Cem Ayyildiz

TeleHealth: intelligent healthcare with M2M communication module

Chronic diseases, such as diabetes, heart disease (e.g. arrhythmia and hypertension), chronic respiratory diseases (e.g. asthma and COPD), are by far the leading cause of mortality in the world, representing 63% of all deaths. Out of the 36 million people who died from chronic disease in 2008, nine million were under 60. Remote patient monitoring is an efficient and cost-effective solution to mitigate this problem. The key technology to facilitate wireless mobile healthcare or TeleHealth is cellular based M2M (machine-to-machine) communication. In this demo, we present a M2M Communication Module and its healthcare application developed at Turkcell Technology, Applied Research Lab.

CP and pilot jamming attacks on SC-FDMA: Performance tests with software defined radios

We designed and implemented single carrier-frequency domain multiple access (SC-FDMA) based system by using software defined radio (SDR) nodes. We aimed to investigate the performance of an LTE system against smart jamming attacks, which target synchronization and channel estimation processes. Cyclic prefix (CP) based jamming attacks and pilot jamming attacks are evaluated and observed for both uplink and downlink directions of an LTE-like system. Based on error vector magnitude measurements, we observed that an SC-FDMA system is more sensitive to CP and pilot jamming attacks when compared to OFDMA.

OFDMA system identification using cyclic autocorrelation function: A software defined radio testbed

In this paper, cyclic autocorrelation function (CAF) is used for signal type identification. Orthogonal frequency division multiple access (OFDMA) signals with different cyclic prefix lengths are generated by using software defined radio nodes. According to test results, by using the OFDMA frame structure, it is possible to distinguished between OFDMA signals, hence signal parameters can be blindly differentiated. Additionally, jamming signal and OFDMA signals can also be distinguished from each other. Real-time measurement results are given to demonstrate the performance of the investigated approach.

Identification of Non-Conforming Cordless Phone Signals in Licensed Bands

Identification of interference signals is critical in telecommunication systems increasing the need for automated signal identification. As deep convolutional neural networks demonstrate significant achievements in pattern recognition problems, it can be inferred that deep learning methods will give successful results in the field of wireless communications, especially about identification of signal. This paper investigates a new approach of signal identification based on deep convolutional neural network with the convolution architecture for feature extraction (CAFFE) framework. Authors provide the identification for the types of interference signals based on non-conforming digital enhanced cordless telecommunications (DECT) devices. For training NVIDIA-DIGITS, the NVIDIA Deep Learning GPU Training System, is used. The classification accuracy of the system under additive white Gaussian noise and Rayleigh fading channel conditions is observed to be high despite low signal to noise ratio values.

Blind recognition of OFDM signals based on cyclostationary signal analysis

Growth in telecommunication world leads to an increase in the importance of efficient usage of frequency spectrum. Increasing number of subscribers and their demands force network operators to use the frequency bands as efficiently as possible. It is necessary to detect and prevent the interference of secondary or unauthorized frequency band occupations. Cyclostationary signal analysis is widely used for detection of interference signals. In this paper, cyclostationary characteristics have been derived for OFDM signals. The associated spectral correlation density function has been computed by using FFT accumulation method. This analysis can be used for blind recognition of OFDM based interference signals in presence of other interference sources.

Chirp subcarrier jamming attacks: an OFDM based smart jammer design

In this study a new jammer design for orthogonal frequency division multiplexing (OFDM) based systems is in- troduced. We present three new vulnerability attack scenarios based on chirp signals. These are the conventional chirp attack, the cyclic prefix chirp attack and the pilot tone chirp attack. Bit error rate (BER) performances are investigated in presence of chirp based attacks. It is shown that the performance degradation is increased when compared to Gaussian distributed jammers. Chirp signals are also preferable due to nominal peak-to-average power ratios (at least 7 dB lower than Gaussian counterparts).

Angle of arrival (AoA) estimation by using software defined radios

Source direction of arrival (DoA) or angle of arrival (AoA) estimation is one of the vital problems in many wireless communication applications such as radar, radio astronomy, sonar and navigation. The resolution of a source direction of arrival estimation can be improved by an uniform linear array (ULA) system with innovative signal processing techniques. Super resolution techniques take the advantage of linear array antenna structures to better process the incoming waves. These techniques also have the ability to describe the direction of multiple targets. In this paper, MUSIC algorithm is implemented on USRP nodes using LabVIEW platform. Four receiver antennas are used to detect the AoA of a single carrier and unmodulated RF signal at 2.45 GHz in an indoor environment. The experimental results are shown for a single source which is tested at two different arbitrary angles to confirm the real-time implementation of MUSIC algorithm with some errors which occur due to lack of some hardware imperfections. Moreover, these hardware imperfections are studied.