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

Beamforming Optimization Based on Kalman Filter for Vehicle in Constrained Route

By analyzing a simplified model for vehicles in a constrained route like High Speed Railway (HSR), it is found that when the distance between transmitter and route is short, the Direction of Arrival (DOA) changing speed is not a negligible effect. A Kalman Filter based solution is then proposed to fuse the vehicle speed sensor and DOA estimation. By using the filtered angle from Kalman filter, power loss due to angle mismatch could be reduced by more than 4dB for 4 elements antenna beamformer in the simulated scenario

Non-Gaussian Colored Noise Generation for Wireless Channel Simulation with Particle Swarm Optimizer

Random Variable (RV) with different Probability Density Function (PDF) and Power Spectral Density (PSD) is a critical component for simulation of different wireless channel fading profile. To get a specific PSD for simulation of different multi-path scenario, the usual method is to pass a white noise through a filter with the required shape. But the filtering process will cause the change of random variable’s PDF unless the input noise follows Gaussian Distribution. In this paper, a Particle Swarm optimization (PSO) based method to generate NonGaussian noise by a pre-distortion filter and Inverse Transform Sampling (ITS) that meets both the requirement of PSD and PDF is described. As the solution is based on filtering, after the filter weight is found using PSO, the simulation could be carried out in a real-time manner compared to block-based methods. The numerical simulation confirms that it can generate the required PDF and more than 90% similar to the required PSD

A zero placement algorithm for synthesis of flat top beam pattern with low sidelobe level

Flat top beam pattern synthesis is increasingly important for beamformer in high mobility scenarios due to the rapid change of Direction of Arrival (DOA). A Zero Placement for Flat Top (ZPFT) beam pattern synthesis algorithm is presented in this paper. It works in Z domain directly and breaks down the total response into two portions. The first portion satisfies the beamwidth requirement with low Sidelobe Level (SLL) which is realized through algorithms like Dolph-Chebyshev algorithm. The second portion is then used to create a broadening effect. The location of the broadening zeros are derived using principles result from the broadening effect analysis of two quadratic functions. Compared to conventional Finite Impulse Response (FIR) method or iterative methods, the proposed method identifies the zeros of the array factor directly and computes the weight without iteration. Since it works in the spatial angle domain directly, the steering of the mainlobe beam could be implemented through a simple angle shift. Numerical simulation confirms the effectiveness of the algorithm. ZPFT can achieve 22dB lower SLL while maintaining the same main beam performance as compared with FIR method for an Uniform Linear Array (ULA) with 7 elements. It can achieve the same optimal performance as the iteration based global optimisation techniques like Semi-Definite Relaxation (SDR) with about 380 times less computing time in an Intel Core i7 Windows platform. ZPFT can steer the main beam easily in real time. All these make it an ideal candidate for high mobility applications where the DOA changes rapidly

Improve Tracking Speed of Beamformer With Simplified Zero Placement Algorithm

This paper presents a new structure and algorithm to improve the tracking speed of a Generalized Sidelobe Canceler (GSC) based adaptive beamformer. Iterative methods like Conjugate Gradient algorithm to calculate the beamformer weight vector eliminates the complexity of Matrix reversing. But the reduced complexity comes with time cost which requires iterations of calculation before converging to the desired direction. To combat the problem, a Simplified Zero Placement algorithm is proposed to set the initial weight vector to make the starting value near the optimum location of weight vector. Numerical simulation and analysis confirms the effectiveness of the proposed solution

A Fast Blocking Matrix Generating Algorithm for Generalized Sidelobe Canceller Beamforming

A fast algorithm to generate the Blocking Matrix for Generalized Sidelobe Canceller (GSC) beamforming is proposed in this paper. The proposed algorithm uses a simplified zero placement algorithm (SZPA) to directly generate the independent null space vectors by specifying the zeros of the corresponding Z domain polynomials following the constrained signal incoming angle. Compared to the conventional methods based on Singular Value Decomposition (SVD), this method can be more than 10 times faster for scenarios with 15 constraints and will be even more advantageous for more constraints with the same converged state performance. Numerical simulation confirms the efficiency and effectiveness of the algorithm

Two-stage deep neural network for diagnosing fungal keratitis via in vivo confocal microscopy images

Timely and effective diagnosis of fungal keratitis (FK) is necessary for suitable treatment and avoiding irreversible vision loss for patients. In vivo confocal microscopy (IVCM) has been widely adopted to guide the FK diagnosis. We present a deep learning framework for diagnosing fungal keratitis using IVCM images to assist ophthalmologists. Inspired by the real diagnostic process, our method employs a two-stage deep architecture for diagnostic predictions based on both image-level and sequence-level information. To the best of our knowledge, we collected the largest dataset with 96,632 IVCM images in total with expert labeling to train and evaluate our method. The specificity and sensitivity of our method in diagnosing FK on the unseen test set achieved 96.65% and 97.57%, comparable or better than experienced ophthalmologists. The network can provide image-level, sequence-level and patient-level diagnostic suggestions to physicians. The results show great promise for assisting ophthalmologists in FK diagnosis

Clustering-based interference management in densely deployed femtocell networks

Deploying femtocells underlaying macrocells is a promising way to improve the capacity and enhance the coverage of a cellular system. However, densely deployed femtocells in urban area also give rise to intra-tier interference and cross-tier issue that should be addressed properly in order to acquire the expected performance gain. In this paper, we propose an interference management scheme based on joint clustering and resource allocation for two-tier Orthogonal Frequency Division Multiplexing (OFDM)-based femtocell networks. We formulate an optimization task with the objective of maximizing the sum throughput of the femtocell users (FUs) under the consideration of intra-tier interference mitigation, while controlling the interference to the macrocell user (MU) under its bearable threshold. The formulation problem is addressed by a two-stage procedure: femtocells clustering and resource allocation. First, disjoint femtocell clusters with dynamic sizes and numbers are generated to minimize intra-tier interference. Then each cluster is taken as a resource allocation unit to share all subchannels, followed by a fast algorithm to distribute power among these subchannels. Simulation results show that our proposed schemes can improve the throughput of the FUs with acceptable complexity

Hardware Efficient Adaptive Beamformer Based on Cyclic Variable Step Size

This paper presents an improved adaptive beamforming solution based on Global Sidelobe Canceler (GSC) and Least Mean Square (LMS). By analyzing the impact of step size on LMS performance, a simple yet effective new Cyclic Variable Step Size (CVSS) algorithm is proposed to enhance the convergence speed and excess MSE performance of LMS. By adding just a few shift operations to adapt the step size cyclically, the CVSS LMS is efficient for hardware implementation. Initial study shows that it's possible to double the convergence speed with minimum overhead. The numerical simulation confirmed the effectiveness and performance of the algorithm