Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

Automatic Modulation Classification Method for Multiple Antenna System Based on Convolutional Neural Network

<div>In order to transmit communication signals of</div><div>different properties, quickly, effectively, and accurately, various</div><div>different modulation styles can be adopted. Accurate recognition</div><div>of signal modulation is required at the receive side. Automatic</div><div>modulation recognition (AMR) is a key technique to identify</div><div>various styles of modulation of signals received in wireless</div><div>channels. It can be used in many kinds of communication systems,</div><div>including single antenna system and multiple antenna system. In</div><div>this paper, we propose a convolutional neural networks (CNN)</div><div>aided AMR method for multiple antenna system. Compared with</div><div>the high order cumulants (HOC) and artificial neural networks</div><div>(ANN) aided traditional AMR classification method, both with</div><div>two specific combination strategies, such as relative majority</div><div>voting method and arithmetic mean method, the proposed</div><div>AMR with arithmetic mean method has the best classification</div><div>performance. The experimental results obtained verify that the</div><div>CNN, one of the representative algorithms of deep learning, has</div><div>a strong ability to exploit dominant features and classify the</div><div>modulation styles.</div>

High-Performance Multiple-Input Multiple-Output Antenna System For 5G Mobile Terminals

In this paper, the systematic design of a multiple antenna system for 5G smartphone operating at 3.5 GHz for multiple-input multiple-output (MIMO) operation in smartphones is proposed. The smartphone is preferred to be lightweight, thin, and attractive, and as a result metal casings have become popular. Using conventional antennas, such as a patch antenna, Inverted-F antennas, or monopole, in proximity to metal casing leads to decreasing its total efficiency and bandwidth. Therefore, a slot antenna embedded in the metal casing can be helpful, with good performance regarding bandwidth and total efficiency. The proposed multiple antenna system adopted the unit open-end slot antenna fed by Inverted-L microstrip with tuning stub. The measured S-parameters results agree fairly with the numerical results. It attains 200 MHz bandwidth at 3.5 GHz with ports isolation of (≤−13 dB) for any two antennas of the system. The influence of the customer’s hand for the proposed multiple antenna system is also considered, and the MIMO channel capacity is computed. The maximum achievable MIMO channel capacity based on the measured result is 31.25 bps/Hz and is about 2.7 times of 2 × 2 MIMO operation.