In the 6th generation (6G), demand for high-capacity wireless communication systems is growing due to the emergence of a wide variety of applications. Small-cell use with millimeter waves and higher frequency bands is key to increasing system capacity. To achieve a large number of small-cell installations, a flexible backhaul is required to support the foundation of high-speed 6G accesses. The orbital angular momentum (OAM) multiplexing technique is effective for achieving high-capacity wireless backhauls. This paper presents the field experimental results of real-time wireless transmission using a combination of OAM and polarization multiplexing in the E-band, from 71 to 86 GHz, using an OAM-multiplexing prototype. Experimental results indicate successful real-time transmission of 139.2 and 104.0 Gb/s at distances of 22.5 and 45 m, respectively. We also demonstrated real-time OAM multiplexing transmission in a reflection scenario using a reflector placed in the middle of the propagation path and successfully achieved 139.2 Gb/s at a distance of 22.5 m.

The use of millimeter wave (mmWave) bands is a promising approach to increase the capacity of mobile networks. However, the mm Wave link quality (LQ) is strongly affected by the condition of the line of sight (LOS) path. In order to stably utilize mm Wave bands, an effective solution is to predict future LQ and adaptively control wireless communication. LQ prediction based on Deep Neural Network (DNN) using physical space information, such as the position information of user equipment (UE), is effective to enable the proactive control. On the other hand, huge training data is required to provide the prediction model. In a supervised learning approach, the prediction models must be generated for all the base stations with which the UE communicates. Thus, it is a key to generate the prediction models for different base stations using no or less amount of the training data. In this paper, we propose a method for preprocessing physical space information to reduce the amount of training data when the UE accesses to the different base station. The proposal normalizes the position and direction information of UE by using the position of the base station. To evaluate the effectiveness of the proposed method, we conducted experiments using an automated humanoid robot in a commercial 5G network. Experiment results show that the proposed method reduces the root-mean-square-error (RMSE) of the differences between the predicted and actual throughputs by 35.1 and 51.0% at the different base station when using no samples and 100 samples as training data, respectively, compared to without physical space information preprocessing.

Millimeter-wave (mm-wave) band use is the key to meet the demand for high-speed wireless communication in future mobile networks. The mm-wave throughput performance is often unstable due to the influence of blocking objects, the fading effect, etc. A promising way to improve the throughput performance of the mm-wave channel is for multiple user equipment (UEs) to be used by the user and to integrate their links to achieve the extremely high requirements of the future mobile network. Since the mm-wave channel throughput strongly depends on where the UE is kept, the simple cooperation of multiple-UE-link of the same user enables stable throughput performance, while there is little gain in the multiple-UE-link cooperation in the micro-wave channel. In this paper, we evaluated the potential throughput of multiple-UE-link cooperation by using measured throughputs in 28 GHz channels in a fifth generation mobile communication system (5G) network. In addition, we propose the link selection by using measured reference signal received power (RSRP) cross-correlations between UE links to reduce power consumption. Experimental results show that the potential throughput of three UE links is 22.2 % greater than when using a single UE link at the 5%-ile cumulative distribution function (CDF). Also, we show that the proposed method achieves throughput performance with two UE links that is the same as when three UE links are used at the 5%-ile CDF and is 9.9% greater than that of the conventional link selection.

This paper proposes a multicomponent topology optimization method that considers assemblability. Generally, it is difficult to consider assemblability in topology optimization; however, in this study, we achieve it by introducing a fictitious physical model. To perform multicomponent topology optimization, the extended level set method is used to represent multiple components. First, the assembly constraints are formulated using a fictitious physical model limited to two components. Then, by considering stepwise assembly, the constraint is extended to three or more components. In addition, topology optimization algorithms are constructed using the finite element method. Several numerical examples demonstrate that the proposed method can obtain structures with assemblability and has low initial structure dependence.

Toward further enhancement of 5G and 6G, metasurface and reconfigurable intelligent surface (RIS) have been attracting much attention thanks to cost-effective and energy-efficient features. Experimental trials have been conducted to verify their effectiveness. In consideration of a legacy deployment, it is difficult to create a coverage area at the foot of a building in millimeter-wave (mmW) bands. Thus, for mmW coverage enhancement, we propose a novel solution that combines multiple transmissive metasurfaces (TMSs) with beamforming (BF) in a base station (BS). To verify the effectiveness of our proposed solution, experimental trials using a 28 GHz-band 5G BS and a user equipment were conducted. The BF function selectively aims the beam at one of five TMSs, and then TMS re-directs the radio wave to the foot area. This paper introduces an overview of the trials and shows received power and downlink throughput measured in the trials. It is verified that our solution can enhance the coverage area to the foot and achieve much higher throughput of approximately 1 Gbps than the legacy deployment.

Full-duplex communications are potential candidate for increasing the spectral efficiency for 5G mobile systems and beyond, which can transmit and receive simultaneously on the same carrier frequency. The key challenge is the self-interference (SI) caused by the coupling between the transmitter (TX) and its own receiver (RX). This paper proposes a new type of SI canceller based on the channel estimation between TX and its own RX through the use of a specific demodulation reference signal (DMRS) which is special type of physical layer signal. In this paper, we first show the configuration of the proposed SI canceller using the DMRS. Then, we demonstrate the BER performance of OFDM-based QPSK and QAM signals under static and Rician fading channels for SI, using link-level simulations. The simulation results indicate that the proposed SI canceller can sufficiently improve the BERs for OFDM-based QPSK and QAM signals.

A method is proposed for preventing unauthorized face image revelation through unintentional capture of facial images. Methods such as covering the face and painting particular patterns on the face effectively prevent detection of facial images but hinder face-to-face communication. The proposed method overcomes this problem through the use of a device worn on the face that corrupts the Haar-like features through the use of light absorbing and reflecting materials, which makes faces in captured images mostly undetectable. The device is similar in appearance to a pair of eyeglasses, so face-to-face communication is only slightly hindered, and it does not need a power supply. Testing of a prototype "privacy visor" showed that captured facial images are sufficiently corrupted to prevent unauthorized face image revelation by face detection.

The Flexible Wireless System (FWS) has been proposed as a networked system for a User-Centric Wireless Networks (UCWN). UCWNs allow users to make network connections easily at all times without being conscious of any upgrades or differences in wireless systems. The FWS is a unified wireless platform that simultaneously deals with various types of wireless signals. It consists of flexible access points and a wireless signal processing platform. Various types of wireless signals are received at a distributed flexible access point and transferred to a server in the wireless signal processing platform through the wired access line. Transferred signals are separated and demodulated at the server. To achieve highly flexible and efficient radio wave data transfer between the access point and the server, this paper proposes a sparse-matrix-based compressed sensing method under the framework of the belief propagation and cavity method. Low cost implementation using interlevers and adders is also proposed. An empirical study with real data shows the proposed method achieves greater efficiency and reduced calculation cost compared to the conventional compressed sensing method.

The “Flexible Wireless System (FWS)” has been proposed as a networked system for the “User-Centric Wireless Network (UCWN)”. The UCWN will allow users to make network connections easily at all times without being conscious of any upgrades or differences in wireless systems. The FWS is a unified wireless platform that simultaneously deals with various types of wireless signals. It consists of flexible access points and a wireless signal processing platform. Various types of wireless signals are received at a distributed flexible access point and transferred to a server in the wireless signal processing platform through the wired access line. Transferred signals are separated and demodulated at the server. To achieve highly flexible and efficient radio wave data transfer between the access point and the FWS server, we consider compression of transfer data. In this paper, we propose 1-bit compressed sensing with smoothed edge detection, which enhances compression and reconstruction performance. This paper shows the performance of the proposed method by using computer simulations to explore the method's validity for transferring compressed radio wave data.

A previously proposed method for preventing the illegal recording of movies, which uses a charge-coupled device or a complementary metal oxide semiconductor device to add noise signals to the recorded content, is ineffective when the digital camera or camcorder used for the recording is equipped with an infrared-cut filter. The method has now been enhanced to make it effective even when an infrared-cut filter is used. Infrared rays specularly reflected from the filter are detected using an IR camcorder with a short-wavelength cut filter. Testing using a prototype system implemented on a 100-inch cinema screen showed that the enhanced method effectively detects in real time a camera or camcorder equipped with an infrared-cut filter.

A method is proposed for preventing privacy invasion through unintentional capture of facial images. Prevention methods such as covering the face and painting particular patterns on the face are effective but hinder face-to-face communication. The proposed method overcomes this problem through the use of a device worn on the face that transmits near-infrared signals that are picked up by camera image sensors, which makes the face in captured images undetectable. The device is similar in appearance to a pair of eyeglasses, and the signals cannot be seen by the human eye, so face-to-face communication is not hindered. Testing of a prototype "privacy visor" showed that it can effectively prevent privacy invasion via face detection by corrupting the facial images.

Recently developed compressed sensing theory enables signal acquisition and reconstruction from incomplete information with high probability provided that the signal is sparsely represented in some basis. This paper applies compressed sensing for spectrum sensing in a networked system. To tackle the calculation and communication cost problems, this paper also applies structured compressed sensing and 1-bit compressed sensing. Measurement using the partial random circulant matrices can reduce the calculation cost at the sacrifice of a slightly increased number of measurements by utilizing the fact that a circulant matrix is decomposed by multiplications of structured matrices. This paper investigates the tradeoff between calculation cost and compression performance. 1-bit compressed sensing extracts only sign data (1-bit quantization) from measured data, and reconstructs the original signal from the extracted sign data. Therefore, 1-bit compressed sensing can save communication costs associated with spectrum sensing in a networked system. This paper evaluates the efficiency of 1-bit compressed sensing. In addition, this paper also proposes a block reconstruction algorithm for 1-bit compressed sensing that uses the block sparsity of the signals. Empirical study shows that partial random circulant matrices work as efficient as completely random measurement matrices for spectrum sensing and that 1-bit compressed sensing can be used for spectrum sensing with greatly reduced communication costs.

A previously proposed method using infrared (IR) rays to prevent the re-recording of images and videos corrupts the recorded content by adding invisible noise signals using a charge-coupled device or CMOS device during recording. However, it is not effective against re-recording using a short wavelength pass filter (SWPF) to eliminate the noise. We have now implemented a countermeasure that makes it effective against re-recording using such filters. It detects IR rays reflected off the filter by using the IR specular reflection properties of the filter. Evaluation of the countermeasure implemented on a functional prototype system showed that it effectively detects re-recording using an SWPF.

A previously proposed method for preventing the illegal recording of displayed content by adding invisible noise signals to the recorded content during recording to make it unrecognizable has now been implemented in a stand-alone cabinet. Testing using the prototype stand-alone implementation demonstrated that it can effectively prevent illegal recording of displayed content by completely corrupting the recorded content. This illegal recording prevention method is applicable not only to content displayed on a screen but also to other things that should be protected against illegal recording such as confidential documents in a public office and art objects in a museum.

We propose a method for preventing illegal re-recording of images and videos with digital camcorders. Conventional digital watermarking techniques involve embedding content ID into images and videos, which helps to identify the place and time where the actual content was recorded. However, digital watermarking technology does not control the illegal re-recording of digital content with camcorders. The proposed method prevents such re-recording by corrupting the recorded content with an invisible noise signal using CCD or CMOS devices during recording. In this way, the re-recorded content will be unusable. To validate the results of this proposed method, we developed a functional prototype system for preventing illegal re-recording and implemented it on a 100-inch cinema screen.

In conventional road-vehicle communication systems, user terminals in the vehicles have to directly connect with wireless access points (APs). However, the speed of vehicles is so fast that the channel condition between the terminals and the APs constantly changes because of changing path-loss and time-varying fading. In this paper, to compensate for such deterioration, we propose to reduce relative speed between user terminals and APs by using an inter-vehicle packet relay technique. If a terminal can send or receive data via other vehicles running at lower speeds, the relative speed will decrease, which suppresses the dynamic range of path loss and deterioration by fading. We evaluate our method by computer simulations using a geometric propagation model. In the simulations, phase difference between multiple paths and path-loss fluctuation within one frame duration affect the performance. From the results of the simulations, we validate our method. Moreover, we evaluate interference in the overlapped zone between two AP areas. From the evaluation, we show that our packet relays do not cause a problem in interference between areas.

The demand for Internet access in vehicles has been growing. To access the Internet from vehicles, a wireless road- vehicle communication system is necessary. In conventional road-vehicle communication systems, user terminals in the vehicles have to directly connect to wireless access points (APs). However, the speed of vehicles is so fast that the channel condition between the terminals and the APs constantly changes due to changing path-loss and time-varying fading. In this paper, we used an inter-vehicle packet relay technique to improve channel quality in road-vehicle communication systems. If a terminal can send data via other vehicles moving at lower speed, the relative speed will decrease, which suppresses the dynamic range of path loss and deterioration by fading. We evaluated this method using numerical analysis to verify the availability of the method.

This paper describes a method for extracting reliable reputation on the Web. In this research, reliable reputation is the information that has an opposite polarity value of contributor's stance (positive or negative). We call this information "fair reputation". In order to extract fair reputations, we develop the following two tasks. The first task is classification of feedback documents into positive or negative classes. For this task, we propose a classification method using 'document level reputation' that can determine the polarity value of the document. The second task is extraction and classification of reputations. Using the classified reputations, we extract "fair reputations". The experimental results using movie reviews showed that the proposed method could classify feedback documents more correctly than the previous method, and that fair reputations are useful for evaluating reputations

A hybrid learning algorithm that combines the genetic algorithm and steepest decent method is proposed for neural networks. Also described is a method for overcoming the Jacobian problem when the hybrid learning algorithm is applied to a neural network controller. In the proposed algorithm, a parallel search by the genetic algorithm and a continuous search by the steepest descent method are carried out sequentially. This may improve the convergence speed and solve the local minimum problem. In this approach, many neural network controllers are generated in the search process of the genetic algorithm. Potential functions, defined by both the control performance of each neural network controller and the reciprocal actions between the neural network controllers, are introduced into the neural network's learning. This allows application of the steepest descent method without either the Jacobian information or the neural network identifier. Simulations were performed using a discrete-time SISO nonlinear system as the objective system. The results confirm the feasibility of the proposed method for the learning of the neural network controller.

A neural network requires the partial derivative of a plant output with regard to its input. However, it is unknown for an unknown nonlinear plant. This paper proposes a hybrid neural network controller which overcomes this problem and which compensates online neural networks for plant fluctuation by using an identifier and a controller with different convergence speeds.

Feedforward feedback neural network controllers have been proposed. These controllers use the sum of neural network output and conventional controller output as the object plant input. The authors proposed an adaptive type feedforward feedback controller using a neural network and confirmed its characteristics. However, th learning type neural network controller is also attractive for servo control applications. Therefore, this paper proposes a learning type feedforward feedback neural network controller for discrete time dynamic systems. Simulated results using a second order plant confirm the characteristics of the proposed controller.

A practical neural network design method for the identification of both the direct transfer function and inverse transfer function of an object plant is proposed. As a practical application of the direct transfer function identifier, a nonlinear plant simulator is also proposed. Simulated and experimental results for a second-order plant show that identification can be satisfactorily achieved and that neural network identifiers can represent nonlinear plant characteristics very well. The characteristics of a neural network direct controller with a feedback control loop, which uses the learning results of the inverse transfer function identifier, is also proposed and confirmed.>

An autotuning method for the optimum sigmoid function of neural networks is proposed. It is based on the steepest descent method. Simulated results using a learning-type direct controller confirm both the practicality and the characteristics of the autotuning method.

The authors attempt to clarify neural network (NN) controller characteristics by comparison with the adaptive control theory. The authors clarify the classification of the NN controller architecture and the dynamic NN structure. Comparison between the NN controller and the adaptive controller clarifies that the framework of a linear two-layer NN-type controller is the same as that of adaptive control and the nonlinear three-layer NN (PDP type) is a nonlinear extension of the adaptive control. The stability characteristics of the NN control system are discussed. NN controller experiments are demonstrated using a force control servomechanism. Experimental results suggest that the nonlinear sigmoid function of the NN can compensate for the nonlinear plant effect.>

The basic features of the learning-type neural network (NN) controller are clarified. Analytical and experimental results show its stability, convergence and generalization ability compared with the adaptive-type NN and conventional learning control. As an application of the learning-type NN, a nonlinear optimum regulator is presented whose learning ability can obtain optimum conditions without solving a difficult Riccati equation. Moreover, it can be applied to a nonlinear control system because of its nonlinear mapping ability, although the conventional optimum regulator can only be applied to a linear system. Finally task planning is proposed in terms of skill acquisition using the learning-type NN, which implies the possibility of making an interface with an upper symbolic-level control.>

Describes the auto-tuning of feedback gains for a small tunnelling robot. The authors (1989) have already proposed the directional control method that the head angle of the control input is the sum of the deviation multiplied by feedback gain Kp and the angular deviation multiplied by feedback gain Ka. In this paper, they use a neural network to obtain feedback gains Kp and Ka. The inputs of the neural network are an initial deviation and an initial angular deviation. The outputs of the neural network are the feedback gains Kp and Ka. This neural network learns from the deviation errors. The optimum gains obtained by the proposed method agreed with the optimum gain obtained by trial and error. The neural network which can apply to any initial deviations were formed by using plural initial deviations in learning. Moreover, this method can tune the optimum gains to any design line. The results showed the validity of the proposed auto-tuning method.>

Proposes a digital control method for master-slave manipulator systems which can realize high-fidelity telemanipulation. In the discrete-time system, the time-delay of sensor signals and the zero order hold effect of command signals on actuators should be considered to make the systems stable. The authors first analyze the dynamic control method of a master-slave system in discrete-time for the stability problem, which can realize high-fidelity telemanipulation in continuous-time. Secondly, using the results of the stability analysis, the robust control scheme for the master-slave system is proposed, and the validity of this scheme is finally confirmed by simulation.>

NN (neural network) controller characteristics are clarified by comparison with the adaptive control theory. The authors explain the classification of the NN controller architecture and the dynamic NN structure. A comparison between the NN controller and the adaptive controller shows that the framework of a linear two-layer NN controller is the same as that of the adaptive controller, and that the nonlinear three-layer NN (PDP, or parallel distributed processing type) is a nonlinear extension of the adaptive control. The stability characteristics of the NN control system, which shows the robustness effect of the generalized delta rule, the plant and the NN mapping function, are treated. Finally, NN controller experiments are demonstrated using a force control servomechanism. Experimental results suggest that the nonlinear sigmoid function of the NN can compensate for the nonlinear plant effect.>

It is shown that a servomechanism with only position/velocity functions can obtain a force control function using the adaptive theory (model-referenced adaptive system and model-referenced control). Experimental results of both the servomechanism and the single-degree-of-freedom force control of the manipulator show that this method leads to suitable force control when the object stiffness changes greatly. The advantage of this method is that a force control function is achieved by implementing software to identify the object stiffness without changing hardware.>