A Fast Adaptive Tunable RBF Network For Nonstationary Systems

Chen, Hao; Gong, Yu; Hong, Xia; Chen, Sheng

doi:10.1109/tcyb.2015.2484378

Cited by 45 publications

(77 citation statements)

References 27 publications

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“…For the NN to predict the minimum path metrics, we employ a Gaussian radial basis function network (G-RBFN) [11], [12] consisting of one hidden layer, as depicted in Fig. 2.…”

Section: A Design Of the Nn For Path Metric Predictionmentioning

confidence: 99%

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Weon

Lee

2020

IEEE Access

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In this paper, we propose a novel learning-aided sphere decoding (SD) scheme for large multipleinput-multiple-output systems, namely, deep path prediction-based sphere decoding (DPP-SD). In this scheme, we employ a neural network (NN) to predict the minimum metrics of the "deep" paths in subtrees before commencing the tree search in SD. To reduce the complexity of the NN, we employ the input vector with a reduced dimension rather than using the original received signals and full channel matrix. The outputs of the NN, i.e., the predicted minimum path metrics, are exploited to determine the search order between the sub-trees, as well as to optimize the initial search radius, which may reduce the computational complexity of SD. For further complexity reduction, an early termination scheme based on the predicted minimum path metrics is also proposed. Our simulation results show that the proposed DPP-SD scheme provides a significant reduction in computational complexity compared with the conventional SD algorithm, despite achieving near-optimal performance. Index TermsMIMO, sphere decoding, tree search, machine learning, deep learning, neural network.The authors are with the 2 interest [2]. To maximize the achievable data rates in large MIMO systems, the base station needs to receive as many symbols as possible simultaneously from multiple terminals, which leads to enhanced multiplexing gains. In this circumstance, a near-optimal receiver like SD plays an important role in approaching the channel capacity. However, the complexity of SD significantly increases with the number of antennas [3], which makes it difficult to apply to large MIMO systems.Recently, deep-learning (DL) techniques have been applied in various fields, exhibiting eminent performance. Motivated by the performance of DL technologies in other fields, there have been attempts to apply DL to MIMO detection [4]-[8]. In particular, the DL-based sphere decoding (DL-SD) algorithm is derived to choose the optimal hypersphere radius [4]. In addition, a deep network architecture, called DetNet, is proposed to estimate the solution of MIMO detection [5]. Furthermore, the sparsely connected neural network (ScNet) is developed to simplify the structure of DetNet for massive MIMO systems [6]. The application of a deep neural network to reduce the computational complexity of the conventional belief propagation detector is proposed in [7], and the orthogonal approximate message-passing network (OAMP-Net) architecture is proposed to improve the performance of the iterative detection algorithm with trainable variables [8].In this paper, a novel learning-aided SD algorithm is proposed. The main idea of the proposed algorithm is to predict the minimum path metric among "deep" paths of each sub-tree in a large tree structure by using a neural network (NN). In large MIMO systems, the required information to estimate the path metrics can have large dimension, which can significantly increase the complexity of the NN. To resolve this problem, the size of the input vector to...

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“…For the NN to predict the minimum path metrics, we employ a Gaussian radial basis function network (G-RBFN) [11], [12] consisting of one hidden layer, as depicted in Fig. 2.…”

Section: A Design Of the Nn For Path Metric Predictionmentioning

confidence: 99%

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Weon

Lee

2020

IEEE Access

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“…In order to use (7) to calculate the linear output weights, the dimension of the desired output matrix D has to be P×2. Then, the actual output matrix Y must be arranged as a P × 2 matrix.…”

Section: Two-output Ghc Learning Algorithmmentioning

confidence: 99%

“…Owing to good approximation capabilities [1], single-output RBF networks are usually utilized to model nonlinear functions in engineering applications [2]- [7], while multioutput RBF networks have been widely applied in increasingly challenging fields, including security domain [8], system identification [9], [10] and pattern recognition [11]- [15], etc. Actually, learning of RBF networks involves two tasks: 1) determining the network structure and 2) optimizing the adjustable parameters (i.e., centers and their radii of hidden neurons, and linear output weights).…”

Section: Introductionmentioning

confidence: 99%

Generalized Hybrid Constructive Learning Algorithm for Multioutput RBF Networks

Qian

Huang

Chen

et al. 2016

IEEE Trans. Cybern.

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An efficient generalized hybrid constructive (GHC) learning algorithm for multioutput radial basis function (RBF) networks is proposed to obtain a compact network with good generalization capability. By this algorithm, one can train the adjustable parameters and determine the optimal network structure simultaneously. First, an initialization method based on the growing and pruning algorithm is utilized to select the important initial hidden neurons and candidate ones. Then, by introducing a generalized hidden matrix, a structured parameter optimization algorithm is presented to train multioutput RBF network with fixed size, which combines Levenberg-Marquardt (LM) algorithm with least-square method together. Beginning from an appropriate number of hidden neurons, new neurons chosen from the candidates are added one by one each time when the training entraps into local minima. By incorporating an improved incremental constructive scheme, the training is built on previous results after adding new neurons such that the GHC learning algorithm avoids a trial-and-error procedure. Furthermore, based on the improved computation for LM training, the memory limitation problem is solved. The computational complexity analysis and experimental results demonstrate that better performance is efficiently achieved by this algorithm.

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“…Furthermore, free open-source libraries such as the Fast Artificial Neural Network Library (FANN) [5] for network learning have already enabled researchers in various fields to use neural networks [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. In fact, neural networks have recently been used for the identification of a wide range of nonlinear systems, including biological systems [23][24][25][26][27][28][29][30][31][32][33][34][35][36]. However, neural networks provide little information regarding the structure of the identified system.…”

Section: Introductionmentioning

confidence: 99%

Derivation of NARX models by expanding activation functions in neural networks

Inaoka

Kobayashi

Nebuya

et al. 2019

IEEJ Transactions Elec Engng

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A method was developed to derive an NARX model from a neural network so that the usability of open‐source libraries for network learning was combined with the NARX advantage of revealing the system structure. After the neural network model was trained on input and output data, the sigmoid activation functions were expanded into Taylor series. Candidate parameters in the NARX model were calculated from the connection weights in the neural network and coefficients in the series. The NARX model structure was determined by the extended least‐squares (ELS) method and the Bayesian information criterion (BIC). Correct NARX models were successfully detected in computer simulations and an experiment. The developed method can be used for any activation functions, including the sigmoid function, if they are expanded into Taylor series. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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A Fast Adaptive Tunable RBF Network For Nonstationary Systems

Cited by 45 publications

References 27 publications

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Learning-Aided Deep Path Prediction for Sphere Decoding in Large MIMO Systems

Generalized Hybrid Constructive Learning Algorithm for Multioutput RBF Networks

Derivation of NARX models by expanding activation functions in neural networks

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