Biblio
With the rapid development of radio detection and wireless communication, narrowband radio-frequency interference (NB-RFI) is a serious threat for GNSS-R (global navigation satellite systems - reflectometry) receivers. However, interferometric GNSS-R (iGNSS-R) is more prone to the NB-RFIs than conventional GNSS-R (cGNSS-R), due to wider bandwidth and unclean replica. Therefore, there is strong demand of detecting and mitigating NB-RFIs for GNSS-R receivers, especially iGNSS-R receivers. Hence, focusing on working with high sampling rate and simplifying the fixed-point implementation on FPGA, this paper proposes a system design exploiting cascading IIR band-stop filters (BSFs) to suppress NB-RFIs. Furthermore, IIR BSF compared with IIR notch filter (NF) and IIR band-pass filter (BPF) is the merely choice that is able to mitigate both white narrowband interference (WNBI) and continuous wave interference (CWI) well. Finally, validation and evaluation are conducted, and then it is indicated that the system design can detect NB-RFIs and suppress WNBI and CWI effectively, which improves the signal-to-noise ratio (SNR) of the Delay-Doppler map (DDM).
Energy efficiency and security is a critical requirement for computing at edge nodes. Unrolled architectures for lightweight cryptographic algorithms have been shown to be energy-efficient, providing higher performance while meeting resource constraints. Hardware implementations of unrolled datapaths have also been shown to be resistant to side channel analysis (SCA) attacks due to a reduction in signal-to-noise ratio (SNR) and an increased complexity in the leakage model. This paper demonstrates optimal leakage models and an improved CFA attack which makes it feasible to extract first-order side-channel leakages from combinational logic in the initial rounds of unrolled datapaths. Several leakage models, targeting initial rounds, are explored and 1-bit hamming weight (HW) based leakage model is shown to be an optimal choice. Additionally, multi-band narrow bandpass filtering techniques in conjunction with correlation frequency analysis (CFA) is demonstrated to improve SNR by up to 4×, attributed to the removal of the misalignment effect in combinational logics and signal isolation. The improved CFA attack is performed on side channel signatures acquired for 7-round unrolled SIMON datapaths, implemented on Sakura-G (XILINX spartan 6, 45nm) based FPGA platform and a 24× reduction in minimum-traces-to-disclose (MTD) for revealing 80% of the key bits is demonstrated with respect to conventional time domain correlation power analysis (CPA). Finally, the proposed method is successfully applied to a fully-unrolled datapath for PRINCE and a parallel round-based datapath for Advanced Encryption Standard (AES) algorithm to demonstrate its general applicability.
Multipath propagation of radio waves negatively affects to the performance of telecommunications and radio navigation systems. When performing time and frequency synchronization tasks of spatially separated standards, the multipath signal propagation aggravates the probability of a correct synchronization and introduces an error. The presence of a multipath signal reduces the signal-to-noise ratio in the received signal, which in turn causes an increase in the synchronization error. If the time delay of the additional beam (s) is less than the useful signal duration, the reception of the useful signal is further complicated by the presence of a partially correlated interference, the level and correlation degree of which increases with decreasing time delay of the interference signals. The article considers with the method of multi-path interference compensation in a multi-position (telecommunication or radio navigation system) or a time and frequency synchronization system for the case if at least one of the receiving positions has no noise signal or does not exceed the permissible level. The essence of the method is that the interference-free useful signal is transmitted to other points in order to pick out the interference component from the signal / noise mix. As a result, an interference-free signal is used for further processing. The mathematical models of multipath interference suppressors in the temporal and in the frequency domain are presented in the article. Compared to time processing, processing in the frequency domain reduces computational costs. The operation of the suppressor in the time domain has been verified experimentally.
This paper investigates closed-form expressions to evaluate the performance of the Compressive Sensing (CS) based Energy Detector (ED). The conventional way to approximate the probability density function of the ED test statistic invokes the central limit theorem and considers the decision variable as Gaussian. This approach, however, provides good approximation only if the number of samples is large enough. This is not usually the case in CS framework, where the goal is to keep the sample size low. Moreover, working with a reduced number of measurements is of practical interest for general spectrum sensing in cognitive radio applications, where the sensing time should be sufficiently short since any time spent for sensing cannot be used for data transmission on the detected idle channels. In this paper, we make use of low-complexity approximations based on algebraic transformations of the one-dimensional Gaussian Q-function. More precisely, this paper provides new closed-form expressions for accurate evaluation of the CS-based ED performance as a function of the compressive ratio and the Signal-to-Noise Ratio (SNR). Simulation results demonstrate the increased accuracy of the proposed equations compared to existing works.
The ultrafast active cavitation imaging (UACI) based on plane wave can be implemented with high frame rate, in which adaptive beamforming technique was introduced to enhance resolutions and signal-to-noise ratio (SNR) of images. However, regular adaptive beamforming continuously updates the spatial filter for each sample point, which requires a huge amount of calculation, especially in the case of a high sampling rate, and, moreover, 3D imaging. In order to achieve UACI rapidly with satisfactory resolution and SNR, this paper proposed an adaptive beamforming on the basis of compressive sensing (CS), which can retain the quality of adaptive beamforming but reduce the calculating amount substantially. The results of simulations and experiments showed that comparing with regular adaptive beamforming, this new method successfully achieved about eightfold in time consuming.
In distributed wireless storage systems, failed recovery probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For efficient utilization of limited storage capacity, we asymptotically analyze the failed recovery probability of a distributed wireless storage system with a sum storage capacity constraint when signal-to-noise ratio goes to infinity, and find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic failed recovery probability. It is also shown that when the number of storage nodes is sufficiently large the storage size required at each node is not so large for high exponential order of the failed recovery probability.
A novel physical layer authentication scheme is proposed in this paper by exploiting the time-varying carrier frequency offset (CFO) associated with each pair of wireless communications devices. In realistic scenarios, radio frequency oscillators in each transmitter-and-receiver pair always present device-dependent biases to the nominal oscillating frequency. The combination of these biases and mobility-induced Doppler shift, characterized as a time-varying CFO, can be used as a radiometric signature for wireless device authentication. In the proposed authentication scheme, the variable CFO values at different communication times are first estimated. Kalman filtering is then employed to predict the current value by tracking the past CFO variation, which is modeled as an autoregressive random process. To achieve the proposed authentication, the current CFO estimate is compared with the Kalman predicted CFO using hypothesis testing to determine whether the signal has followed a consistent CFO pattern. An adaptive CFO variation threshold is derived for device discrimination according to the signal-to-noise ratio and the Kalman prediction error. In addition, a software-defined radio (SDR) based prototype platform has been developed to validate the feasibility of using CFO for authentication. Simulation results further confirm the effectiveness of the proposed scheme in multipath fading channels.
Wireless channel reciprocity can be successfully exploited as a common source of randomness for the generation of a secret key by two legitimate users willing to achieve confidential communications over a public channel. This paper presents an analytical framework to investigate the theoretical limits of secret-key generation when wireless multi-dimensional Gaussian channels are used as source of randomness. The intrinsic secrecy content of wide-sense stationary wireless channels in frequency, time and spatial domains is derived through asymptotic analysis as the number of observations in a given domain tends to infinity. Some significant case studies are presented where single and multiple antenna eavesdroppers are considered. In the numerical results, the role of signal-to-noise ratio, spatial correlation, frequency and time selectivity is investigated.
The electric network frequency (ENF) criterion is a recently developed technique for audio timestamp identification, which involves the matching between extracted ENF signal and reference data. For nearly a decade, conventional matching criterion has been based on the minimum mean squared error (MMSE) or maximum correlation coefficient. However, the corresponding performance is highly limited by low signal-to-noise ratio, short recording durations, frequency resolution problems, and so on. This paper presents a threshold-based dynamic matching algorithm (DMA), which is capable of autocorrecting the noise affected frequency estimates. The threshold is chosen according to the frequency resolution determined by the short-time Fourier transform (STFT) window size. A penalty coefficient is introduced to monitor the autocorrection process and finally determine the estimated timestamp. It is then shown that the DMA generalizes the conventional MMSE method. By considering the mainlobe width in the STFT caused by limited frequency resolution, the DMA achieves improved identification accuracy and robustness against higher levels of noise and the offset problem. Synthetic performance analysis and practical experimental results are provided to illustrate the advantages of the DMA.