MS004121-移动通信多用户传输技术

研究生课程开设申请表

开课院（系、所）： 信息科学与工程学院

课程申请开设类型： 新开□     重开     更名□（请在□内打勾，下同）

一、课程介绍（含教学目标、教学要求等）（300字以内）

课程主要瞄准无线通信多用户传输场景的PHY/MAC关键问题，如多用户信道获取与反馈、上行接收机合并器与检测器设计、下行预编码设计、用户时频资源/功率分配等，一方面讲授这些技术的基础原理，并辅以经典文献介绍典型的代表性研究成果；另一方面，通过分析经典模型设计方法存在的不足，介绍最新的借鉴机器学习技术解决上述问题的设计思路。课程采用讲授为主、讨论为辅的教学方式，使硕士研究生同学通过本课程的学习，熟悉无线通信多用户传输技术研究的已有代表性成果与最新研究动态，掌握其工作原理与设计方法，并理解各项技术之间的区别与内在联系。

二、教学大纲（含章节目录）：（可附页）

第一讲：移动通信多用户传输基本系统模型（2学时）

• 教学目标：介绍多用户传输系统的基本构成，重点讲解通信链路中的基本模型。

• 知识点：

  1. 基本系统模型：多用户无线通信系统的架构，包括用户设备、基站和信道。

  2. 传输模型：讨论信息传输的基本概念，包括信道容量、传输速率和误码率。

  3. 信道模型：介绍常见的无线信道模型，如瑞利衰落、Rician模型等，分析信道衰减、路径损耗及多径效应对系统性能的影响。

第二讲：移动通信多用户传输中的信道获取与反馈 (2学时)

• 教学目标：讲解信道估计和反馈机制，重点是如何在多用户场景下有效获取信道信息。

• 知识点：

  1. 上行信道估计：介绍如何在上行链路中进行信道估计，包括基于导频的估计方法。

  2. 下行信道估计与反馈：讲解下行信道估计的典型方案，以及反馈机制的设计，包括量化、压缩感知和反馈延迟问题。

第三讲：移动通信多用户传输中的上行接收机设计 (2学时)

• 教学目标：介绍多用户接收机的设计原理和技术，重点分析不同接收机的性能和适用场景。

• 知识点：

  1. 上行接收机结构：多用户检测技术，如基于最大比合并（MRC）、串行干扰消除（SIC）、最小均方误差（MMSE）等方法。

  2. 性能分析：介绍接收机性能评估指标，包括误码率（BER）和信噪比（SNR）等。

  3. 非理想条件下的设计：讨论非理想情况对接收机设计的影响，如信道估计误差、反馈延迟、噪声干扰等。

第四讲：移动通信多用户传输中的下行预编码设计 (2学时)

• 教学目标：重点介绍下行链路的预编码设计，如何通过预编码技术优化系统性能。

• 知识点：

  1. 速率最大化的预编码方案：讨论如何设计预编码矩阵以最大化系统的传输速率，包含基于信道状态信息的优化方法。

  2. 用户公平性考虑：介绍公平性问题，如公平调度和公平预编码方案，以保证多用户之间的公平性。

  3. 预编码算法的实现：具体算法介绍，如最小均方误差（MMSE）预编码、ZF（迫零）预编码和注水算法等。

第五讲：移动通信多用户传输中的时频资源分配 (2学时)

• 教学目标：讲解时频资源的管理和优化，如何根据系统需求进行资源分配。

• 知识点：

  1. 时频资源分配模型：介绍资源分配的基本问题模型，包括时隙、频带的分配，以及如何在多用户场景下进行优化。

  2. 优化求解方法：分析常见的优化方法，如线性规划、整数规划、动态规划等，如何用这些方法求解资源分配问题。

  3. 应用场景：讨论不同的应用场景，如信道状态变化、用户需求不平衡等情况下的资源分配策略。

第六讲：移动通信多用户传输中的功率分配问题 (2学时)

• 教学目标：介绍在多用户传输中如何进行功率分配，重点在不同场景下的功率优化。

• 知识点：

  1. 单小区功率分配方案：讨论单小区环境下的功率分配问题，包括最大化总吞吐量或最小化总功率等目标。

  2. 多小区功率分配方案：研究多小区间干扰情况下的功率分配问题，如何通过功率控制算法降低干扰。

  3. 上行与下行功率分配：区分上行和下行链路的功率分配问题，并讲解各自的优化方法，如基于反向传播的功率控制。

第七讲：机器学习辅助的多用户无线资源管理 (2学时)

• 教学目标：探讨如何通过机器学习提升传统资源管理方法的性能，解决多用户传输中的资源分配问题。

• 知识点：

  1. 机器学习基础：简单介绍机器学习的基本概念和方法，如监督学习、强化学习等。

  2. 基于机器学习的资源管理：介绍如何利用机器学习模型（如深度学习、强化学习）来优化波束成形、时频资源分配等。

  3. 与传统方法的对比：分析传统模型驱动方法和机器学习辅助方法的优势与不足，举例说明机器学习如何改善传统方法的性能。

第八讲：分小组进行学习报告

• 教学目标：通过小组报告促进学生对所学知识的深入理解和实践应用。

• 知识点：

  1. 分组主题设定：设定具体的学习主题，如“多用户资源分配优化算法”或“机器学习在移动通信中的应用”。

  2. 报告要求与讨论：学生分组研究主题，撰写报告并进行汇报，教师根据报告内容和展示情况进行点评和指导。

  3. 互动与反馈：报告结束后，教师与学生互动，针对报告中的关键点进行深入讨论，帮助学生巩固知识。

三、教学周历

注：1.以上一、二、三项内容将作为中文教学大纲，在研究生院中文网页上公布，四、五内容将保存在研究生院。2.开课学期为：春季、秋季或春秋季。3.授课语言为：汉语、英语或双语教学。4.适用学科范围为：公共，一级，二级，三级。5.实践环节为：实验、调研、研究报告等。6.教学方式为：讲课、讨论、实验等。7.学位课程考试必须是笔试。8.课件地址指在网络上已经有的课程课件地址。9.主讲教师简介主要为基本信息（出生年月、性别、学历学位、专业职称等）、研究方向、教学与科研成果，以100至500字为宜。

四、主讲教师简介：

张铖，1988年生，男，工学博士学位，副研究员，研究方向主要为面向B5G/6G无线通信的空时信号处理及机器学习辅助优化。目前担任本科生三年级必修课《人工智能与深度学习》的主讲教师，曾获得校授课竞赛三等奖。研究成果方面发表IEEE论文60余篇，授权国内发明专利20余项，目前主持国自然面上项目1项、江苏省优青项目1项。

五、任课教师信息（包括主讲教师）：

六、课程必要性说明

本课程《移动通信多用户传输技术》旨在填补当前硕士研究生课程中在无线通信多用户传输领域的空白，尤其是在PHY/MAC层面上对多用户场景的关键问题进行深入探讨。与现有课程相比，该课程的内容不仅涵盖传统的无线通信技术，如信道获取、上行接收机设计、下行预编码以及时频资源和功率分配等基础原理和经典设计方案，还结合最新的研究动态，特别是机器学习技术的应用，来解决经典设计方法中的不足。通过引入机器学习辅助的资源管理方案，课程将为学生提供一种新的视角，帮助他们在解决多用户无线通信中的实际问题时，能够灵活地运用传统技术与前沿方法的结合。

与传统的通信课程（如基础通信原理、现代通信技术等）相比，本课程更加专注于多用户传输场景下的系统级设计与优化，填补了现有课程在这一特定领域的深入探讨。在现有课程基础上，本课程进一步强化了多用户通信的关键技术，特别是通过引入机器学习在波束控制、时频资源分配等方面的创新应用，提升了课程的前瞻性和实践性，能够帮助学生更好地理解现代无线通信中的复杂问题和最新技术发展。因此，本课程不仅是对现有课程的有力补充，也是对未来无线通信技术发展趋势的回应。

七、课程开设审批意见

所在院（系）

审 批 意 见

负责人：

日  期：

所在学位评定分

委员会审批意见

分委员会主席：

日  期：

研究生院审批意见

负责人：

日  期：

注

说明：1．研究生课程重开、更名申请也采用此表。表格下载：http: /seugs.seu.edu.cn/down/1.asp

2．此表一式三份，交研究生院、院（系）和自留各一份，同时提交电子文档交研究生院。

Application Form For Opening Graduate Courses

School (Department/Institute)：

Course Type: New Open □   Reopen    Rename □（Please tick in □, the same below）

1. Course Introduction (including teaching goals and requirements) within 300 words:

The course mainly targets the key PHY/MAC problems in multi-user transmission scenarios of wireless communications, such as multi-user channel acquisition and feedback, uplink receiver combiner and detector design, downlink precoding design, user power allocation, etc. On the one hand, the course teaches the basic principles of these techniques, and introduces the representative research results of each with the help of classical literature; on the other hand, it analyzes the shortcomings of classical model design methods and introduces the latest design solutions to solve the above problems with the help of machine learning techniques. The course adopts a lecture-based approach, supplemented by discussions, to familiarize the master students with the representative results and latest research developments of multi-user transmission technologies for wireless communication, to master their working principles and design methods, and to understand the differences and interconnections among the technologies.

2. Teaching Syllabus (including the content of chapters and sections. A sheet can be attached):

Lecture 1: Basic System Models for Multi-User Transmission in Mobile Communications (2 hours)

• Teaching Objective: Introduce the basic structure of multi-user transmission systems, with a focus on basic models in communication links.

• Key Topics:

  1. Basic System Models: Architecture of multi-user wireless communication systems, including user equipment, base stations, and channels.

  2. Transmission Models: Discuss fundamental concepts of information transmission, including channel capacity, transmission rate, and error rate.

  3. Channel Models: Introduce common wireless channel models such as Rayleigh fading, Rician models, etc., and analyze the impact of channel attenuation, path loss, and multipath effects on system performance.

Lecture 2: Channel Estimation and Feedback in Multi-User Transmission of Mobile Communications (2 hours)

• Teaching Objective: Explain channel estimation and feedback mechanisms, focusing on how to effectively acquire channel information in multi-user scenarios.

• Key Topics:

  1. Uplink Channel Estimation: Introduce channel estimation methods in the uplink, including pilot-based estimation techniques.

  2. Downlink Channel Estimation and Feedback: Explain typical methods for downlink channel estimation and feedback mechanisms, including quantization, compressed sensing, and feedback delay issues.

Lecture 3: Uplink Receiver Design in Multi-User Transmission of Mobile Communications (2 hours)

• Teaching Objective: Introduce the design principles and techniques of multi-user receivers, focusing on performance and applicability in various scenarios.

• Key Topics:

  1. Uplink Receiver Structure: Multi-user detection techniques such as Maximum Ratio Combining (MRC), Successive Interference Cancellation (SIC), Minimum Mean Square Error (MMSE), etc.

  2. Performance Analysis: Introduce performance evaluation metrics for receivers, including Bit Error Rate (BER) and Signal-to-Noise Ratio (SNR).

  3. Design under Non-Ideal Conditions: Discuss the impact of non-ideal conditions such as channel estimation errors, feedback delay, and noise interference on receiver design.

Lecture 4: Downlink Precoding Design in Multi-User Transmission of Mobile Communications (2 hours)

• Teaching Objective: Focus on downlink precoding design and how precoding techniques can optimize system performance.

• Key Topics:

  1. Rate Maximization Precoding: Discuss how to design precoding matrices to maximize system transmission rate, including optimization based on channel state information.

  2. User Fairness Considerations: Introduce fairness issues such as fair scheduling and fair precoding schemes to ensure fairness among multiple users.

  3. Precoding Algorithm Implementation: Specific algorithms for precoding, such as MMSE precoding, Zero-Forcing (ZF) precoding, and water-filling algorithms.

Lecture 5: Time-Frequency Resource Allocation in Multi-User Transmission of Mobile Communications (2 hours)

• Teaching Objective: Explain the management and optimization of time-frequency resources, and how to allocate resources based on system requirements.

• Key Topics:

  1. Time-Frequency Resource Allocation Models: Introduce the basic problem models of resource allocation, including time slots and frequency band allocation, and how to optimize them in multi-user scenarios.

  2. Optimization Methods: Analyze common optimization techniques such as Linear Programming (LP), Integer Programming (IP), Dynamic Programming (DP), etc., and how to use them to solve resource allocation problems.

  3. Application Scenarios: Discuss different application scenarios such as channel state variations and user demand imbalances, and corresponding resource allocation strategies.

Lecture 6: Power Allocation in Multi-User Transmission of Mobile Communications (2 hours)

• Teaching Objective: Introduce power allocation in multi-user transmissions, with a focus on optimizing power distribution in different scenarios.

• Key Topics:

  1. Single-Cell Power Allocation Schemes: Discuss power allocation in single-cell environments, including goals such as maximizing total throughput or minimizing total power.

  2. Multi-Cell Power Allocation Schemes: Study power allocation in multi-cell scenarios with interference, and how power control algorithms can reduce interference.

  3. Uplink and Downlink Power Allocation: Distinguish between uplink and downlink power allocation and explain optimization methods for each, such as power control based on backpropagation.

Lecture 7: Machine Learning-Assisted Multi-User Wireless Resource Management (2 hours)

• Teaching Objective: Explore how machine learning can enhance the performance of traditional resource management methods and address multi-user transmission resource allocation issues.

• Key Topics:

  1. Machine Learning Basics: A brief introduction to the basics of machine learning, including supervised learning and reinforcement learning.

  2. Machine Learning-Based Resource Management: Introduce how machine learning models (such as deep learning and reinforcement learning) can be used to optimize beamforming, time-frequency resource allocation, etc.

  3. Comparison with Traditional Methods: Analyze the advantages and disadvantages of traditional model-driven methods versus machine learning-assisted methods, and provide examples of how machine learning improves traditional methods.

Lecture 8: Group Reports (2 hours)

• Teaching Objective: Facilitate in-depth understanding and practical application of the knowledge learned through group reports.

• Key Topics:

  1. Group Theme Setting: Assign specific topics for group study, such as "Multi-User Resource Allocation Optimization Algorithms" or "Applications of Machine Learning in Mobile Communications."

  2. Report Requirements and Discussion: Students will research their assigned topics, write reports, and present their findings. The instructor will evaluate the content and presentation and provide feedback.

  3. Interaction and Feedback: After the reports, the instructor will interact with students and discuss key points from the reports to reinforce the knowledge.

3. Teaching Schedule:

 Note: 1.Above one, two, and three items are used as teaching Syllabus in Chinese and announced on the Chinese website of Graduate School. The four and five items are preserved in Graduate School.

 2. Course terms: Spring, Autumn , and Spring-Autumn term.   

 3. The teaching languages for courses: Chinese, English or Chinese-English.  

 4. Applicable range of discipline: public, first-class discipline, second-class discipline, and third-class discipline.  

 5. Practice includes: experiment, investigation, research report, etc.  

 6. Teaching methods: lecture, seminar, practice, etc.  

 7. Examination for degree courses must be in paper.  

 8. Teaching material websites are those which have already been announced.  

 9. Brief introduction of chief lecturer should include: personal information (date of birth, gender, degree achieved, professional title), research direction, teaching and research achievements. (within 100-500 words)  

4. Brief Introduction of Chief lecturer:

Cheng Zhang, May 1988, male, PhD in engineering, associate researcher, research interests are mainly on B5G/6G wireless communications for space-time signal processing and machine learning-assisted optimization. He is currently the lecturer of the third-year undergraduate course "Artificial Intelligence and Deep Learning" and has won the third prize in the university lecture competition. He has published more than 60 IEEE papers and granted 20 domestic invention patents, and is currently presiding over one general project from National Natural Science foundation and one project under the Jiangsu Province Outstanding Youth Program.

5. Lecturer Information (include chief lecturer)