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 …

Blindly decoding a signal requires estimating its unknown transmit parameters,
compensating for the wireless channel impairments, and identifying the modulation type …

Soft demodulation is a basic module of traditional communication receivers. It converts
received symbols into soft bits, that is, log likelihood ratios (LLRs). However, in the non-ideal …

Noncoherent demodulation is an attractive choice for many wireless communication
systems. It requires minimal protocol overhead for carrier synchronization, and it is robust to …

Modulation classification, an intermediate process between signal detection and
demodulation in a physical layer, is now attracting more interest to the cognitive radio field …

The extended binary phase shift keying (EBPSK) is an efficient modulation technique, and a
special impacting filter (SIF) is used in its demodulator to improve the bit error rate (BER) …

Deep learning has been widely exploited in radio modulation recognition in recent years. In
this paper, we exploit empirical mode decomposition (EMD) and variational mode …

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 …

In recent years, deep learning is becoming more and more popular. It has been widely
applied to fields including image recognition, automatic speech recognition and natural …

Recently, deep learning (DL) is an innovative machine learning (ML) technique that has
gained the outstanding achievements in computer vision and natural language processing …