Unmanned aerial vehicle (UAV) communication can be used in overcrowded areas and either during or postdisaster situations as an evolving technology to provide ubiquitous connections for wireless devices due to its flexibility, mobility, and good condition of the line of sight channels. In this paper, a single UAV is used as an aerial relay node to provide connectivity to wireless devices because of the considerable distance between wireless devices and the ground base station. Specifically, two path loss models have been utilized; a cellular-to-UAV path loss for a backhaul connection and an air-to-ground path loss model for a downlink connection scenario. Then, the tradeoff introduced by these models is discussed. The problem of efficient placement of an aerial relay node is formulated as an optimization problem, where the objective is to maximize the total throughput of wireless devices. To find an appropriate location for a relay aerial node that maximizes the overall throughput, we first use the particle swarm optimization algorithm to find the drone location; then, we use three different approaches, namely, (1) the equal power allocation approach, (2) water filling approach, and (3) modified water filling approach to maximize the total users’ throughput. The results show that the modified water filling outperforms the other two approaches in terms of the average sum rate of all users and the total number of served users. More specifically, in the best-case scenario, it was observed that the average sum rate of the modified water filling is better than the equal power allocation and ensuring 100% coverage. In contrast, the water filling provides a very close average sum rate to the modified water filling, but it only provides a 28% user coverage.
This paper propose a fast, matched-filtered based imaging algorithm to detect below ground object To image below ground objects, a set of distributed transmitters and receivers are placed above the ground, or slightly buried. These transmitters radiate waveforms into the subsurface. The resulting wavefront impinges upon underground objects, scattering electromagnetic energy in all directions. Receivers collects the reflected electromagnetic signal, retrieve the phasor of the scattered signals, and transmit this information to systems for post-processing. After applying adaptive signal processing algorithms to collected data, an image of the buried objects can be reconstructed. Reconstructed 2D of buried objects are computed via numerical discretization and match filtering techniques. Match filtering technique is faster and it reduces computational power that required to process the collected data. The matched-filtered approach is easier to implement as compared to matrix inversion. Results from simulation analysis are used to validate this method.
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