel symbol recovery schemes to extract data from a signal transmitted under a steganographic technique and interfered with by a simulated underwater acoustic channel. We apply techniques for reliably extracting imperceptible information from unremarkable acoustic events robust to the variability of the hostile operating environment. The system is evaluated based on performance metrics, such as transmission rate and bit error rate, and we show that our scheme is sufficient to conduct covert communications through acoustic transmissions, though we do not solve the problems of synchronization or equalization.Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Neural Network Based Robust Adaptive Beamforming for Smart Antenna System

As the growing demand for mobile communications is constantly increasing, the need for better coverage, improved capacity, and higher transmission quality rises. Thus, a more efficient use of the radio spectrum is required. A smart antenna system is capable of efficiently utilizing the radio spectrum and is a promise for an effective solution to the present wireless system problems while achieving reliable and robust high-speed, high-data-rate transmission. Smart antenna technology offer significantly improved solution to reduce interference level and improve system capacity. With this technology, each user’s signal is transmitted and received by the base station only in the direction of that particular user. Smart antenna technology attempts to address this problem via advanced signal processing technology called beamforming. The adaptive algorithm used in the signal processing has a profound effect on the performance of a Smart Antenna system that is known to have resolution and interference rejection capability when array steering vector is precisely known. Adaptive beamforming is used for enhancing a desired signal while suppressing noise and interference at the output of an array of sensors. However the performance degradation of adaptive beamforming may become more pronounced than in an ideal case because some of underlying assumptions on environment, sources or sensor array can be violated and this may cause mismatch. There are several efficient approaches that provide an improved robustness against mismatch as like LSMI algorithm. Neural network is a massively parallel distributed processor made up of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use. Neural network methods possess such advantages as general purpose nature, nonlinear property, passive parallelism, adaptive learning capability, generalization capability and fast convergence rates. Motivated by these inherent advantages of the neural network, in this thesis work, a robust adaptive beamforming algorithm using neural network is investigated which is effective in case of signal steering vector mismatch. This technique employs a three-layer radial basis function neural network (RBFNN), which treats the problem of computing the weights of an adaptive array antenna as a mapping problem. The robust adaptive beamforming algorithm using RBFNN, provides excellent robustness to signal steering vector mismatches, enhances the array system performance under non ideal conditions and makes the mean output array SINR (Signal-to-Interference-plus- Noise Ratio) consistently close to the optimal one

Complex-valued Adaptive Digital Signal Enhancement For Applications In Wireless Communication Systems

In recent decades, the wireless communication industry has attracted a great deal of research efforts to satisfy rigorous performance requirements and preserve high spectral efficiency. Along with this trend, I/Q modulation is frequently applied in modern wireless communications to develop high performance and high data rate systems. This has necessitated the need for applying efficient complex-valued signal processing techniques to highly-integrated, multi-standard receiver devices. In this dissertation, novel techniques for complex-valued digital signal enhancement are presented and analyzed for various applications in wireless communications. The first technique is a unified block processing approach to generate the complex-valued conjugate gradient Least Mean Square (LMS) techniques with optimal adaptations. The proposed algorithms exploit the concept of the complex conjugate gradients to find the orthogonal directions for updating the adaptive filter coefficients at each iteration. Along each orthogonal direction, the presented algorithms employ the complex Taylor series expansion to calculate time-varying convergence factors tailored for the adaptive filter coefficients. The performance of the developed technique is tested in the applications of channel estimation, channel equalization, and adaptive array beamforming. Comparing with the state of the art methods, the proposed techniques demonstrate improved performance and exhibit desirable characteristics for practical use. The second complex-valued signal processing technique is a novel Optimal Block Adaptive algorithm based on Circularity, OBA-C. The proposed OBA-C method compensates for a complex imbalanced signal by restoring its circularity. In addition, by utilizing the complex iv Taylor series expansion, the OBA-C method optimally updates the adaptive filter coefficients at each iteration. This algorithm can be applied to mitigate the frequency-dependent I/Q mismatch effects in analog front-end. Simulation results indicate that comparing with the existing methods, OBA-C exhibits superior convergence speed while maintaining excellent accuracy. The third technique is regarding interference rejection in communication systems. The research on both LMS and Independent Component Analysis (ICA) based techniques continues to receive significant attention in the area of interference cancellation. The performance of the LMS and ICA based approaches is studied for signals with different probabilistic distributions. Our research indicates that the ICA-based approach works better for super-Gaussian signals, while the LMS-based method is preferable for sub-Gaussian signals. Therefore, an appropriate choice of interference suppression algorithms can be made to satisfy the ever-increasing demand for better performance in modern receiver design

On transmitter power control for cellular mobile radio networks

Master'sMASTER OF ENGINEERIN