• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.33-39
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• 2014

We propose an optimal number of transmit antennas which maximizes energy-efficiency (EE) in multi-user massive multiple-input multiple-output (MIMO) downlink system with the maximal ratio transmission (MRT) precoding. With full channel state information at the transmitter (CSIT), we find a closed form solution by partial differential function with proper approximations using average channel gain, independence of individual channels, and average path loss. With limited feedback, we get a solution numerically by the bisection with approximations in the same manner, and analyze an effect of feedback bits on the optimal number of transmit antennas. Simulation results show that the optimal numbers of transmit antenna getting from proposed closed form solution and exhaustive search are nearly same.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2021-2026
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• 2007

In the parer, we design a non-coherent UWB system by adopting the architecture of a simplified asynchronous transmission and the edge-triggered pulse transmission, which makes e system performance independent of the share of the transmitted waveform, robust to multipath channels. The designed non-coherent UWB transceiver architecture has an advantage of the simple realization since any mixer, high-speed correlator, and high-sampling A/D converter are not necessary at the cost of performance degradation of about 3dB. Further, the designed non-coherent UWB transceiver is actually implemented with the wireless CANVAS prototype testbed in short range indoor application environments such as a lecture room. The implemented prototype testbed is proven to offer the data rate of 115kbps on the conditions of Peer-to-Peer(P-to-P) in the indoor channel within the range of about 10m.

• Journal of IKEEE
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• v.26 no.3
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• pp.408-415
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• 2022

In this paper, we propose a 3D point cloud reconstruction technique from 2D images using efficient feature map extraction network. The originality of the method proposed in this paper is as follows. First, we use a new feature map extraction network that is about 27% efficient than existing techniques in terms of memory. The proposed network does not reduce the size to the middle of the deep learning network, so important information required for 3D point cloud reconstruction is not lost. We solved the memory increase problem caused by the non-reduced image size by reducing the number of channels and by efficiently configuring the deep learning network to be shallow. Second, by preserving the high-resolution features of the 2D image, the accuracy can be further improved than that of the conventional technique. The feature map extracted from the non-reduced image contains more detailed information than the existing method, which can further improve the reconstruction accuracy of the 3D point cloud. Third, we use a divergence loss that does not require shooting information. The fact that not only the 2D image but also the shooting angle is required for learning, the dataset must contain detailed information and it is a disadvantage that makes it difficult to construct the dataset. In this paper, the accuracy of the reconstruction of the 3D point cloud can be increased by increasing the diversity of information through randomness without additional shooting information. In order to objectively evaluate the performance of the proposed method, using the ShapeNet dataset and using the same method as in the comparative papers, the CD value of the method proposed in this paper is 5.87, the EMD value is 5.81, and the FLOPs value is 2.9G. It was calculated. On the other hand, the lower the CD and EMD values, the better the accuracy of the reconstructed 3D point cloud approaches the original. In addition, the lower the number of FLOPs, the less memory is required for the deep learning network. Therefore, the CD, EMD, and FLOPs performance evaluation results of the proposed method showed about 27% improvement in memory and 6.3% in terms of accuracy compared to the methods in other papers, demonstrating objective performance.