Abstract
This paper proposes simulated annealing (SA) assisted deep learning (DL) based sparse array selection approach. Conventional DL-based antenna selectors are primarily data-driven techniques. As a result, the required dataset is generated by listing all possible combinations of selecting M sensors given N uniform array, which is computationally expensive. A simulated annealing algorithm is proposed to assist dataset generation as an initializer to circumvent the above limitation. The SA algorithm sequentially samples and optimizes the subarrays that constitute the training data samples while retaining specific array characteristics. Hence, it simplifies the dataset annotation as most array configurations generated contain desired properties, thereby reducing the computation complexity of the overall data annotation processes. Therefore, the initializer reduces computation costs related to data generation considerably. Simulation examples show that using the dataset generated by the proposed method improves the DL-based array selector’s accuracy compared to the one generated by the conventional random sampler. Moreover, the realized sparse arrays show better sparse array configuration characteristics and enhanced DOA estimation performance.
Highlights
T HE design of optimum sparse arrays via machine learning (ML) for direction-of-arrival (DOA) and beamforming (BF) has recently received tremendous attention [1]-[15]
This paper presents simulated annealing assisted deep learning (DL)-based antenna selection approach for 2D sparse array selection
The results show that the rendered 2D sparse arrays have improved DOA estimation resolution compared to the parent 2D array and other 2D sparse arrays
Summary
T HE design of optimum sparse arrays via machine learning (ML) for direction-of-arrival (DOA) and beamforming (BF) has recently received tremendous attention [1]-[15]. A deep learning-based antenna selection approach was proposed in [11] to predict planar or two-dimensional (2D) sparse arrays using covariance matrix as input. Most conventional antenna selection techniques employ heuristic or population-based optimization methods Practical, they are computationally expensive and prone to local minima [6]. This paper presents simulated annealing assisted DL-based antenna selection approach for 2D sparse array selection. The proposed technique is a hybrid two-stage approach for the generation of the dataset, and sparse array selection using features extracted from DOA estimation environment [19]. The proposed hybrid approach has improved sparse array estimation accuracy and reduced computation complexity compared to the conventional deep learning-based methods and traditional SA-based antenna optimization approach. ⊙ and E · denote the Khatri-Rao product and statistical expectation operator
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