Automatic modulation classification (AMC) plays a critical role in wireless communications
by autonomously classifying signals transmitted over the radio spectrum. Deep learning (DL) …

In this paper, to investigate sparse channel estimation in OFDM communication systems, we
propose a novel binary particle swarm optimization (BPSO) based pilot pattern design …

Intelligent communication is gradually becoming a mainstream direction. As a major branch
of machine learning, deep learning (DL) has been applied in physical layer communications …

In next-generation communications, massive machine-type communications (mMTC) induce
severe burden on base stations. To address such an issue, automatic modulation …

In the LTE system, uplink synchronization can be established through the random access
channel, by which timing and frequency offsets between transceivers can be estimated and …

An efficient data-driven prediction strategy for multi-antenna frequency-selective channels
must operate based on a small number of pilot symbols. This paper proposes novel channel …

Blind modulation classification is a fundamental step before signal detection for cognitive
radio networks where the users may not have the complete knowledge of the modulation …

Automatic Modulation Classification (AMC) is a crucial task in the field of wireless
communication, allowing for the identification of the modulation scheme of a received radio …

Many fields, like document tagging, video labeling, and medical analysis, require
associating the samples with multiple non-exclusive labels, driving the research in multi …

As one of the main scenarios in 5G mobile networks, ultra-reliable low-latency
communication (URLLC) can satisfy the stringent requirements of many emerging …