A new antenna array beamformer based on neural networks (NNs) is presented. The NN training is performed by using optimized data sets extracted by a novel invasive weed …
Y Kim, S Keely, J Ghosh, H Ling - IEEE Transactions on …, 2007 - ieeexplore.ieee.org
An artificial neural network (ANN) is proposed to predict the input impedance of a broadband antenna as a function of its geometric parameters. The input resistance of the …
Conventional reservoir computing (RC) is a shallow recurrent neural network (RNN) with fixed high dimensional hidden dynamics and one trainable output layer. It has the nice …
This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main …
S Mishra, RN Yadav, RP Singh - IEEE Antennas and Wireless …, 2015 - ieeexplore.ieee.org
The role of directivity is very important in the operation of an array as it gives a measure of the effectiveness of the array in pointing the radiations in a specific direction. Traditional …
Demodulation techniques are of central importance for achieving intelligent receiving. Improvement in demodulation performance enhances the overall performance of a …
M Zhang, Z Liu, L Li, H Wang - IEEE Access, 2018 - ieeexplore.ieee.org
In this paper, a novel binary phase shift keying demodulator based on 1-D convolutional neural network (1-D CNN) is proposed. The utilization of neural networks to detect the …
For nano-scale communications, there must be cooperation and simultaneous communication between nano devices. To this end, we investigate two way (aka bi …
H Klessig, D Öhmann, AI Reppas… - IEEE Journal on …, 2016 - ieeexplore.ieee.org
In order to cope with the wireless traffic demand explosion within the next decade, operators are underlying their macrocellular networks with low power base stations in a more dense …