Neural networks in wireless networks: Techniques, applications and guidelines

Ahad, Nauman; Qadir, Junaid; Ahsan, Nasir

doi:10.1016/j.jnca.2016.04.006

Cited by 66 publications

(33 citation statements)

References 112 publications

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“…DNNs consist of many hidden layers and can have various combination of activation functions between the layers. Deep learning neural networks are useful for classification [22], regression [23], clustering [24] and prediction tasks [25]. A neural network which uses any combination of binary weight or hard threshold activation functions is typically known as BNN.…”

Section: Background a Learning Algorithms And Biologically Inspimentioning

confidence: 99%

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

Krestinskaya

Salama

James

2019

IEEE Trans. Circuits Syst. I

123

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The on-chip implementation of learning algorithms would speed-up the training of neural networks in crossbar arrays. The circuit level design and implementation of backpropagation algorithm using gradient descent operation for neural network architectures is an open problem. In this paper, we proposed the analog backpropagation learning circuits for various memristive learning architectures, such as Deep Neural Network (DNN), Binary Neural Network (BNN), Multiple Neural Network (MNN), Hierarchical Temporal Memory (HTM) and Long-Short Term Memory (LSTM). The circuit design and verification is done using TSMC 180nm CMOS process models, and TiO2 based memristor models. The application level validations of the system are done using XOR problem, MNIST character and Yale face image databases.

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Section: Background a Learning Algorithms And Biologically Inspimentioning

confidence: 99%

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

Krestinskaya

Salama

James

2019

IEEE Trans. Circuits Syst. I

123

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“…A different approach resorting to the Taylor Series Expansion was followed in [11], yielding expression (12) as an approximation of I T . The interested reader is referred to [11] for the definitions of each element of the equation.…”

Section: Capacity and Mutual Informationmentioning

confidence: 99%

Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation

Tato¹,

Mosquera²,

Henarejos³

et al. 2020

IEEE Trans. Commun.

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Index Modulations, in the form of Spatial Modulation or Polarized Modulation, are gaining traction for both satellite and terrestrial next generation communication systems. Adaptive Spatial Modulation based links are needed to fully exploit the transmission capacity of time-variant channels. The adaptation of code and/or modulation requires a real-time evaluation of the channel achievable rates. Some existing results in the literature present a computational complexity which scales quadratically with the number of transmit antennas and the constellation order. Moreover, the accuracy of these approximations is low and it can lead to wrong Modulation and Coding Scheme selection. In this work we apply a Multilayer Feedforward Neural Network to compute the achievable rate of a generic Index Modulation link. The case of two antennas/polarizations is analyzed throughly showing the neural network not only a one-hundred fold decrement of the Mean Square Error in the estimation of the capacity compared with existing analytical approximations, but it also reduces fifty times the computational complexity. Moreover, the extension to an arbitrary number of antennas is explained and supported with simulations. More generally, neural networks can be considered as promising candidates for the practical estimation of complex metrics in communication related settings.

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“…Experimental results show that this scheme has high detection accuracy. Moreover, machine learning approaches can also be used to design secure network protocol in WSNs [225]. • Consumer behaviour prediction.…”

Section: ) Internet Of Thingsmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

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The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

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Neural networks in wireless networks: Techniques, applications and guidelines

Cited by 66 publications

References 112 publications

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

Learning in Memristive Neural Network Architectures Using Analog Backpropagation Circuits

Neural Network Aided Computation of Mutual Information for Adaptation of Spatial Modulation

Big Data Analytics for Large-scale Wireless Networks

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