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The objective of this study is to investigate a high-throughput cipher-block chaining (CBC) mode crypto circuit, which can be embedded in commercial home gateways or switches/routers. Concurrently, the area efficiency of block ciphers can be improved as well. However, the CBC mode encounters the problem of data dependency. To solve this issue, a data scheduling mechanism of network packets is proposed to eliminate the data dependency of input data for CBC mode pipelined crypto engines. The proposed CBC mode architecture can be applied to advanced encryption standards (AES), triple data encryption standards (3DES), and other block ciphers. In addition, to increase the throughput, deeply pipelined AES-CBC and 3DES-CBC circuits with balanced paths are proposed. With the proposed scheduling and pipelined circuits, the authors can effectively encrypt the packet data of multiple network channels at the same time. Using the proposed architecture, throughputs of 137.8 and 44.75 Gbps using a copy of pipelined AES-CBC and 3DES-CBC circuits can be achieved in TSMC 45 nm and TSMC 130 nm processes, respectively.

In this paper, we explore the problem of removing atmospheric turbulence to obtain better images of remote sidereal star systems. The imaging process is remodeled as a transmit–receive wireless communication paradigm in a novel approach for correcting space- and time-varying blur in stellar images. In particular, the effect of starlight passing through atmospheric turbulence is modeled as multipath fading communication channels. The problem is then transformed into channel equalization in space and time domains to produce a sharp stellar image. This approach involves first estimating the center of a blurred stellar image. Next, linear regression obtains an equivalent two-dimensional channel response through decomposition of the blurred image into the weighted sum of the diffraction-limited patterns in the spatial domain. Finally, an alignment algorithm is implemented for synthesis, and a final output is generated. Experiments were performed with real field-captured images; the results revealed that this approach could be used to effectively correct image blur and obtain diffraction-limited stellar images.

LBS has become an integral part of people's life nowadays. However, the GPS restricted by the shielding effect is unavailable for indoor environments. Therefore, accurately locating an electronic device indoors has become a challenging issue in recent years. This work employs the CSi combined with neural networks to achieve an accurate indoor positioning. The CSi refers to known channel properties of a communication link in wireless communications. This information describes how a signal propagates from the transmitter to the receiver and represents the combined effects of, for example, scattering, fading, and power decay with distance. This work will evaluate several neural networks for the positioning, including the FCNN, CNN, and GCNN. in multi-carrier communication systems, the CSi of adjacent subcarriers has a high correlation, and hence, the CNN is promising to learn and extract the features of this input information corresponding to the location of radio devices. Beyond that, we also investigate an improved CNN, that is, the GCNN, which has more talent to locate in indoor environments than traditional CNNs. Experimental results show that the proposed GCNN can achieve a root-mean-square error (RMSE) of less than 0.08m and 0.3m for 16 and two antennas, respectively. in addition, the computational complexities and required numbers of parameters of compared deep neural networks have been analyzed as well.

Wireless devices, including smartphones/PDAs and the network equipment that connects them, consume a significant amount of power. The global greenhouse gas emissions produced by the operation of information and communication technology (ICT) infrastructure is comparable to that produced by the aviation industry. This situation is getting worse since billions of wireless devices, such as the Internet of Things devices, are expected to be newly added to the Internet in the coming decade. Consequently, there are multiple research and development projects that aim at addressing this issue, including some IEEE standards intended to reduce the environmental impact of the ICT industry. These standards include energy-efficient communications and networking. Although these emerging techniques have attracted considerable attention and have been studied recently, there are still many open research challenges to be addressed.

Spectrum sensing for fast-moving vehicles in cognitive radios over a general line-of-sight (LOS) doubly selective fading channel, which is a practical environment for modern mobile orthogonal frequency-division multiplexing communications, should be addressed. However, time- and frequency-selectivity inherent in radio channels is a limiting factor for the detection performance. By discovering the complement property of correlation profile (introduced by the multipath delay spread) and adopting the proposed estimation for the correlation function of LOS channels (introduced by the mobility), the decision metric can be transformed to the ideal one without time- and frequency-selectivity. As such, the detection performance can be greatly enhanced by the proposed technique without needing any channel equalization techniques on received signals.

Orthogonal frequency-division multiplexing (OFDM) is a promising technology for communication systems. This paper investigates the impacts of unknown noise variance on the popular spectrum sensing scheme, i.e., energy detection, for OFDM cognitive radios over multipath fading channels. To study the effects of unknown parameters on the energy detector, a new maximum-likelihood estimation of noise and signal powers employing the cyclic prefix of OFDM is presented in this paper. The mean values and Cram $\acute{\text{e}}$ r–Rao lower bounds of the estimation are obtained. Furthermore, the performances of the energy detector for both hypotheses, i.e., false-alarm rate and detection probability, under the influence of unknown noise variance are validated by both simulation and analytical results. The assessment on the required number of samples for the proposed energy detector is conducted, which indicates that an amount of 40–50% samples can be saved compared to the conventional energy detector.

Electricity is the lifeline of civilization. To reduce carbon impacts associated with electricity generation and delivery, the smart grid can improve the efficiency and reliability of power grids by integrating power systems with modern information and communication technology and control systems. However, based on the integrated infrastructure and two-way communication, the risk and vulnerability in the power grid have increased. In this paper, recent advances in dc blind and stealthy attacks are reported. Moreover, we focus on the false data attacks (FDAs) in smart grids with an emphasis on ac nonlinear grid model. Specifically, we present a more general class of blind attacks based on the geometric approach without any need of the power grid topology and transmission-line admittances. The criteria for successful ac blind and non-blind FDAs are derived. Furthermore, with additional information of hijacked original system states, a targeted ac blind attack that can manipulate the desired state is proposed as well. Simulations confirm the advantages of the proposed approach.

This paper introduces a time-domain nondata-aided maximum-likelihood (ML) estimation of Doppler frequency for orthogonal frequency-division multiplexing (OFDM) over doubly selective fading channels with the Clarke–Jakes–Doppler spectrum. The proposed approach adopts the repetitive signal structure of OFDM, such as the cyclic prefix, which can be used for synchronization purpose. The originally derived ML estimation results in a high computational cost; therefore, after gaining insights into the log-likelihood function, we further design an algorithm with reduced complexity. The proposed algorithm only has a complexity $\sim O(QN_{d})$ , where $Q$ and $N_{d}$ denote the numbers of repetitive parts and samples in a repetitive part, respectively. In contrast to conventional works based on (data aided) channel estimates and/or assuming that channels are flat fading, the proposed method employs the cyclostationary feature of received signals and is not sensitive to channel estimation error and unknown channel phases. Furthermore, the Cramer–Rao lower bounds of the Doppler frequency estimate over nonline-of-sight (NLOS) and line-of-sight multipath fading channels are analyzed. Simulations confirm the advantages of the proposed estimator.

Increased intelligence and automation in smart grid results in many heterogeneous applications benefiting from the Internet of Things, such as demand response, energy delivery efficiency/reliability, and fault recovery. However, vulnerabilities in smart grid arise due to public communication infrastructure and Internet-based protocols. To deal with security threats, energy big data should be thoughtfully stored and processed to extract critical information, and security and blackout warnings should be given in an early stage. This work gives a comprehensive tutorial and survey to highlight research challenges on the aforementioned issues in the Internet-of-Things-based smart grid. We demonstrate that a stealthy and blind energy big data attack can be launched using a replay scheme. Also, we elucidate an intuitive geometric viewpoint for this type of attack. The proposed attack can bypass bad data detection successfully using either DC or AC state estimation.

In this work, a new symbol time synchronization and its architecture design for high-throughput millimeter wave systems based on orthogonal frequency-division multiplexing (OFDM) are introduced. Complementary Golay sequences with good signal properties are popular training sequences used for the preamble design of a multi-gigabit communication system, which is the promising technology for future 5G communications. The basic idea of our contribution is to obtain a time estimate based on the aperiodic autocorrelation function (ACF) of complementary Golay sequences. Besides, achieving low power consumption and less chip area remains the challenge of high-throughput millimeter wave systems. To achieve over 2.64 GSamples/s throughput requirement, the proposed algorithm is especially suitable for the parallel design architecture of very high throughput receivers. Moreover, the complexity is further reduced by employing the correlation characteristic of the ACF of complementary Golay sequences and the regularity of the algorithm. Simulations confirm the advantages of the proposed synchronizer.

Today’s electric power grids have been ageing and are ill-suited to meet fast-growing demands for electricity energy generation, delivery, and supply. Global climate change and greenhouse gas emissions on the Earth caused by power industries put a high pressure on the existing power grids. Consequently, smart grid (SG) has emerged to address these challenges. The SG can achieve improved load balancing through accessing instantaneous electricity demand information via two-way communication and power flows, which help power plants match their output to the demand precisely. To this end, SG works based on the exchanges of a large amount of information generated from metering, sensing, and monitoring. Hence, the choice of communication infrastructure for SG is critical to provide secure, reliable, and efficient data delivery between various SG components. Cognitive radio (CR) network has been recognized as a promising technology to address communication requirements, standardization, and security problems of SG. Moreover, possible solutions in CR-based SG communications are also identified. In particular, we identify the major challenges of communication architecture, standardization, and security issues to implement CR-based SG communications. The aim of this paper is to offer a comprehensive review on the state-of-the-art researches on CR-based SG communications, to highlight what have been investigated and what still remain to be addressed, particularly, in standardization and security aspects.

This study addresses an important challenge of signal detection for the moving devices in cognitive radios over a general non-line-of-sight (NLOS) or line-of-sight (LOS) time-variant multipath fading channel, which is a practical environment for modern mobile communications. To this aim, we propose a new feature detector (FD) based on the likelihood-ratio test (LRT), which suggests us to employ the stationarity of received signals for the signal detection. As such, the detection performance can be greatly enhanced by the proposed technique without needing any channel equalization techniques on received signals. Additionally, two major synchronization impairments are considered which were often neglected in conventional detectors. According to simulation results, the performance of the proposed detector can achieve the detection probability of 0.9 under the false-alarm rate of 0.1 and vehicular speed of 100 km/hr.

The detection of QRS complex is the start point for almost all electrocardiogram (ECG) applications. However, due to the complexity and non-causality of their mathematical computation, lots of QRS detectors can only be operated by using software but in a non-real-time fashion. This paper implements a real-time QRS complex detection system based on the difference operation method (DOM) and its efficient hardware architecture design. After simulations verified by MATLAB programs, we used the Verilog HDL (hardware description language) to perform the task of hardware design using the FPGA (field programmable gate array) system. The hardware design was tested with ECG records in the MIT-BIH (Massachusetts Institute of Technology- Beth Israel Hospital) arrhythmia database. The achievable accuracy exceeds 99% for records including typical and worst cases in the database.

Despite the promising role of orthogonal frequency-division multiplexing (OFDM) in communication systems, the spectrum sensing of OFDM signals and its practical considerations for cognitive radios (CRs) remain vital and challenging topics. This paper presents a new scheme for detecting OFDM signals based on the Neyman–Pearson (NP) principle. In contrast to conventional approaches in which additive white Gaussian noise (AWGN) channels are considered or empirical second-order statistics based on correlation coefficients are employed, to improve the detection performance, the proposed approach involves considering multipath fading channels and the classical NP detector. The log-likelihood ratio (LLR) test is formulated without requiring additional pilot symbols by using the redundancy of the cyclic prefix. Analytical results indicate that the LLR of received samples is the sum of the log-likelihood function (LLF) of the samples, which is typically used for estimating unknown parameters, and the LLR of an energy detector (ED). These results provide insight into the NP detector and the relationship between the NP detector, a detector based on the LLF, and the ED. 1 Because many unknown parameters must be estimated in the NP detector, two practical generalized log-likelihood ratio test (GLRT) detectors are designed. To develop a channel-independent GLRT, which is crucial for achieving favorable performance over multipath fading channels, the complementary property of the correlation coefficient is employed to derive an estimate independent of multipath channel profiles. Simulation results confirm the advantages of the proposed detector compared with the state-of-the-art detectors. 1 EDs are optimal only for independent and identically distributed random samples.