1,502 research outputs found
Short-range ultrasonic communications in air using quadrature modulation
A study has been undertaken of ultrasonic communications methods in air, using a quadrature modulation method. Simulations were first performed to establish the likely performance of quadrature phase shift keying over the limited bandwidth available in an ultrasonic system. Quadrature phase shift keying modulation was then implemented within an experimental communication system, using capacitive ultrasonic sources and receivers. The results show that such a system is feasible in principle for communications over distances of several meters, using frequencies in the 200 to 400 kHz range
Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting
Radio frequency fingerprinting (RFF) is a promising device authentication
technique for securing the Internet of things. It exploits the intrinsic and
unique hardware impairments of the transmitters for RF device identification.
In real-world communication systems, hardware impairments across transmitters
are subtle, which are difficult to model explicitly. Recently, due to the
superior performance of deep learning (DL)-based classification models on
real-world datasets, DL networks have been explored for RFF. Most existing
DL-based RFF models use a single representation of radio signals as the input.
Multi-channel input model can leverage information from different
representations of radio signals and improve the identification accuracy of the
RF fingerprint. In this work, we propose a novel multi-channel attentive
feature fusion (McAFF) method for RFF. It utilizes multi-channel neural
features extracted from multiple representations of radio signals, including IQ
samples, carrier frequency offset, fast Fourier transform coefficients and
short-time Fourier transform coefficients, for better RF fingerprint
identification. The features extracted from different channels are fused
adaptively using a shared attention module, where the weights of neural
features from multiple channels are learned during training the McAFF model. In
addition, we design a signal identification module using a convolution-based
ResNeXt block to map the fused features to device identities. To evaluate the
identification performance of the proposed method, we construct a WiFi dataset,
named WFDI, using commercial WiFi end-devices as the transmitters and a
Universal Software Radio Peripheral (USRP) as the receiver. ..
Transition technologies towards 6G networks
[EN] The sixth generation (6G) mobile systems will create new markets, services, and industries making possible a plethora of new opportunities and solutions. Commercially successful rollouts will involve scaling enabling technologies, such as cloud radio access networks, virtualization, and artificial intelligence. This paper addresses the principal technologies in the transition towards next generation mobile networks. The convergence of 6G key-performance indicators along with evaluation methodologies and use cases are also addressed. Free-space optics, Terahertz systems, photonic integrated circuits, softwarization, massive multiple-input multiple-output signaling, and multi-core fibers, are among the technologies identified and discussed. Finally, some of these technologies are showcased in an experimental demonstration of a mobile fronthaul system based on millimeter 5G NR OFDM signaling compliant with 3GPP Rel. 15. The signals are generated by a bespoke 5G baseband unit and transmitted through both a 10 km prototype multi-core fiber and 4 m wireless V-band link using a pair of directional 60 GHz antennas with 10 degrees beamwidth. Results shown that the 5G and beyond fronthaul system can successfully transmit signals with both wide bandwidth (up to 800 MHz) and fully centralized signal processing. As a result, this system can support large capacity and accommodate several simultaneous users as a key candidate for next generation mobile networks. Thus, these technologies will be needed for fully integrated, heterogeneous solutions to benefit from hardware commoditization and softwarization. They will ensure the ultimate user experience, while also anticipating the quality-of-service demands that future applications and services will put on 6G networks.This work was partially funded by the blueSPACE and 5G-PHOS 5G-PPP phase 2 projects, which have received funding from the European Union's Horizon 2020 programme under Grant Agreements Number 762055 and 761989. D. PerezGalacho acknowledges the funding of the Spanish Science Ministry through the Juan de la Cierva programme.Raddo, TR.; Rommel, S.; Cimoli, B.; Vagionas, C.; Pérez-Galacho, D.; Pikasis, E.; Grivas, E.... (2021). Transition technologies towards 6G networks. EURASIP Journal on Wireless Communications and Networking. 2021(1):1-22. https://doi.org/10.1186/s13638-021-01973-91222021
Deep Learning Methods for Device Identification Using Symbols Trace Plot
Devices authentication is one crucial aspect of any communication system.
Recently, the physical layer approach radio frequency (RF) fingerprinting has
gained increased interest as it provides an extra layer of security without
requiring additional components. In this work, we propose an RF fingerprinting
based transmitter authentication approach density trace plot (DTP) to exploit
device-identifiable fingerprints. By considering IQ imbalance solely as the
feature source, DTP can efficiently extract device-identifiable fingerprints
from symbol transition trajectories and density center drifts. In total, three
DTP modalities based on constellation, eye and phase traces are respectively
generated and tested against three deep learning classifiers: the 2D-CNN,
2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is
verified using a practical dataset collected from the ADALM-PLUTO
software-defined radios (SDRs)
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