Automatic modulation classification (AMC) using convolutional neural networks (CNNs) is
an active area of research that has the potential to improve the efficiency and reliability of …

Deep learning (DL) is a newly addressed area of research in the field of modulation
classification. In this letter, a constellation density matrix (CDM) based modulation …

Neural networks (NNs) based digital predistortion (DPD) have been shown to be a very
promising technique to enhance power amplifier linearity. However, studies consider high …

Deep learning has been recently applied to many problems in wireless communications
including modulation classification and symbol decoding. Many of the existing end-to-end …

Automatic modulation classification (AMC) is becoming increasingly important in spectrum
monitoring and cognitive radio. However, most existing modulation classification algorithms …

We consider orthogonal frequency division multiplexing over rapidly time-varying multipath
channels, for which performance of standard channel estimation and equalization …

This paper details the derivation of the closed-form BER expressions of PSK modulation in
Rayleigh faded AWGN channel for OFDM and SC-FDMA under jamming and channel …

This paper proposes a bandwidth efficient Gaussian Minimum Frequency-Shift Keying
(GMSK) approach for software defined radio (SDR) platforms. The proposed demodulator …

A novel simple receiver structure for M-ary differential phase shift keying (MDPSK)
transmission over flat Rayleigh fading channels without channel state information is …

Deep learning has recently been used for this issue with superior results in automatic
modulation classification. Previous studies state that it is challenging to categorize a variety …