Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric Learning

1 code implementation • 1 Dec 2022 • Cheng Wang, Xue Fu, Yu Wang, Guan Gui, Haris Gacanin, Hikmet Sari, Fumiyuki Adachi

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication.

Data Augmentation Metric Learning

Rogue Emitter Detection Using Hybrid Network of Denoising Autoencoder and Deep Metric Learning

1 code implementation • 1 Dec 2022 • Zeyang Yang, Xue Fu, Guan Gui, Yun Lin, Haris Gacanin, Hikmet Sari, Fumiyuki Adachi

Rogue emitter detection (RED) is a crucial technique to maintain secure internet of things applications.

Denoising Metric Learning

Semi-Supervised Specific Emitter Identification Method Using Metric-Adversarial Training

1 code implementation • 28 Nov 2022 • Xue Fu, Yang Peng, Yuchao Liu, Yun Lin, Guan Gui, Haris Gacanin, Fumiyuki Adachi

Specifically, pseudo labels are innovatively introduced into metric learning to enable semi-supervised metric learning (SSML), and an objective function alternatively regularized by SSML and virtual adversarial training (VAT) is designed to extract discriminative and generalized semantic features of radio signals.

Decision Making Metric Learning

A Real-World Radio Frequency Signal Dataset Based on LTE System and Variable Channels

1 code implementation • 25 May 2022 • Shupeng Zhang, Yibin Zhang, Xixi Zhang, Jinlong Sun, Yun Lin, Haris Gacanin, Fumiyuki Adachi, Guan Gui

However, most of these datasets are collected from 2. 4G WiFi devices and through similar channel environments.

A Novel Channel Identification Architecture for mmWave Systems Based on Eigen Features

no code implementations • 11 Apr 2022 • Yibin Zhang, Jinlong Sun, Guan Gui, Haris Gacanin, Fumiyuki Adachi

Hence, the ideal communication environment for mmWave is line of sight (LOS) channel.

Blocking

On Deep Learning for Radio Resource Management in A Non-stationary Radio Environment

no code implementations • 5 May 2020 • Suren Sritharan, Harshana Weligampola, Haris Gacanin

This paper studies practical limitations of learning methods for resource management in non-stationary radio environment.

Management

Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence

no code implementations • 23 Feb 2020 • Haris Gacanin, Marco Di Renzo

We introduce "Wireless 2. 0": The future generation of wireless communication networks, where the radio environment becomes controllable, programmable, and intelligent by leveraging the emerging technologies of reconfigurable metasurfaces and artificial intelligence (AI).

Management reinforcement-learning +1

Autonomous Wireless Systems with Artificial Intelligence

no code implementations • 27 Jun 2018 • Haris Gacanin

This paper discusses technology and opportunities to embrace artificial intelligence (AI) in the design of autonomous wireless systems.

Active Learning Management