Broadcast services for wireless local area networks (WLANs) are being standardized in the IEEE 802.11 task group bc. Envisaging the upcoming coexistence of broadcast access points (APs) with densely-deployed legacy APs, this paper addresses a learning-based spatial reuse with only partial receiver-awareness. This partial awareness means that the broadcast APs can leverage few acknowledgment frames (ACKs) from recipient stations (STAs). This is in view of the specific concerns of broadcast communications. In broadcast communications for a very large number of STAs, ACK implosions occur unless some STAs are stopped from responding with ACKs. Given this, the main contribution of this paper is to demonstrate the feasibility to improve the robustness of learning-based spatial reuse to hidden interferers only with the partial receiver-awareness while discarding any re-training of broadcast APs. The core idea is to leverage robust adversarial reinforcement learning (RARL), where before a hidden interferer is installed, a broadcast AP learns a rate adaptation policy in a competition with a proxy interferer that provides jamming signals intelligently. Therein, the recipient STAs experience interference and the partial STAs provide a feedback overestimating the effect of interference, allowing the broadcast AP to select a data rate to avoid frame losses in a broad range of recipient STAs. Simulations demonstrate the suppression of the throughput degradation under a sudden installation of a hidden interferer, indicating the feasibility of acquiring robustness to the hidden interferer.

Quadrature voltage-controlled oscillators (VCOs) with current-weight-average and voltage-weight-average phase-adjusting architectures are studied. The phase adjusting equalizes the oscillation frequency to the LC-resonant frequency. The merits of the equalization are explained by using Leeson's phase noise equation and the impulse sensitivity function (ISF). Quadrature VCOs with the phase-adjusting architectures are fabricated using 180-nm TSMC CMOS and show low-phase-noise performances compared to a conventional differential VCO. The ISF analysis and small-signal analysis also show that the drawbacks of the current-weight-average phase-adjusting and voltage-weight-average phase-adjusting architectures are current-source noise effect and large additional capacitance, respectively. A voltage-average-adjusting circuit with a source follower at its input alleviates the capacitance increase.

Mobile communication systems are not only the core of the Information and Communication Technology (ICT) infrastructure but also that of our social infrastructure. The 5th generation mobile communication system (5G) has already started and is in use. 5G is expected for various use cases in industry and society. Thus, many companies and research institutes are now trying to improve the performance of 5G, that is, 5G Enhancement and the next generation of mobile communication systems (Beyond 5G (6G)). 6G is expected to meet various highly demanding requirements even compared with 5G, such as extremely high data rate, extremely large coverage, extremely low latency, extremely low energy, extremely high reliability, extreme massive connectivity, and so on. Artificial intelligence (AI) and machine learning (ML), AI/ML, will have more important roles than ever in 6G wireless communications with the above extreme high requirements for a diversity of applications, including new combinations of the requirements for new use cases. We can say that AI/ML will be essential for 6G wireless communications. This paper introduces some ML techniques and applications in 6G wireless communications, mainly focusing on the physical layer.

Deep neural networks (DNNs) perform well for image recognition, speech recognition, and pattern analysis. However, such neural networks are vulnerable to adversarial examples. An adversarial example is a data sample created by adding a small amount of noise to an original sample in such a way that it is difficult for humans to identify but that will cause the sample to be misclassified by a target model. In a military environment, adversarial examples that are correctly classified by a friendly model while deceiving an enemy model may be useful. In this paper, we propose a method for generating a selective adversarial example that is correctly classified by a friendly gait recognition system and misclassified by an enemy gait recognition system. The proposed scheme generates the selective adversarial example by combining the loss for correct classification by the friendly gait recognition system with the loss for misclassification by the enemy gait recognition system. In our experiments, we used the CASIA Gait Database as the dataset and TensorFlow as the machine learning library. The results show that the proposed method can generate selective adversarial examples that have a 98.5% attack success rate against an enemy gait recognition system and are classified with 87.3% accuracy by a friendly gait recognition system.

Intelligent reconfigurable surfaces (IRS) have attracted much attention from both industry and academia due to their performance improving capability and low complexity for 6G wireless communication systems. In this letter, we introduce an IRS-assisted space-time line code (STLC) technique. The STLC was introduced as a promising technique to acquire the optimal diversity gain in 1×2 single-input multiple-output (SIMO) channel without channel state information at receiver (CSIR). Using the cosine similarity theorem, we propose a novel phase-steering technique for the proposed IRS-assisted STLC technique. We also mathematically characterize the proposed IRS-assisted STLC technique in terms of outage probability and bit-error rate (BER). Based on computer simulations, it is shown that the results of analysis shows well match with the computer simulation results for various communication scenarios.

This letter solves the energy efficiency optimization problem for the simultaneous wireless information and power transfer (SWIPT) systems with non-orthogonal multiple access (NOMA), multiple input single output (MISO) and power-splitting structures, where each user may have different individual quality of service (QoS) requirements about information and energy. Nonlinear energy harvesting model is used. Alternate optimization approach is adopted to find the solution, which shows a fast convergence behavior. Simulation results show the proposed scheme has higher energy efficiency than existing dual-layer iteration and throughput maximization methods.

In this letter, we propose an energy beamforming empowered relaying scheme for a batteryless IoT network, where wireless-powered relays are deployed between the hybrid access point (HAP) and batteryless IoT devices to assist the uplink information transmission from the devices to the HAP. In particular, the HAP first exploits energy beamforming to efficiently transmit radio frequency (RF) signals to transfer energy to the relays and as the incident signals to enable the information backscattering of batteryless IoT devices. Then, each relay uses the harvested energy to forward the decoded signals from its corresponding batteryless IoT device to the HAP, where the maximum-ratio combing is used for further performance improvement. To maximize the network sum-rate, the joint optimization of energy beamforming vectors at the HAP, network time scheduling, power allocation at the relays, and relection coefficient at the users is investigated. As the formulated problem is non-convex, we propose an alternating optimization algorithm with the variable substitution and semi-definite relaxation (SDR) techniques to solve it efficiently. Specifically, we prove that the obtained energy beamforming matrices are always rank-one. Numerical results show that compared to the benchmark schemes, the proposed scheme can achieve a significant sum-rate gain.

In this study, we have proposed a millimeter-wave (MMW) single-pixel imaging (SPI) system with a liquid-crystal (LC) mask cell. The LC cell functions as an electrically switchable mask based on the change in absorption properties, which depend on the orientation of the LC. We investigated the influence of noise on the measured and estimated data (reconstructed image). The proposed system exhibited moderate robustness against random noise (that were added) compared to raster scan-based and Hadamard matrix-based SPI systems. Finally, the results of some demonstrative experiments were introduced to ensure the applicability of the constructed MMW-SPI system, and steps for improving the reconstructed image quality were discussed.

Local (private) 5G system can provide a secure and flexible network using the cellular-based technologies at their facilities (e.g., factories, agricultural lands, and buildings). We constructed a small-scale demonstration system that exhibits the remote control of a patrol and work robot with arms using a local 5G system. The constructed robot comprises a robot operating system-based unmanned ground vehicle, two laser range finders, a webcam, an omnidirectional camera, and a six-axis robot arm. To fabricate a demonstration system with open-source software, we assessed the one-way delay of video streaming by changing different CPU, camera types, drivers, applications, and video resolutions. According to the assessment findings, it was demonstrated that it is possible to realize approximately 100ms delay under the limited resolution condition, and the allowable maximum absolute delay of 300ms can be attained even for full HD (1920 × 1080) resolution of this demonstration. Furthermore, local 5G was demonstrated to reduce delay variations to the same level as wired systems. It was also clarified that the increase in delay due to the application of local 5G is relatively small (5-25% in total delay) in this demonstration. Finally, we employed the small-scale demonstration system for the online and onsite campus tours for high school students.

The mean, median, and mode are usually calculated from univariate observations as the most basic representative values of a random variable. To measure the spread of the distribution, the standard deviation, interquartile range, and modal interval are also calculated. When we analyze continuous relations between a pair of random variables from bivariate observations, regression analysis is often used. By minimizing appropriate costs evaluating regression errors, we estimate the conditional mean, median, and mode. The conditional standard deviation can be estimated if the bivariate observations are obtained from a Gaussian process. Moreover, the conditional interquartile range can be calculated for various distributions by the quantile regression that estimates any conditional quantile (percentile). Meanwhile, the study of the modal interval regression is relatively new, and spline regression models, known as flexible models having the optimality on the smoothness for bivariate data, are not yet used. In this paper, we propose a modal interval regression method based on spline quantile regression. The proposed method consists of two steps. In the first step, we divide the bivariate observations into bins for one random variable, then detect the modal interval for the other random variable as the lower and upper quantiles in each bin. In the second step, we estimate the conditional modal interval by constructing both lower and upper quantile curves as spline functions. By using the spline quantile regression, the proposed method is widely applicable to various distributions and formulated as a convex optimization problem on the coefficient vectors of the lower and upper spline functions. Extensive experiments, including settings of the bin width, the smoothing parameter and weights in the cost function, show the effectiveness of the proposed modal interval regression in terms of accuracy and visual shape for synthetic data generated from various distributions. Experiments for real-world meteorological data also demonstrate a good performance of the proposed method.

Increased demand for DNS privacy has driven the creation of several encrypted DNS protocols, such as DNS over HTTPS (DoH), DNS over TLS (DoT), and DNS over QUIC (DoQ). Recently, DoT and DoH have been deployed by some vendors like Google and Cloudflare. This paper addresses privacy leakage in these three encrypted DNS protocols (especially DoQ) with different DNS recursive resolvers (Google, NextDNS, and Bind) and DNS proxy (AdGuard). More particularly, we investigate encrypted DNS traffic to determine whether the adversary can infer the category of websites users visit for this purpose. Through analyzing packet traces of three encrypted DNS protocols, we show that the classification performance of the websites (i.e., user's privacy leakage) is very high in terms of identifying 42 categories of the websites both in public (Google and NextDNS) and local (Bind) resolvers. By comparing the case with cache and without cache at the local resolver, we confirm that the caching effect is negligible as regards identification. We also show that discriminative features are mainly related to the inter-arrival time of packets for DNS resolving. Indeed, we confirm that the F1 score decreases largely by removing these features. We further investigate two possible countermeasures that could affect the inter-arrival time analysis in the local resolver: AdBlocker and DNS prefetch. However, there is no significant improvement in results with these countermeasures. These findings highlight that information leakage is still possible even in encrypted DNS traffic regardless of underlying protocols (i.e., HTTPS, TLS, QUIC).

When a disaster hits a network, network service disruptions can occur even if the network facilities have survived and battery and power generators are provided. This is because in the event of a disaster, the power supply will not be restarted within the lifetime of the battery or oil transportation will not be restarted before running out of oil and power will be running out. Therefore, taking a power grid into account is important. This paper proposes a polynomial-time algorithm to identify the critical location C*D of a communications network Nc when a disaster hits. Electrical power grid Np supplies power to the nodes of Nc, and a link in Nc is disconnected when a node or a link in Nc or Np fails. Here, the disaster area is modeled as co-centric disks and the failure probability is higher in the inner disk than the outer one. The location of the center of the disaster with the greatest expected number of disconnected links in Nc is taken as the critical location C*D.

In this paper, we propose a novel radial line planar phased array in which helical antenna elements are individually rotated by their respective connected micromotors to realize dynamic beam-scanning. To our knowledge, this is the first radial line planar array (RLPA) that has antenna elements electromechanically rotated by their individual micromotors. To facilitate its fabrication, helix and its probe are directly metallized on a plastic shaft using molded interconnect device technology, and a motor shaft is press-fitted into the plastic shaft. We also present a new design methodology for RLPA, which combines the equivalent circuit theory and electromagnetic simulations of the unit cell element. The proposed procedure is practical to design an RLPA of antenna elements with arbitrary probe shape without large-scale full-wave analysis of the whole structure of the RLPA. We design, fabricate, and evaluate a 7-circle array with 168 helical antenna elements fabricated using molded interconnect device technology. The prototype antenna exhibits dynamic and accurate beam-scanning performance. Furthermore, the prototype antenna exhibits a low reflection coefficient (less than -17dB) and high antenna efficiency (above 77%), which validates the proposed design methodology.

This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4×4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0dB better transmission performance at the BER of 10-5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

Network-on-Chips (NoCs) are important components for scalable many-core processors. Because the performance of parallel applications is usually sensitive to the latency of NoCs, reducing it is a primary requirement. In this study, a compression router that hides the (de)compression-operation delay is proposed. The compression router (de)compresses the contents of the incoming packet before the switch arbitration is completed, thus shortening the packet length without latency penalty and reducing the network injection-and-ejection latency. Evaluation results show that the compression router improves up to 33% of the parallel application performance (conjugate gradients (CG), fast Fourier transform (FT), integer sort (IS), and traveling salesman problem (TSP)) and 63% of the effective network throughput by 1.8 compression ratio on NoC. The cost is an increase in router area and its energy consumption by 0.22mm2 and 1.6 times compared to the conventional virtual-channel router. Another finding is that off-loading the decompressor onto a network interface decreases the compression-router area by 57% at the expense of the moderate increase in communication latency.

Mixed-Polarity Multiple-Control Toffoli (MPMCT) gates are generally used to implement large control logic functions for quantum computation. A logic circuit consisting of MPMCT gates needs to be mapped to a quantum computing device that invariably has a physical limitation, which means we need to (1) decompose the MPMCT gates into one- or two-qubit gates, and then (2) insert SWAP gates so that all the gates can be performed on Nearest Neighbor Architectures (NNAs). Up to date, the above two processes have only been studied independently. In this work, we investigate that the total number of gates in a circuit can be decreased if the above two processes are considered simultaneously as a single step. We developed a method that inserts SWAP gates while decomposing MPMCT gates unlike most of the existing methods. Also, we consider the effect on the latter part of a circuit carefully by considering the qubit placement when decomposing an MPMCT gate. Experimental results demonstrate the effectiveness of our method.

Aiming at the problem of spatial focus regions distribution dispersion and dislocation in feature pyramid networks and insufficient feature dependency acquisition in both spatial and channel dimensions, this paper proposes a spatial-temporal aggregated shuffle attention for video instance segmentation (STASA-VIS). First, an mixed subsampling (MS) module to embed activating features from the low-level target area of feature pyramid into the high-level is designed, so as to aggregate spatial information on target area. Taking advantage of the coherent information in video frames, STASA-VIS uses the first ones of every 5 video frames as the key-frames and then propagates the keyframe feature maps of the pyramid layers forward in the time domain, and fuses with the non-keyframe mixed subsampled features to achieve time-domain consistent feature aggregation. Finally, STASA-VIS embeds shuffle attention in the backbone to capture the pixel-level pairwise relationship and dimensional dependencies among the channels and reduce the computation. Experimental results show that the segmentation accuracy of STASA-VIS reaches 41.2%, and the test speed reaches 34FPS, which is better than the state-of-the-art one stage video instance segmentation (VIS) methods in accuracy and achieves real-time segmentation.

Recent developments in engineering applications of stochastic resonance have expanded to various fields, especially biomedicine. Deterministic chaos generates a phenomenon known as chaotic resonance, which is similar to stochastic resonance. However, engineering applications of chaotic resonance are limited owing to the problems in controlling chaos, despite its uniquely high sensitivity to weak signal responses. To tackle these problems, a previous study proposed “reduced region of orbit” (RRO) feedback methods, which cause chaotic resonance using external feedback signals. However, this evaluation was conducted under noise-free conditions. In actual environments, background noise and measurement errors are inevitable in the estimation of RRO feedback strength; therefore, their impact must be elucidated for the application of RRO feedback methods. In this study, we evaluated the chaotic resonance induced by the RRO feedback method in chaotic neural systems in the presence of stochastic noise. Specifically, we focused on the chaotic resonance induced by RRO feedback signals in a neural system composed of excitatory and inhibitory neurons, a typical neural system wherein chaotic resonance is observed in the presence of additive noise and feedback signals including the measurement error (called contaminant noise). It was found that for a relatively small noise strength, both types of noise commonly degenerated the degree of synchronization in chaotic resonance induced by RRO feedback signals, although these characteristics were significantly different. In contrast, chaos-chaos intermittency synchronization was observed for a relatively high noise strength owing to the noise-induced attractor merging bifurcation for both types of noise. In practical neural systems, the influence of noise is unavoidable; therefore, this study highlighted the importance of the countermeasures for noise in the application of chaotic resonance and utilization of noise-induced attractor merging bifurcation.

An aperture-shared multi-port waveguide antenna with multiple feeds is presented in this paper. The antenna consists of sequentially rotated four traditional WR-28 waveguides at 28GHz so as to create a multi-polarized function with decoupling between the ports. In addition, a rectangular DR (Dielectric resonator) is mounted at the center of the four apertures to obtain lower mutual coupling over a wide band and to suppress the cross-polarization in the antenna boresight direction. The proposed antenna achieves high gain of 14.4dBi, low mutual coupling of ≤-20dB on average, sufficient cross-polarization discrimination level at ≃20dB in the 27-29GHz frequency band.

Solar energy is an important energy resource for a sustainable society and is massively introduced these days. Household generally sells their excess solar energy by the reverse power flow, but the massive reverse power flow usually sacrifices the grid stability. In order to utilize renewable energy effectively and reduce solar energy waste, electric vehicles (EVs) takes an important role to fill in the spatiotemporal gap of solar energy. This paper proposes a novel EV aggregation framework for spatiotemporal shifting of solar energy without any reverse power flow. The proposed framework causes charging and discharging via an EV aggregator by intentionally changing the price, and the solar energy waste is expected to reduce by the energy trade. Simulation results show the proposed framework reduced the solar energy waste by 68%.