Recently, classifying the modulation schemes of signals using deep neural network has
received much attention. In this paper, we introduce a general model of deep neural network …

The number of base stations (BSs) in remote areas is poor, and seamless coverage cannot
be achieved. This paper investigates a cognitive satellite-terrestrial network, including the …

Global navigation satellite systems (GNSSs) are used in numerous fields, but their
vulnerability is a global problem that has yet to be solved. A promising way to effectively …

Cognitive satellite communications (SatCom) is considered to be able to alleviate the
bottleneck of spectrum resource shortage due to traditional spectrum allocation. This paper …

Automatic modulation classification (AMC) is a crucial task for various communications
applications. Deep learning (DL) based classifier is emerging as a prevalent choice for …

Automatic modulation classification (AMC) is an important technology in military signal
reconnaissance and civilian communications such as cognitive radios. Most of the existing …

With the rapidly increasing spectrum demand by multimedia applications, the limitation of
the spectrum resource restricts the improvement of the performance for communication …

With the increase in user demand for internet access on move, spectrum resource seems to
deplete and leads to spectrum crunch. Recent researches reports that this spectrum crunch …

In this paper, an intelligent probing method for interference constraint learning is proposed
to allow a centralized cognitive radio network (CRN) to access the frequency band of a …

Modulation recognition has always been an important task in the development of the
cognitive radio. At present, there are two main application methods for signal data, namely …