This paper proposes a probabilistic interference constraint method with a radio environment map (REM) for spatial spectrum sharing. The REM stores the spatial distribution of the average received signal power. We can optimize the accuracy of the measurement-based REM using the Kriging interpolation. Although several researchers have maintained a continuous interest in improving the accuracy of the REM, sufficient study has not been done to actually explore the interference constraint considering the estimation error. The proposed method uses ordinary Kriging interpolation for the spectrum cartography. According to the predicted distribution of the estimation error, the allowable interference power to the primary user is approximately formulated. Numerical results show that the proposed method can achieve the probabilistic interference constraint asymptotically. Additionally, we compare the performance of the proposed technique with three methods: the perfect estimation, the path loss-based method, and the Kriging-based method without the error prediction. The comparison results show that the proposed method has a higher spectrum sharing opportunity than the path loss-based method, even if only a small amount of measurement data is available. It is also shown that the proposed method dramatically improves the outage probability of the interference power compared to the conventional Kriging-based method.

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.

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.

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 proposes an RGB-D sensor-aided ray-tracing simulation framework for indoor radio map construction that models indoor information, such as walls and obstacles, as a set of cuboids. Radio maps can accelerate indoor wireless systems, including resource optimization and fingerprint-based localization. We can construct an accurate radio map using a ray-tracing algorithm with precise indoor structures and obstacles information. However, it is challenging to acquire this indoor information because manual observations of these are expensive. To mitigate this critical drawback, we propose utilizing three-dimensional information obtained from RGB-D images for ray-tracing simulation. The proposed method semi-automatically models indoor information as cuboids from three-dimensional information with RGB-D images. The proposed method then feeds these cuboids into a ray-tracing simulation. Indoor experiments using wireless LANs over 5180 and 2452 MHz clarified that the proposed method accurately constructs the radio map.