Side-channel (SC) leakage from a cryptographic device chip is simulated as the dynamic current flowing out of the chip. When evaluating the simulated current, an evaluation by comparison with an actual measurement is essential; however, it is difficult to compare them directly. This is because a measured waveform is typically the output voltage of probe placed at the observation position outside the chip, and the actual dynamic current is modified by several transfer impedances. Therefore, in this paper, the probe voltage is converted into the dynamic current by using an EMC macro-model of a cryptographic device being evaluated. This paper shows that both the amplitude and the SC analysis (correlation power analysis and measurements to disclosure) results of the simulated dynamic current were evaluated appropriately by using the EMC macro-model. An evaluation confirms that the shape of the simulated current matches the measured one; moreover, the SC analysis results agreed with the measured ones well. On the basis of the results, it is confirmed that a register-transfer level (RTL) simulation of the dynamic current gives a reasonable estimation of SC traces.

Artificial intelligence (AI), especially deep learning (DL), has been remarkable and applied to various industries. However, adversarial examples (AE), which add small perturbations to input data of deep neural networks (DNNs) for misclassification, are attracting attention. In this paper, we propose a novel black-box attack to craft AE using only processing time which is side-channel information of DNNs, without using training data, model architecture and parameters, substitute models or output probability. While, several existing black-box attacks use output probability, our attack exploits a relationship between the number of activated nodes and the processing time of DNNs. The perturbations for AE are decided by the differential processing time according to input data in our attack. We show experimental results in which our attack's AE increase the number of activated nodes and cause misclassification to one of the incorrect labels effectively. In addition, the experimental results highlight that our attack can evade gradient masking countermeasures which mask output probability to prevent crafting AE against several black-box attacks.

Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called “conditional information leakage given the eavesdropper's received signals,” which expresses the amount of information that an eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length n, while the complexity for computing the usual information leakage is exponential in n. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.

In this paper, we consider interference suppression for a full-duplex (FD) multiuser system based on single-carrier transmission in frequency-selective channels where a FD base-station (BS) simultaneously communicates with half-duplex (HD) uplink and downlink mobile users. We propose a design method for time-domain filtering where the filters in the BS transmitter suppress inter-symbol interference (ISI) and downlink inter-user interference (IUI); those in the BS receiver, self-interference, ISI, and uplink IUI; and those in the downlink mobile users, co-channel interference (CCI) without the channel state information of the CCI channels. Simulation results indicate that the FD system based on the proposed method outperforms the conventional HD system and FD system based on multicarrier transmission.

Dynamic spectrum access (DSA) exploits vacant frequency resources via distributed wireless access. The two nodes of DSA, master and slave, access different channels, and thus, cannot communicate with each other. To compensate for the access channel mismatch between the two nodes, a rendezvous channel, which exchanges control signals between two nodes, has been considered. The rendezvous channel based on channel-occupancy ratio (COR) adaptively constructs the channel in accordance with the channel occupancy of other systems, and both a high-speed rendezvous channel and high usage efficiency of the frequency resource are accomplished owing to exploitation of the vacant channel. In the rendezvous channel based on COR, the master and slave recognize the channel with minimum measured COR as the superior channel. As the master sends the control signals through the superior channel recognized by the master, the slave accesses to the superior channel recognized by the slave with higher access rate than to the other channels. As a result, the slave can receive the control signals with highly probability and thus high speed rendezvous channel is achieved. If the master and the slave recognize the different channel as the superior channel, the access rate to the other channel should be larger. This is because the slave obtains the opportunity of receiving the control signals through the different channel from the superior channel recognized by slave and thus the high probability that the slave can receive the control signals is maintained. Therefore, the access rate of slave should be constructed in accordance with the recognition of superior channel by master and slave. In this paper, the access rate of slave to the superior channel is optimally constructed using the analyzed probability of completion of rendezvous channel. The analysis of the probability of completion of rendezvous channel includes the recognition of superior channel by master and slave. Even if the master and the slave recognize the different channel, the constructed access rate of slave can maintain the high speed rendezvous channel. From the theoretical analysis and computer simulation, the rendezvous channel based on COR with the optimal access rate to the channel with the lowest COR achieves reduced time for the rendezvous channel.

Pilot contamination is addressed in massive multiple-input multiple-output (MIMO) uplink. The main ideas of pilot decontamination are twofold: One is to design transmission timing of pilot sequences such that the pilot transmission periods in different cells do not fully overlap with each other, as considered in previous works. The other is joint channel and data estimation via approximate message-passing (AMP) for bilinear inference. The convergence property of conventional AMP is bad in bilinear inference problems, so that adaptive damping was required to help conventional AMP converge. The main contribution of this paper is a modification of the update rules in conventional AMP to improve the convergence property of AMP. Numerical simulations show that the proposed AMP outperforms conventional AMP in terms of estimation performance when adaptive damping is not used. Furthermore, it achieves better performance than state-of-the-art methods based on subspace estimation when the power difference between cells is small.

In a multiple-user MIMO system in which numerous users simultaneously communicate in a cell, the channel matrix properties depend on the parameters of the individual users in such a way that they can be modeled as points randomly moving within the cell. Although these properties can be simulated by computer, they need to be expressed analytically to develop MIMO systems with diversity. Given a small area with an equivalent multi-path, we assume that a user u is at a certain “user point” $P^u(lambda _p^u,xi _p^u)$ in a cell, or (radius $lambda _p^u$ from origin, angle $xi _p^u)$ and that the user moves with movement $M^u(f_{max}^u, xi_v^u)$ around that point, or (Doppler frequency $f_{max}^u$, direction $xi_v^u$). The MU-MIMO channel model consists of a multipath environment, user parameters, and antenna configuration. A general formula of the correlation $ ho_{i - j,i' - j'}^{u - u'} (bm)$ between the channel matrix elements of users u and u' and one for given multipath conditions are derived. As a feature of the MU-MIMO channel, the movement factor $F^{u - u'}(gamma^u,xi_n ,xi_v^u)$, which means a fall coefficient of the spatial correlation calculated from only the user points of u and u', is also derived. As the difference in speed or direction between u and u' increases, $F^{u - u'}(gamma^u,xi_n ,xi_v^u)$ becomes smaller. Consequently, even if the path is LOS, $ ho_{i - j,i' - j'}^{u - u'} (bm)$ becomes low enough owing to the movement factor, even though the correlation in the single-user MIMO channel is high. If the parameters of u and u' are the same, the factor equals 1, and the channels correspond to the users' own channels and work like SU-MIMO channel. These analytical findings are verified by computer simulation.

Recently, the ECG-based diagnosis system based on wearable devices has attracted more and more attention of researchers. Existing studies have achieved high classification accuracy by using deep neural networks (DNNs), but there are still some problems, such as: imprecise heart beat segmentation, inadequate use of medical knowledge, the same treatment of features with different importance. To address these problems, this paper: 1) proposes an adaptive segmenting-reshaping method to acquire abundant useful samples; 2) builds a set of hand-crafted features and deep features on the inner-beat, beat and inter-beat scale by integrating enough medical knowledge. 3) introduced a modified channel attention module (CAM) to augment the significant channels in deep features. Following the Association for Advancement of Medical Instrumentation (AAMI) recommendation, we classified the dataset into four classes and validated our algorithm on the MIT-BIH database. Experiments show that the accuracy of our model reaches 96.94%, a 3.71% increase over that of a state-of-the-art alternative.

Motion deblurring for noisy and blurry images is an arduous and fundamental problem in image processing community. The problem is ill-posed as many different pairs of latent image and blur kernel can render the same blurred image, and thus, the optimization of this problem is still unsolved. To tackle it, we present an effective motion deblurring method for noisy and blurry images based on prominent structure and a data-driven heavy-tailed prior of enhanced gradient. Specifically, first, we employ denoising as a preprocess to remove the input image noise, and then restore strong edges for accurate kernel estimation. The image extreme channels-based priors (dark channel prior and bright channel prior) as sparse complementary knowledge are exploited to extract prominent structure. High closeness of the extracted structure to the clear image structure can be obtained via tuning the parameters of extraction function. Next, the integration term of enhanced interim image gradient and clear image heavy-tailed prior is proposed and then embedded into the image restoration model, which favors sharp images over blurry ones. A large number of experiments on both synthetic and real-life images verify the superiority of the proposed method over state-of-the-art algorithms, both qualitatively and quantitatively.

A spectrum suppressed transmission that increases the frequency utilization efficiency, defined as throughput/bandwidth, by suppressing the required bandwidth has been proposed. This is one of the most effective schemes to solve the exhaustion problem of frequency bandwidths. However, in spectrum suppressed transmission, its transmission quality potentially degrades due to the ISI making the bandwidth narrower than the Nyquist bandwidth. In this paper, in order to improve the transmission quality degradation, we propose the spectrum suppressed transmission applying both FEC (forward error correction) and LE (linear equalization). Moreover, we also propose a new channel allocation scheme for the spectrum suppressed transmission, in multi-channel environments over a satellite transponder. From our computer simulation results, we clarify that the proposed schemes are more effective at increasing the system throughput than the scheme without spectrum suppression.

This paper proposes a deterministic pilot pattern placement optimization scheme based on the quantum genetic algorithm (QGA) which aims to improve the performance of sparse channel estimation in orthogonal frequency division multiplexing (OFDM) systems. By minimizing the mutual incoherence property (MIP) of the sensing matrix, the pilot pattern placement optimization is modeled as the solution of a combinatorial optimization problem. QGA is used to solve the optimization problem and generate optimized pilot pattern that can effectively avoid local optima traps. The simulation results demonstrate that the proposed method can generate a sensing matrix with a smaller MIP than a random search or the genetic algorithm (GA), and the optimized pilot pattern performs well for sparse channel estimation in OFDM systems.

Green cognitive radio (CR) plays an important role in offering secondary users (SUs) with more spectrum with smaller energy expenditure. However, the energy efficiency (EE) issues associated with green CR for fading channels have not been fully studied. In this paper, we investigate the average EE maximization problem for spectrum-sharing CR in fading channels. Unlike previous studies that considered either the peak or the average transmission power constraints, herein, we considered both of these constraints. Our aim is to maximize the average EE of SU by optimizing the transmission power under the joint peak and average transmit power constraints, the rate constraint of SU and the quality of service (QoS) constraint of primary user (PU). Specifically, the QoS for PU is guaranteed based on either the average interference power constraint or the PU outage constraint. To address the non-convex optimization problem, an iterative optimal power allocation algorithm that can tackle the problem efficiently is proposed. The optimal transmission powers are identified under both of perfect and imperfect channel side information (CSI). Simulations show that our proposed scheme can achieve higher EE over the existing scheme and the EE achieved under perfect CSI is better than that under imperfect CSI.

In radio channel measurements and modeling, directional scanning via highly directive antennas is the most popular method to obtain angular channel characteristics to develop and evaluate advanced wireless systems for high frequency band use. However, it is often insufficient for ray-/cluster-level characterizations because the angular resolution of the measured data is limited by the angular sampling interval over a given scanning angle range and antenna half power beamwidth. This study proposes the sub-grid CLEAN algorithm, a novel technique for high-resolution multipath component (MPC) extraction from the multi-dimensional power image, so called double-directional angular delay power spectrum. This technique can successfully extract the MPCs by using the multi-dimensional power image. Simulation and measurements showed that the proposed technique could extract MPCs for ray-/cluster-level characterizations and channel modeling. Further, applying the proposed method to the data captured at 58.5GHz in an atrium entrance hall environment which is an indoor hotspot access scenario in the fifth generation mobile system, the multipath clusters and corresponding scattering processes were identified.

This paper proposes a novel flexible receiver with virtual channels for overloaded multiple-input multiple-output (MIMO) channels. The receiver applies extended rotation matrices proposed in the paper for the flexibility. In addition, adaptive selection of the extended rotation matrices is proposed for further performance improvement. We propose two techniques to reduce the computational complexity of the adaptive selection. As a result, the proposed receiver gives us an option to reduce the complexity with a slight decrease in the transmission performance by changing receiver configuration parameters. A computer simulation reveals that the adaptive selection attains a gain of about 3dB at the BER of 10-3.

In this study, simulations are performed to design an optimal device for thinning the GaN channel layer on the semi-insulating layer in HEMT. When the gate length is 50nm, the thickness of the undoped channel must be thinner than 300nm to observe the off state. When the GaN channel layer is an Fe-doped, an on/off ratio of ~300 can be achieved even with a gate length of 25nm, although the transconductance is slightly reduced.

This paper presents a real-time body motion classification system using the radio channel characteristics of a wearable body area network (BAN). We developed a custom wearable BAN radio channel measurement system by modifying an off-the-shelf ZigBee-based sensor network system, where the link quality indicator (LQI) values of the wireless links between the coordinator and four sensor nodes can be measured. After interpolating and standardizing the raw data samples in a pre-processing stage, the time-domain features are calculated, and the body motion is classified by a decision-tree based random forest machine learning algorithm which is most suitable for real-time processing. The features were carefully chosen to exclude those that exhibit the same tendency based on the mean and variance of the features to avoid overfitting. The measurements demonstrated successful real-time body motion classification and revealed the potential for practical use in various daily-life applications.

This paper presents an algorithm to arrange a large number of antenna elements in the limited space of massive MIMO base station antenna without degrading the communication quality under a street-cell line-of-sight environment in mobile communications. The proposed algorithm works by using mathematical optimization in which the objective function is the correlation coefficient between the channel responses of two elements of the base station antenna, according to an algorithm constructed based on the results obtained through basic examinations of the characteristics of the correlation coefficient between channel responses. The channel responses are computed by using the propagation path information obtained by ray-tracing. The arrangements output by the proposed algorithm are mainly evaluated by channel capacity comparison with uniformly spaced arrangements on the vertical plane in single user and multiuser environments. The evaluation results of these arrangements in downlink demonstrate the superiority of the arrangements generated by the proposed algorithm, especially in term of robustness against an increase in the number of users.

Side-channel Attack, such as simple power analysis and differential power analysis (DPA), is an efficient method to gather the key, which challenges the security of crypto chips. Side-channel Attack logs the power trace of the crypto chip and speculates the key by statistical analysis. To reduce the threat of power analysis attack, an innovative method based on random execution and register randomization is proposed in this paper. In order to enhance ability against DPA, the method disorders the correspondence between power trace and operands by scrambling the data execution sequence randomly and dynamically and randomize the data operation path to randomize the registers that store intermediate data. Experiments and verification are done on the Sakura-G FPGA platform. The results show that the key is not revealed after even 2 million power traces by adopting the proposed method and only 7.23% slices overhead and 3.4% throughput rate cost is introduced. Compared to unprotected chip, it increases more than 4000× measure to disclosure.

The increase in computation cost and storage of convolutional neural networks (CNNs) severely hinders their applications on limited-resources devices in recent years. As a result, there is impending necessity to accelerate the networks by certain methods. In this paper, we propose a loss-driven method to prune redundant channels of CNNs. It identifies unimportant channels by using Taylor expansion technique regarding to scaling and shifting factors, and prunes those channels by fixed percentile threshold. By doing so, we obtain a compact network with less parameters and FLOPs consumption. In experimental section, we evaluate the proposed method in CIFAR datasets with several popular networks, including VGG-19, DenseNet-40 and ResNet-164, and experimental results demonstrate the proposed method is able to prune over 70% channels and parameters with no performance loss. Moreover, iterative pruning could be used to obtain more compact network.

In this study, product of two independent and non-identically distributed (i.n.i.d.) random variables (RVs) for κ-µ fading distribution and α-µ fading distribution is considered. The statistics of the product of RVs has been broadly applied in a large number of communications fields, such as cascaded fading channels, multiple input multiple output (MIMO) systems, radar communications and cognitive radios (CR). Exact close-form expressions of probability density function (PDF) and cumulative distribution function (CDF) with exact series formulas for the product of two i.n.i.d. fading distributions κ-µ and α-µ are deduced more accurately to represent the provided product expressions and generalized composite multipath shadowing models. Furthermore, ergodic channel capacity (ECC) is obtained to measure maximum fading channel capacity. At last, interestingly unlike κ-µ, η-µ, α-µ in [9], [17], [18], these analytical results are validated with Monte Carlo simulations and it shows that for provided κ-µ/α-µ model, non-linear parameter has more important influence than multipath component in PDF and CDF, and when the ratio between the total power of the dominant components and the total power of the scattered waves is same, higher α can significantly improve channel capacity over composite fading channels.