Orthogonal time frequency space (OTFS) modulation is a new 2-dimensional technique that encodes information in the delay and Doppler domains. It can outperform orthog-onal frequency division multiplexing (OFDM) in high-mobility wireless communication scenarios. The advantage of the OTFS technique is it exploits full diversity over both time and frequency [2]. However, an ideal OTFS rectangular waveform that satisfies the bi-orthogonality conditions is unrealistic for implementation. Therefore, in order to suppress inter-symbol interference (ISI), techniques such as a guard intervals (GI) should be considered in signal generation. In this paper, we present an overview of different OTFS GI schemes, including the cyclic prefix (CP) as well as the zero suffix (ZS). We also propose a new scheme by inserting zeros into the information stream randomly. This simple approach can reduce the eigen-value ratio (EVR) of the channel matrix thus providing more robust performance. Finally, the simulation results demonstrate the proposed zero insertion technique can help to improve the transmission performance compared to other OTFS schemes.

This paper presents a novel radio frequency fingerprint (RFF) enhancement strategy by exploiting the physical unclonable function (PUF) to tune the RF hardware impairments in a unique and secure manner, which is exemplified by taking power amplifiers (PAs) in RF chains as an example. This is achieved by intentionally and slightly tuning the PA non-linearity characteristics using the active load-pulling technique. The motivation driving the proposed research is to enlarge the RFF feature differences among wireless devices of same vendor, in order to massively improve their RFF classification accuracy in low to medium signal to noise ratio (SNR) channel conditions. PUF is employed to dynamically tune the PA’s RFF feature which guarantees the security since the PUF response cannot be cloned. Specifically, a ring oscillator (RO)-based PUF is implemented to control the PA non-linearity by selecting unique but random configuration parameters. This approach is proposed to amplify the distinctions across same model PAs, thereby enhancing the RFF classification performance. In the meantime, our innovative strategy of PUF-assisted RFF does not noticeably compromise communication link performance which is experimentally tested. The resulting RFF features can be extracted from the received distorted constellation diagrams with the help of image recognition-based machine learning classification algorithms. Extensive experimental evaluations are carried out using both cable-connected and over-the-air (OTA) measurements. Our proposed approach, when classifying eight PAs from a same vendor, achieves 11% to 24% average classification accuracy improvement by enlarging the RFF feature differences arising from the PA non-linearity.

The millimetre-wave (mmWave) band communication system suffers from its susceptibility to blockage. Unlike human blockage, which has been extensively investigated in the literature, the impact of other small objects on signal strength, such as metal road signs, is not completely understood. In this study, we measure and investigate the attenuation of different types of relatively small objects. It is found that the resulting loss is in the range of 15 to 30 dB depending on the blocker size and type. We have investigated the applicability of the existing 3rd generation partnership project (3GPP) blockage model for small-size blockers, with and without including the antenna gain. Our results suggest that the 3GPP model underestimates the attenuation caused by small-size blockers, and including the antenna gain into the 3GPP model does not show a significant improvement. The multiple knife-edge (MKE) model shows a better performance in capturing the measured attenuation with a smaller error.