Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

Li, Yifan; Hu, Jinsong; Yan, Shihao; Song, Haiwei; Zhu, Weiqiang; Tian, Da; Song, Yunpeng; Wang, Jiangzhou

doi:10.3390/drones7040256

Cited by 4 publications

(3 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The hurdles and solutions discussed in study provide useful information for the adaptability of machine learning-based solutions to variable network conditions echoing with dynamic nature of 5G networks that require mobile beam steering. Within UAV communication studies, Li et al [18] consider machine learning techniques to estimate the number of UAV emitters based on Massive MIMO receive arrays. This work is especially important because it analyzes the possible nexus of Massive MIMO, machine learning, and new technologies such as UAV communication peculiarities emerging from intelligent beamforming.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Shrikant Upadhyay

2024

jes

View full text Add to dashboard Cite

The following research focuses on the use of machine learning-based beamforming algorithms to improve Massive Multiple Input Multiple Output (MIMO) systems in 5G networks. Four unique algorithms namely, the Deep Learning Beamforming Algorithm (DLBA), Reinforcement Learning-Based Doa Estimation Algorithm (RLBEA), Clustering based beam forming algorithm(CBA) and GeneticAlgorithm Based Beam Forming Algoeithm were developed after which each of them was undertook evaluation. Widespread trials, in a simulated 5G environment, have revealed that the DLBA and RLBA considerably outperform other technologies by means of system throughput SINR as well Both the DLBA and RLBA achieved high system throughput, increased SINR levels and low BER. CBA and GABA, using clustering and genetic algorithms as their approaches, displayed moderate values on all assessed composite measures. This research offers important insights on the adaptability and learning potential of machine-learning based beamforming algorithms highlighting their ability to improve efficiency in wireless communication networks during the 5G revolution

show abstract

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Shrikant Upadhyay

2024

jes

View full text Add to dashboard Cite

show abstract

“…The experimental results demonstrate that this approach can significantly enhance system throughput and energy efficiency. To determine the number of radiation nodes, the authors propose a UAV-based comprehensive DOA preprocessing system in [37]. This system consists of two signal detectors and a verifier that can make precise inferences based on the MIMO receiver array, which provides the foundation for the following DOA estimation.…”

Section: Related Workmentioning

confidence: 99%

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-Massive Receive Array

Chen,

Jie,

Zhang

et al. 2023

Drones

Self Cite

View full text Add to dashboard Cite

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide good directional measurement results, the rapid power iterative polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with the polynomial solution method to obtain the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPI-PR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to the Cramér–Rao Lower Bound (CRLB). Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance with respect to the computational complexity.

show abstract

“…Direction-of-arrival-(DOA) estimation is a technique to obtain the direction of the target signal based on the array receiving data. It has been widely used and rapidly developed in the fields of radar, sonar, detection, and mobile communication [1][2][3]. The classical high-resolution algorithms [4,5] mostly study incoherent signals.…”

Section: Introductionmentioning

confidence: 99%

DOA-Estimation Method Based on Improved Spatial-Smoothing Technique

Hou,

Wang,

Ding

et al. 2023

Mathematics

View full text Add to dashboard Cite

To improve the data utilization of the sensor array and direction-of-arrival-(DOA)-estimation performance for coherent signals, a DOA-estimation method with a modified spatial-smoothing technique is proposed. The covariance matrix of the received data of the sensor array is decomposed to obtain the signal subspace vectors, and the obtained vectors are smoothed in the proposed method. Then, a new data covariance matrix is constructed by using the autocorrelation covariance matrix of the intra-subarrays and the cross-correlation covariance matrix of the inter-subarrays. Finally, the DOA of the incident signal is estimated, using the new data covariance matrix with the multiple-signal-classification-(MUSIC) algorithm. The data-processing results of the simulation and lake test show that the modified method has stronger decoherence ability, as it utilizes more comprehensive coherent signal information. It also has better azimuth-estimation performance and robustness in the case of a low signal-to-noise ratio (SNR), few snapshots, and small incident-angle intervals when compared to the traditional method. Therefore, the modified method has great engineering application value.

show abstract

Machine Learning Methods for Inferring the Number of UAV Emitters via Massive MIMO Receive Array

Cited by 4 publications

References 44 publications

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Machine Learning-Based Beamforming Algorithm for Massive MIMO Systems in 5G Networks

Two Rapid Power Iterative DOA Estimators for UAV Emitter Using Massive/Ultra-Massive Receive Array

DOA-Estimation Method Based on Improved Spatial-Smoothing Technique

Contact Info

Product

Resources

About