Signal-independent RFF Identification for LTE Mobile Devices via Ensemble Deep Learning

Radio frequency fingerprint (RFF)-based wireless device authentication is an emerging technique to prevent potential spoofing attacks in wireless communications. The random access preamble of the physical random access channel (PRACH) in Long Term Evolution (LTE) systems is the first message sent from a user equipment (UE). However, PRACH preambles change under different evolved Node B (eNB), which will affect the RFF extraction. In this paper, a signal-independent RFF extraction method is first proposed to extract varying LTE PRACH preambles under different LTE eNBs. Residual transient segment (RTS) features from the varying PRACH preambles are extracted for RFF identification. A convolutional neural network (CNN) based ensemble deep learning scheme is proposed to integrate benefits from different RFF features. An experimental system under real operator LTE eNB is designed to capture and identify real UE signals. Experimental results show that the classification accuracy of five UEs can reach more than 95% under the same eNB and 85% under different eNBs. Furthermore, longtime evaluations show that the UE RTS feature is robust over time

IMSI-based care of-address creation for fast binding update in MIPv6

The growth of Internet user forced the fixed line Internet user to migrate from IPv4 to IPv6 due to the address availability.The similar situation will arise in mobile Internet, in which will forced the users to migrate to IPv6-based network.The numbers of Internet user also affect the access router work load and may grounds the latency in data reception.Handover from one access router to another needs a mechanism called binding update which produces latency.The most liable process in this mechanism is Duplicate Address Detection (DAD) in which take longer time than any other process.This paper proposes a mechanism to reduce the handover latency by eliminating the DAD process, using IMSI number

On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection

In the development of radiofrequency fingerprinting (RFF), one of the major challenges is to extract subtle and robust features from transmitted signals of wireless devices to be used in accurate identification of possible threats to the wireless network. To overcome this challenge, the use of the transient region of the transmitted signals could be one of the best options. For an efficient transient-based RFF, it is also necessary to accurately and precisely estimate the transient region of the signal. Here, the most important difficulty can be attributed to the detection of the transient starting point. Thus, several methods have been developed to detect transient start in the literature. Among them, the energy criterion method based on the instantaneous amplitude characteristics (EC-a) was shown to be superior in a recent study. The study reported the performance of the EC-a method for a set of Wi-Fi signals captured from a particular Wi-Fi device brand. However, since the transient pattern varies according to the type of wireless device, the device diversity needs to be increased to achieve more reliable results. Therefore, this study is aimed at assessing the efficiency of the EC-a method across a large set of Wi-Fi signals captured from various Wi-Fi devices for the first time. To this end, Wi-Fi signals are first captured from smartphones of five brands, for a wide range of signal-to-noise ratio (SNR) values defined as low (−3 to 5 dB), medium (5 to 15 dB), and high (15 to 30 dB). Then, the performance of the EC-a method and well-known methods was comparatively assessed, and the efficiency of the EC-a method was verified in terms of detection accuracy.publishedVersio

Multi-Channel CNN-Based Open-Set RF Fingerprint Identification for LTE Devices

Radio frequency fingerprint identification (RFFI) is a promising technique that exploits the transmitter-specific characteristics of the RF chain for identification. Disregarding its massive deployment, long-term evolution (LTE) systems have not fully benefited from RFFI. In this paper, an RFFI technique is designed to authenticate LTE devices. Three segments of the LTE physical layer random access channel (PRACH) preambles are captured, namely the transient-on, transient-off, and modulation parts. The segments are first converted into differential constellation trace figures (DCTFs), and then a specific type of neural network called multi-channel convolutional neural network (MCCNN) is used for identification. Additionally, the protocol is able to be applied for open-set identification, i.e., unknown device detection. Experiments are conducted with ten LTE mobile phones. The results show that the proposed RFFI scheme is robust against location changes. In the known device classification problem, the classification accuracy can reach 98.70% in the line-of-sight (LOS) scenario and 89.40% in the non-line-of-sight (NLOS) scenario. In the open-set unknown device detection problem, the identification equal error rate (EER) and area under the curve (AUC) reach 0.0545 and 0.9817, respectively, among six known devices and four unknown devices

Semi-Analytical Model of the Rician K-Factor

The analysis of the performance of 5G wireless communication systems employing Massive MIMO at millimeter-wave frequencies is of great practical relevance. Of special relevance are the signal fluctuations. In the present paper, we introduce a semi-analytical model for a generic scattering environment by using randomly distributed resonant scatterers to investigate the impact of the size of the scattering environment, the scatterer density, and the number of scatterers on the signal variability in terms of the Rician K-factor as a function of frequency. We further present an investigation of the impact of scattering on the frequency dependence of the signal fading statistics in the 500 MHz–100 GHz band. The simplified model is also verified against full-wave simulation using the Method of Moments (MoM)

Authorized and Rogue LTE Terminal Identification Using Wavelet Coefficient Graph with Auto-encoder

The wide popularity of 4G/5G mobile terminals increase the requirements of wireless security. Radio frequency fingerprint (RFF) technology can strengthen 4G/5G air interface accessing security at the physical layer. In this paper, a wavelet transform (WT) coefficient graphs RFF extraction with auto-encoder (AE) based rogue terminal detection scheme is proposed. At first, WT coefficients at 48 scales are extracted from the transient-power-off part of LTE physical random access channel (PRACH) preamble. Then, an AE network structure aimed for 2D WT coefficient graph is designed for rogue terminal detection. We successfully distinguish 7 mobile phones and 1 USRP under the proposed mechanism, where the authorized terminals from the same manufacturer can be identified with an accuracy of 90.08%. In addition, extensive experiments are carried out at LOS and NOLS scenarios, respectively, the proposed LTE identification scheme has demonstrated robustness in dynamic environments

Physical Layer Security for the Internet of Things: Authentication and Key Generation

A low-complexity, yet secure framework is proposed for protecting the Internet of Things (IoT) and for achieving both authentication and secure communication. In particular, the slight random difference among transceivers is extracted for creating a unique radio frequency fingerprint and for ascertaining the unique user identity. The wireless channel between any two users is a perfect source of randomness and can be exploited as cryptographic keys. This can be applied to the physical layer of the communications protocol stack. This article reviews these protocols and shows how they can be integrated to provide a complete IoT security framework. We conclude by outlining the future challenges in applying these compelling physical layer security techniques to the IoT.<br/

Unification of Treatments and Interventions for Tinnitus Patients (UNITI): a study protocol for a multi-center randomized clinical trial

The UNITI project has received funding from the European Union's Horizon 2020 Research and Innovation Program (grant agreement number 848261).Background: Tinnitus represents a relatively common condition in the global population accompanied by various comorbidities and severe burden in many cases. Nevertheless, there is currently no general treatment or cure, presumable due to the heterogeneity of tinnitus with its wide variety of etiologies and tinnitus phenotypes. Hence, most treatment studies merely demonstrated improvement in a subgroup of tinnitus patients. The majority of studies are characterized by small sample sizes, unstandardized treatments and assessments, or applications of interventions targeting only a single organ level. Combinatory treatment approaches, potentially targeting multiple systems as well as treatment personalization, might provide remedy and enhance treatment responses. The aim of the present study is to systematically examine established tinnitus therapies both alone and in combination in a large sample of tinnitus patients. Further, it wants to provide the basis for personalized treatment approaches by evaluating a specific decision support system developed as part of an EU-funded collaborative project (Unification of treatments and interventions for tinnitus patients; UNITI project). Methods/study design: This is a multi-center parallel-arm randomized clinical trial conducted at five different clinical sites over the EU. The effect of four different tinnitus therapy approaches (sound therapy, structured counseling, hearing aids, cognitive behavioral therapy) applied over a time period of 12 weeks as a single or rather a combinatory treatment in a total number of 500 chronic tinnitus patients will be investigated. Assessments and interventions are harmonized over the involved clinical sites. The primary outcome measure focuses on the domain tinnitus distress assessed via the Tinnitus Handicap Inventory. Discussion: Results and conclusions from the current study might not only provide an essential contribution to combinatory and personalized treatment approaches in tinnitus but could also provide more profound insights in the heterogeneity of tinnitus, representing an important step towards a cure for tinnitus.European Union's Horizon 2020 Research and Innovation Program 84826

Wireless Power Transfer Techniques for Implantable Medical Devices:A Review

Wireless power transfer (WPT) systems have become increasingly suitable solutions for the electrical powering of advanced multifunctional micro-electronic devices such as those found in current biomedical implants. The design and implementation of high power transfer efficiency WPT systems are, however, challenging. The size of the WPT system, the separation distance between the outside environment and location of the implanted medical device inside the body, the operating frequency and tissue safety due to power dissipation are key parameters to consider in the design of WPT systems. This article provides a systematic review of the wide range of WPT systems that have been investigated over the last two decades to improve overall system performance. The various strategies implemented to transfer wireless power in implantable medical devices (IMDs) were reviewed, which includes capacitive coupling, inductive coupling, magnetic resonance coupling and, more recently, acoustic and optical powering methods. The strengths and limitations of all these techniques are benchmarked against each other and particular emphasis is placed on comparing the implanted receiver size, the WPT distance, power transfer efficiency and tissue safety presented by the resulting systems. Necessary improvements and trends of each WPT techniques are also indicated per specific IMD