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

No abstract

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.

In the packet access of wireless sensor networks, a distributed access protocol is employed to avoid packet collision but it also causes delay. Therefore, real time data collection is difficult. In wireless communication for simultaneous multidata collection (WC-SDC), sensed data are projected onto the parameters of the wireless communication. The specific feature of the sensed data appears in received signals. Even if the transmitted signal from each sensor collides by simultaneous access, the mixture sensed can be separated by using the specific features. Therefore, the real time data collection is achieved. However, frequency mismatch causes the fluctuation of sensed data, which gives the adverse impact to data separation. In this paper, a data tracking method is used for the data separation in the WC-SDC. We clarify the accuracy of data separation and the impact from the frequency offset. We propose a method for coping with the frequency offset and the error tracking. From the numerical results, our proposed method accurately achieves data separation even under 7% frequency offset normalized by the minimum frequency resolution.

No abstract