Spectrum Sharing Policy in the Asia-Pacific Region

In this chapter, we investigate the spectrum measurement results in Asia-Pacific region. Then the spectrum sharing policy in the Asia-Pacific region is reviewed in details, where the national projects and strategies on spectrum refarming and spectrum sharing in China, Japan, Singapore, India, Korea and Australia are investigated. Then we introduce the spectrum sharing test-bed that is developed in China, which is a cognitive radio enabled TD-LTE test-bed utilizing TVWS. This chapter provides a brief introduction of the spectrum sharing mechanism and policy of Asia-Pacific region.Comment: 33 pages, 17figure

RORS: Enhanced Rule-based OWL Reasoning on Spark

The rule-based OWL reasoning is to compute the deductive closure of an ontology by applying RDF/RDFS and OWL entailment rules. The performance of the rule-based OWL reasoning is often sensitive to the rule execution order. In this paper, we present an approach to enhancing the performance of the rule-based OWL reasoning on Spark based on a locally optimal executable strategy. Firstly, we divide all rules (27 in total) into four main classes, namely, SPO rules (5 rules), type rules (7 rules), sameAs rules (7 rules), and schema rules (8 rules) since, as we investigated, those triples corresponding to the first three classes of rules are overwhelming (e.g., over 99% in the LUBM dataset) in our practical world. Secondly, based on the interdependence among those entailment rules in each class, we pick out an optimal rule executable order of each class and then combine them into a new rule execution order of all rules. Finally, we implement the new rule execution order on Spark in a prototype called RORS. The experimental results show that the running time of RORS is improved by about 30% as compared to Kim & Park's algorithm (2015) using the LUBM200 (27.6 million triples).Comment: 12 page

On the Construction of Radio Environment Maps for Cognitive Radio Networks

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.Comment: 6 pages, 7 figures, IEEE WCNC conferenc

Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments

We propose a geographic and spatio-temporal information based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.Comment: 6 pages, 5 figures, accepted to appear on IEEE Globecom, Aug. 2022. arXiv admin note: text overlap with arXiv:2208.1185

SLAM for Multiple Extended Targets using 5G Signal

5th Generation (5G) mobile communication systems operating at around 28 GHz have the potential to be applied to simultaneous localization and mapping (SLAM). Most existing 5G SLAM studies estimate environment as many point targets, instead of extended targets. In this paper, we focus on the performance analysis of 5G SLAM for multiple extended targets. To evaluate the mapping performance of multiple extended targets, a new mapping error metric, named extended targets generalized optimal sub-pattern assignment (ET-GOPSA), is proposed in this paper. Compared with the existing metrics, ET-GOPSA not only considers the accuracy error of target estimation, the cost of missing detection, the cost of false detection, but also the cost of matching the estimated point with the extended target. To evaluate the performance of 5G signal in SLAM, we analyze and simulate the mapping error of 5G signal sensing by ET-GOPSA. Simulation results show that, under the condition of SNR = 10 dB, 5G signal sensing can barely meet to meet the requirements of SLAM for multiple extended targets with the carrier frequency of 28 GHz, the bandwidth of 1.23 GHz, and the antenna size of 32