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Resource allocation under massive connectivity is one of the most important research topics due to widespread Internet-of-Things and machine-to-machine communication. Since multiple systems share the same frequency band due to the scarcity of frequency spectrum, resource allocation that takes into account the intersystem interference is essential. In this letter, an interference detection scheme that detects the change of the intersystem interference state is proposed. The proposed scheme is based on density ratio estimation using the available information at the resource controller. For change detection, the controller generates standard samples and test samples. Estimating the density ratio between test and standard samples enables the controller to detect change of the intersystem interference state. Under long-range wide area networks and a wireless-sensor utility network coexisting environment, the numerical results elucidate that the proposed scheme can improve average packet delivery rate by 10% compared to the system without interference detection.

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.

With the progress of internet of things (IoT), devices with a wireless ad-hoc function are being increasingly used. For future wireless communication systems, the use of millimeter waves, which are strongly affected by shadowing, is considered. We focus on energy-efficiency [bits/J] that combines throughput and energy consumption, which is expected to be important in future wireless communication systems, to realize smart IoT in a shadowing environment. In this paper, we compare the effects of two parameters, including shadowing deviation values, on energy-efficiency in three dimensions for both AODV and OLSR protocols in a shadowing environment using network simulator, ns3.

With the further development of the internet-of-things (IoT), numerous nodes are expected to be densely deployed. If multiple nodes simultaneously transmit packets on the same frequency channel, packet collision can occur at the gateway leading to packet loss. By transmitting an acknowledgment (ACK) signal to the node whose packet was correctly received by the gateway, the other nodes can initiate packet retransmission in order to resolve packet loss. However, ACK signal transmission is required from the gateway to all the nodes, increasing the gateway processing load and impacting the duty cycle (DC) restriction on the gateway. In this paper, we propose an autonomous decentralized frequency resource allocation scheme for LoRaWAN, aiming to reduce the number of packet collisions and the gateway processing load. We implement the proposed approach, and demonstrate its effectiveness through computer simulation and experimental evaluation.

Due to the wide spread of machine-to-machine (M2M) communications and Internet-of-things (IoT), large number of wireless terminals are densely deployed. Under such dense deployment, it is necessary to manage the mutual interference among wireless terminals. Carrier-sense multiple access/collision avoidance (CSMA/CA) is one of the random access schemes that allow wireless terminals to access a channel while avoiding such mutual interference. However, if two wireless terminals are in the hidden terminal relation, a packet collision may happen as the carrier sense mechanism does not work. This results in the degradation of packet delivery rate (PDR) performance. Whether or not particular two wireless terminals are in the hidden terminal relation is an unobservable information from a network controller such as an access point (AP). In this paper, we tackle this problem by using machine learning. By using machine learning, the wireless controller makes a guess of the unobservable information from the observable information. We will use the wireless terminal locations and received signal strength at APs as the observable information. Based on the estimated unobservable information, the wireless controller assigns orthogonal resources to the wireless terminals that are in the relationship of hidden terminal in order to avoid the packet collision. Numerical results confirm that the proposed approach can improve the PDR performance up to 15% compared to the random resource allocation scheme.

A low power wide area network (LPWAN) is one of the promising technologies intended for Internet of Things (IoT). The LPWAN features a wide communication area that is a few kilometers to a few tens of kilometers; low power consumption is achieved using a battery. This study measures the transmission characteristics of a LoRaWAN which is one of the LPWAN. Therefore this paper shows the measured results in field experiment of certain cases: a road along the Tamagawa river in Tokyo, inside and around campuses, and a medium city environment. These measured results indicate that it is difficult to accurately perform a carrier sensing for LoRaWAN. Subsequently, we propose a method to realize the accurate spectrum sensing for spectrum sharing considering sensing sensitivity.