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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 ongoing 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. INDEX TERMS Frequency sharing, machine learning, resource allocation, LoRaWAN, CSMA/CA. The associate editor coordinating the review of this manuscript and approving it for publication was Kun Yang. node adopts pure ALOHA. Due to this simple MAC protocol, increased packet collision due to the large number of LoRaWAN nodes is a critical factor in the limitation of the network performance. One of the countermeasures is the introduction of a duty cycle, which limits the transmission interval of each node to a predetermined threshold [2]. Recently, the application of carrier sense multiple access with collision avoidance (CSMA/CA) was proposed to improve the performance of LoRaWAN [3]. For example, CSMA/CA is essential for LoRaWAN in Japan [4]. In this protocol, LoRaWAN nodes detect the wireless medium before starting packet transmission. However, due to LoRaWAN's wide communication area and the low transmission power of its nodes, packet collision happens quite often in comparison to legacy wireless LAN systems. Because the LoRaWAN III. SYSTEM MODEL A. SYSTEM MODEL

Crystalline thin films of polytetrafluoroethylene were deposited on Si (100) wafers by F2 laser (157 nm) ablation. X-ray photoemission spectra indicated that the composition of deposited films was similar to the source material. The surface morphology of films deposited at room temperature contained numerous fibrous structures in size of 100N40o nm, but they were smoothed out at elevated wafer temperature of N370 K. The refractive index was N1.35 at 633 nm. Ionized fragments in the ablation plume were measured by a Faraday cup assembly, but their effect on the deposited films was not observed at the present ionization ratio.

This paper addresses the research question: can feedforward neural network (FFNN)-based path loss modeling improve the accuracy of Kriging? Radio propagation factors, which consist of path loss and shadowing, can accurately be obtained via crowdsourcing with Kriging. In most works on Kriging-aided radio environment mapping, measurement datasets are first regressed via linear path loss modeling to ensure spatial stationarity of the shadowing. However, in practical situations, the path loss often contains an anisotropy owing to terrain and obstacle effects. Thus, Kriging may not perform an optimal interpolation because of the errors in path loss modeling. In this paper, an FFNN is used for path loss modeling. Then, ordinary Kriging is applied to interpolate the shadowing. We first evaluate the performance of this method in a case where the transmitter is fixed. It is shown that this method does not improve Kriging in a large-scale and fixed transmitter system; although the FFNN outperforms OLS in path loss modeling. Then, this method is extended to distributed wireless networks where transmitters are arbitrarily located, such as in mobile ad hoc networks (MANETs) and vehicular ad hoc networks (VANETs). The results of a measurement-based experiment show that the FFNN is capable of improving Kriging in such a distributed network case.

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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 highspeed 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.

Millimeter wave provides high data rates for Vehicle-to-Everything (V2X) communications. This paper motivates millimeter wave to support automated driving and begins by explaining V2X use cases that support automated driving with references to several standardization bodies. The paper gives a classification of existing V2X standards: IEEE802.11p and LTE V2X, along with the status of their commercial deployment. Then, the paper provides a detailed assessment on how millimeter wave V2X enables the use case of cooperative perception. The explanations provide detailed rate calculations for this use case and show that millimeter wave is the only technology able to achieve the requirements. Furthermore, specific challenges related to millimeter wave for V2X are described, including coverage enhancement and beam alignment. The paper concludes with some results from three studies, i.e. IEEE802.11ad (WiGig) based V2X, extension of 5G NR (New Radio) toward mmWave V2X, and prototypes of intelligent street with mmWave V2X.

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