6,276 research outputs found
Wireless Powered Dense Cellular Networks: How Many Small Cells Do We Need?
This paper focuses on wireless powered 5G dense cellular networks, where base
station (BS) delivers energy to user equipment (UE) via the microwave radiation
in sub-6 GHz or millimeter wave (mmWave) frequency, and UE uses the harvested
energy for uplink information transmission. By addressing the impacts of
employing different number of antennas and bandwidths at lower and higher
frequencies, we evaluate the amount of harvested energy and throughput in such
networks. Based on the derived results, we obtain the required small cell
density to achieve an expected level of harvested energy or throughput. Also,
we obtain that when the ratio of the number of sub-6 GHz BSs to that of the
mmWave BSs is lower than a given threshold, UE harvests more energy from a
mmWave BS than a sub-6 GHz BS. We find how many mmWave small cells are needed
to perform better than the sub-6 GHz small cells from the perspectives of
harvested energy and throughput. Our results reveal that the amount of
harvested energy from the mmWave tier can be comparable to the sub-6 GHz
counterpart in the dense scenarios. For the same tier scale, mmWave tier can
achieve higher throughput. Furthermore, the throughput gap between different
mmWave frequencies increases with the mmWave BS density.Comment: pages 1-14, accepted by IEEE Journal on Selected Areas in
Communication
Research on a Pulmonary Nodule Segmentation Method Combining Fast Self-Adaptive FCM and Classification
The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms
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