The spectrum access rights granted by the Federal government to spectrum users come with the expectation of protection from harmful interference. As a consequence of the growth of wireless demand and services of all types, technical progress enabling smart agile radio networks, and on-going spectrum management reform, there is both a need and opportunity to use and share spectrum more intensively and dynamically. A key element of any framework for managing harmful interference is the mechanism for enforcement of those rights. Since the rights to use spectrum and to protection from harmful interference vary by band (licensed/unlicensed, legacy/newly reformed) and type of use/users (primary/secondary, overlay/underlay), it is reasonable to expect that the enforcement mechanisms may need to vary as well.In this paper, we present a taxonomy for evaluating alternative mechanisms for enforcing interference protection for spectrum usage rights, with special attention to the potential changes that may be expected from wider deployment of Dynamic Spectrum Access (DSA) systems. Our exploration of how the design of the enforcement regime interacts with and influences the incentives of radio operators under different rights regimes and market scenarios is intended to assist in refining thinking about appropriate access rights regimes and how best to incentivize investment and growth in more efficient and valuable uses of the radio frequency spectrum.

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Recent studies have forecasted major growth in mobile broadband traffic. Due to the predicted high growth rate of mobile broadband traffic over the coming years (demand), there is a need for more wireless network capacity (supply). One of the major approaches to expand mobile wireless capacity is to add more spectrum to the market by enabling "spectrum sharing". The FCC has issued many reports indicating that the US is dangerously close to running out of capacity for mobile data, which is why the FCC and the NTIA have been working continually to enable spectrum sharing. Spectrum sharing has moved from being a radical notion to a principal policy focus in the past decade. Enabling spectrum sharing regimes means that sharing agreements must be implemented.To have meaning, those agreements must be enforceable. The focus of this paper is to determine the relationship between enforcement methodologies and benefits of spectrum sharing through sharing between government and commercial users. Sharing between the government incumbents (i.e. Federal or non-Federal agencies) and commercial wireless broadband operators/users is one of the key forms of spectrum sharing that is recommended by the NTIA, the FCC, and the PCAST report. To address this problem, we build a model to quantitatively examine the relationships between different enforcement scenarios and sharing benefits. We model two case studies, 1695-1710 MHz band and 3550-3650 MHz band.

Hacks, unauthorised access, and other problems have increased the risk to the healthcare system dependent on data analytics in recent years. When a system is kept in its factory settings, it provides an easier target for hackers who wish to get access to the server and steal data. In order to protect the privacy of patients, we use an innovative encryption approach called the Whale-based Random Forest (WbRF) Scheme in this research. Furthermore, ciphertext is made by layering micro-electronic sensors and employing Identity-based Encryption (IBE) on plaintext. The purpose of this surveillance is to ensure the model’s continued health while keeping a vigilant eye out for threats. Therefore the framework is programmed into the Python tool, and the system is trained on more than 200 patient datasets. Medical records for patients can be encrypted and stored safely in the cloud using nano-electronic jargon, in the end. The generated model is subjected to various attacks in order to determine how secure and effective it really is. Energy consumption, execution time, encryption time, latency, accuracy, and decryption time are compared between the created framework and conventional methods.