Single Perceptron Model for Smart Beam forming in Array Antennas

In this paper, a single neuron neural network beamformer is proposed. A perceptron model is designed to optimize the complex weights of a dipole array antenna to steer the beam to desired directions. The objective is to reduce the complexity by using a single neuron neural network and utilize it for adaptive beamforming in array antennas. The selection of nonlinear activation function plays the pivotal role in optimization depends on whether the weights are real or complex. We have appropriately proposed two types of activation functions for respective real and complex weight values.   The optimized radiation patterns obtained from the single neuron neural network are compared with the respective optimized radiation patterns from the traditional Least Mean Square (LMS) method. Matlab is used to optimize the weights in neural network and LMS method as well as display the radiation patterns

FPGA-Based Adaptive Digital Beamforming Using Machine Learning for MIMO Systems

In modern Multiple-Input and Multiple-Output (MIMO) systems, such as cellular and Wi-Fi technology, an array of antenna elements is used to spatially steer RF signals with the goal of changing the overall antenna gain pattern to achieve a higher Signal-to-interference-plus-noise ratio (SINR). Digital Beamforming (DBF) achieves this steering effect by applying weighted coefficients to antenna elements- similar to digital filtering- which adjust the phase and gain of the received, or transmitted, signals. Since real world MIMO systems are often used in dynamic environments, Adaptive Beamforming techniques have been used to overcome variable challenges to system SINR- such as dispersive channels or inter-device interference- by applying statistically-based algorithms to calculate weights adaptively. However, large element count array systems, with their high degrees of freedom (DOF), can face many challenges in real application of these adaptive algorithms. These statistical matrix methods can be either computationally prohibitive, or utilize non-optimal simplifications, in order to provide adaptive weights in time for an application, especially given a certain system's computational capability; for instance, MIMO communication devices with strict size, weight and power (SWaP) constraints often have processing limitations due to use of low-power processors or Field-Programmable Gate Arrays (FPGAs). Thus, this thesis research investigation will show novel progress in these adaptive MIMO challenges in a twofold approach. First, it will be shown that advances in Machine Learning (ML) and Deep Neural Networks (DNNs) can be directly applied to the computationally complex problem of calculating optimal adaptive beamforming weights via a custom Convolutional Neural Net (CNN). Secondly, the derived adaptive beamforming CNN will be shown to efficiently map to programmable logic FPGA resources which can update adaptive coefficients in real-time. This machine learning implementation is contrasted against the current state-of-the-art FPGA architecture for adaptive beamforming- which uses traditional, Recursive Least Squares (RLS) computation- and is shown to provide adaptive beamforming weights faster, and with fewer FPGA logic resources. The reduction in both processing latency and FPGA fabric utilization enables SWaP constrained MIMO processors to perform adaptive beamforming for higher channel count systems than currently possible with traditional computation methods

Neural Networks for improved signal source enumeration and localization with unsteered antenna arrays

Direction of Arrival estimation using unsteered antenna arrays, unlike mechanically scanned or phased arrays, requires complex algorithms which perform poorly with small aperture arrays or without a large number of observations, or snapshots. In general, these algorithms compute a sample covriance matrix to obtain the direction of arrival and some require a prior estimate of the number of signal sources. Herein, artificial neural network architectures are proposed which demonstrate improved estimation of the number of signal sources, the true signal covariance matrix, and the direction of arrival. The proposed number of source estimation network demonstrates robust performance in the case of coherent signals where conventional methods fail. For covariance matrix estimation, four different network architectures are assessed and the best performing architecture achieves a 20 times improvement in performance over the sample covariance matrix. Additionally, this network can achieve comparable performance to the sample covariance matrix with 1/8-th the amount of snapshots. For direction of arrival estimation, preliminary results are provided comparing six architectures which all demonstrate high levels of accuracy and demonstrate the benefits of progressively training artificial neural networks by training on a sequence of sub- problems and extending to the network to encapsulate the entire process