我似乎无法让作者所属区块显示为\thanks。它根本就没有出现。我添加了\saythanks等等，而 IEEEtran 类显示它已被锁定。有没有办法像往常一样将所属区块放入脚注中？
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\begin{document}
\title{Title
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\author{one, \textit{Member, IEEE}, two, and three, \textit{Senior Member, IEEE} %
\thanks{one is with the School of Physical and Mathematical Sciences, two is with the School of Mathematics, University of Bristol, Bristol, U.K., and three is with the Wireless Systems Laboratory, School of Electronic and Electrical Engineering, Hanyang University, Seoul, Republic of South Korea. We thank G. Sicuro and Springer International Publishing AG for allowing reproduction of Fig. \ref{fig:torus} from his PhD thesis \cite{sicuro2017}. This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program(IITP-2021-2017-0-01637) supervised by the IITP (Institute for Information \& Communications Technology Planning \& Evaluation).}}
%Warwick Manufacturing Group (WMG), University of Warwick, Coventry,
\maketitle
\begin{abstract}
The Poisson bipolar model considers user-base station pairs distributed at random on a flat domain, similar to matchsticks scattered onto a table. Though this is a simple and tractable setting in which to study dense networks, it doesn't properly characterise the stochastic geometry of user-base station interactions in some dense deployment scenarios, which may involve short and long range links, with some paired very nearby optimally, and others sub-optimally due to local crowding. Since the users will pair one-to-one with base stations, we can consider using the popular bipartite Euclidean matching (BEM) from spatial combinatorics, and study the corresponding (meta) distribution of the signal-to-interference-ratio (SIR). This provides detailed information about the proportion of links in the network meeting a target reliability constraint. We can then observe via comparison the impact of taking into account the variable/correlated short-range distances between the transmitter-receiver pairs on the communication statistics. We illustrate and quantify how the widely-accepted bipolar model fails to capture the network-wide reliability of communication in a typical ultra-dense setting based on a binomial point process. We also show how assuming a Gamma distribution for link distances may be a simple improvement on the bipolar model. Overall, BEMs provide good grounds for understanding more sophisticated pairing features in ultra-dense networks.
%Overall, we can conclude that there is good grounds for understanding the effect of more sophisticated geometric pairing features in random models of ultra-dense networks in the future.
\end{abstract}
\begin{IEEEkeywords}
Matching theory, stochastic geometry, Internet of Things, random geometric graphs, data capacity, interference, wireless networks.
\end{IEEEkeywords}
\thanks
\section{Introduction}
答案1
添加\IEEEoverridecommandlockouts 于\documentclass[conference, a4paper]{IEEEtran}
