Related papers: Modulation Classification Using Received Signal's …
In this letter, we derive the optimal discriminant functions for modulation classification based on the sampled distribution distance. The proposed method classifies various candidate constellations using a low complexity approach based on…
Modulation format identification (MFI) is crucial in next-generation optical networks such as cognitive optical networks. An integrated-clustering-algorithm-based MFI scheme in a coherent optical communication system is proposed herein.…
In this paper, we address the problem of Identifying the modulation level of the received signal under an unknown frequency selective channel. The modulation level classification is performed using reduced-complexity Kuiper (rcK) test which…
Modulation classification is an essential step of signal processing and has been regularly applied in the field of tele-communication. Since variations of frequency with respect to time remains a vital distinction among radio signals having…
In this study, an algorithm to blind and automatic modulation classification has been proposed. It well benefits combined machine leaning and signal feature extraction to recognize diverse range of modulation in low signal power to noise…
Modulation recognition is a challenging task while performing spectrum sensing in a cognitive radio setup. Recently, the use of deep convolutional neural networks (CNNs) has shown to achieve state-of-the-art accuracy for modulation…
Blind algorithms for multiple-input multiple-output (MIMO) signals interception have recently received considerable attention because of their important applications in modern civil and military communication fields. One key step in the…
A modulation classification (MC) scheme based on Independent Component Analysis (ICA) in conjunction with either maximum likelihood (ML) or Support Vector Machines (SVM) is proposed for MIMO-OFDM signals over frequency selective, time…
Deployment of machine learning algorithms into real-world practice is still a difficult task. One of the challenges lies in the unpredictable variability of input data, which may differ significantly among individual users, institutions,…
The state of polarization and the carrier phase drift dynamically during transmission in a random fashion in coherent optical fiber communications. The typical digital signal processing solution to mitigate these impairments consists of two…
This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (CAPs) together with cyclic cumulant (CC) features of the…
In this paper, we propose an algorithm to perform modulation classification on a 5-class problem consisting of AM, 2-PSK, 4-PSK, 8-PSK and 16-QAM modulation schemes using a combination of features based on the first order cyclostationarity,…
The problem of modulation classification for a multiple-antenna (MIMO) system employing orthogonal frequency division multiplexing (OFDM) is investigated under the assumption of unknown frequency-selective fading channels and…
With the rapid development of information nowadays, spectrum resources are becoming more and more scarce, leading to a shift in the research direction from the modulation classification of a single signal to the modulation classification of…
We consider binary continuous phase modulation (CPM) signals used in some recent low-cost and low-power consumption telecommunications standard. When these signals are generated through a low-cost transmitter, the real modulation index can…
Automatic modulation classification (AMC) is essential for wireless communication systems in both military and civilian applications. However, existing deep learning-based AMC methods often require large labeled signals and struggle with…
This work initiates the efforts to design an intelligent/cognitive nano receiver operating in Terahertz (THz) band. Specifically, we investigate two essential ingredients of an intelligent nano receiver---modulation mode detection (to…
Automatic Modulation Classification (AMC) is an essential technology that is widely applied into various communications scenarios. In recent years, many Machine Learning and Deep-Learning methods have been introduced into AMC, and a lot of…
Modulation classification (MC) is the first step performed at the receiver side unless the modulation type is explicitly indicated by the transmitter. Machine learning techniques have been widely used for MC recently. In this paper, we…
Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades…