This paper presents a novel method for radio map construction that simultaneously interpolates the received signal power values over space and frequency domains. Radio maps can be used to improve spectrum management and for localization systems, which are related to wireless systems in general such as cellular systems, Internet of Things (IoT) networks, and vehicular communications. Researchers have shown that crowdsourcing using spatial interpolation techniques can be used to accurately construct a radio map to improve these applications; however, because of the limitation in the spatial domain, conventional methods can build a radio map for only those frequencies at which sensing can be performed. Our proposed method focuses on the fact that the shadowing values show strong correlation over a wide range of frequency domains. The main idea is to treat the shadowing values obtained over various frequencies as the ones over target frequency. Using the actual datasets obtained over 870, 2115, and 3500 MHz in a cellular system, we show that the proposed method can accurately generate a radio map, even if no (or few) datasets are available in the target frequency.

Radio maps are a promising technology that can boost the capability of wireless networks by enhancing spectrum efficiency. Since spatial interpolation is a critical challenge to construct a precise radio map, the latest works have proposed deep learning (DL)-based interpolation methods. However, a DL model that achieves enough estimation accuracy for practical uses has not yet been established due to the complexity of radio propagation characteristics. Therefore, we propose a novel DL framework that transforms the spatial interpolation problem into a shadowing adjustment problem suitable for DL-based approaches. We evaluate the performance using real measurement data in urban and suburban areas to show that the proposed framework outperforms the state-of-the-art deep learning models.

This paper discusses a spreading factor allocation for Long Range Wide Area Network (LoRaWAN). Because Long Range (LoRa) is based on chirp spread spectrum that each spreading factor is approximately orthogonal to each other, the performance of LoRaWAN can be enhanced by allocating the spreading factor appropriately to end devices (EDs). Several spreading factor allocation techniques have been reported. Techniques shown in existing studies can improve some characteristics (e.g. throughput or packet reception probability (PRP)); however, there are a few studies that have focused on the energy consumption of the EDs. The LoRa communication offers a low power communication and this enables the improvement of the performance in exchange for the energy consumption. This paper presents a performance improvement technique via spreading factor allocations for LoRaWAN. We define the optimization problem for the spreading factor allocation to maximize the PRP under a constraint for the average energy consumption of all the EDs. It enables for the performance improvement under the constraint of the average energy consumption of all the EDs by solving the problem. This study further develops a method to solve the defined problem based on a distributed genetic algorithm, which is metaheuristics method. Although the techniques shown in the existing studies give the average energy consumption as a result of the performance improvement by the spreading factor allocation, the presented technique can enhance the LoRaWAN performance by allocating the spreading factor to EDs under the constraint for the average energy consumption of all the EDs. Numerical examples validate the effectiveness of the presented technique. The PRP performance of the presented technique is superior to that of the techniques shown in the existing studies despite that the average energy consumption of all the EDs of the presented technique is less than that of the techniques shown in the existing studies.

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.

Radio environment map (REM) provides detailed information on the coverage of the primary terminals of wireless communication systems. The spatial estimation of the channel condition is directly related to the radio environment. However, the open characteristics of a system are always accompanied by life-threatening conditions. This study addresses this by proposing the Kriging-based trust nodes aided (KTNA) REM construction method with a channel estimation (KTNA+) system. The proposed method adds a small number of trust nodes to avoid malicious impacts and precisely estimate the channel conditions. The simulation results show that the proposed method succeeded in estimating the average path-loss and shadowing impacts under threatening environmental conditions.

The cognitive radio is a secondary wireless communication system that can overlap the frequency band assigned to the primary system by recognizing the frequency status. Ad-hoc cognitive radio is considered for realizing the cognitive radio by using multi-hop communication networks without giving the large interference toward the primary system. We have proposed the STBC distributed ARQ system applied to the ad-hoc cognitive radio for realizing effective cognitive radio. This system can realize a multi-hop communication from the source terminal to the destination terminal without deciding the reliable route in advance. However, the affect of inter-system interference has not been evaluated yet. In this paper, in order to keep the interference level low at the receiver of the primary system, the permissible maximum transmit power of the cognitive terminals is analytically derived. Moreover, we propose a novel STBC distributed ARQ using multifrequency band for improving the performance of the ad-hoc cognitive radio due to the small transmit power and the large interference from the primary system. Finally, we confirm the effectiveness of the proposed system by using computer simulations