2 edition of Dynamic Spectrum Management found in the catalog.
Open Access Unrestricted online accessCreative Commons https://creativecommons.org/licenses/by/4.0English
|The Physical Object|
|Pagination||xvi, 65 p. :|
|Number of Pages||94|
This open access book, authored by a world-leading researcher in this field, describes fundamentals of dynamic spectrum management, provides a systematic overview on the enabling technologies covering cognitive radio, blockchain, and artificial intelligence, and offers valuable guidance for designing advanced wireless communications systems. This book is intended for a broad range of readers, including students and professionals in this field, as well as radio spectrum policy makers. File Size: 1MB.
The team, including researchers from Michigan State and Cornell with expertise in property rights and networking fairness, is developing a general framework for understanding cooperation in unlicensed band wireless networks by studying the following issues:• In centralized spectrum management, the common approach for spectrum monitoring is to build infrastructures e.
degree Doctor of Philosophy en thesis.  Dynamic Spectrum Management Project Objectives: The establishment of unlicensed communication bands has successfully encouraged innovation, most recently in wireless devices and infrastructure that use unlicensed spectrum Dynamic Spectrum Management provide connections to the Internet.
In this dissertation, we investigate the above-mentioned spectrum management issues in both types of DSA systems, specifically, the spectrum sensing challenges with licensed user location privacy issues in centralized DSA, and the spectrum sharing problems in distributed DSA systems. For example, CR as a paradigm for wireless communications represents the use of devices in a network where both Dynamic Spectrum Management devices and the network could change or adapt their characteristics to achieve higher efficiency levels.
Abstract Advancing technologies and bandwidth-hungry applications have increased mobile data traffic in the radio spectrum. But what has been less discussed is how DSS will extend the life of IoT devices that use Long Term Evolution LTE technologies, like LTE Machine Type Communication LTE-M and Narrowband IoT NB-IoT.
As a remedy, we design novel schemes to preserve both static and moving IU's location information by adjusting IU's radiation pattern and transmit power.
abstractgeneral Due to the rapid growth in wireless communication demands, the frequency spectrum is becoming increasingly crowded. Results from the project are expected tobe of value to both policy makers and emerging unlicensed band wireless Internet providers as well as wireless technologists. We also provide insight on how vulnerabilities in system design could become potential threats.
21 also identifies future capabilities as part of the system. This paper argues that the conditions required by Coase are gradually achieved by the introduction of Dynamic Spectrum Management DSMwhich enables a dynamic reassignment of spectrum bands at different times and places.
5 GHz to sense the IU activities for protecting them from SUs' interference. Different approaches have been considered including cognitive radio, machine learning for dynamic spectrum management, spectrum sharing, spectrum harmonization, Dynamic Spectrum Management identification strategies, etc.
Moreover, problems arise when regulatory constraints are not followed. By starting small and then expanding the amount of spectrum they allocate to NR, MNOs can quickly begin offering customers NR services today, and then scale up their NR network capacity as adoption of NR devices rises. Also, traffic monitoring can help provide LBS services to traffic commuters, and the sensing of some parameters will make the device adapt to channel conditions by changing its transceiver.
The coverage of a satellite beam is typically orders of magnitude larger than a terrestrial cell, and thus the number of secondary users inside a beam can easily be much higher than in the terrestrial sharing scenario.
The combination of increasing data rates and the proliferation of devices could easily lead to inefficiency in the use of unlicensed spectrum due to a combination of overuse and failure to develop mechanisms for efficient sharing of this resource.
A radio can be induced to learn false information by malicious or selfish entities, the effect of which can sometimes propagate to the entire network.