Related papers: On Oversampling-Based Signal Detection
We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there may be multiple primary users. A detector based on the spherical test is analyzed in such a scenario. Based on the moments of the distributions…
Nowadays, cognitive radio is one of the most promising paradigms in the arena of wireless communications, as it aims at the proficient use of radio resources. Proper utilization of the radio spectrum requires dynamic spectrum accessing. To…
Radio Frequency (RF) sensing is attracting interest in research, standardization, and industry, especially for its potential in Internet of Things (IoT) applications. By leveraging the properties of the ElectroMagnetic (EM) waves used in…
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. In this paper, we propose a data-driven CS framework that learns signal characteristics and individual…
Due to the increased usage of spectrum caused by the exponential growth of wireless devices, detecting and avoiding interference has become an increasingly relevant problem to ensure uninterrupted wireless communications. In this paper, we…
Receivers with joint channel estimation and signal detection using superimposed pilots (SP) can achieve high transmission efficiency in orthogonal time frequency space (OTFS) systems. However, existing receivers have high computational…
Cognitive radio is a potential solution to meet the upcoming spectrum crunch issue. In a cognitive radio, spectrum holes can be identified using spectrum sensing techniques. A high resolution spectrum hole detection can ensure even the…
Spectrum sensing is an essential component of modern wireless networks as it offers a tool to characterize spectrum usage and better utilize it. Deep Learning (DL) has become one of the most used techniques to perform spectrum sensing as…
Quantum sensors using solid-state spin defects excel in the detection of radiofrequency (RF) fields, serving various purposes in communication, ranging, and sensing. For this purpose, pulsed dynamical decoupling (PDD) protocols are…
Hardware imperfections in RF transmitters introduce features that can be used to identify a specific transmitter amongst others. Supervised deep learning has shown good performance in this task but using datasets not applicable to real…
Overlapping Speech Detection (OSD) aims to identify regions where multiple speakers overlap in a conversation, a critical challenge in multi-party speech processing. This work proposes a speaker-aware progressive OSD model that leverages a…
Pulse coding is an effective method to overcome the trade-off between signal-to-noise ratio (SNR) and spatial resolution in optical-fiber sensing systems based on optical time-domain reflectometry (OTDR). However, the coding gain has not…
Detecting anomalous behavior in wireless spectrum is a demanding task due to the sheer complexity of the electromagnetic spectrum use. Wireless spectrum anomalies can take a wide range of forms from the presence of an unwanted signal in a…
Applications of deep learning to the radio frequency (RF) domain have largely concentrated on the task of narrowband signal classification after the signals of interest have already been detected and extracted from a wideband capture. To…
Orthogonal time frequency space (OTFS) modulation stands out as a promising waveform for sixth generation (6G) and beyond wireless communication systems, offering superior performance over conventional methods, particularly in high-mobility…
In this work, we discuss techniques for coherently detecting turbo coded orthogonal frequency division multiplexed (OFDM) signals, transmitted through frequency selective Rayleigh (the magnitude of each channel tap is Rayleigh distributed)…
The proliferation of wireless communications has recently created a bottleneck in terms of spectrum availability. Motivated by the observation that the root of the spectrum scarcity is not a lack of resources but an inefficient managing…
We consider multi-antenna cooperative spectrum sensing in cognitive radio networks, when there may be multiple primary users. A noise-uncertainty-free detector that is optimal in the low signal to noise ratio regime is analyzed in such a…
A waveform channel is considered where the transmitted signal is corrupted by Wiener phase noise and additive white Gaussian noise (AWGN). A discrete-time channel model is introduced that is based on a multi-sample receiver. Tight lower…
Spectrum scarcity is a prevalent problem in wireless networks due to the strict allotment of the spectrum (frequency bands) to licensed users by network regulatory bodies. Such an operation implies that the unlicensed users (secondary…