Hao Liu

A Multi-Reference Calibration Method for Coherent Joint Transmission in Asynchronous Cell-Free Massive MIMO With Imperfect RF Chains

Liyuan Qin, Rui Zhang, Yinghui Ye, Yuhan Ruan et autres

Cell–free massive multiple–input multiple–output (CF–mMIMO) has attracted significant research interest by leveraging coherent joint transmission (CJT) for achieving excellent performance. However, this performance is degraded by phase offsets arising from imperfect radio frequency (RF) chains and asynchronous reception. To significantly recover CJT …

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Guided by visual-textual feature and wavelet similarity for zero-shot anomaly detection

Hao Liu, Shiliang Wang, Zhaofeng Wu, Ze Yu et autres

Zero-shot anomaly detection (ZSAD) is of utmost importance in industrial domains. It aims to detect anomalies in various fields without training samples, yet faces challenges due to significant differences in anomalies. Although large pre-trained vision-language models like CLIP are powerful in zero-shot …

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A junction temperature prediction model for 3D stacked packaging chip based on thermal resistance matrix

Jiahao Liu, Fangzhou Chen, Hao Zhao, Hui Xiao et autres

As the package size of 3D stacked chip continues to decrease and the power density continues to increase, the internal temperature of the chip rises sharply. In order to meet the heat dissipation and reliability requirements of the chip, it is important …

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Joint Covering Congestion Rents in Multi-area Power Systems Considering Loop Flow Effects

Hao Liu, Ye Guo, Haitian Liu, Hongbin Sun

We consider the problem of how multiple areas should jointly cover congestion rents of internal and tie-lines in an interconnected power system. A key issue of our concern is the loop flow problem, which represents discrepancies between scheduled and actual power flow …

Entropy Targets for Adaptive Distillation

Hao Liu, Haowen Yan, Jinxiang Xia, Ying Ai

The focus of this paper is the problem of targets in knowledge distillation. Compared with hard targets, soft targets can provide extra information which compensates for the lack of supervision signals in classification problems, but there are still many defects such as …

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Cellular base station downlink power allocation using model-free reinforcement learning

Hao Liu

With the maturity of 5G and IoT technologies, the number of base stations(BS) and end-user equipment(UE) is expected to increase dramatically. Therefore, the problem of an optimal solution for complex resource and power allocation in cellular networks has become a research topic. …

A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems

Fangli Jin, Qiufeng Liu, Hao Liu, Peng Wu

Massive multiple-input multiple-output (MIMO) systems need to handle a large number of matrix inversion operations during the signal detection process. Several methods have been proposed to avoid exact matrix inversion in massive MIMO systems, which can be roughly divided into approximation methods …

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Resource Allocation Scheme for D2D Communication Based on ILA

Zhifang Gu, Pingping Xu, Guilu Wu, Hao Liu

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Variational Inference with Tail-adaptive f-Divergence

Dilin Wang, Hao Liu, Qiang Liu

Variational inference with α-divergences has been widely used in modern probabilistic machine learning. Compared to Kullback-Leibler (KL) divergence, a major advantage of using α-divergences (with positive α values) is their mass-covering property. However, estimating and optimizing α-divergences require to use importance sampling, …

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Nicotinamide N-methyltransferase promotes epithelial-mesenchymal transition in gastric cancer cells by activating transforming growth factor-β1 expression

Liang Liang, Ming Zeng, Haixia Pan, Hao Liu et autres

Previous studies have demonstrated that nicotinamide N-methyltransferase (NNMT) is aberrantly expressed in a number of tumors. In the present study, it was demonstrated that the gene and protein levels of NNMT were significantly increased in gastric cancer cells. Furthermore, upregulation of NNMT …

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Action-dependent Control Variates for Policy Optimization via Stein Identity

Hao Liu, Yihao Feng, Yi Mao, Dengyong Zhou et autres

Policy gradient methods have achieved remarkable successes in solving challenging reinforcement learning problems. However, it still often suffers from the large variance issue on policy gradient estimation, which leads to poor sample efficiency during training. In this work, we propose a control …

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Fast spatial correlation acquisition for hybrid precoding using sequential compressive sensing

Jinping Hao, Hao Liu

This paper addresses the critical problem of high overhead in channel information acquisition for hybrid precoding in massive multiple-input multiple-output (MIMO) system. With the employment of large-scale massive antennas in the next generation 5G wireless system, the high overhead in the estimation …

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