In this paper, we investigate a multi-user one-way dual-hop multi-relay cognitive radio underlay spectrum sharing network. .The joint optimal resource allocation of the bandwidth and power allocation in underlay Cooperative Cognitive Radio Relay Network (CCRRN) was studied and a convex optimization analytical framework is presented. A combined optimal power and bandwidth allocation (COPBA) scheme for the minimization of the total network power and blocking probability of the call admission control stage of the CCRRN is investigated. Our goal is to jointly optimize the bandwidth and power allocation such that the total transmission power in the CCRRN is minimized without compromising the Quality of Service (QoS) demands of the secondary users (SUs) and the interference constraint thresholds of the primary users (PUs) in the primary networks. The mathematical model and convex optimization problem with the aim of minimizing the total transmission power of the CCRRN was formulated. The resulting optimization problem was solved in the standard computer based numerical optimization solver package CONOPT in MATLAB/TOMLAB environment. Simulation results obtained is compared the equal-bandwidth optimal power allocation scheme. Keywords—Radio resource allocations; Cognitive radio Networks; Convex optimization; Cooperative relay networks, Transmit power minimization
The increased use of drones and aerial vehicles in applications poses challenges of airspace safety for aviation organizations. It is important to ensure the safety of the airspace when a significant number of unmanned aerial vehicles are deployed by civilian users. A solution that meets this requirement is important to promote innovation in the commercialization of air space for civilian users deploying unmanned aerial vehicle. The discussion in this paper proposes a mechanism that uses artificial intelligence to address this challenge. The proposed mechanism utilizes a low altitude platform (LAP) and entities in terrestrial wireless networks. The low altitude platform (LAP) observes, develops insights and training data (with human aid). The training data is used to develop learning mechanisms which determine the suitable unmanned aerial vehicles flight parameters in different scenarios. The use of the LAP reduces the burden of communicating with terrestrial base stations. The unmanned aerial vehicles have a reduced altitude between the LAPs in comparison to terrestrial base stations. This reduces the free space path loss and rain-induced attenuation. The performance benefit of the proposed mechanism in comparison to existing solution is examined via MATLAB simulations. Evaluation shows that the proposed mechanism reduces the network access costs by up to 90% on average. The proposed mechanism also increases available flight power and improves airspace safety by 37.3% and up to 53.2% on average respectively.
Keywords: Autonomous unmanned aerial vehicles, Intelligence Paradigm; Aviation Safety, Capital Constrained Aviation Organizations.
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