Related papers: Time-Frequency Analysis based Deep Interference Cl…
In a frequency hopping (FH) scheme users communicate simultaneously using FH sequences defined on the same set of frequency channels. An FH sequence specifies the frequency channel to be used as communication progresses. Much of the…
Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning…
The recent success in implementing supervised learning to classify modulation types suggests that other problems akin to modulation classification would eventually benefit from that implementation. One of these problems is classifying the…
This paper proposes an anti-interference affine frequency division multiplexing (AFDM) system to ensure reliability and resource efficiency under malicious high-power interference originating from adversarial devices in high-mobility…
This paper proposes a methodology for optimizing a frequency-hopping network that uses continuous-phase frequency-shift keying and adaptive capacity-approaching channel coding. The optimization takes into account the spatial distribution of…
With the increasingly complex and changeable electromagnetic environment, wireless communication systems are facing jamming and abnormal signal injection, which significantly affects the normal operation of a communication system. In…
Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions involving three or more components of a network system. Most of these methods are defined only in the time domain and rely…
Interference Management is a vast topic present in many disciplines. The majority of wireless standards suffer the drawback of interference intrusion and the network efficiency drop due to that. Traditionally, interference management has…
The integration of Fourier transform and deep learning opens new avenues for time series forecasting. We reconsider the Fourier transform from a basis functions perspective. Specifically, the real and imaginary parts of the frequency…
Blind modulation classification is an important step to implement cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information…
By combining traditional frequency hopping ideas with the concepts of subcarriers and sampling points in OFDM baseband systems, this paper proposes a frequency hopping technology within the baseband called micro frequency hopping. Based on…
This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and…
Interference at the radio receiver is a key source of degradation in quality of service of wireless communication systems. This paper presents a unified framework for OFDM/FBMC interference characterization and analysis in asynchronous…
We demonstrate quantum interference of three photons that are distinguishable in time, by resolving them in the conjugate parameter, frequency. We show that the multiphoton interference pattern in our setup can be manipulated by tuning the…
Collision-free Frequency Hopping Sequences (FHS) are crucial for maintaining the throughput of Frequency Hopping Multiple Access (FHMA) communication systems. When multiple FHS deployed in the same geographical area hop into the same…
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…
In this paper, we address the problem of spectrum estimation of multiple frequency-hopping (FH) signals in the presence of random missing observations. The signals are analyzed within the bilinear time-frequency (TF) representation…
In wireless networks, interferences between trans- missions are modelled either in time or frequency domain. In this article, we jointly analyze interferences in the time- frequency domain using a stochastic geometry model assuming the…
This paper investigates deep neural networks for radio signal classification. Instead of performing modulation recognition and combining it with further analysis methods, the classifier operates directly on the IQ data of the signals and…
Integrated sensing and communication is a key feature in next-generation wireless networks, enabling joint data transmission and environmental radar sensing on shared spectrum. In multi-user scenarios, simultaneous transmissions cause…