你现在是无线通信方向的专家，正在提交论文的bio。把下面的bio改写成标准的IEEE期刊论文的lat...

latex
% IEEE-style author biographies (with photo placeholders)
% Make sure you have: \usepackage{graphicx}

\newcommand{\IEEEbioPhotoPlaceholder}{%
\fbox{\parbox[b][1.25in][c]{1in}{\centering Photo}}}

\begin{IEEEbiography}[\IEEEbioPhotoPlaceholder]{Jingcheng Shi} (Student Member, IEEE) received the B.E. degree in communication engineering from Dalian Maritime University, Dalian, China, in 2022. He is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. His research interests include integrated sensing and communication, signal processing, and wireless resource allocation.
\end{IEEEbiography}

\begin{IEEEbiography}[\IEEEbioPhotoPlaceholder]{Yifeng Xiong} (Member, IEEE) received the Ph.D. degree in electronic and electrical engineering from the University of Southampton, Southampton, U.K., in 2022. He received the B.S. degree in information engineering and the M.S. degree (with highest honor) in information and communication engineering from Beijing Institute of Technology (BIT), Beijing, China, in 2015 and 2018, respectively. He is currently an Associate Professor with Beijing University of Posts and Telecommunications (BUPT), Beijing, China. His research interests include integrated sensing and communications, quantum computation, quantum information theory, and statistical inference over networks. He received the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the Best Paper Award at IEEE GLOBECOM 2025, the Best Master Thesis Award from the Chinese Institute of Electronics, and the Best Paper Award at IEEE/CIC ICCC 2023.
\end{IEEEbiography}

\begin{IEEEbiography}[\IEEEbioPhotoPlaceholder]{Xu Guo} (Student Member, IEEE) received the B.E. degree in network engineering from Southwest Jiaotong University, Chengdu, China, in 2021. She is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. Her research interests include UAV communications, wireless resource allocation, and integrated sensing and communication networks.
\end{IEEEbiography}

\begin{IEEEbiography}[\IEEEbioPhotoPlaceholder]{Jianjun Wu} (Member, IEEE) received the B.S., M.S., and Ph.D. degrees from Peking University, Beijing, China, in 1989, 1992, and 2006, respectively. Since 1992, he has been with Peking University, where he became a Professor with the School of Electronics Engineering and Computer Science in 2014. His research interests include satellite communications, wireless communications, and communications signal processing.
\end{IEEEbiography}

\begin{IEEEbiography}[\IEEEbioPhotoPlaceholder]{Fan Liu} (Senior Member, IEEE) is currently a Professor with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China. From 2020 to 2024, he was an Assistant Professor with the Southern University of Science and Technology, Shenzhen, China. He received the B.Eng. and Ph.D. degrees from Beijing Institute of Technology (BIT), Beijing, China, in 2013 and 2018, respectively. He was with University College London (UCL), London, U.K., as a Visiting Researcher from 2016 to 2018, and a Marie Curie Research Fellow from 2018 to 2020.

His research interests lie in signal processing and wireless communications, in particular integrated sensing and communications (ISAC). He is the founding Academic Chair of the IEEE ComSoc ISAC Emerging Technology Initiative (ISAC--ETI), the Vice Chair and a founding member of the IEEE SPS ISAC Technical Working Group (ISAC--TWG), an elected member of the IEEE SPS Sensor Array and Multichannel Technical Committee (SAM--TC), an Associate Editor of \emph{IEEE Transactions on Communications}, \emph{IEEE Transactions on Mobile Computing}, and \emph{IEEE Open Journal of Signal Processing}, and a Guest Editor of \emph{IEEE Journal on Selected Areas in Communications}, \emph{IEEE Wireless Communications}, and \emph{IEEE Vehicular Technology Magazine}. He served as the TPC Co-Chair of the inaugural IEEE ISAC Conference 2026 and the 2nd--4th IEEE Joint Communication and Sensing (JC\&S) Symposium, Special Session Co-Chair of IEEE SPAWC 2026, and Symposium Co-Chair for IEEE ICC 2026, IEEE/CIC ICCC 2025, IEEE WCNC 2024, and IEEE GLOBECOM 2023. He is a member of the IMT-2030 (6G) ISAC Task Group. He was listed as a 2025 Clarivate Highly Cited Researcher. He received multiple Best Paper Awards, including the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the 2024 IEEE Signal Processing Society Best Paper Award, the 2024 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, the 2024 IEEE Communications Society Asia-Pacific Outstanding Paper Award, the 2023 IEEE Communications Society Stephan O.\ Rice Prize, and the 2021 IEEE SPS Young Author Best Paper Award.
\end{IEEEbiography}

如果要在 IEEEbiography 里放照片，做法是把可选参数里那个占位框，换成 \includegraphics。

前提：导言区确保有

latex
\usepackage{graphicx}

然后（假设照片文件名就叫 jingcheng shi.jpeg，并且和 .tex 在同一目录）：

latex
\begin{IEEEbiography}[\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{jingcheng shi.jpeg}]{Jingcheng Shi}
% ... biography text ...
\end{IEEEbiography}

更稳妥的写法（建议）：文件名里尽量别用空格，改成 jingcheng_shi.jpeg 或 jingcheng-shi.jpeg，然后：

latex
\begin{IEEEbiography}[\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{jingcheng_shi.jpeg}]{Jingcheng Shi}
% ... biography text ...
\end{IEEEbiography}

如果你必须保留空格，也可以用 \detokenize：

latex
\begin{IEEEbiography}[\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{\detokenize{jingcheng shi.jpeg}}]{Jingcheng Shi}
% ... biography text ...
\end{IEEEbiography}

width=1in,height=1.25in 这两个尺寸就是 IEEE 常见的头像框大小；keepaspectratio 可以避免图片被拉伸变形。

你这段最常见的致命原因在这里：

IEEEbiography 的照片参数本身是一个可选参数 [...]，而你在里面直接写了 \includegraphics[...]{...}，它自己也带 [...]。如果不把 \includegraphics... 整体用花括号包起来，外层的 [...] 会在遇到 \includegraphics 的第一个 ] 时就提前结束，导致后面的内容被错当成别的参数，从而编译报错（常见报错类似 Runaway argument? / Missing ] inserted / Extra } 等）。

关键改法（每个 biography 都要这样改）

把

latex
\begin{IEEEbiography}[\includegraphics[width=1in,...]{Jingcheng_Shi.jpeg}]{Jingcheng Shi}

改成

latex
\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Jingcheng_Shi.jpeg}}]{Jingcheng Shi}

注意：外层 [...] 里面我加了 { ... } 把整条 \includegraphics[...] 包住，这就是能跑起来的关键。

给你一份可直接编译的修正版（按你原文改最少）

latex
\documentclass[journal]{IEEEtran}
\usepackage{graphicx}

\hyphenation{op-tical net-works semi-conduc-tor}

\begin{document}

\title{Bare Demo of IEEEtran.cls\\ for IEEE Journals}

\markboth{Journal of \LaTeX\ Class Files,~Vol.~14, No.~8, August~2015}%
{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for IEEE Journals}

\maketitle

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Jingcheng_Shi.jpeg}}]{Jingcheng Shi}
(Student Member, IEEE) received the B.E. degree in communication engineering from Dalian Maritime University, Dalian, China, in 2022. He is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. His research interests include integrated sensing and communication, signal processing, and wireless resource allocation.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Yifeng_Xiong.jpeg}}]{Yifeng Xiong}
(Member, IEEE) received the Ph.D. degree in electronic and electrical engineering from the University of Southampton, Southampton, U.K., in 2022. He received the B.S. degree in information engineering and the M.S. degree (with highest honor) in information and communication engineering from Beijing Institute of Technology (BIT), Beijing, China, in 2015 and 2018, respectively. He is currently an Associate Professor with Beijing University of Posts and Telecommunications (BUPT), Beijing, China. His research interests include integrated sensing and communications, quantum computation, quantum information theory, and statistical inference over networks. He received the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the Best Paper Award at IEEE GLOBECOM 2025, the Best Master Thesis Award from the Chinese Institute of Electronics, and the Best Paper Award at IEEE/CIC ICCC 2023.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Xu_Guo.jpeg}}]{Xu Guo}
(Student Member, IEEE) received the B.E. degree in network engineering from Southwest Jiaotong University, Chengdu, China, in 2021. She is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. Her research interests include UAV communications, wireless resource allocation, and integrated sensing and communication networks.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Jianjun_Wu.jpeg}}]{Jianjun Wu}
(Member, IEEE) received the B.S., M.S., and Ph.D. degrees from Peking University, Beijing, China, in 1989, 1992, and 2006, respectively. Since 1992, he has been with Peking University, where he became a Professor with the School of Electronics Engineering and Computer Science in 2014. His research interests include satellite communications, wireless communications, and communications signal processing.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Fan_Liu.jpeg}}]{Fan Liu}
(Senior Member, IEEE) is currently a Professor with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China. From 2020 to 2024, he was an Assistant Professor with the Southern University of Science and Technology, Shenzhen, China. He received the B.Eng. and Ph.D. degrees from Beijing Institute of Technology (BIT), Beijing, China, in 2013 and 2018, respectively. He was with University College London (UCL), London, U.K., as a Visiting Researcher from 2016 to 2018, and a Marie Curie Research Fellow from 2018 to 2020.

His research interests lie in signal processing and wireless communications, in particular integrated sensing and communications (ISAC). He is the founding Academic Chair of the IEEE ComSoc ISAC Emerging Technology Initiative (ISAC--ETI), the Vice Chair and a founding member of the IEEE SPS ISAC Technical Working Group (ISAC--TWG), an elected member of the IEEE SPS Sensor Array and Multichannel Technical Committee (SAM--TC), an Associate Editor of \emph{IEEE Transactions on Communications}, \emph{IEEE Transactions on Mobile Computing}, and \emph{IEEE Open Journal of Signal Processing}, and a Guest Editor of \emph{IEEE Journal on Selected Areas in Communications}, \emph{IEEE Wireless Communications}, and \emph{IEEE Vehicular Technology Magazine}. He served as the TPC Co-Chair of the inaugural IEEE ISAC Conference 2026 and the 2nd--4th IEEE Joint Communication and Sensing (JC\&S) Symposium, Special Session Co-Chair of IEEE SPAWC 2026, and Symposium Co-Chair for IEEE ICC 2026, IEEE/CIC ICCC 2025, IEEE WCNC 2024, and IEEE GLOBECOM 2023. He is a member of the IMT-2030 (6G) ISAC Task Group. He was listed as a 2025 Clarivate Highly Cited Researcher. He received multiple Best Paper Awards, including the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the 2024 IEEE Signal Processing Society Best Paper Award, the 2024 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, the 2024 IEEE Communications Society Asia-Pacific Outstanding Paper Award, the 2023 IEEE Communications Society Stephan O.\ Rice Prize, and the 2021 IEEE SPS Young Author Best Paper Award.
\end{IEEEbiography}

\end{document}

另外两点也经常导致“跑不出来”（但和你这段相比是次要）

1. 图片文件不存在 / 路径不对 / 大小写不一致
   Overleaf/Linux 区分大小写：Jingcheng_Shi.jpeg 和 jingcheng_shi.jpeg 是两个文件名。

2. 编译引擎用错
   jpeg/jpg/png 建议用 pdfLaTeX / XeLaTeX / LuaLaTeX。如果你用的是老的 latex -> dvi，就不支持 jpeg（需要 eps）。

如果你把报错信息（第一屏那几行）贴出来，我也可以直接定位到你那边到底是“括号解析问题”还是“找不到图片/引擎不支持”。

下面把你模板里的题目和页眉（running head）按 IEEE TWC 论文的常见写法改掉，同时保留你已经修正过的 biography+照片写法（注意我继续用了 [{...}] 把 \includegraphics[...] 整体包起来）。

你只需要把原来的 \title{...} 和 \markboth{...}{...} 换成下面这一版即可（我也给出一份可直接编译的完整示例）。

只改两处（最推荐）

把原来的：

latex
\title{Bare Demo of IEEEtran.cls\\ for IEEE Journals}

\markboth{Journal of \LaTeX\ Class Files,~Vol.~14, No.~8, August~2015}%
{Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for IEEE Journals}

改成：

latex
\title{Doppler Ambiguity-Resolving Waveform Design\\
Based on Ziv--Zakai Bound Optimization}

\markboth{IEEE Transactions on Wireless Communications}%
{Shi \MakeLowercase{\textit{et al.}}: Doppler Ambiguity-Resolving Waveform Design Based on Ziv--Zakai Bound Optimization}

说明：

• Ziv--Zakai 用 -- 是 LaTeX 正确的长破折号排版。
• 如果你后续拿到卷期页码，可以把第一行写成：
  \markboth{IEEE Transactions on Wireless Communications,~vol.~XX, no.~X, Month~20XX}{...}

完整可编译示例（含你的 biographies）

latex
\documentclass[journal]{IEEEtran}
\usepackage{graphicx}

\hyphenation{op-tical net-works semi-conduc-tor}

\begin{document}

\title{Doppler Ambiguity-Resolving Waveform Design\\
Based on Ziv--Zakai Bound Optimization}

\markboth{IEEE Transactions on Wireless Communications}%
{Shi \MakeLowercase{\textit{et al.}}: Doppler Ambiguity-Resolving Waveform Design Based on Ziv--Zakai Bound Optimization}

\maketitle

% 注：严格的 IEEE 排版习惯是把 biography 放在参考文献后面（\bibliography 之后）
% 这里只是演示，位置你可按需要挪到文末。

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Jingcheng_Shi.jpeg}}]{Jingcheng Shi}
(Student Member, IEEE) received the B.E. degree in communication engineering from Dalian Maritime University, Dalian, China, in 2022. He is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. His research interests include integrated sensing and communication, signal processing, and wireless resource allocation.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Yifeng_Xiong.jpeg}}]{Yifeng Xiong}
(Member, IEEE) received the Ph.D. degree in electronic and electrical engineering from the University of Southampton, Southampton, U.K., in 2022. He received the B.S. degree in information engineering and the M.S. degree (with highest honor) in information and communication engineering from Beijing Institute of Technology (BIT), Beijing, China, in 2015 and 2018, respectively. He is currently an Associate Professor with Beijing University of Posts and Telecommunications (BUPT), Beijing, China. His research interests include integrated sensing and communications, quantum computation, quantum information theory, and statistical inference over networks. He received the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the Best Paper Award at IEEE GLOBECOM 2025, the Best Master Thesis Award from the Chinese Institute of Electronics, and the Best Paper Award at IEEE/CIC ICCC 2023.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Xu_Guo.jpeg}}]{Xu Guo}
(Student Member, IEEE) received the B.E. degree in network engineering from Southwest Jiaotong University, Chengdu, China, in 2021. She is currently pursuing the Ph.D. degree with the School of Electronics, Peking University, Beijing, China. Her research interests include UAV communications, wireless resource allocation, and integrated sensing and communication networks.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Jianjun_Wu.jpeg}}]{Jianjun Wu}
(Member, IEEE) received the B.S., M.S., and Ph.D. degrees from Peking University, Beijing, China, in 1989, 1992, and 2006, respectively. Since 1992, he has been with Peking University, where he became a Professor with the School of Electronics Engineering and Computer Science in 2014. His research interests include satellite communications, wireless communications, and communications signal processing.
\end{IEEEbiography}

\begin{IEEEbiography}[{\includegraphics[width=1in,height=1.25in,clip,keepaspectratio]{Fan_Liu.jpeg}}]{Fan Liu}
(Senior Member, IEEE) is currently a Professor with the National Mobile Communications Research Laboratory, School of Information Science and Engineering, Southeast University, Nanjing, China. From 2020 to 2024, he was an Assistant Professor with the Southern University of Science and Technology, Shenzhen, China. He received the B.Eng. and Ph.D. degrees from Beijing Institute of Technology (BIT), Beijing, China, in 2013 and 2018, respectively. He was with University College London (UCL), London, U.K., as a Visiting Researcher from 2016 to 2018, and a Marie Curie Research Fellow from 2018 to 2020.

His research interests lie in signal processing and wireless communications, in particular integrated sensing and communications (ISAC). He is the founding Academic Chair of the IEEE ComSoc ISAC Emerging Technology Initiative (ISAC--ETI), the Vice Chair and a founding member of the IEEE SPS ISAC Technical Working Group (ISAC--TWG), an elected member of the IEEE SPS Sensor Array and Multichannel Technical Committee (SAM--TC), an Associate Editor of \emph{IEEE Transactions on Communications}, \emph{IEEE Transactions on Mobile Computing}, and \emph{IEEE Open Journal of Signal Processing}, and a Guest Editor of \emph{IEEE Journal on Selected Areas in Communications}, \emph{IEEE Wireless Communications}, and \emph{IEEE Vehicular Technology Magazine}. He served as the TPC Co-Chair of the inaugural IEEE ISAC Conference 2026 and the 2nd--4th IEEE Joint Communication and Sensing (JC\&S) Symposium, Special Session Co-Chair of IEEE SPAWC 2026, and Symposium Co-Chair for IEEE ICC 2026, IEEE/CIC ICCC 2025, IEEE WCNC 2024, and IEEE GLOBECOM 2023. He is a member of the IMT-2030 (6G) ISAC Task Group. He was listed as a 2025 Clarivate Highly Cited Researcher. He received multiple Best Paper Awards, including the 2025 IEEE Communications Society \& Information Theory Society Joint Paper Award, the 2024 IEEE Signal Processing Society Best Paper Award, the 2024 IEEE Signal Processing Society Donald G. Fink Overview Paper Award, the 2024 IEEE Communications Society Asia-Pacific Outstanding Paper Award, the 2023 IEEE Communications Society Stephan O.\ Rice Prize, and the 2021 IEEE SPS Young Author Best Paper Award.
\end{IEEEbiography}

\end{document}

如果你希望 running head 的作者显示不是 “Shi et al.”（比如用 “Liu et al.” 或者按通讯作者），我也可以按你们投稿的作者顺序帮你改成最标准的一版。

可以把 \title{...} 写成“本文档用途”的标题，比如 Author Biographies 或 “Biographies for …”。IEEEtran 不会强制你标题必须是论文题目，所以做一个 bio 文档时这样写最清晰。

下面给你两种常用写法（二选一）：

写法 A：纯粹的 bio 文档（最简洁）

latex
\title{Author Biographies}
\markboth{Author Biographies}{Author Biographies}

写法 B：bio 文档 + 标注对应的论文题目（更正式）

latex
\title{Author Biographies for\\
``Doppler Ambiguity-Resolving Waveform Design Based on Ziv--Zakai Bound Optimization''}

\markboth{Author Biographies}%
{Author Biographies for ``Doppler Ambiguity-Resolving Waveform Design Based on Ziv--Zakai Bound Optimization''}

小提示：如果你不想用英文引号，可以用 \emph{...}：

latex
\title{Author Biographies for\\
\emph{Doppler Ambiguity-Resolving Waveform Design Based on Ziv--Zakai Bound Optimization}}

你把上面任意一组替换掉原来的 \title 和 \markboth 就行，其它 biography（含照片）代码不需要改。

长文版本与先前会议论文的差异说明

本文档旨在系统阐明本次提交的期刊长文版本（下称“长文”）与先前发表的会议论文（下称“会议论文”）之间的主要差异与新增贡献。总体而言，长文在会议论文的基础上进行了面向期刊要求的全面扩展与深入完善，在理论分析、模型推广、算法细化与实验验证等方面显著提升了研究的完整性、严谨性与工程可用性。

1) 向量形式 ZZB 的复杂度分析与指标对比的系统化扩展

长文新增了多目标场景下向量形式 Ziv–Zakai Bound（ZZB）的计算复杂度分析，明确指出其在多维积分与最大化约束下的计算瓶颈，从而解释了向量 ZZB 难以直接嵌入波形设计优化流程的根本原因。该分析为长文提出的多普勒差标量化 ZZB提供了更充分的动机与理论依据，而会议论文未包含上述复杂度层面的论证。

此外，长文进一步扩展了与**贝叶斯克拉美–罗界（BCRB）以及模糊函数零延迟切片（AFZDC）**的对比分析，定量刻画了不同信噪比（SNR）区间内各性能指标的适用范围与边界条件。尤其是，长文揭示了在低 SNR 区域，BCRB 由于未显式表征模糊效应，可能出现对性能的系统性低估。相比之下，会议论文仅对 ZZB 的优势进行概念性讨论，未展开上述定量比较与适用性边界的系统分析。

2) 从双目标到多目标的推广与跨参数适配性讨论

长文在会议论文双目标设定的基础上，提出了面向多目标场景的扩展思路与适配框架，给出了基于多普勒差标量化的多目标可扩展方案，并补充了相关建模假设与适用条件。与此同时，长文新增了对延迟估计问题的扩展模型讨论，并从估计机理与误差表征角度阐明其与多普勒估计的本质区别，从而进一步增强了所提方法在更一般参数估计场景中的可迁移性与解释力。会议论文则主要聚焦双目标情形，未涉及多目标推广与跨参数适配性分析。

3) 算法流程的可复现细化与 SNR 自适应设计机制完善

长文对基于 Adam 的脉冲调制优化算法进行了可复现级别的流程拆解，给出了梯度计算、动量更新、偏差修正与投影操作等关键步骤的明确公式及参数设置，并提供了迭代终止准则的量化定义（如容忍度阈值）。相比之下，会议论文仅给出算法框架性描述，缺乏实现细节，难以直接复现实验结果。

进一步地，长文提出并阐释了基于 ZZB 与均方误差（MSE）对齐的 SNR 自适应波形设计方法，明确说明不同设计 SNR 下波形选择的内在逻辑，并通过定量实验展示该自适应策略对阈值效应的抑制作用。会议论文虽提出 SNR 自适应的概念，但未给出对齐机制与效果量化。

4) 仿真设置的多元化与验证深度增强

长文显著扩展了仿真覆盖范围，新增多组关键参数变量的系统评估，包括目标幅度比、多普勒差取值区间以及 SNR 适配范围等，覆盖对称/非对称目标强度与不同分辨率需求下的典型应用场景。会议论文的仿真主要局限于单一幅度比与固定区间，场景覆盖相对有限。

同时，长文补充了双目标联合 MSE的计算方式与仿真结果，用以验证多普勒差 ZZB 对多目标估计性能的表征能力；并新增 4D AFZDC 的多区间对比分析，从旁瓣抑制等角度定量呈现波形优化带来的改进幅度，进一步提升了结论的可验证性与说服力。

5) 新增附录推导以增强理论严谨性与可复现性

长文新增完整附录，给出了多普勒差最小错误概率解析表达式的详细推导过程，涵盖协方差矩阵构建、极坐标变换以及 Marcum Q 函数的引入与化简等关键步骤，从而补齐会议论文中未公开的核心推导细节，显著提升了研究的可复现性与理论透明度。会议论文未包含相关推导附录，仅给出了最终结论。

总结

综上所述，长文并非对会议论文的简单篇幅扩展，而是在理论基础、模型广度、算法可复现性与验证深度等方面实现了系统性提升。通过补充关键推导、推广多目标模型、完善优化流程并丰富仿真验证，长文进一步增强了研究成果的学术贡献与工程应用潜力，满足 IEEE 顶级期刊对论文完整性、严谨性与深度的要求。

Statement of Differences Between the Journal Manuscript and the Prior Conference Paper

This document aims to systematically delineate the principal differences and additional contributions of the submitted journal manuscript (hereafter, the “journal version”) relative to the previously published conference paper (hereafter, the “conference version”). Overall, the journal version constitutes a comprehensive extension and substantial refinement of the conference version to meet journal-level requirements, significantly enhancing the completeness, rigor, and practical relevance of the study in terms of theoretical analysis, model generalization, algorithmic elaboration, and experimental validation.

1) Complexity analysis of the vector-form ZZB and systematic extension of performance-metric comparisons
The journal version introduces a computational complexity analysis of the vector-form Ziv–Zakai bound (ZZB) in multi-target scenarios, explicitly identifying the computational bottlenecks arising from multi-dimensional integrations and maximization constraints. This analysis clarifies the fundamental reason why the vector-form ZZB is difficult to directly incorporate into waveform-design optimization procedures. It further provides stronger motivation and theoretical justification for the proposed Doppler-difference scalarized ZZB, whereas the conference version did not include such complexity-oriented arguments.
In addition, the journal version substantially extends the comparative analysis against the Bayesian Cramér–Rao bound (BCRB) and the ambiguity-function zero-delay cut (AFZDC), quantitatively characterizing the applicability regimes and boundary conditions of these metrics across different signal-to-noise ratio (SNR) ranges. In particular, the journal version reveals that, in the low-SNR regime, the BCRB may systematically underestimate performance because it does not explicitly capture ambiguity effects. By contrast, the conference version only provided a qualitative discussion of the advantages of ZZB and did not conduct the above quantitative comparisons or a systematic analysis of applicability boundaries.

2) Extension from the two-target case to multi-target scenarios and discussion on cross-parameter adaptability
Building on the two-target setting considered in the conference version, the journal version develops an extension rationale and an adaptation framework for multi-target scenarios. Specifically, it provides a scalable multi-target solution based on Doppler-difference scalarization and supplements the associated modeling assumptions and applicability conditions. Meanwhile, the journal version adds an extended modeling discussion for delay estimation and clarifies, from the perspectives of estimation mechanisms and error characterization, its essential differences from Doppler estimation, thereby further strengthening the transferability and interpretability of the proposed approach in more general parameter-estimation settings. The conference version primarily focused on the two-target case and did not address multi-target extensions or cross-parameter adaptability analyses.

3) Reproducible-level elaboration of the algorithmic pipeline and refinement of the SNR-adaptive design mechanism
The journal version provides a reproducible-level decomposition of the Adam-based pulse-modulation optimization algorithm, including explicit formulas and parameter settings for key steps such as gradient evaluation, momentum updates, bias correction, and projection operations, together with a quantitative definition of the stopping criterion (e.g., tolerance thresholds). In contrast, the conference version only outlined the algorithmic framework without sufficient implementation details.
Furthermore, the journal version proposes and explains an SNR-adaptive waveform-design method based on aligning ZZB with the mean-squared error (MSE), explicitly articulating the underlying rationale for waveform selection under different design SNRs and demonstrating, via quantitative experiments, the effectiveness of the adaptive strategy in mitigating threshold effects. Although the conference version introduced the notion of SNR adaptivity, it did not provide the alignment mechanism or quantitative evaluations of its impact.

4) Diversification of simulation settings and strengthened validation depth
The journal version substantially broadens the simulation coverage by conducting systematic evaluations over multiple key parameters, including the target amplitude ratio, the Doppler-difference interval, and the SNR adaptation range, thereby encompassing representative practical scenarios with symmetric/asymmetric target strengths and varying resolution requirements. The conference version primarily considered a single amplitude ratio and a fixed interval, resulting in relatively limited scenario coverage.
Moreover, the journal version supplements the computation and simulation results of the joint MSE for the two-target case to validate the representational capability of the Doppler-difference ZZB for multi-target estimation performance. It also adds multi-interval comparative analyses based on 4D AFZDC, quantitatively demonstrating the gains brought by waveform optimization from aspects such as sidelobe suppression, which further improves the verifiability and persuasiveness of the conclusions.

Summary
In summary, the journal version is not a mere lengthened version of the conference paper; rather, it achieves systematic advancements in theoretical foundations, model scope, algorithmic reproducibility, and validation depth. By supplementing key derivations, extending to multi-target models, refining the optimization procedure, and enriching simulation-based verification, the journal version further strengthens the academic significance and engineering potential of the results, meeting the expectations of IEEE top-tier journals regarding completeness, rigor, and depth.

Below is a journal-ready “Differences Document” written to match the requirement: “A document detailing how your submission differs from the earlier publication(s).” (IEEE top-tier journal tone, focused, and non-redundant).

Differences Between the Submitted Journal Manuscript and the Prior Conference Publication

This document summarizes the principal differences and additional contributions of the submitted journal manuscript (the “journal submission”) relative to the authors’ earlier conference publication (the “conference paper”). The journal submission is a substantial extension and refinement of the conference paper, with enhanced rigor and broader validation in theoretical analysis, model generalization, algorithmic specification, and experimental evaluation.

1) Complexity Analysis of Vector-Form ZZB and Expanded Metric Comparisons

The journal submission adds a computational complexity analysis of the vector-form Ziv–Zakai bound (ZZB) in multi-target settings. It identifies the key bottlenecks induced by multi-dimensional integrations and maximization constraints, thereby clarifying why the vector-form ZZB is not readily embedded into waveform-design optimization. This analysis provides stronger motivation and theoretical support for the Doppler-difference scalarized ZZB, whereas the conference paper did not include this complexity-oriented justification.

In addition, the journal submission substantially extends the comparative study with the Bayesian Cramér–Rao bound (BCRB) and the ambiguity-function zero-delay cut (AFZDC). It quantitatively characterizes the applicability regimes and boundary conditions of these metrics across different SNR ranges. In particular, it shows that in the low-SNR regime, BCRB may systematically underestimate performance when ambiguity effects are not explicitly captured. By contrast, the conference paper provided only a qualitative discussion of ZZB’s advantages without a systematic, quantitative applicability analysis.

2) Extension from Two-Target to Multi-Target Scenarios and Cross-Parameter Adaptability

Building upon the two-target formulation in the conference paper, the journal submission develops an extension rationale and an adaptation framework for multi-target scenarios. It presents a scalable approach based on Doppler-difference scalarization, together with the corresponding modeling assumptions and applicability conditions.

Moreover, the journal submission includes an extended modeling discussion for delay estimation, and clarifies—via estimation mechanisms and error characterization—its essential differences from Doppler estimation. This strengthens the interpretability and portability of the proposed methodology to broader parameter-estimation settings. The conference paper focused on the two-target Doppler case and did not address these multi-target and cross-parameter considerations.

3) Reproducible-Level Algorithmic Specification and Refined SNR-Adaptive Design Mechanism

The journal submission provides a reproducible, implementation-level description of the Adam-based pulse-modulation optimization procedure, including explicit formulations and parameter settings for gradient evaluation, momentum updates, bias correction, and projection steps, as well as a quantitative stopping criterion (e.g., tolerance thresholds). The conference paper described the algorithm at a high level without sufficient details for direct reproduction.

Further, the journal submission proposes and details an SNR-adaptive waveform design strategy via alignment between ZZB and MSE, explicitly explaining the waveform-selection logic under different design SNRs and demonstrating, through quantitative results, its effectiveness in mitigating threshold effects. The conference paper mentioned SNR adaptivity conceptually but did not provide the alignment mechanism or quantified performance impact.

4) Broader Simulation Coverage and Stronger Validation Depth

The journal submission significantly broadens the simulation study by systematically varying key parameters, including target amplitude ratio, Doppler-difference intervals, and SNR adaptation ranges, thereby covering representative scenarios with symmetric/asymmetric target strengths and different resolution demands. The conference paper evaluated a more limited set of configurations (e.g., a single amplitude ratio and fixed intervals).

In addition, the journal submission includes the computation and simulation of the two-target joint MSE, validating the capability of the Doppler-difference ZZB to characterize multi-target estimation performance. It also adds multi-interval comparisons using 4D AFZDC, quantitatively demonstrating waveform-optimization gains (e.g., sidelobe suppression), which further strengthens the verifiability and persuasiveness of the conclusions.

Summary

In summary, the journal submission is not a mere length expansion of the conference paper. It provides systematic advancements in theoretical grounding, model scope, algorithmic reproducibility, and validation depth, thereby meeting the expectations of IEEE top-tier journals in terms of completeness, rigor, and technical depth.

If you want, I can also output this in a clean LaTeX template (one-page PDF-friendly) with your paper title and the conference paper citation placeholder formatted in IEEE style.

latex
% Differences Document (IEEE-style tone)
% You can paste this into a standalone .tex file, or into your submission package.

\documentclass[10pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{times}
\usepackage{enumitem}
\usepackage[hidelinks]{hyperref}

\begin{document}

\begin{center}
{\Large \textbf{Differences Between the Submitted Journal Manuscript and the Prior Conference Publication}}\\[6pt]
\end{center}

\noindent
This document summarizes the principal differences and additional contributions of the submitted journal manuscript (the ``journal submission'') relative to the authors' earlier conference publication (the ``conference paper''). The journal submission is a substantial extension and refinement of the conference paper, with enhanced rigor and broader validation in theoretical analysis, model generalization, algorithmic specification, and experimental evaluation.

\vspace{6pt}
\begin{enumerate}[leftmargin=*, itemsep=6pt]

\item \textbf{Complexity Analysis of Vector-Form ZZB and Expanded Metric Comparisons.}
The journal submission adds a computational complexity analysis of the \emph{vector-form} Ziv--Zakai bound (ZZB) in multi-target settings. It identifies the key bottlenecks induced by multi-dimensional integrations and maximization constraints, thereby clarifying why the vector-form ZZB is not readily embedded into waveform-design optimization. This analysis provides stronger motivation and theoretical support for the \emph{Doppler-difference scalarized ZZB}, whereas the conference paper did not include this complexity-oriented justification.

In addition, the journal submission substantially extends the comparative study with the Bayesian Cram\'er--Rao bound (BCRB) and the ambiguity-function zero-delay cut (AFZDC). It quantitatively characterizes the applicability regimes and boundary conditions of these metrics across different SNR ranges. In particular, it shows that in the low-SNR regime, BCRB may systematically underestimate performance when ambiguity effects are not explicitly captured. By contrast, the conference paper provided only a qualitative discussion of ZZB's advantages without a systematic, quantitative applicability analysis.

\item \textbf{Extension from Two-Target to Multi-Target Scenarios and Cross-Parameter Adaptability.}
Building upon the two-target formulation in the conference paper, the journal submission develops an extension rationale and an adaptation framework for multi-target scenarios. It presents a scalable approach based on Doppler-difference scalarization, together with the corresponding modeling assumptions and applicability conditions.

Moreover, the journal submission includes an extended modeling discussion for delay estimation, and clarifies---via estimation mechanisms and error characterization---its essential differences from Doppler estimation. This strengthens the interpretability and portability of the proposed methodology to broader parameter-estimation settings. The conference paper focused on the two-target Doppler case and did not address these multi-target and cross-parameter considerations.

\item \textbf{Reproducible-Level Algorithmic Specification and Refined SNR-Adaptive Design Mechanism.}
The journal submission provides a reproducible, implementation-level description of the Adam-based pulse-modulation optimization procedure, including explicit formulations and parameter settings for gradient evaluation, momentum updates, bias correction, and projection steps, as well as a quantitative stopping criterion (e.g., tolerance thresholds). The conference paper described the algorithm at a high level without sufficient details for direct reproduction.

Further, the journal submission proposes and details an SNR-adaptive waveform design strategy via alignment between ZZB and MSE, explicitly explaining the waveform-selection logic under different design SNRs and demonstrating, through quantitative results, its effectiveness in mitigating threshold effects. The conference paper mentioned SNR adaptivity conceptually but did not provide the alignment mechanism or quantified performance impact.

\item \textbf{Broader Simulation Coverage and Stronger Validation Depth.}
The journal submission significantly broadens the simulation study by systematically varying key parameters, including target amplitude ratio, Doppler-difference intervals, and SNR adaptation ranges, thereby covering representative scenarios with symmetric/asymmetric target strengths and different resolution demands. The conference paper evaluated a more limited set of configurations (e.g., a single amplitude ratio and fixed intervals).

In addition, the journal submission includes the computation and simulation of the two-target joint MSE, validating the capability of the Doppler-difference ZZB to characterize multi-target estimation performance. It also adds multi-interval comparisons using 4D AFZDC, quantitatively demonstrating waveform-optimization gains (e.g., sidelobe suppression), which further strengthens the verifiability and persuasiveness of the conclusions.

\end{enumerate}

\vspace{6pt}
\noindent
\textbf{Summary:}
In summary, the journal submission is not a mere length expansion of the conference paper. It provides systematic advancements in theoretical grounding, model scope, algorithmic reproducibility, and validation depth, thereby meeting the expectations of IEEE top-tier journals in terms of completeness, rigor, and technical depth.

\end{document}

latex
% Differences Document (explicitly referencing IEEE TWC and IEEE GLOBECOM 2025)
% Paste into a standalone .tex file.

\documentclass[10pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{times}
\usepackage{enumitem}
\usepackage[hidelinks]{hyperref}

\begin{document}

\begin{center}
{\Large \textbf{Differences Between the Submitted IEEE TWC Manuscript and the Prior IEEE GLOBECOM 2025 Publication}}\\[6pt]
\end{center}

\noindent
This document summarizes the principal differences and additional contributions of the submitted journal manuscript \emph{under review at} \emph{IEEE Transactions on Wireless Communications (IEEE TWC)} (the ``journal submission'') relative to the authors' earlier conference publication at \emph{IEEE GLOBECOM 2025} (the ``conference paper''). The journal submission is a substantial extension and refinement of the conference paper, with enhanced rigor and broader validation in theoretical analysis, model generalization, algorithmic specification, and experimental evaluation.

\vspace{6pt}
\begin{enumerate}[leftmargin=*, itemsep=6pt]

\item \textbf{Complexity Analysis of Vector-Form ZZB and Expanded Metric Comparisons.}
The journal submission adds a computational complexity analysis of the \emph{vector-form} Ziv--Zakai bound (ZZB) in multi-target settings. It identifies the key bottlenecks induced by multi-dimensional integrations and maximization constraints, thereby clarifying why the vector-form ZZB is not readily embedded into waveform-design optimization. This analysis provides stronger motivation and theoretical support for the \emph{Doppler-difference scalarized ZZB}, whereas the conference paper did not include this complexity-oriented justification.

In addition, the journal submission substantially extends the comparative study with the Bayesian Cram\'er--Rao bound (BCRB) and the ambiguity-function zero-delay cut (AFZDC). It quantitatively characterizes the applicability regimes and boundary conditions of these metrics across different SNR ranges. In particular, it shows that in the low-SNR regime, BCRB may systematically underestimate performance when ambiguity effects are not explicitly captured. By contrast, the conference paper provided only a qualitative discussion of ZZB's advantages without a systematic, quantitative applicability analysis.

\item \textbf{Extension from Two-Target to Multi-Target Scenarios and Cross-Parameter Adaptability.}
Building upon the two-target formulation in the conference paper, the journal submission develops an extension rationale and an adaptation framework for multi-target scenarios. It presents a scalable approach based on Doppler-difference scalarization, together with the corresponding modeling assumptions and applicability conditions.

Moreover, the journal submission includes an extended modeling discussion for delay estimation, and clarifies---via estimation mechanisms and error characterization---its essential differences from Doppler estimation. This strengthens the interpretability and portability of the proposed methodology to broader parameter-estimation settings. The conference paper focused on the two-target Doppler case and did not address these multi-target and cross-parameter considerations.

\item \textbf{Reproducible-Level Algorithmic Specification and Refined SNR-Adaptive Design Mechanism.}
The journal submission provides a reproducible, implementation-level description of the Adam-based pulse-modulation optimization procedure, including explicit formulations and parameter settings for gradient evaluation, momentum updates, bias correction, and projection steps, as well as a quantitative stopping criterion (e.g., tolerance thresholds). The conference paper described the algorithm at a high level without sufficient details for direct reproduction.

Further, the journal submission proposes and details an SNR-adaptive waveform design strategy via alignment between ZZB and MSE, explicitly explaining the waveform-selection logic under different design SNRs and demonstrating, through quantitative results, its effectiveness in mitigating threshold effects. The conference paper mentioned SNR adaptivity conceptually but did not provide the alignment mechanism or quantified performance impact.

\item \textbf{Broader Simulation Coverage and Stronger Validation Depth.}
The journal submission significantly broadens the simulation study by systematically varying key parameters, including target amplitude ratio, Doppler-difference intervals, and SNR adaptation ranges, thereby covering representative scenarios with symmetric/asymmetric target strengths and different resolution demands. The conference paper evaluated a more limited set of configurations (e.g., a single amplitude ratio and fixed intervals).

In addition, the journal submission includes the computation and simulation of the two-target joint MSE, validating the capability of the Doppler-difference ZZB to characterize multi-target estimation performance. It also adds multi-interval comparisons using 4D AFZDC, quantitatively demonstrating waveform-optimization gains (e.g., sidelobe suppression), which further strengthens the verifiability and persuasiveness of the conclusions.

\end{enumerate}

\vspace{6pt}
\noindent
\textbf{Summary:}
In summary, the IEEE TWC journal submission is not a mere length expansion of the IEEE GLOBECOM 2025 conference paper. It provides systematic advancements in theoretical grounding, model scope, algorithmic reproducibility, and validation depth, thereby meeting the expectations of IEEE top-tier journals in terms of completeness, rigor, and technical depth.

\end{document}

latex
\documentclass[10pt]{article}
\usepackage[margin=1in]{geometry}
\usepackage{times}
\usepackage{enumitem}
\usepackage[hidelinks]{hyperref}

\begin{document}

\begin{center}
{\Large \textbf{Differences Between This Manuscript and the Prior Publication}}\\[6pt]
\end{center}

\noindent
This document summarizes the principal differences and additional contributions of \textbf{this manuscript} relative to the authors' earlier conference publication at the \emph{IEEE Global Communications Conference (GLOBECOM) 2025} (the ``conference paper''). Overall, this manuscript constitutes a substantial extension and refinement of the conference paper, with enhanced rigor and broader validation in theoretical analysis, model generalization, algorithmic specification, and experimental evaluation.

\vspace{6pt}
\begin{enumerate}[leftmargin=*, itemsep=6pt]

\item \textbf{Complexity Analysis of Vector-Form ZZB and Expanded Metric Comparisons.}
This manuscript adds a computational complexity analysis of the \emph{vector-form} Ziv--Zakai bound (ZZB) in multi-target settings. It identifies the key bottlenecks induced by multi-dimensional integrations and maximization constraints, thereby clarifying why the vector-form ZZB is not readily embedded into waveform-design optimization. This analysis provides stronger motivation and theoretical support for the \emph{Doppler-difference scalarized ZZB}, whereas the conference paper did not include this complexity-oriented justification.

In addition, this manuscript substantially extends the comparative study with the Bayesian Cram\'er--Rao bound (BCRB) and the ambiguity-function zero-delay cut (AFZDC). It quantitatively characterizes the applicability regimes and boundary conditions of these metrics across different SNR ranges. In particular, it shows that in the low-SNR regime, BCRB may systematically underestimate performance when ambiguity effects are not explicitly captured. By contrast, the conference paper provided only a qualitative discussion of ZZB's advantages without a systematic, quantitative applicability analysis.

\item \textbf{Extension from Two-Target to Multi-Target Scenarios and Cross-Parameter Adaptability.}
Building upon the two-target formulation in the conference paper, this manuscript develops an extension rationale and an adaptation framework for multi-target scenarios. It presents a scalable approach based on Doppler-difference scalarization, together with the corresponding modeling assumptions and applicability conditions.

Moreover, this manuscript includes an extended modeling discussion for delay estimation, and clarifies---via estimation mechanisms and error characterization---its essential differences from Doppler estimation. This strengthens the interpretability and portability of the proposed methodology to broader parameter-estimation settings. The conference paper focused on the two-target Doppler case and did not address these multi-target and cross-parameter considerations.

\item \textbf{Reproducible-Level Algorithmic Specification and Refined SNR-Adaptive Design Mechanism.}
This manuscript provides a reproducible, implementation-level description of the Adam-based pulse-modulation optimization procedure, including explicit formulations and parameter settings for gradient evaluation, momentum updates, bias correction, and projection steps, as well as a quantitative stopping criterion (e.g., tolerance thresholds). The conference paper described the algorithm at a high level without sufficient details for direct reproduction.

Further, this manuscript proposes and details an SNR-adaptive waveform design strategy via alignment between ZZB and MSE, explicitly explaining the waveform-selection logic under different design SNRs and demonstrating, through quantitative results, its effectiveness in mitigating threshold effects. The conference paper mentioned SNR adaptivity conceptually but did not provide the alignment mechanism or quantified performance impact.

\item \textbf{Broader Simulation Coverage and Stronger Validation Depth.}
This manuscript significantly broadens the simulation study by systematically varying key parameters, including target amplitude ratio, Doppler-difference intervals, and SNR adaptation ranges, thereby covering representative scenarios with symmetric/asymmetric target strengths and different resolution demands. The conference paper evaluated a more limited set of configurations (e.g., a single amplitude ratio and fixed intervals).

In addition, this manuscript includes the computation and simulation of the two-target joint MSE, validating the capability of the Doppler-difference ZZB to characterize multi-target estimation performance. It also adds multi-interval comparisons using 4D AFZDC, quantitatively demonstrating waveform-optimization gains (e.g., sidelobe suppression), which further strengthens the verifiability and persuasiveness of the conclusions.

\end{enumerate}

\vspace{6pt}
\noindent
\textbf{Summary:}
In summary, this manuscript is not a mere length expansion of the prior publication in IEEE GLOBECOM 2025. It provides systematic advancements in theoretical grounding, model scope, algorithmic reproducibility, and validation depth, thereby meeting the expectations of IEEE top-tier journals in terms of completeness, rigor, and technical depth.

\end{document}