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Female Directors’ Foreign Experience and Environmental and Sustainable Performance
Ping Jiang,Farid Ullah,Collins G. Ntim,Yasir Shahab,Xianling Jiang 한국증권학회 2022 Asia-Pacific Journal of Financial Studies Vol.51 No.2
We examine the impact of female directors’ foreign experience on environmental and sustainable (ES) performance in Chinese listed firms from 2010 to 2016. We find that female directors’ foreign experience, especially work experience, significantly positively impacts firms’ ES performance. The results are robust, and self-selection concerns are addressed using the Heckman two-step model and propensity score matching. Also, female directors’ foreign experience impacts ES performance more significantly when female directors gain foreign experience from a Scandinavian law country or a civil law country. Overall, our results reveal that female directors with foreign experience transmit ES knowledge and practices to Chinese firms.
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Channel Prediction Based on Non-Uniform Pilot Pattern for Mobile Massive MIMO Scenarios
Shi, Yi,Wang, Xianling,Jiang, Zhiyuan 한국통신학회 2023 Journal of communications and networks Vol.25 No.4
Massive multiple input multiple output (MIMO) isa broadly used technique that can provide numerous gains inspectral efficiency. However, the degradation of beamformingperformance due to outdated channel state information at thetransmitter side (CSIT) induced by the mobility of users hasbeen a significant problem waiting to be solved. It is reported thatsystem performance will decrease 50 percent even in a moderate30 km/h speed scenario. However, the CSI cannot be simplyreconstructed through interpolation in high mobility scenariosdue to the limitation of pilot density — the phenomenon is knownas “Doppler aliasing”. To address this, we propose a novel non-uniform pilot pattern that can provide more spectrum resolutioncompared with the uniform pilot currently used in most commu-nication protocols. Meanwhile, we maintain the density of pilotsin order not to sacrifice the payload resources. Based on the novelpilot setting, we propose two-channel prediction schemes withcompressive sensing and matrix completion methods. Simulationresults show our scheme can outperform deep learning-based andauto-regressive-based methods for about 15 percent in terms ofaverage throughput in the simulated channel generated from theCOST2100 channel model. To further verify the applicability,we apply our schemes in real channels measured from a channelsounding campaign, the proposed methods also achieve 5 percentgain which validates their superiority over conventional methods.
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