Throughput Maximization for Backscatter- and Cache-Assisted Wireless Powered UAV Technology


In this paper, we investigate a wireless Communication network for unmanned aerial vehicles (UAVs) that is powered wirelessly and uses backscatter and caching technologies. To be more specific, we will assume that a self-energizing UAV equipped with a cache memory is used as a flying backscatter device (BD), which we will refer to as the UAV-enabled BD (UB), in order to transmit the signals coming from the source to the destination. On the other hand, the source S has the potential to function as either a wireless charging station or a base station, and it can use the dynamic time splitting (DTS) method to either supply the UB with power or transmit information to it. The unmanned aerial vehicle (UAV) makes use of the energy it has harvested in order to backscatter (also known as engage in passive Communication) and actively communicate (also known as engage in active Communication) with the destination. Within the scope of this discussion, our objective is to maximize the total throughput by simultaneously optimizing the DTS ratio and the trajectory of the UB while utilizing the caching capacity of the UB. Due to the fact that the formulation is a non-convex problem, finding a solution to it can be challenging. In order to find solutions, we break the original problem down into two smaller problems. First, we optimize the DTS ratio for a given UB's trajectory, and then we optimize the UB's trajectory for a given DTS ratio. Both of these optimizations take place simultaneously. When the KKT conditions are applied, a closed-form expression for the optimal value of the DTS ratio can be obtained. This results in a significant reduction in the amount of time required for the computation. In addition, the successive convex approximation (SCA) method can be utilized to acquire the answer to the second sub-problem. This will allow for the solution to be obtained. As a result, we propose an effective alternating algorithm by making use of the block coordinate descent (BCD) method. In order to demonstrate the benefits of the BCD-based algorithm that we have proposed, we have also provided a solution to the initial problem that was reached by using the inner approximation (IA) method. In conclusion, the exhaustive numerical results show that the proposed schemes achieve a significant throughput gain compared to the benchmark schemes.

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