Secure Directional Modulation to Enhance Physical Layer Security in IoT Networks
This work addresses security vulnerabilities in IoT wireless networks, though it is incremental as it builds on existing directional modulation and null-space projection techniques.
The authors tackled the problem of enhancing physical layer security in IoT networks by proposing an adaptive robust null-space projection scheme for directional modulation, which achieved significantly lower bit error rates at the desired user and higher secrecy rates compared to conventional methods, with performance gains increasing with estimation error ranges.
In this work, an adaptive and robust null-space projection (AR-NSP) scheme is proposed for secure transmission with artificial noise (AN)-aided directional modulation (DM) in wireless networks. The proposed scheme is carried out in three steps. Firstly, the directions of arrival (DOAs) of the signals from the desired user and eavesdropper are estimated by the Root Multiple Signal Classificaiton (Root-MUSIC) algorithm and the related signal-to-noise ratios (SNRs) are estimated based on the ratio of the corresponding eigenvalue to the minimum eigenvalue of the covariance matrix of the received signals. In the second step, the value intervals of DOA estimation errors are predicted based on the DOA and SNR estimations. Finally, a robust NSP beamforming DM system is designed according to the afore-obtained estimations and predictions. Our examination shows that the proposed scheme can significantly outperform the conventional non-adaptive robust scheme and non-robust NSP scheme in terms of achieving a much lower bit error rate (BER) at the desired user and a much higher secrecy rate (SR). In addition, the BER and SR performance gains achieved by the proposed scheme relative to other schemes increase with the value range of DOA estimation error.