Hybrid precoding can reduce the number of required radio frequency(RF)chains in millimeter-Wave(mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition(SVD) requires the ...Hybrid precoding can reduce the number of required radio frequency(RF)chains in millimeter-Wave(mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition(SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios(SNRs) of different sub-channels. In this paper,we propose a geometric mean decomposition(GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically,we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit(OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.展开更多
Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we...Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.展开更多
基金supported by the National Natural Science Foundation of China for Outstanding Young Scholars (Grant No. 61722109)the National Natural Science Foundation of China (Grant No. 61571270)the Royal Academy of Engineering through the UK–China Industry Academia Partnership Programme Scheme (Grant No. UK-CIAPP\49)
文摘Hybrid precoding can reduce the number of required radio frequency(RF)chains in millimeter-Wave(mmWave) massive MIMO systems. However, existing hybrid precoding based on singular value decomposition(SVD) requires the complicated bit allocation to match the different signal-to-noise-ratios(SNRs) of different sub-channels. In this paper,we propose a geometric mean decomposition(GMD)-based hybrid precoding to avoid the complicated bit allocation. Specifically,we seek a pair of analog and digital precoders sufficiently close to the unconstrained fully digital GMD precoder. To achieve this, we fix the analog precoder to design the digital precoder, and vice versa. The analog precoder is designed based on the orthogonal matching pursuit(OMP) algorithm, while GMD is used to obtain the digital precoder. Simulations show that the proposed GMD-based hybrid precoding achieves better performance than the conventional SVD-based hybrid precoding with only a slight increase in complexity.
基金supported by the National Nature Science Foundation of China under Grant No.61502226the Jiangsu Provincial Transportation Science and Technology Project No.2017X04the Fundamental Research Funds for the Central Universities
文摘Lane line detection is a fundamental step in applications like autonomous driving and intelligent traffic monitoring. Emerging applications today have higher requirements for accurate lane detection. In this paper, we present a precise information extraction algorithm for lane lines. Specifically, with Gaussian Mixture Model(GMM), we solved the issue of lane line occlusion in multi-lane scenes. Then, Progressive Probabilistic Hough Transform(PPHT) was used for line segments detection. After K-Means clustering for line segments classification, we solved the problem of extracting precise information that includes left and right edges as well as endpoints of each lane line based on geometric characteristics. Finally, we fitted these solid and dashed lane lines respectively. Experimental results indicate that the proposed method performs better than the other methods in both single-lane and multi-lane scenarios.
