China
Africa
Explore chapters and articles related to this topic
Cognitive Radio Networks: Concepts and Applications
Published in Nazmul Siddique, Syed Faraz Hasan, Salahuddin Muhammad Salim Zabir, Opportunistic Networking, 2017
S. M. Kamruzzaman, Abdullah Alghamdi, M. Anwar Hossain
Due to the rapid advance of wireless communications, a tremendous number of different communication systems exist in licensed and unlicensed bands, suitable for different demands and applications such as GSM/GPRS, IEEE 802.11, Bluetooth, UWB, ZigBee, 3G (CDMA series), HSPA, 3G LTE, IEEE 802.16, and so on. In contrast, radio propagation favors the use of spectrums under 3 GHz due to non-line-of-sight propagation. Consequently, many more devices, up to 1 trillion wireless devices by 2020, require radio spectrum allocation in order to respond to the challenge for further advances in wireless communications [29].
An improved LSE-EKF optimisation algorithm for UAV UWB positioning in complex indoor environments
Published in Journal of Control and Decision, 2022
However, the above methods have limited solutions to the multi-path effect and non-line-of-sight propagation problems in the indoor complex environment positioning of UAVs. Therefore, this paper proposes an improved LSE-EKF UWB indoor UAV positioning method. Through BP the neural network corrects the original positioning data, introduces a redundant base station positioning system, uses the least squares estimation to optimise the pre-positioning coordinate error and then removes the Gaussian white noise in the pre-positioning signal through the extended Kalman filter algorithm. Compared with the traditional UWB positioning system, the improved LSE-EKF algorithm can effectively solve the problems of multi-path effect and non-line-of-sight (NLOS) propagation, which greatly improves the positioning accuracy of indoor multi-rotor UAVs.
A New Method of Spectrum Sensing in Cognitive Radio for Dynamic and Randomly Modelled Primary Users
Published in IETE Journal of Research, 2022
To facilitate the theoretical analysis of the newly proposed CuS-WED, the following mathematical model can be used where and refer to the absence and presence, respectively, of the PU in the channel. In Equation (1), N represents the total number of observed data samples and denotes the additive channel noise modelled as zero-mean white Gaussian noise (WGN) with variance . The real-valued PU signal, , is represented using zero-mean Gaussian distribution with variance as in order to more realistically model the PU signal which is formed by the addition of multiple non-line-of-sight propagation signals arriving at the CR receiver [16,17].