The growing popularity of unmanned aerial vehicle (UAV) attracts significant research interests and applications including low-altitude and airborne vehicles. Since there is no declared spectrum allocated to UAV communications, opportunistic transmission has been commonly considered as an important way for supporting UAV communications. When sharing the same spectrum with other users such as satellites and mobile base stations, accurate spectrum sensing and allocation are of critical importance for UAV communications to avoid serious interference. As the UAVs can constantly move to different locations with various spectrum environments, the spectrum decision may be invalid only in a short period, leading to require fast spectrum sensing. Furthermore, an UAV needs to predict possible temporal and spatial vacations of the spectrum. In this case, the spectrum prediction has a high dimensional state space which is notoriously difficult to solve. In this paper, some other issues such as how to determine the spectrum processing time and how to detect the primary signals with high priority to avoid interference, are also discussed. Finally, a fast spectrum sensing algorithm is proposed to improve the energy detection performance by optimizing the error estimation and a constant ratio of missed detection. Our proposed algorithm does not require high computational capability and can achieve relatively accurate sensing in low signal-to-noise ratio scenarios.INDEX TERMS Unmanned aerial vehicles, ultra dense networks, spectrum management, spectrum sensing.
Performance analysis of connectivity-based geolocation in ultra-dense networks (UDNs) is a very important task. Although several performance analyses have been presented for range-free localization, determining the best achievable positioning accuracy of range-free localization remains an open problem. In this paper, we first derive the Cramer-Rao lower bound (CRLB) for the performance evaluation of range-free localization. All the current performance analyses in the literature for range-free localization are used to evaluate the real performance of a given algorithm, whereas the proposed CRLB provides a benchmark to evaluate the performance of any unbiased range-free location algorithm and determines the physical impossibility of the variance of an unbiased estimator being less than the bound. To the best of our knowledge, this is the first time in the literature that the CRLB for range-free localization has been derived. Second, the theoretical variance of centroid-based localization (CL) with an arbitrary node distribution is derived in this paper. In contrast to the existing theoretical variance of CL for uniform node distribution, the proposed theoretical variance can be used to evaluate the performance of CL in the case of an arbitrary node distribution. Additionally, characteristics of the proposed CRLB and theoretical variance are given in this paper. Finally, an optimal estimator based on a maximum likelihood estimator (MLE) is proposed to improve positioning accuracy. Since both prior information on the spatial node distribution and the connectivity property are effectively utilized in our algorithm, the proposed method performs better than the CL method and can asymptotically attain the CRLB. INDEX TERMS Connectivity-based localization, Cramer-Rao lower bound (CRLB), theoretical variance, maximum likelihood estimator (MLE), centroid-based localization (CL).
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