6G research has been extensively conducted by individual organizations as well as pre-competitive joint research initiatives. One of the joint initiatives is the Hexa-X European 6G flagship project. This paper shares the up-to-date deliverables through which Hexa-X is envisioning the 6G era. The Hexa-X deliverables presented in this paper encompass the overall 6G vision, use cases and technical enablers. The latest deliverables on tenets of 6G architectural design and central pillars of technical enablers are presented. In conclusion, the authors encourage joint research and PoC collaboration with Japanese industry, academia and research initiatives for the potential technical enablers presented in this paper, aimed at global harmonization towards 6G standards.

Technology for sixth-generation (6G) mobile communication system is now being widely studied. A sub-Terahertz band is expected to play a great role in 6G to enable extremely high data-rate transmission. This paper has two goals. (1) Introduction of 6G concept and propagation characteristics of sub-Terahertz-band radio waves. (2) Performance evaluation of intelligent reflecting surfaces (IRSs) based on beamforming in a sub-Terahertz band for smart radio environments (SREs). We briefly review research on SREs with reconfigurable intelligent surfaces (RISs), and describe requirements and key features of 6G with a sub-Terahertz band. After that, we explain propagation characteristics of sub-Terahertz band radio waves. Important feature is that the number of multipath components is small in a sub-Terahertz band in indoor office environments. This leads to an IRS control method based on beamforming because the number of radio waves out of the optimum beam is very small and power that is not used for transmission from the IRS to user equipment (UE) is little in the environments. We use beams generated by a Butler matrix or a DFT matrix. In simulations, we compare the received power at a UE with that of the upper bound value. Simulation results show that the proposed method reveals good performance in the sense that the received power is not so lower than the upper bound value.

Cell-free massive MIMO (CF-mMIMO), which cooperatively utilizes a large number of antennas deployed over a communication area, has been attracting great attention as an important technology for realizing 5G-advanced and 6G systems. Recently, to ensure system scalability and mitigate inter-user interference in CF-mMIMO, a user-centric (UC) approach was investigated. In this UC approach, user-centric antenna-sets are formed by selecting appropriate antennas for each user, and postcoding is applied to reduce the strong interference from users whose antenna-sets overlap. However, in very high user density environments, since the number of interfering users increases due to increased overlapping of antenna-sets, the achievable link capacity may degrade. In this paper, we propose a user-cluster-centric (UCC) approach, which groups neighborhood users into a user-cluster and associates the predetermined number of antennas to this user-cluster for spatial multiplexing. We derive the uplink postcoding weights and explain the effectiveness of the proposed UCC approach in terms of the computational complexity of the weight computation. We also compare the uplink user capacities achievable with UC and UCC approaches by computer simulation and clarify situations where the UCC approach is effective. Furthermore, we discuss the impact of the number of interfering users considered in the zero-forcing and minimum mean square error postcoding weight computation on the user capacity.

With the rapid increase in the amount of data communication in 5G networks, there is a strong demand to reduce the power of the entire network, so the use of highly power-efficient millimeter-wave (mm-wave) networks is being considered. However, while mm-wave communication has high power efficiency, it has strong straightness, so it is difficult to secure stable communication in an environment with blocking. Especially when considering use cases such as autonomous driving, continuous communication is required when transmitting streaming data such as moving images taken by vehicles, it is necessary to compensate the blocking problem. For this reason, the authors examined an optimum radio access technology (RAT) selection scheme which selects mm-wave communication when mm-wave can be used and select wide-area macro-communication when mm-wave may be blocked. In addition, the authors implemented the scheme on a prototype device and conducted field tests and confirmed that mm-wave communication and macro communication were switched at an appropriate timing.

Machine learning (ML) has been used for various tasks in network operations in recent years. However, since the scale of networks has grown and the amount of data generated has increased, it has been increasingly difficult for network operators to conduct their tasks with a single server using ML. Thus, ML with edge-cloud cooperation has been attracting attention for efficiently processing and analyzing a large amount of data. In the edge-cloud cooperation setting, although transmission latency, bandwidth congestion, and accuracy of tasks using ML depend on the load balance of processing data with edge servers and a cloud server in edge-cloud cooperation, the relationship is too complex to estimate. In this paper, we focus on monitoring anomalous traffic as an example of ML tasks for network operations and formulate transmission latency, bandwidth congestion, and the accuracy of the task with edge-cloud cooperation considering the ratio of the amount of data preprocessed in edge servers to that in a cloud server. Moreover, we formulate an optimization problem under constraints for transmission latency and bandwidth congestion to select the proper ratio by using our formulation. By solving our optimization problem, the optimal load balance between edge servers and a cloud server can be selected, and the accuracy of anomalous traffic monitoring can be estimated. Our formulation and optimization framework can be used for other ML tasks by considering the generating distribution of data and the type of an ML model. In accordance with our formulation, we simulated the optimal load balance of edge-cloud cooperation in a topology that mimicked a Japanese network and conducted an anomalous traffic detection experiment by using real traffic data to compare the estimated accuracy based on our formulation and the actual accuracy based on the experiment.

This paper proposes a multiple code block transmission scheme using hierarchical modulation (HM) for a broadcast channel in the orthogonal frequency division multiplexing (OFDM) downlink. We investigate the average bit error rate (BER) performance of two-layer HM using 16 quadrature amplitude modulation (QAM) and three-layer HM using 64QAM in multipath Rayleigh fading channels. In multiple code block transmission using HM, the basic information bits are demodulated and decoded to all users within a cell that satisfy the bit error rate (BER) requirement. Hence, we investigate non-uniform QAM constellations to find one that suppresses the loss in the average BER of the basic information bits for HM to a low level compared to that using the original constellation in which only the basic information bits are transmitted while simultaneously minimizing the loss in the average BER of the secondary and tertiary information bits from the original constellations in which the information bits of the respective layers are transmitted alone. Based on the path loss equations in the Urban Macro and Rural Macro scenarios, we also investigate the maximum distance from a base station (BS) for the information bits of each layer to attain the required average received signal-to-noise power ratio (SNR) that achieves the average BER of 10-3.

To enhance safety and efficiency in the traffic environment, developing intelligent transportation systems (ITSs) is of paramount importance. In ITSs, roadside units (RSUs) are critical components that enable the environment awareness and connectivity via using radar sensing and communications. In this paper, we focus on RSUs with multiple radar systems. Specifically, we propose a parameter selection method of multiple radar systems to enhance the overall sensing performance. Furthermore, since different radars provide different sensing and tracking results, to benefit from multiple radars, we propose fusion algorithms to integrate the tracking results of different radars. We use two commercial frequency-modulated continuous wave (FMCW) radars to conduct experiments at Hsinchu city in Taiwan. The experimental results validate that our proposed approaches can improve the overall sensing performance.

We experimentally evaluated transmission characteristics of 120-GHz-band close-proximity wireless link that employs a split-ring resonator (SRR) millimeter-wave (MMW) absorber integrated on planar slot antennas in 120-GHz-band close-proximity wireless links. We fabricated the SRR MMW absorber made of a 0.28-μm-thick TaN film on a quartz substrate, and integrated it on planar single slot antennas. When the TaN SRRs are not integrated on the planar slot antennas, multiple reflections between the two antennas occur, and a >10-dB fluctuation of S21 at 100-140GHz is observed. When the TaN SRRs are integrated on the planar antennas, the fluctuation of S21 is suppressed to be 3.5dB at 100-140GHz. However, the transmittance of the close-proximity wireless link decreases by integrating TaN SRRs on the planar slot antenna because of reflection at the quartz substrate surface. The integration of the radiator that is composed of single SRR with two capacitors just above the slot antenna increased S21 by 3.5dB at 125GHz. We conducted a data transmission experiment over a close-proximity wireless link that employs radiator-and-TaN-SRR-integrated slot antennas for Tx and Rx, and succeeded to transmit 10-Gbit/s data over the close-proximity wireless link for the first time.

We developed a self-controllable voltage level (SVL) circuit and applied this circuit to a single-power-supply, six-transistor complementary metal-oxide-semiconductor static random-access memory (SRAM) to not only improve both write and read performances but also to achieve low standby power and data retention (holding) capability. The SVL circuit comprises only three MOSFETs (i.e., pull-up, pull-down and bypass MOSFETs). The SVL circuit is able to adaptively generate both optimal memory cell voltages and word line voltages depending on which mode of operation (i.e., write, read or hold operation) was used. The write margin (VWM) and read margin (VRM) of the developed (dvlp) SRAM at a supply voltage (VDD) of 1V were 0.470 and 0.1923V, respectively. These values were 1.309 and 2.093 times VWM and VRM of the conventional (conv) SRAM, respectively. At a large threshold voltage (Vt) variability (=+6σ), the minimum power supply voltage (VMin) for the write operation of the conv SRAM was 0.37V, whereas it decreased to 0.22V for the dvlp SRAM. VMin for the read operation of the conv SRAM was 1.05V when the Vt variability (=-6σ) was large, but the dvlp SRAM lowered it to 0.41V. These results show that the SVL circuit expands the operating voltage range for both write and read operations to lower voltages. The dvlp SRAM reduces the standby power consumption (PST) while retaining data. The measured PST of the 2k-bit, 90-nm dvlp SRAM was only 0.957µW at VDD=1.0V, which was 9.46% of PST of the conv SRAM (10.12µW). The Si area overhead of the SVL circuits was only 1.383% of the dvlp SRAM.

A fully analog pipelined deep neural network (DNN) accelerator is proposed, which is constructed by using pipeline registers based on master-slave switched capacitors. The idea of the master-slave switched capacitors is an analog equivalent of the delayed flip-flop (D-FF) which has been used as a digital pipeline register. To estimate the performance of the pipeline register, it is applied to a conventional DNN which performs non-pipeline operation. Compared with the conventional DNN, the cycle time is reduced by 61.5% and data rate is increased by 160%. The accuracy reaches 99.6% in MNIST classification test. The energy consumption per classification is reduced by 88.2% to 0.128µJ, achieving an energy efficiency of 1.05TOPS/W and a throughput of 0.538TOPS in 180nm technology node.

Memory corruption can modify the kernel data of an operating system kernel through exploiting kernel vulnerabilities that allow privilege escalation and defeats security mechanisms. To prevent memory corruption, the several security mechanisms are proposed. Kernel address space layout randomization randomizes the virtual address layout of the kernel. The kernel control flow integrity verifies the order of invoking kernel codes. The additional kernel observer focuses on the unintended privilege modifications. However, illegal writing of kernel data is not prevented by these existing security mechanisms. Therefore, an adversary can achieve the privilege escalation and the defeat of security mechanisms. This study proposes a kernel data protection mechanism (KDPM), which is a novel security design that restricts the writing of specific kernel data. The KDPM adopts a memory protection key (MPK) to control the write restriction of kernel data. The KDPM with the MPK ensures that the writing of privileged information for user processes and the writing of kernel data related to the mandatory access control. These are dynamically restricted during the invocation of specific system calls and the execution of specific kernel codes. Further, the KDPM is implemented on the latest Linux with an MPK emulator. The evaluation results indicate the possibility of preventing the illegal writing of kernel data. The KDPM showed an acceptable performance cost, measured by the overhead, which was from 2.96% to 9.01% of system call invocations, whereas the performance load on the MPK operations was 22.1ns to 1347.9ns. Additionally, the KDPM requires 137 to 176 instructions for its implementations.

Although security and privacy technologies are incorporated into every device and service, the complexity of these concepts confuses non-expert users. Prior research has shown that non-expert users ask strangers for advice about digital media use online. In this study, to clarify the security and privacy concerns of non-expert users in their daily lives, we investigated security- and privacy-related question posts on a Question-and-Answer (Q&A) site for non-expert users. We conducted a thematic analysis of 445 question posts. We identified seven themes among the questions and found that users asked about cyberattacks the most, followed by authentication and security software. We also found that there was a strong demand for answers, especially for questions related to privacy abuse and account/device management. Our findings provide key insights into what non-experts are struggling with when it comes to privacy and security and will help service providers and researchers make improvements to address these concerns.

Theoretically secure cryptosystems, digital signatures may not be secure after being implemented on Internet of Things (IoT) devices and PCs because of side-channel attacks (SCA). Because RSA key generation and ECDSA require GCD computations or modular inversions, which are often computed using the binary Euclidean algorithm (BEA) or binary extended Euclidean algorithm (BEEA), the SCA weaknesses of BEA and BEEA become a serious concern. Constant-time GCD (CT-GCD) and constant-time modular inversion (CTMI) algorithms are effective countermeasures in such situations. Modular inversion based on Fermat's little theorem (FLT) can work in constant time, but it is not efficient for general inputs. Two CTMI algorithms, named BOS and BY in this paper, were proposed by Bos, Bernstein and Yang, respectively. Their algorithms are all based on the concept of BEA. However, one iteration of BOS has complicated computations, and BY requires more iterations. A small number of iterations and simple computations during one iteration are good characteristics of a constant-time algorithm. Based on this view, this study proposes new short-iteration CT-GCD and CTMI algorithms over Fp borrowing a simple concept from BEA. Our algorithms are evaluated from a theoretical perspective. Compared with BOS, BY, and the improved version of BY, our short-iteration algorithms are experimentally demonstrated to be faster.

In recent years, the market size for internet advertising has been increasing with the expansion of the Internet. Among the internet advertising technologies, affiliate services, which are a performance-based service, use cookies to track and measure the performance of affiliates. However, for the purpose of safeguarding personal information, cookies tend to be regulated, which leads to concerns over whether normal tracking by cookies works as intended. Therefore, in this study, the recent problems from the perspectives of affiliates, affiliate service providers, and advertisers are extracted, and a framework of cookie-independent measuring engagement method using access logs is proposed and open issues are discussed for future affiliate services.

With a rapidly escalating number of sophisticated cyber-attacks, protecting Internet of Things (IoT) networks against unauthorized activity is a major concern. The detection of malicious attack traffic is thus crucial for IoT security to prevent unwanted traffic. However, existing traditional malicious traffic detection systems which relied on supervised machine learning approach need a considerable number of benign and malware traffic samples to train the machine learning models. Moreover, in the cases of zero-day attacks, only a few labeled traffic samples are accessible for analysis. To deal with this, we propose a few-shot malicious IoT traffic detection system with a prototypical graph neural network. The proposed approach does not require prior knowledge of network payload binaries or network traffic signatures. The model is trained on labeled traffic data and tested to evaluate its ability to detect new types of attacks when only a few labeled traffic samples are available. The proposed detection system first categorizes the network traffic as a bidirectional flow and visualizes the binary traffic flow as a color image. A neural network is then applied to the visualized traffic to extract important features. After that, using the proposed few-shot graph neural network approach, the model is trained on different few-shot tasks to generalize it to new unseen attacks. The proposed model is evaluated on a network traffic dataset consisting of benign traffic and traffic corresponding to six types of attacks. The results revealed that our proposed model achieved an F1 score of 0.91 and 0.94 in 5-shot and 10-shot classification, respectively, and outperformed the baseline models.

Different types of malicious attacks have been increasing simultaneously and have become a serious issue for cybersecurity. Most attacks leverage domain URLs as an attack communications medium and compromise users into a victim of phishing or spam. We take advantage of machine learning methods to detect the maliciousness of a domain automatically using three features: DNS-based, lexical, and semantic features. The proposed approach exhibits high performance even with a small training dataset. The experimental results demonstrate that the proposed scheme achieves an approximate accuracy of 0.927 when using a random forest classifier.

This paper first takes IoT as an example to provide the motivation for eliminating the single point of trust (SPOT) in a CA-based private PKI. It then describes a distributed public key certificate-issuing infrastructure that eliminates the SPOT and its limitation derived from generating signing keys. Finally, it proposes a method to address its limitation by all certificate issuers.

Class imbalance is one of the challenges faced in the field of machine learning. It is difficult for traditional classifiers to predict the minority class data. If the imbalanced data is not processed, the effect of the classifier will be greatly reduced. Aiming at the problem that the traditional classifier tends to the majority class data and ignores the minority class data, imbalanced data over-sampling method based on iterative self-organizing data analysis technique algorithm(ISODATA) clustering is proposed. The minority class is divided into different sub-clusters by ISODATA, and each sub-cluster is over-sampled according to the sampling ratio, so that the sampled minority class data also conforms to the imbalance of the original minority class data. The new imbalanced data composed of new minority class data and majority class data is classified by SVM and Random Forest classifier. Experiments on 12 datasets from the KEEL datasets show that the method has better G-means and F-value, improving the classification accuracy.

Aiming at the problem of low classification accuracy of surface defects of lithium battery pole pieces by traditional classification methods, an image classification algorithm for surface defects of lithium battery pole piece based on deep learning is proposed in this paper. Firstly, Wavelet Threshold and Histogram Equalization are used to preprocess the detect image to weaken influence of noise in non-defect regions and enhance defect features. Secondly, a VGG-InceptionV2 network with better performance is proposed by adding InceptionV2 structure to the improved VGG network structure. Then the original data set is expanded by rotating, flipping and contrast adjustment, and the optimal value of the model hyperparameters is determined by experiments. Finally, the model in this paper is compared with VGG16 and GoogLeNet to realize the recognition of defect types. The results show that the accuracy rate of the model in this paper for the surface pole piece defects of lithium batteries is 98.75%, and the model parameters is only 1.7M, which has certain significance for the classification of lithium battery surface pole piece defects in industry.

In recent years, there has been a growing demand for urban digitization using cameras and light detection and ranging (LiDAR). Shadows are a condition that affects measurement the most. Therefore, shadow detection technology is essential. In this study, we propose shadow detection utilizing the LiDAR intensity that depends on the surface properties of objects but not on irradiation from other light sources. Unlike conventional LiDAR-intensity-aided shadow detection methods, our method embeds the un-correlation between luminance and LiDAR intensity in each position into the optimization. The energy, which is defined by the un-correlation between luminance and LiDAR intensity in each position, is minimized by graph-cut segmentation to detect shadows. In evaluations on KITTI and Waymo datasets, our shadow-detection method outperformed the previous methods in terms of multiple evaluation indices.