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This paper introduces a preamble arbitration rule and interference suppression (PARIS) method for ultra-wideband (UWB) in-vehicle networks. Advancements in the automotive technology have led to increased reliance on wire harnesses, resulting in higher costs, electronic integration challenges, and adverse environmental effects. To address these problems, we explored the use of UWB wireless networks, which are characterized by low transmission power and superior signal penetration capabilities. A significant challenge associated with implementing UWB in automotive environments is the increased frame error rate (FER) caused by UWB interference. Our experiments indicate that vehicles equipped with identical UWB networks exhibit an FER of approximately 6% when positioned closely. This level of FER is problematic for automotive applications, where reliable communication is paramount. To mitigate this problem, we developed an PARIS communication algorithm that is robust against interference. As identified in this study, PARIS leverages two key characteristics of UWB. First, it prioritizes the timing of signal reception over radio signal power, enhancing interference suppression by activating the receiver at the optimal moment before the desired frame arrives, thereby minimizing data loss. Second, the algorithm exploits the hierarchical nature of preamble codes in simultaneously received frames, reducing data loss rate to the order of $10^{-5}$ by prioritizing frames from critical communication devices based on the preamble code hierarchy. Implementing the UWB-based PARIS method in wireless vehicle networks can reduce the weight of the wire harnesses by approximately 20%, offering a promising solution to the challenges posed by traditional wiring systems.

In current mobile communication networks, handover substantially influences the performance of heterogeneous networks (HetNet). The decision process and trigger timing of the handover decision algorithm (HDA) are integral aspects of handover and are indispensable for mobile user equipment (UE). However, the major A3RSRP (reference signal received power) HDA with the parameter of handover margin (HOM) used in co-frequency environments has poor performance with low-speed pedestrian scenarios in HetNet, including high handover failure (HoF) ratio, frequent handover (FHO), and high number of HoFs. In this paper, we focus on reducing the HoF ratio and propose a novel CQI and hysteretic (CH)-based HDA using downlink channel quality indicator (CQI) and hysteretic control (HC). This novel HDA enables the cell to check the downlink CQI of the UE during HDA, thereby correctly determining the trigger timing of the handover. Moreover, the cell can use the HC to dynamically adjust the HOM based on the CQI of the UE to adapt the handover to the HetNet environment. We evaluate and compare the performance of the CH and A3RSRP HDAs in three scenarios with different speeds, each with multiple HOM settings. The ns-3 simulation results indicate that the CH HDA shows a maximum improvement of 100% in HoF ratio and 5% and 100% in the number of handovers and HoFs, respectively, in the small-scale environment with a speed of 0.5 m/s and an initial HOM of 2.0 dB, while the CH HDA shows a minimum improvement of -4% in HoF ratio and 58% and 44% improvement in the number of handover and HoFs, respectively, in the large-scale environment with a speed of 0.5 m/s and an initial HOM of 3.0 dB, compared with the A3RSRP HDA.

This article presents a method for estimating the power consumption of radio frequency (RF) power amplifiers (PAs) within the 5G new radio (NR) downlink (from the base station to user equipment), particularly in the absence of user data. We also outline an RF PA design strategy for low power consumption and high efficiency. When no user data signal is transmitted, in the 5G NR downlink, considerably fewer fixed-transmission signals, such as reference signals, are involved compared to 4G long-term evolution (LTE). This results in a reduced operating time for the PA and decreased power consumption. The PA power consumption was calculated by analyzing the signal and nonsignaling periods in a 5G NR downlink without user data. Furthermore, the power consumption is estimated based on measurements obtained from the continuous wave (CW) or the full-buffer signal input/output of a fabricated PA. The fabricated PA is a Doherty amplifier, whose impedance conversion circuit consists of a transmission line connected in parallel to two T-shaped stubs. It can short-circuit the second to the fifth harmonics at the same connection point. The fabricated Doherty amplifier achieved a drain efficiency of 60.2% at a saturated output power of 48.6 dBm at 1852.5 MHz. At a 6 dB back-off from saturation, its drain efficiency was 51.9%. In addition, at a 39.6 dBm modulated signal output, the spurious levels up to the fifth harmonic were −15.8 dBm/MHz or less.

In outdoor positioning, the global positioning system (GPS) is currently the most commonly used method. Considering the power consumption required to use GPS, it may not be efficient for tracking in smaller areas, such as outdoor grazing areas in Japan, and using wireless sensor networks seems more feasible. There are several methods for BLE-based positioning. Because the angles of arrival (AoA) and time of flight (ToF) require additional equipment, the RSSI-based localization method is the most cost-efficient. Owing to the outdoor environment, the RSSI transmission model follows a two-ray ground-reflection model, this can lead to large errors in the trilateration positioning method. On the other hand, fingerprint positioning requires the creation and maintenance of a large database. This paper proposes an RSSI-based positioning method formulated as an optimization problem. We evaluated the performance of various algorithms in two application scenarios. Our simulation results show that a high localization accuracy can be obtained using this localization method. In contrast to other methods, this approach does not necessitate the use of supplementary equipment, as is the case with AoA and ToF, nor does it require the establishment and upkeep of an RSSI fingerprint database.

Wireless sensor networks are a collection of tiny sensor devices powered by energy-scarce batteries. Depending on the nature of the application, those tiny devices might be deployed in areas that make it difficult to recharge or change their battery. Therefore, it is important to optimize the energy consumption of these devices to prolong their lifetime for as long as possible. Clustering is regarded as the most efficient approach to extend the lifetime of wireless sensor networks. This study proposes a novel method integrating cluster head selection and cluster formation to obtain as long a network lifetime as is possible. We employ a linear programming model to resolve the issue of cluster head selection and a Hierarchical Agglomerative Clustering (HAC) method to form clusters in the network. We evaluated HAC on different sets of inputs where we varied the position of the sink from the corner of the network to the center. HAC outperformed other existing clustering techniques for the first node death (FND) and half node death (HND) lifetime definitions in the cases where the sink is at the corner of the network. In the cases where the sink is located at the center of the network, HAC outperformed the other existing techniques for all definitions of network lifetime.

Establishing a highly accurate positioning of radio sources for radio wave monitoring and frequency spectrum sharing is attracting considerable attention. Because the positioning method generally requires specific processing of the position target, it is not applicable when the position target cannot perform any processing. The location fingerprinting method uses multiple sensors to observe the received signal strength indication (RSSI) emitted by a radio source and estimates the position from the radio wave propagation characteristics. This does not require a certain process for the target position. However, it takes time to gather RSSI from many sensors by wireless communication. In this study, we propose an RSSI gathering method using physical wireless parameter conversion sensor networks (PhyC-SN) for the high-speed positioning of radio sources. In the proposed method, each sensor selects the radio carrier frequency corresponding to its measured RSSI and transmits the signal. Projecting the RSSI distribution of each sensor onto the frequency distribution of the received signal enables the center to detect the RSSI of multiple sensors simultaneously. Furthermore, to improve the gathering accuracy, we established a sensor group method by considering the regional characteristics and access timing control based on each sensor group. Computer simulations and experimental evaluations show that the proposed method significantly reduces the data-gathering time compared with conventional packet communications and achieves a high positioning accuracy.

To monitor dopamine neural activities in the ventral tegmental area (VTA) in response to nicotine, we designed a dopamine platform based on a microimaging device and a microdialysis system. We measured dopamine (DA) release in the prefrontal cortex (PFC) and the nucleus accumbens (NAc) by a micro dialysis system and monitored DA neurons activities by fluorescence imaging in the VTA using a microimaging device simultaneously. GCaMP6 transgenic mice were used in this study, and the change in the fluorescence ratio intensity associated with nicotine administration was estimated. Our results suggested that nicotine administration increased the DA neurons activity in the VTA as well as the DA release in the PFC and the NAc. The proposed platform has the potential to monitor neural activities in the deep brain regions of rodents.

Optogenetics is a powerful tool for controlling biological functions using light. Optical fibers have been extensively utilized in optical stimulation devices for optogenetics. However, the use of optical fibers results in a small photo-stimulation region. In this study, micro-LED array devices were developed to achieve large-area photo-stimulation in the brain of a large animal, such as macaque monkeys. Planar and linear micro-LED array devices were designed and fabricated to photo-stimulate the prefrontal cortex (PFC) and ventral tegmental area (VTA) of the brain and induce a neurochemical response. Device operation, optical intensity, and safety were first characterized using rats. Subsequently, the devices were used to photo-stimulate the brain of macaque monkeys. In addition, microdialysis in the PFC was performed. The devices detected modulated levels of dopamine in the brains. Thus, the photo-stimulation of both the PFC and VTA were successfully achieved, and the effectiveness of the developed micro-LED array devices was demonstrated. The study will help facilitate further studies on micro-LED array stimulation for system-wide optogenetic manipulation in large animals.

Measurements of the deep brain area of rodents has been one of the most important methods to study deep brain activities, in part due to limited access of this region. In order to address this problem, we have designed and fabricated an implantable microimaging device for fluorescence imaging in the deep brain of rodents. In this study, we established a new fabrication method of a flat and uniformed fluorescence filter for an implantable microimaging device which obtained higher quality images, and aimed to show the performance of the device in the ventral tegmental area (VTA) of rats. We introduced GCaMP6s in the VTA of rats by adeno-associated viral (AAV) injection. The change in the fluorescence intensity was associated with nicotine administration and was observed by using our custom implantable microimaging device. We showed that nicotine administration significantly affected the excitation of DA neurons in different sub-regions of the VTA, reaching a maximum activation between 6-10 minutes following nicotine exposure, followed by a decrease after 31-35 minutes. Therefore, we believe our implantable microimaging device can potentially be used to monitor the neural activation within the sub-regions of the VTA in response to acute nicotine exposure by using GCaMP.

Microrobots are promising tools for applications that require micromanipulation, such as single-cell manipulation and surgery, tissue engineering, and desktop manufacturing. This paper briefly reviews common microrobot actuation mechanisms, then reviews current progress in several capabilities that are desirable for micromanipulation, with an emphasis on optothermal microrobots.

The long range wide area network (LoRaWAN) is one of the enabling technologies for low power wide area (LPWA) networks. In LoRaWAN, a node transmits its packet to a gateway (GW) in an autonomous and decentralized manner. The quantity of data that can be transmitted by each node is limited by the duty cycle (DC). Furthermore, it is not easy for the nodes to perform sophisticated techniques for increasing the data quantity due to their limited functionality, especially when the nodes are battery-powered. If the network size increases, packet collision may occur more frequently. Since the packet transmission drains the LoRaWAN nodes' battery, the packet collision results in a waste of limited power. Thus, it is necessary to develop a simple but effective transmission strategy that efficiently utilizes limited battery at each LoRaWAN node. This study proposes packet-level index modulation (PLIM), which is suitable for such LPWA networks. PLIM takes advantage of the sparse data packet transmission in time and the selection of a frequency channel among multiple ones by each node. A time slot and frequency channel combination is selected, i.e., the index, to increase the data quantity. For long-range communication, it is necessary to use a higher spreading factor (SF) in LoRaWAN, which results in a lower data rate due to the DC. The proposed PLIM can compensate for such data rate loss by taking advantage of the sparse transmission in time. Numerical evaluation elucidates that the proposed PLIM can increase the data quantity of LoRaWAN system without requiring any modification in the specification. When the SF is 10, the proposed PLIM can increase the data quantity up to 32.5%, compared to the conventional LoRaWAN system.

Owing to the recent research and development on the Internet-of-Things (IoT) and machine-to-machine (M2M) communication, wireless sensor networks have attracted considerable attention. Among these networks, low power wide area networks (LPWANs), which realize low power, low data rate, and wide communication area, are most commonly used for long-range communication. These networks adopt asynchronous random-access protocols, such as the pure ALOHA protocol in the medium access control (MAC) layer. Thus, there is a high possibility that multiple nodes transmit packets simultaneously on the same frequency channel, resulting in packet collisions. Carrier-sense multiple access/collision avoidance (CSMA/CA) and centralized resource allocation are effective for avoiding packet collisions. However, these schemes increase the energy consumption of battery-powered LPWAN nodes. In addition, LPWAN has a large coverage area; hence, there is a high possibility that the carrier sense will not work successfully. Thus, this paper proposes a simple but effective machine-learning-based scheme that tackles the packet collision problem by offsetting the transmission timings and avoiding unnecessary packet transmission in an autonomous decentralized manner. Each LPWAN node adjusts the transmission probability and timing using the Q-learning technique. The proposed scheme provides effective packet collision avoidance for LPWAN nodes without the need for an additional control signal. The computer simulation results show that the proposed scheme can improve the average packet delivery ratio (PDR) by 60% compared to the pure ALOHA protocol.

In the fifth-generation of mobile communications, network slicing is used to provide an optimal network for various services as a slice. In this paper, we propose a radio access network (RAN) slicing method that flexibly allocates RAN resources using deep reinforcement learning (DRL). In RANs, the number of slices controlled by a base station fluctuates in terms of user ingress and egress from the base station coverage area and service switching on the respective sets of user equipment. Therefore, when resource allocation depends on the number of slices, resources cannot be allocated when the number of slices changes. We consider a method that makes optimal-resource allocation independent of the number of slices. Resource allocation is optimized using DRL, which learns the best action for a state through trial and error. To achieve independence from the number of slices, we show a design for a model that manages resources on a one-slice-by-one-agent basis using Ape-X, which is a DRL method. In Ape-X, because agents can be employed in parallel, models that learn various environments can be generated through trial and error of multiple environments. In addition, we design a model that satisfies the slicing requirements without over-allocating resources. Based on this design, it is possible to optimally allocate resources independently of the number of slices by changing the number of agents. In the evaluation, we test multiple scenarios and show that the mean satisfaction of the slice requirements is approximately 97%.

Gallium-based liquid metals have attracted attention as a conductive adaptable material that can be shaped to obtain reconfigurability. We demonstrate a two-dimensional array of liquid-metal conductive nodes, in which reconfigurability is achieved by merging and splitting adjacent nodes to reversibly shape a two-dimensional structure. The nodes are reconfigured using three different actuation schemes: 1) pressure-point actuation, where the liquid metal is actuated by pressing on the flexible superstrate; 2) surface-tension self-splitting, where the geometric shape of the node itself exerts a separating force on merged nodes; and 3) electrocapillary actuation where the liquid metal is actuated by a DC electrical signal. This is the first demonstration of a reconfigurable liquid-metal pixel array using liquid-liquid interconnects for prototyping reconfigurable devices. As a proof of concept, the proposed actuation techniques are used to reconfigure a liquid-metal nodal patch antenna, in which the liquid-metal nodes are reversibly connected and disconnected to change the operating frequency and polarization.

High-altitude platform stations (HAPSs) are expected to provide ultrawide-coverage areas and disaster-resilient networks from the stratosphere at around 20 km by installing wireless equipment on HAPS. Because their altitude is much lower than that of communications satellites, HAPSs can provide mobile communications services directly to smartphones, which are commonly used in terrestrial networks, such as fourth generation Long Term Evolution. Considering the widespread nature of mobile broadband communications and the importance as a backup line in case of disaster, HAPSs are expected to provide a large capacity in the future. A cellular system with single-cell frequency reuse using multiple cells similar to terrestrial mobile communications should be introduced to achieve such a capacity. The number of cells that a HAPS can accommodate ranges from 1 to more than 100, depending on unmanned aerial vehicle (UAV) ability. By contrast, the optimal cell configuration, which depends on the number of available cells, has not been clarified in previous research. In this paper, we propose an optimization method for the cell configuration for HAPS mobile communications using a genetic algorithm, which can be generally applied regardless of the number of cells and can clarify the optimal cell configuration. Although many cells are required to achieve gigabit-class HAPS mobile communications, the heightened power consumption due to the large number of cells is a critical problem for UAVs. Thus, we also investigate the reduction of the total transmission power and demonstrate the feasibility of energy-efficient gigabit HAPS mobile communications with wide coverage.

Applications related to the Internet of Things are widely diversified and require both low latency and the access of massive sensors to wireless sensor networks. In physical wireless parameter conversion sensor networks (PhyC-SN), a fusion center (FC) can recognize information from all sensors on the frequency spectrum of the received signals via conversion from sensor information to signal frequency. The higher resolution of sensor information of PhyC-SNs requires securing more frequency bandwidth. Therefore, spectrum sharing between PhyC-SNs and other systems is essential. For this study, we assume that primary systems (PSs) refer to other wireless systems and that secondary systems (SSs) refer to the PhyC-SN. The SS detects any access from the PS and immediately stops access to an FC. This results in a loss of sensor information. Thus, the accuracy of the gathered sensor information by an FC is degraded. This paper proposes an adaptive channel assignment based on two predictions; sensor information and channel occupancy rate. In the proposed method, the predicted error caused by the cessation of channel access is calculated and the assignment is constructed by minimizing this predicted error. Since the sensor can select channels in accordance with the error of an instantaneous sensor result, the proposed channel assignment can utilize awareness of the instantaneous sensor result. The proposed method achieves high accuracy of gathered sensor information while delivering less frequent sensor information to the FC, thereby improving the utilization efficiency of frequency channels.

The mutual interference among wireless nodes is a critical factor in the Internet-of-Things (IoT) era due to its dense deployment. Due to its large coverage area, wireless nodes may not be able to detect the on-going communication of other nodes in a long range wide area network (LoRaWAN), which is one of the low power wide area (LPWA) standards. This results in packet collision. The packet collision among LoRaWAN nodes significantly deteriorates network performance functions such as packet delivery rate (PDR). Furthermore, if packet collision happens, LoRaWAN nodes must retransmit packets, draining their limited battery power. Thus, mutual interference management among LoRaWAN nodes is important from the perspectives of both network performance and network lifetime. However, due to its large network size, it is difficult to explicitly comprehend the wireless channel environment around each LoRaWAN node, such as the relation among other LoRaWAN nodes. Thus, in this paper, we utilize the powerful machine learning technique. The wireless environment around LoRaWAN nodes are learned, and the knowledge is utilized for resource allocation in order to improve PDR performance. In the proposed method, Q-learning is adopted in a LoRaWAN system, and the weighted sum of the number of successfully received packets is treated as a Q-reward. The gateway (GW) allocates resources to maximize this Q-reward. The numerical results considering LoRaWAN elucidate that the proposed scheme can improve average PDR performance by about 20% compared to the random resource allocation scheme.

Gallium alloys are liquids at room temperature, and are suitable as conductors in electronic circuits. Furthermore, gallium-based liquid metals immersed in a water-based electrolyte such as sodium hydroxide (NaOH) can be electrically actuated, enabling reconfigurable electronics such as RF switches, tunable filters, and tunable antennas. However, NaOH in liquid-metal reconfigurable electronics also causes RF losses that should be minimized by careful design and simulation. To accurately simulate the effects of NaOH at microwave frequencies, the complex permittivity of NaOH is required over the operating frequency range. Here, the complex dielectric permittivity of aqueous NaOH solutions is determined from 0.2 to 20 GHz by dielectric spectroscopy. NaOH solutions with concentrations of 0.01 moles/liter (M), 0.1 M, 0.25 M, 0.5 M, 0.75 M, 1.0 M, 1.25 M, and 1.5 M are investigated at 20 °C. The complex permittivity spectra are fitted by a Cole-Cole relaxation time distribution. In addition, the fitting parameters, including static permittivity εs and relaxation time τ are reported, along with the distribution parameter α. The measured permittivity of NaOH is used to simulate two liquid-metal RF components using NaOH. The measured RF performance are in good agreement with the simulated results that include the effects of NaOH.

A liquid-metal RF shunt switch is presented that combines a dielectric spacer over the RF path with electrolyte-filled capillary troughs to facilitate low-power electrical actuation. The switch demonstrates between 20and 30-dB isolation in the dc to 5-GHz range, and greater than 10-dB isolation from 5 to 11 GHz. This switch has between 0.2and 1.2-dB insertion loss from dc to 5 GHz.