arXiv subjects

Yiming Liu

Publications and source records attributed to Yiming Liu.

115 records · Page 7Linked to original sources

arXiv2020

Reconfigurable Intelligent Surface Aided Wireless Localization

The advantages of millimeter-wave and large antenna arrays technologies for accurate wireless localization received extensive attentions recently. However, how to further improve the accuracy of wireless localization, even in the case of obstructed line-of-sight, is largely undiscovered. In this paper, the reconfigurable intelligent surface (RIS) is introduced into the system to make the positioning more accurate. First, we establish the three-dimensional RIS-assisted wireless localization channel model. After that, we derive the Fisher information matrix and the Cramer-Rao lower bound for evaluating the estimation of absolute mobile station position. Finally, we propose an alternative optimization method and a gradient decent method to optimize the reflect beamforming, which aims to minimize the Cramer-Rao lower bound to obtain a more accurate estimation. Our results show that the proposed methods significantly improve the accuracy of positioning, and decimeter-level or even centimeter-level positioning can be achieved by utilizing the RIS with a large number of reflecting elements.

cs.IT↗

arXiv2020

Investigating Bottom-Quark Yukawa Interaction at Higgs Factory

Measuring the fermion Yukawa coupling constants is important for understanding the origin of the fermion masses and its relationship to the spontaneously electroweak symmetry breaking. On the other hand, some new physics models will change the Lorentz structure of the Yukawa interactions between the standard model (SM) fermions and the SM-like Higgs boson even in their decoupling limit. Thus the precisely measurement of the fermion Yukawa interactions is a powerful tool of new physics searching in the decoupling limit. In this work, we show the possibility of investigating the Lorentz structure of the bottom-quark Yukawa interaction with the 125GeV SM-like Higgs boson at future $e^+e^-$ colliders.

hep-ph↗

arXiv2020

Channel Estimation and Power Scaling Law of Large Reflecting Surface with Non-Ideal Hardware

Large reflecting surface (LRS) has emerged as a new solution to improve the energy and spectrum efficiency of wireless communication system. Most existing studies were conducted with an assumption of ideal hardware, and the impact of hardware impairments receives little attention. However, the non-negligible hardware impairments should be taken into consideration when we evaluate the system performance. In this paper, we consider an LRS assisted communication system with hardware impairments, and focus on the channel estimation study and the power scaling law analysis. First, with linear minimum mean square error estimation, we theoretically characterize the relationship between channel estimation performance and impairment level, number of reflecting elements, and pilot power. After that, we analyze the power scaling law and reveal that if the base station (BS) has perfect channel state information, the transmit power of user can be made inversely proportional to the number of BS antennas and the square of the number of reflecting elements with no reduction in performance; If the BS has imperfectly estimated channel state information, to achieve the same performance, the transmit power of user can be made inversely proportional to the square-root of the number of BS antennas and the square of the number of reflecting elements.

cs.IT↗

arXiv2020

Energy Efficiency Analysis of Intelligent Reflecting Surface System with Hardware Impairments

Recently, as explosive growth of mobile data traffic, the performance of wireless communication systems requires to be enhanced significantly in future. Intelligent reflecting surface (IRS) can be used as a promising way to improve the energy efficiency of wireless communications with less complexity and hardware cost. Most existing studies consider the cases with ideal hardware, however, both physical transceiver and IRS suffer from non-negligible hardware impairments which may greatly degrade the system performance. In this paper, by taking hardware impairments into consideration, we focus on the energy efficiency analysis of IRS system. Our first contribution is to derive the optimal receive combining and transmit beamforming vectors. After that, we characterize the asymptotic channel capacity. With the derived asymptotic channel capacity and the power consumption model, the analytical upper and lower bounds of energy efficiency are provided. Our results show that an IRS system can achieve both high spectral efficiency and high energy efficiency with moderate number of antennas. This observation is encouraging for that there is no need to cost a lot on expensive high-quality antennas, which corresponds to the requirements of new communication paradigms.

cs.IT↗

arXiv2019

Addressing band-edge-property spatial variations and localized-state carrier trapping and recombination in solar cell numerical modeling

Conduction and valence band-edge-property variations with position as well as defects giving rise to localized states in the energy gap can play a significant role in determining solar cell performance. Understanding their effects on a device is necessary in interpreting complex experimental observations and in optimizing the performance of solar cells. In this overview, we include the effective forces arising from electron and hole band-edge-property variations with position in a numerical formulation of solar cell performance. Further we systematically catalogue and review a variety of localized states with different types and distributions, and include in our numerical transport model the carrier trapping, electric field modification, and recombination caused by these localized states. The successful implementation of the numerical modeling of band-edge-property variations and defect state effects is demonstrated using the methodology of the solar cell simulation code Analysis of Microelectronic and Photonic Structures (AMPS) and its derivatives.

physics.app-ph↗

arXiv2019

Blockchain based Privacy-Preserving Software Updates with Proof-of-Delivery for Internet of Things

A large number of IoT devices are connected via the Internet. However, most of these IoT devices are generally not perfect-by-design even have security weaknesses or vulnerabilities. Thus, it is essential to update these IoT devices securely, patching their vulnerabilities and protecting the safety of the involved users. Existing studies deliver secure and reliable updates based on blockchain network which serves as the transmission network. However, these approaches could compromise users privacy when updating the IoT devices. In this paper, we propose a new blockchain based privacy-preserving software updates protocol, which delivers secure and reliable updates with an incentive mechanism, as well protects the privacy of involved users. The vendor delivers the updates and it makes a commitment by using a smart contract to provide financial incentive to the transmission nodes who deliver the updates to the IoT devices. A transmission node gets financial incentive by providing a proof-of-delivery. The transmission node uses double authentication preventing signature (DAPS) to carry out the fair exchange to obtain the proof-of-delivery. Specifically, the transmission node exchanges an attribute-based signature from a IoT device by using DAPS. Then, it uses the attribute-based signature as a proof-of-delivery to receive financial incentives. Generally, the IoT device has to execute complex computation for an attribute-based signature (ABS). It is intolerable for resource limited devices. We propose a concrete outsourced attribute-based signature (OABS) scheme to resist the weakness. Then, we prove the security of the proposed OABS and the protocol as well. Finally, we implement smart contract in Solidity to demonstrate the validity of the proposed protocol.

cs.CR↗

arXiv2017

Video Frame Synthesis using Deep Voxel Flow

We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-of-the-art.

cs.CV↗