Cognitive Unmanned Aerial Vehicle-Aided Human Bond Communication System: Modeling and Performance Analysis


As a result of an increase in the number of Internet of things (IoT) devices communicating with one another, there is a limited spectrum resource that can be used, and effective NetWorking over wireless media has become an extremely essential component in today's technological landscape. Specifically, maintaining continuous Communication between the macro base station and the Internet of Things devices or user nodes is an absolute necessity for healthcare infrastructure that is located in a remote area or is operating in an emergency setting. However, due to their limited spectral capacity, networks based on unmanned aerial vehicles (UAVs) can provide an effective solution and make use of both licensed and unlicensed bands for Communication among users or devices. This can be accomplished by utilizing both licensed and unlicensed bands. In this paper, our focus is on cache-enabled cognitive NetWorking for secondary users (SUs), which accredits precise Communication delivery for critical healthcare systems that are performed by the cognitive UAV. Specifically, we are interested in the following: (CUAV). In addition, we develop a caching strategy in which a CUAV is capable of caching relevant information from high-power (HP) and moderate-power (MP) devices in both its local storage and cloud storage by employing a non-orthogonal multiple-access method. This strategy was developed by us. The CUAV will proactively send the requested HP and MP information to the designated SUs in the downlink scenario. The CUAV will consider this entire model to be in two states: the effective state and the interference state. We can determine whether an interference state exists or does not exist based on the presence or absence of interference. In order to achieve the highest possible level of energy efficiency with this system, we have formulated an optimization problem with the goal of reducing the amount of transmission power while maintaining the desired level of throughput for SUs. We find a solution to the optimization problem by employing a Lagrangian strategy in conjunction with the Karush-Kuhn-Tucker conditions. When compared to the energy efficiency during the interference state, the effectual state's energy efficiency results in an average performance that is approximately 400% higher. This holds true across all simulations.

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