RNN based MIMO channel prediction

Potter, Chris; Venayagamoorthy, G.K.; Kosbar, Kurt

doi:10.1016/j.sigpro.2009.07.013

Cited by 44 publications

(20 citation statements)

References 13 publications

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“…Recurrent neural networks have been widely used in MIMO systems [17][18][19][20][21][22][23]. Echo state networks are a way to train recurrent neural networks.…”

Section: Mimo Channel Predictionmentioning

confidence: 99%

Real-Time Echo State Network Based on FPGA and Its Applications

Liao¹

2020

Field Programmable Gate Arrays (FPGAs) II

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In this chapter, a hardware processing architecture of real-time echo state network based on field-programmable gate array (FPGA) is proposed, which solves the problem that it is difficult to obtain the output weight of the network in real time. The design of this architecture strictly follows the reservoir calculation (RC) theory, and its five components are established in FPGA: input module, reservoir module, output module, training module, and system switch module. This paper implements the architecture in Altera FPGA chip and verifies it through the application of pattern recognition, waveform generation, and multiple-input multiple-output (MIMO) channel prediction. Experimental results show that the hardwareimplemented real-time echo state network can identify the duty cycle of different input signals, generate floating-point waveforms, and predict the MIMO channel by training. In this paper, a real-time echo state network based on field programmable gate array is proposed, which has the advantages of fast computation speed, less resource consumption, and ideal simple task execution.

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“…Recurrent neural networks have been widely used in MIMO systems [17][18][19][20][21][22][23]. Echo state networks are a way to train recurrent neural networks.…”

Section: Mimo Channel Predictionmentioning

confidence: 99%

Real-Time Echo State Network Based on FPGA and Its Applications

Liao¹

2020

Field Programmable Gate Arrays (FPGAs) II

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“…The recurrent neural network (RNN) [9,10] is a popular learning method in the field of deep learning in recent years and is a kind of neural network for processing sequence data. Compared with the independent calculation results of common neural networks, the results of each hidden layer in RNN are correlated with the current input and the last hidden layer, that is, the input of hidden layer includes not only the output of the input layer but also the output of the previous hidden layer.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Zhao

Zhu

Wan

et al. 2018

Water

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Ensuring the quality of hydrological data has become a key issue in the field of hydrology. Based on the characteristics of hydrological data, this paper proposes a data-driven quality control method for hydrological data. For continuous hydrological time series data, two combined forecasting models and one statistical control model are constructed from horizontal, vertical, and statistical perspectives and the three models provide three confidence intervals. Set the suspicious level based on the number of confidence intervals for data violations, control the data, and provide suggested values for suspicious and missing data. For the discrete hydrological data with large time-space difference, the similar weight topological map between the neighboring stations is established centering on the hydrological station under the test and it is adjusted continuously with the seasonal changes. Lastly, a spatial interpolation model is established to detect the data. The experimental results show that the quality control method proposed in this paper can effectively detect and control the data, find suspicious and erroneous data, and provide suggested values.

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“…The generalization ability of neural networks provides flexible representation of complicated channel-state changes and high prediction capability. For instance, prediction methods based on an echo state network (ESN) [14] and an extreme learning machine (ELM) [15] as well as real-valued recurrent neural network (RNN) [16], [17] have been reported, and their prediction performance has been evaluated in some simulated communication situations. Luo et al recently combined a convolutional neural network and a long short-term memory (LSTM) network for learning and predicting channel states under some communication situations with static communication ends [18].…”

Section: Introductionmentioning

confidence: 99%

Online Regularization of Complex-Valued Neural Networks for Structure Optimization in Wireless-Communication Channel Prediction

Ding

Hirose

2020

IEEE Access

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This paper proposes online-learning complex-valued neural networks (CVNNs) to predict future channel states in fast-fading multipath mobile communications. CVNN is suitable for dealing with a fading communication channel as a single complex-valued entity. This framework makes it possible to realize accurate channel prediction by utilizing its high generalization ability in the complex domain. However, actual communication environments are marked by rapid and irregular changes, thus causing fluctuation of communication channel states. Hence, an empirically selected stationary network gives only limited prediction accuracy. In this paper, we introduce regularization in updates of the CVNN weights to develop online dynamics that can self-optimize its effective network size in response to such channelstate changes. It realizes online adaptive, highly accurate and robust channel prediction with dynamical adjustment of the network size. We characterize its online adaptability in a series of simulations and our practical wireless-propagation experiments demonstrate that the proposed channel prediction scheme provide 2.5 dB and 5.5 dB improvement of bit error rate (BER) at 10 −3 and 5 × 10 −4 , and achieve 10 −5 BER with E b /N 0 = 23 − 24 dB. INDEX TERMS Adaptive communications, channel prediction, channel state information (CSI), complexvalued neural network (CVNN), fading, 5G wireless communications (5G-NR)

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RNN based MIMO channel prediction

Cited by 44 publications

References 13 publications

Real-Time Echo State Network Based on FPGA and Its Applications

Real-Time Echo State Network Based on FPGA and Its Applications

Research on the Data-Driven Quality Control Method of Hydrological Time Series Data

Online Regularization of Complex-Valued Neural Networks for Structure Optimization in Wireless-Communication Channel Prediction

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