Beamforming management and beam training in 5G system

Massive multiple-input-multiple-output (MIMO) antenna system with beamforming technique is an integral part of upcoming 5G new radio (NR) system. For the upcoming deployment of 5G NR system in both stand-alone (SA) and non-stand-alone (NSA) structure, beamforming plays an important role to achieve its key features and meet the estimated requirement. To be employed with massive MIMO antenna structure, beamforming will allow 5G system to serve several users at a time with better throughput and spectral usage. Beamforming will also minimize the path loss due to high susceptibility of millimetre wave and provide beamforming gain. For a wide range of benefit scheme, beamforming is currently a hot topic regarding the deployment of 5G. With the advantage of both analog and digital beamforming, hybrid beamforming structure can provide better system benchmark performance in terms of cost and flexibility. Switched beam training and adaptive beam training approaches and algorithms are developed in order to reduce training time, signalling overhead and misdetection probability. Some of the approaches and algorithm are addressed in this thesis. Beamforming management ensures the initiation and sustainability of the established link between transmitter and receiver through different processes. Beam tracking helps to keep track of the receiver devices during mobility. As beamforming is related to antenna configuration, near-field spherical wave front incident problem was ignored, and all the references and examples presented in this topic was obtained with a far-field propagation perspective. To avoid mutual coupling between antenna elements and grating lobe problems in antenna radiation pattern, each element is separated by half of the wavelength. This thesis paper aims to provide a broader view into beamforming scenario, starting from the basics of beamforming to training the beams and management aspects in the hardware part of 5G structure. Another goal is to present the necessity of beamforming in a 5G system by stating different benefits scheme such as spatial diversity, interference suppression, energy efficiency, spectral efficiency and so on. These benefits are justified by evaluating various research paper and MATLAB simulations

A pre-filtering maximum likelihood approach to multiple source direction estimation.

Imperial Users onl

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

Multiple Antenna-based GPS Multipath Mitigation using Code Carrier Information

학위논문 (박사)-- 서울대학교 대학원 : 전기공학부, 2013. 8. 최진영.여러 응용분야에서 수 억대의 GPS(Global Positioning System) 수신기가 사용되고 있지만, GPS을 기반으로 하는 위치기반 서비스(LBS: Location Based Services)에서는 여전히 다중경로 오차와 같은 전파 방해가 발생하고 있으며, 이러한 오차들로 인하여 상관함수의 왜곡은 거리 오차가 발생에 영향을 미치고 있다. 이러한 이유로 인하여 GPS을 이용한 항법 시스템에서의 위치 정확도 향상을 위하여, 다중경로 오차를 효과 적으로 줄이기 위한 강인하고 현실적인 방법이 요구된다. 다중경로는 GPS 신호가 장애물에 의해 반사나 회절 되어 수신기에 도착할 때 잘 일어난다. 가시경로 신호에 결합된 다중경로 신호는 GPS 수신기의 상관함수의 변형을 일으키며 궁극적으로 차별함수에 영향을 미치므로 거리오차를 발생시킨다. 그러므로 다중경로 오차는 위성항법 시스템에서의 위치정확도 향상을 위해 해결 되어야 될 문제로 쟁점이 되어왔다. 최근에는 이러한 전파 간섭신호를 줄이기 위하여 다중개의 안테나(Multiple Antenna)를 이용하는 방법이 GPS 항법 시스템에서 이용되고 있다. 현 시점에서, 다중개의 안테나를 사용하는 응용분야는 주로 학술적인 연구 및 복잡한 군사용 연구로 주로 진행 되었다. 그러나 안테나 제작 방법 및 전기적 시스템의 급격한 발전으로 인해 이전의 하드웨어 및 소프웨어적인 문제를 쉽게 해결 됨에 따라 가까운 미래에는 다중 안테나 기반의 수신기가 민간 상용분야로 확대 될 것으로 예상이 된다. 또한 안테나 수신기 RF단의 소형화로 인하여 다중 안테나 시스템에서의 안테나 크기 문제점 또한 해결 가능하다. 그러므로 본 논문에서는 다중 GPS 안테나를 이용하여 GPS 항법에서의 전파 간섭 및 다중경로 오차 감쇄에 대한 연구를 목적으로 한다. 본 연구는 강한 전파 간섭 및 다중경로 신호에 대하여 공간 처리 기법을 적용한다. 제안된 새로운 방법은 다중 안테나를 기반의 코드 케리어 정보를 이용한 공간처리 기법으로 전파 간섭 및 다중경로 오차를 완화시키며, 또한 빔형성 기법을 이용하여 신호 대 잡음 비율을 최대로 한다. 제안된 성능을 검증하기 위하여 소프트웨어 GPS 수신기를 사용된다. 소프트웨어 GPS 수신기를 이용한 신호처리 기법은 새로운 장비의 제품화 및 GPS 신호 분석에 장점을 가지고 있다. 또한 GPS 알고리즘 분석 및 수신기 성능 향상 검증 등 여러 연구분야에서 널리 이용되고 있다. 본 논문에서는 제안된 방법의 성능 검증을 위하여 컴퓨터 시뮬레이션 및 가공 IF 데이터를 이용한 소프트웨어 수신기 결과를 제시한다. 그 결과 제안된 방법은 전파 간섭 및 다중경로 오차 감쇄에 강인하며, GPS 항법시스템에서의 위치정확도 향상에 가능성을 보여준다. 그로므로 제안된 방법은 차량 항법 응용분야에서 방해신호 감쇄에 사용될 것으로 예상된다.Although hundreds of millions of receivers are used all around the world, the performance of location-based services(LBS) provided by GPS is still compromised by interference which includes unintentional distortion of correlation function due to multipath propagation. For this reason, the requirement for proper mitigation techniques becomes crucial in GPS receivers for robust, accurate, and reliable positioning. Multipath propagation can easily occur when environmental features cause combinations of reflected and diffracted replica signals to arrive at the receiving antenna. These signals which are combined with the original line-of-sight (LOS) signal can cause distortion of the receiver correlation function and ultimately distortion of the discrimination functionhence, errors in range estimation occur. Therefore, multipath error in the satellite navigation system to improve location accuracy is an important issue to be addressed. Recently, interference mitigation techniques utilizing multiple antennas have gained significant attention in GPS navigation systems. Although at the time of this dissertation, employing multiple antennas in GPS applications is mostly limited to academic research and possibly complicated military applications, it is expected that in the near future, antenna array-based receivers will also become widespread in civilian commercial markets. Rapid advances in antenna design technology and electronic systems make previously challenging problems in hardware and software easier to solve. Furthermore, due to the significant effort devoted to miniaturization of RF front-ends and antennas, the size of antenna array based receivers will no longer be a problem. Given the above, this dissertation investigates multiple antenna-based GPS the interference suppression and multipath mitigation. Firstly, a modified spatial processing technique is proposed that is capable of mitigating both high power interference and coherent and correlated GPS multipath signals. The use of spatial-temporal processing for GPS multipath mitigation is studied. A new method utilizing code carrier information based on multiple antennas is proposed to deal with highly correlated multipath components and to increase the signal to noise ratio of the beamformer by synthesizing antenna array processing. In order to verify the proposed method, a software defined GPS receiver is used. Software-based GPS signal processing technique has already produced benefits for prototyping new equipment and analyzing GPS signal quality. Not only do such receivers provide an excellent research tool for GPS algorithm verification, they also improve GPS receiver performance in a wide range of conditions. In this dissertation, the enhancement of the proposed method is presented in terms of the simulations and software defined GPS receiver using simulated IF data. From the result, the proposed method is robust to interference suppression, and multipath mitigation, and shows a strong possibility for use in improving location accuracy. Thus, this method can be employed to mitigate interference signals in vehicular navigation applications.Contents Abstract i Acknowledgements iv Contents v List of Figures x List of Tables xiv Chapter 1.Introduction 1 1.1 Introduction 1 1.2 Background and Motivation 2 1.2.1 Strong Narrowband and Wideband Interference 6 1.2.2 Multipath 7 1.3 Antenna Array Processing in GPS 11 1.3.1 Interference Suppression 11 1.3.2 Multipath Mitigation 13 1.4 Software-Defined GPS Receiver 15 1.5 Objective and Contribution 17 1.6 Dissertation Outline 18 Chapter 2. Global Positioning System 21 2.1 GPS System Overview 21 2.2 Basic Concept of GSP 25 2.3 Determining Satellite to User 28 2.4 Calculation of User Position 33 2.5 GPS Error Sources 40 2.5.1 Receiver Clock Bias 41 2.5.2 Satellite Clock Bias 42 2.5.3 Atmospheric Delay 43 2.5.4 Ephemeris Delay 46 2.5.5 Multipath Error 47 2.5.6 Receiver Noise 55 2.6 Summary 55 Chapter 3. Antenna Array Processing and Beamforming 56 3.1 Background on Antenna Arrays and Beamformers 56 3.1.1 Signal Model 59 3.2 Conventional Optimum Beamformers 69 3.2.1 Minimum Variance Distortionless Response Beamformer 69 3.2.2 Maximum Likelihood Estimator 71 3.2.3 Maximum Signal to Noise Interference Ratio Beamformer 72 3.2.4 Minimum Power Distortionless Response Beamformer 75 3.2.5 Linear Constrained Minimum Variance and Linear Constrained Minimum Power Beamformers 76 3.2.6 Eigenvector Beamformer 77 3.3 Space-Time Processing 81 3.4 Array Calibration 85 3.5 Summary 86 Chapter 4. Multipath Mitigation using Code-Carrier Information 87 4.1 Introduction 87 4.2 Interference Suppression and Multipath Mitigation 88 4.2.1 Signal Model 88 4.2.2 Interference Suppression by Subspace Projection 90 4.2.3 Multipath Mitigation by Subspace Projection 93 4.3 Determination of Multipath Satellites using Code-carrier Information 95 4.4 MSR Beamformer 100 4.5 Simulation Results 102 4.5.1 Subspace Projection and Beamforming 102 4.5.2 Performance Comparison 109 4.6 Summary 111 Chapter 5. Performance Verification using Software-Defined GPS Receiver 113 5.1 Introduction 113 5.2 Software-Defined GPS Receiver Methodology 114 5.2.1 Software-Defined GPS Receiver Signals 115 5.2.2 Software-Defined GPS Receiver Modules 116 5.3 Architecture of Software-Defined GPS Receiver 120 5.3.1 GPS Signal Generation 120 5.3.2 Interference Signal Generation 124 5.3.1 Front-End Signal Processing 125 5.4 Experimental Results 126 5.3.1 Static Environments 128 5.3.2 Dynamic Environments 133 5.5 Summary 136 Chapter 6. Conclusions and Future Work 138 6.1 Conclusions 138 6.2 Future Work 139 Bibliography 142 Appendix 168 Abstract in Korean 170 Acknowledgments 173Docto

Aperture-Level Simultaneous Transmit and Receive (STAR) with Digital Phased Arrays

In the signal processing community, it has long been assumed that transmitting and receiving useful signals at the same time in the same frequency band at the same physical location was impossible. A number of insights in antenna design, analog hardware, and digital signal processing have allowed researchers to achieve simultaneous transmit and receive (STAR) capability, sometimes also referred to as in-band full-duplex (IBFD). All STAR systems must mitigate the interference in the receive channel caused by the signals emitted by the system. This poses a significant challenge because of the immense disparity in the power of the transmitted and received signals. As an analogy, imagine a person that wanted to be able to hear a whisper from across the room while screaming at the top of their lungs. The sound of their own voice would completely drown out the whisper. Approaches to increasing the isolation between the transmit and receive channels of a system attempt to successively reduce the magnitude of the transmitted interference at various points in the received signal processing chain. Many researchers believe that STAR cannot be achieved practically without some combination of modified antennas, analog self-interference cancellation hardware, digital adaptive beamforming, and digital self-interference cancellation. The aperture-level simultaneous transmit and receive (ALSTAR) paradigm confronts that assumption by creating isolation between transmit and receive subarrays in a phased array using only digital adaptive transmit and receive beamforming and digital self-interference cancellation. This dissertation explores the boundaries of performance for the ALSTAR architecture both in terms of isolation and in terms of spatial imaging resolution. It also makes significant strides towards practical ALSTAR implementation by determining the performance capabilities and computational costs of an adaptive beamforming and self-interference cancellation implementation inspired by the mathematical structure of the isolation performance limits and designed for real-time operation

Temporal and Spatial Interference Mitigation Strategies to Improve Radar Data Quality

The microwave band is well suited to wireless applications, including radar, communications, and electronic warfare. While radar operations currently have priority in a portion of the microwave band, wireless companies are lobbying to change that; such a change would force current operators into a smaller total bandwidth. Interference would occur, and has already occurred at the former National Weather Radar Testbed Phased Array Radar. The research in this dissertation was motivated by this interference --- it occurred even without a change to radar's primacy in the microwave band. If microwave operations had to squeeze into a smaller overall bandwidth, such interference, whether originating from other radars or some other source, would only become more common. The radio frequency interference (RFI) present at the National Weather Radar Testbed Phased Array Radar altered the statistical properties at certain locations, causing targets to be erroneously detected. While harmless enough in clear air, it could affect National Weather Service decisions if it occurred during a weather event. The initial experiments, covered in Chapter 2, used data comprised of a single channel of in-phase and quadrature (IQ) data, reflecting the resources available to the National Weather Service's weather radar surveillance network. A new algorithm, the Interference Spike Detection Algorithm, was developed with these restrictions in mind. This new algorithm outperforms several interference detection algorithms developed by industry. Tests on this data examined algorithm performance quantitatively, using real and simulated weather data and radio frequency interference. Additionally, machine learning classification algorithms were employed for the first time to the RFI classification problem and it was found that, given enough resources, machine learning had the potential to perform even better than the other temporal algorithms. Subsequent experiments, covered in Chapter 3, used spatial data from phased arrays and looked at methods of interference mitigation that leveraged this spatial data. Specifically, adaptive beamforming techniques could be used to mitigate interference and improve data quality. A variety of adaptive digital beamforming techniques were evaluated in terms of their performance at interference mitigation for a communications task. Additionally, weather radar data contaminated with ground clutter was collected from the sidelobe canceller channels of the former National Weather Radar Testbed Phased Array Radar and, using the reasoning that ground clutter is simply interference from the ground, adaptive digital beamforming was successfully employed to mitigate the impact of ground clutter and restore the data to reflect the statistics of the underlying weather data. Tests on digital equalization, covered in Chapter 4, used data from a prototype receiver for Horus, a digital phased array radar under development at the University of Oklahoma. The data suffered from significant channel mismatch, which can severely negatively impact the performance of phased arrays. Equalization, implemented both via older digital filter design methods and, for the first time, via newer machine learning regression methods, was able to improve channel matching. When used before adaptive digital beamforming, it was found that digital equalization always improved system performance

Robust Multichannel Microphone Beamforming

In this thesis, a method for the design and implementation of a spatially robust multichannel microphone beamforming system is presented. A set of spatial correlation functions are derived for 2D and 3D far-field/near-field scenarios based on von Mises(-Fisher), Gaussian, and uniform source location distributions. These correlation functions are used to design spatially robust beamformers and blocking beamformers (nullformers) designed to enhance or suppress a known source, where the target source location is not perfectly known due to either an incorrect location estimate or movement of the target while the beamformers are active. The spatially robust beam/null-formers form signal and interferer plus noise references which can be further processed via a blind source separation algorithm to remove mutual components - removing the interference and sensor noise from the signal path and vice versa. The noise reduction performance of the combined beamforming and blind source separation system approaches that of a perfect information MVDR beamformer under reverberant conditions. It is demonstrated that the proposed algorithm can be implemented on low-power hardware with good performance on hardware similar to current mobile platforms using a four-element microphone array

Voice inactivity ranking for enhancement of speech on microphone arrays

Motivated by the problem of improving the performance of speech enhancement algorithms in non-stationary acoustic environments with low SNR, a framework is proposed for identifying signal frames of noisy speech that are unlikely to contain voice activity. Such voice-inactive frames can then be incorporated into an adaptation strategy to improve the performance of existing speech enhancement algorithms. This adaptive approach is applicable to single-channel as well as multi-channel algorithms for noisy speech. In both cases, the adaptive versions of the enhancement algorithms are observed to improve SNR levels by 20dB, as indicated by PESQ and WER criteria. In advanced speech enhancement algorithms, it is often of interest to identify some regions of the signal that have a high likelihood of being noise only i.e. no speech present. This is in contrast to advanced speech recognition, speaker recognition, and pitch tracking algorithms in which we are interested in identifying all regions that have a high likelihood of containing speech, as well as regions that have a high likelihood of not containing speech. In other terms, this would mean minimizing the false positive and false negative rates, respectively. In the context of speech enhancement, the identification of some speech-absent regions prompts the minimization of false positives while setting an acceptable tolerance on false negatives, as determined by the performance of the enhancement algorithm. Typically, Voice Activity Detectors (VADs) are used for identifying speech absent regions for the application of speech enhancement. In recent years a myriad of Deep Neural Network (DNN) based approaches have been proposed to improve the performance of VADs at low SNR levels by training on combinations of speech and noise. Training on such an exhaustive dataset is combinatorically explosive. For this dissertation, we propose a voice inactivity ranking framework, where the identification of voice-inactive frames is performed using a machine learning (ML) approach that only uses clean speech utterances for training and is robust to high levels of noise. In the proposed framework, input frames of noisy speech are ranked by ‘voice inactivity score’ to acquire definitely speech inactive (DSI) frame-sequences. These DSI regions serve as a noise estimate and are adaptively used by the underlying speech enhancement algorithm to enhance speech from a speech mixture. The proposed voice-inactivity ranking framework was used to perform speech enhancement in single-channel and multi-channel systems. In the context of microphone arrays, the proposed framework was used to determine parameters for spatial filtering using adaptive beamformers. We achieved an average Word Error Rate (WER) improvement of 50% at SNR levels below 0dB compared to the noisy signal, which is 7±2.5% more than the framework where state-of-the-art VAD decision was used for spatial filtering. For monaural signals, we propose a multi-frame multiband spectral-subtraction (MF-MBSS) speech enhancement system utilizing the voice inactivity framework to compute and update the noise statistics on overlapping frequency bands. The proposed MF-MBSS not only achieved an average PESQ improvement of 16% with a maximum improvement of 56% when compared to the state-of-the-art Spectral Subtraction but also a 5 ± 1.5% improvement in the Word Error Rate (WER) of the spatially filtered output signal, in non-stationary acoustic environments