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OFDM-Subcarrier Index Selection with Artificially Interfering Signals for Improving the Physical Layer Security of IoT-5G Systems
7 Feb 2019
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Jehad Jehad M. Hamamreh
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Disciplines
Keywords
Ofdm
Waveforms
Ofdm-sis
Eavesdropping
Physical Layer Security
Subcarrier Selection
Abstract

Ensuring the security of the Internet of Things (IoT) is deemed as one of the most critical challenges and needs that have to be addressed in order to guarantee the successful deployment of IoT in emerging technologies like 5G. In an effort to address this challenge, in this work, an improved and flexible physical layer security technique referred to as orthogonal frequency division multiplexing with subcarrier index selection and artificially interfering signals (OFDM-SIS-AIS), is developed for protecting the transmission of OFDM-based waveforms against eavesdropping in 5G and beyond wireless networks. In this technique, the frequency response of correlated subchannels is first converted into a completely randomized and independent response by means of adaptive interleaving. Then, the whole OFDM block is divided into small sub-blocks, each containing a set of subcarriers, from which a subset of these subcarriers, which are corresponding to high subchannel gains, are selected and used for data transmission, while the remaining ones, which are corresponding to low subchannel gains, are used for sending artificially interfering signals. The selected subcarriers are determined through an optimization problem that can effectively maximize the signal-to-noise ratio (SNR) at only the legitimate receiver. The obtained results demonstrate a significant improvement in the secrecy gap performance without considering the knowledge of the eavesdropper’s channel nor sharing any keys, while maintaining low complexity and high reliability at the legitimate user. These numerous advantages have the potential to make the proposed scheme a consistent candidate technique for secure IoT-5G based services.

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Submitted by7 Feb 2019
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Jehad Hamamreh
Antalya international University
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