An official website of the United States government
To address practical challenges in establishing and maintaining robust wireless connectivity such as multi-path effects, low latency, size reduction, and high data rate, we have deployed the digital beamformer, as a spatial filter, by using the hybrid antenna array at an operating frequency of 10 GHz. The proposed digital beamformer utilizes a combination of the two well-established beamforming techniques of minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV). In this case, the MVDR beamforming method updates weight vectors on the FPGA board, while the LCMV beamforming technique performs nullsteering in directions of interference signals in the real environment. The most well-established machine learning technique of support vector machine (SVM) for the Direction of Arrival (DoA) estimation is limited to problems with linearly-separable datasets. To overcome the aforementioned constraint, the quadratic surface support vector machine (QS-SVM) classifier with a small regularizer has been used in the proposed beamformer for the DoA estimation in addition to the two beamforming techniques of LCMV and MVDR. In this work, we have assumed that five hybrid array antennas and three sources are available, at which one of the sources transmits the signal of interest. The QS-SVM-based beamformer has been deployed on the FPGA board for spatially filtering two signals from undesired directions and passing only one of the signals from the desired direction. The simulation results have verified the strong performance of the QS-SVM-based beamformer in suppressing interference signals, which are accompanied by placing deep nulls with powers less than −10 dB in directions of interference signals, and transferring the desired signal. Furthermore, we have verified that the performance of the QS-SVM-based beamformer yields other advantages including average latency time in the order of milliseconds, performance efficiency of more than 90%, and throughput of nearly 100%.
more »
« less
This content will become publicly available on June 1, 2026
Deep Learning of the Sparse Array Configurations in Optimum Beamforming
The article examines neural network learning of the sparse array configurations in optimum beamforming. Unlike iterative greedy, convex, and global optimization methods for optimum array design, deep learning enables fast reconfigurations of the sparse array in rapid dynamic propagation environments. We employ three different convolutional neural network architectures with varying simplification and parameter counts. The network is trained to select M out of N uniformly spaced antennas to achieve maximum signal-to-interference and noise ratio (SINR) beamforming. Different values of M are considered, including N = 2 M, for studying network performance under an increased number of subarray classes. We consider one desired source and one interference of arbitrary angle, and delineate the learning results for the two cases where the network is trained with the desired source assuming fixed and varying angles. We discuss the benefits of reducing the number of possible configurations due to sidelobe level reductions. It is also shown that the network performance significantly improves with data augmentations and by removing redundant array configurations which produce the same SINR.
more »
« less
- PAR ID:
- 10615873
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Aerospace and Electronic Systems
- Volume:
- 61
- Issue:
- 3
- ISSN:
- 0018-9251
- Page Range / eLocation ID:
- 5718 to 5730
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Sparse Bayesian Learning (SBL) is a popular sparse signal recovery method, and various algorithms exist under the SBL paradigm. In this paper, we introduce a novel re-parameterization that allows the iterations of existing algorithms to be viewed as special cases of a unified and general mapping function. Furthermore, the re-parameterization enables an interesting beamforming interpretation that lends insights to all the considered algorithms. Utilizing the abstraction allowed by the general mapping viewpoint, we introduce a novel neural network architecture for learning improved iterative update rules under the SBL framework. Our modular design of the architecture enables the model to be independent of the size of the measurement matrix and provides us a unique opportunity to test the generalization capabilities across different measurement matrices. We show that the network when trained on a particular parameterized dictionary generalizes in many ways hitherto not possible; different measurement matrices, both type and dimension, and number of snapshots. Our numerical results showcase the generalization capability of our network in terms of mean square error and probability of support recovery across sparsity levels, different signal-to-noise ratios, number of snapshots and multiple measurement matrices of different sizes.more » « less
-
This article presents a new notch steering scheme for hybrid beamforming transmitters (TXs) aimed at suppressing spatial interference, thereby enhancing the signal-to-interference-plus-noise ratio (SINR) to support spatial multiplexing. Built upon existing phased arrays, this scheme integrates an auxiliary-path vector modulator (VM) into each antenna element, which in turn, forms an interference-canceling beam. By spatially combining the array factors (AFs) of the main beam and the interference-canceling beam, a deep spatial notch is created while ensuring minimal main-beam power degradation. Unlike the conventional zero-forcing method that requires matrix inversion in digital for spatial notch creation, our scheme enables the computation of antenna weights in analog, significantly reducing the computational cost and latency. Leveraging this new notch steering scheme, we develop a 28-GHz four-element fully connected (FC) hybrid beamforming TX array using the GlobalFoundries 45-nm CMOS Silicon-on-Insulator (SOI) process. It is capable of simultaneously transmitting two independent, wideband data streams (DSs) in the same polarization toward two directions. In probing-based measurements, each TX channel delivers 19.7-dBm OP1 dB, 20.4-dBm PSAT , and 30.6% peak power-added efficiency (PAE) at 29 GHz, demonstrating state-of-the-art TX linearity and efficiency. In over-the-air (OTA) measurements, the packaged TX array achieves 29.8-dBm EIRP1 dB and is able to steer a spatial notch outside the −10-dB beamwidth of the main beam, with a notch depth of >35 dB and a main-beam power degradation of < 0.8 dB. Moreover, in spatial multiplexing demonstrations, the TX array is capable of transmitting a 400-MHz 64-quadrature amplitude modulation (QAM) signal to the intended receiver (RX) in the first DS, while suppressing the co-channel continuous-wave or wideband modulated interference created by the second DS with a high SINR.more » « less
-
There has been recent work on the design of antenna arrays for beamforming in dynamic evolving environments such as in vehicle-to-vehicle communication systems. A key problem is that of determining how to optimally use a large antenna array to communicate with multiple spatially located vehicles in dynamically changing channel conditions with minimal co-channel interference while minimizing overall power consumption of the wireless system. We envision disjoint subsets of antennas in the array being used to direct beams concurrently to different vehicles. The number of antennas, gain and phase of each RF-chain driving an antenna are optimized dynamically using a constrained quadratic cost formulation encompassing channel quality, interference and power consumption. This quadratic optimization problem is solved using behavior constrained bandit algorithm, a reinforcement learning based technique. A gaussian kernel is used to perform data clustering of vehicle environment and resulting solutions, allowing quick bootstrapping of the bandit solver to find optimal array configurations in real-time vehicle environments. Simulation studies prove the viability of the proposed scheme.more » « less
-
This paper characterizes the impact of interference on vehicular communications employing beamforming links between transmitters and receivers operating within a multipath propagation environment. Different road scenarios, such as an intersection and a roundabout are considered to determine the performance characteristics of the vehicle communication link. By employing the results for different antenna array elements and by varying distance, we analyse the bit error rate (BER) of vehicles operating in multipath propagation environments with an interferer within the vicinity of an communication link.more » « less
