Browsing by Author "Chang, Ting-Wei"

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Learning-based Beam Training Algorithms for IEEE 802.11ad/ay Networks
(2019-01-01)
Chang, Ting-Wei
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Shen, Li-Hsiang
;
Feng, Kai-Ten
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電機工程學系
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Department of Electrical and Computer Engineering
Recently, many researches are focusing on millimeter wave (mmWave) due to its large bandwidth resources. In the next 5G generation, wireless multi-gigabit (WiGig) is developed based on IEEE 802.11ad/ay standards at unlicensed mmWave bands for providing extremely high throughput. However, mmWave has high propagation loss due to high frequency transmission properties. Beamforming (BF) technique is adopted to solve this problem, and therefore, we have to perform beam training before data transmission. In the standard, the conventional method for beam training is exhaustive beam search (EBS) which takes too much time so that the data transmission time will decrease. On the other hand, blocked environments may severely degrade WiGig beam training performance, and most of existing algorithms do not consider this issue. Recently, machine and deep learning have been widely used in the wireless communication field. We propose a learning-based beam training (LBT) to simultaneously learn about wireless environments and beam training candidates. We select simplified neural network (NN) model to achieve lower computation overhead. To further refine learning information, we propose two enhanced algorithm, expanded LBT (LBT-E) and history-aided expanded LBT(LBT-HE). LBT-E aims to tackle unexpected slight deviation and LBT-HE make use of historical information to improve beam matching accuracy with acceptable latency. In our simulation, our proposed learning-based schemes achieve much higher throughput compared to EBS and algorithms in existing literatures.
Low Complexity Subdivision Algorithm to Approximate Phong Shading Using Forward Difference
(2009)
Sheu, Teng-Yao
;
Van, Lan-Da
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Jung, Tzung-Rung
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Lin, Cheng-Wei
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Chang, Ting-Wei
In this paper, we proposed a low complexity subdivision algorithm to approximate Phong shading. It is a combination of a subdivision scheme using forward difference and a recovery scheme to prevent rasterization anomaly. Dual space subdivision, triangle filtering and variable sharing schemes are also proposed to reduce the computation. Compared with the conventional recursive subdivision shading algorithm, the proposed algorithm reduces 46.6% memory access, 80% projective transformations and perspective division, 93.75% clipping/culling tests and 60% 3x3 matrix multiplications for setup operation.