Smart antennas have received increasing interest for improving the performance of wireless radio systems. These systems of antennas include a large number of techniques that attempt to enhance the received signal, suppress all interfering signals, and increase capacity, in general. The main purpose of this article is to provide an overview of the current state of research in the area of smart antennas, and to describe how they can be used in wireless systems. Thus, this article provides a basic model for determining the angle of arrival for incoming signals, the appropriate antenna beamforming, and the adaptive algorithms that are currently used for array processing. Moreover, it is shown how smart antennas, with spatial processing, can provide substantial additional improvement when used with TDMA and CDMA digital-communication systems. The material presented is tutorial in nature, leaving the details for further study from the papers appearing in the reference list.

Smart antennas

This paper reviews the current state-of-the-art in electromagnetic (EM)-based design and optimization of microwave circuits using artificial neural networks (ANNs). Measurement-based design of microwave circuits using ANNs is also reviewed. The conventional microwave neural optimization approach is surveyed, along with typical enhancing techniques, such as segmentation, decomposition, hierarchy, design of experiments, and clusterization. Innovative strategies for ANN-based design exploiting microwave knowledge are reviewed, including neural space-mapping methods. The problem of developing synthesis neural networks is treated. EM-based statistical analysis and yield optimization using neural networks is reviewed. The key issues in transient EM-based design using neural networks are summarized. The use of ANNs to speed up "global modeling" for EM-based design of monolithic microwave integrated circuits is briefly described. Future directions in ANN techniques to microwave design are suggested.

https://desi.iteso.mx/erayas/documents/cad_course/lectures/ANNs/EM_ANN_State_Art_paper.pdf

EM-based optimization of microwave circuits using artificial neural networks: the state-of-the-art

Lacking of adaptation to various array imperfections is an open problem for most high-precision direction-of-arrival (DOA) estimation methods. Machine learning-based methods are data-driven, they do not rely on prior assumptions about array geometries, and are expected to adapt better to array imperfections when compared with model-based counterparts. This paper introduces a framework of the deep neural network to address the DOA estimation problem, so as to obtain good adaptation to array imperfections and enhanced generalization to unseen scenarios. The framework consists of a multitask autoencoder and a series of parallel multilayer classifiers. The autoencoder acts like a group of spatial filters, it decomposes the input into multiple components in different spatial subregions. These components thus have more concentrated distributions than the original input, which helps to reduce the burden of generalization for subsequent DOA estimation classifiers. The classifiers follow a one-versus-all classification guideline to determine if there are signal components near preseted directional grids, and the classification results are concatenated to reconstruct a spatial spectrum and estimate signal directions. Simulations are carried out to show that the proposed method performs satisfyingly in both generalization and imperfection adaptation.

/pdf/direction-of-arrival-estimation-based-on-deep-neural-5d6ze1o10h.pdf

Direction-of-Arrival Estimation Based on Deep Neural Networks With Robustness to Array Imperfections

The estimation of the directions of arrival (DoAs) of narrow-band signals impinging on a linear antenna array is addressed within the Bayesian compressive sensing (BCS) framework. Unlike several state-of-the-art approaches, the voltages at the output of the receiving sensors are directly used to determine the DoAs of the signals thus avoiding the computation of the correlation matrix. Towards this end, the estimation problem is properly formulated to enforce the sparsity of the solution in the linear relationships between output voltages (i.e., the problem data) and the unknown DoAs. Customized implementations exploiting the measurements collected at a unique time instant (single-snapshot) and multiple time instants (multiple-snapshots) are presented and discussed. The effectiveness of the proposed approaches is assessed through an extensive numerical analysis addressing different scenarios, signal configurations, and noise conditions. Comparisons with state-of-the-art methods are reported, as well.

Directions-of-Arrival Estimation Through Bayesian Compressive Sensing Strategies

This paper considers the problem of multiple-source tracking with neural network-based smart antennas for wireless terrestrial and satellite mobile communications. The neural multiple-source tracking (N-MUST) algorithm is based on an architecture of a family of radial basis function neural networks (RBFNN) to perform both detection and direction of arrival (DOA) estimation. The field of view of the antenna array is divided into spatial angular sectors, which are in turn assigned to a different pair of RBFNNs. When a network detects one or more sources in the first stage, the corresponding second stage network(s) are activated to perform the DOA estimation. Simulation results are performed to investigate the performance of the algorithm for various angular separations, with sources of random relative signal-to-noise ratio and when the system suffers from Doppler spread.

A neural network-based smart antenna for multiple source tracking

The problem of direction of arrival (DOA) estimation of mobile users using linear antenna arrays is addressed. To reduce the computational complexity of superresolution algorithms, e.g. multiple signal classification (MUSIC), the DOA problem is approached as a mapping which can be modeled using a suitable artificial neural network trained with input output pairs. This paper discusses the application of a three-layer radial-basis function neural network (RBFNN), which can learn multiple source-direction findings of a six-element array. The network weights are modified using the normalized cumulative delta rule. The performance of this network is compared to that of the MUSIC algorithm for both uncorrelated and correlated signals. It is also shown that the RBFNN substantially reduced the CPU time for the DOA estimation computations.

Performance of radial-basis function networks for direction of arrival estimation with antenna arrays

We present a neural network approach to the problem of finding the weights of one- (1-D) and two-dimensional (2-D) adaptive arrays. In modern cellular satellite mobile communications systems and in global positioning systems (GPSs), both desired and interfering signals change their directions continuously. Therefore, a fast tracking system is needed to constantly track the users and then adapt the radiation pattern of the antenna to direct multiple narrow beams to desired users and nulls interfering sources. In the approach suggested in this paper, the computation of the optimum weights is accomplished using three-layer radial basis function neural networks (RBFNN). The results obtained from this network are in excellent agreement with the Wiener solution.

Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays

A neural network architecture is applied to the problem of direction of arrival (DOA) estimation using a linear array. A three layer radial basis function network (RBFN) is trained with input output pairs. The network is then capable of estimating DOA not included in the training set through generalization. This approach reduces the extensive computations required by conventional superresolution algorithms such as MUSIC and is easier to implement in real-time. The results suggest that the performance of the RBFNN method approaches that of the MUSIC algorithm. In real time the fast convergence rates of neural networks will allow the array to track mobile sources.

A.H. El Zooghby

Papers

A neural network-based smart antenna for multiple source tracking

Performance of radial-basis function networks for direction of arrival estimation with antenna arrays

Neural network-based adaptive beamforming for one- and two-dimensional antenna arrays

Antenna array signal processing with neural networks for direction of arrival estimation

A neural‐network‐based linearly constrained minimum variance beamformer