ENGD3105 Mobile Communication 1Matlab

Java Python Faculty of Computing, Engineering & Media (CEM)

Coursework Brief 2024/25

Module name:

Mobile Communication 1

Module code:

ENGD3105

Title of the Assessment:

Design and Analysis of Image Communication System

ENGD3105 Mobile Communication 1

Coursework Assignment

2024-2025

Design and Analysis of Image Communication System

(Maximum Marks: 50)

Issue date: 16/10/2024

Submission deadline: 13/12/2024 noon

Feedback date: 08/01/2025

Aim: In this assignment, you will design, implement, and test an image communication system consisting of a channel coder, modulator, demodulator, and channel decoder. You will use MATLAB for your implementation.

What to submit: Your coursework must be submitted as a repor ENGD3105 Mobile Communication 1Matlab t in Word format. Your MATLAB codes must be provided as text (not pictures) and included as appendices at the end of the report. All code lines must be commented to explain them. Your answers must be supported by references as appropriate. All references must be in IEEE style. Please note that 5 marks will be allocated to the presentation and organisation of your report. Your report, excluding the cover page and MATLAB codes, must not exceed eight pages (minimum font size 11 pt).

1) Design and implement in MATLAB a communication system that uses channel coding with an LDPC code and digital modulation with QPSK to transmit digital images over an additive white Gaussian noise (AWGN) channel. Your system must be implemented as a function. You must clearly describe all the components of your system and motivate your design.

[20 marks: Design: 10 marks, quality of the implementation: 10 marks]

2) Use your system to simulate the transmission of the image Fruits.jpg. Discuss the results.

[10 marks: Design of the simulation: 2 marks, discussion of the results: 8 marks]

3)  Replace the LDPC code with a Turbo code and compare the results to those in 2).

[15 marks: Design: 3 marks, quality of the implementation: 4 marks, discussion of the results: 8 marks]

The following MATLAB documentation can help you with your coursework

· Reading images: https://uk.mathworks.com/help/matlab/ref/imread.html

· LDPC encoder: https://uk.mathworks.com/help/comm/ref/ldpcencode.html

· LDPC decoder: https://uk.mathworks.com/help/comm/ref/ldpcdecode.html

· Turbo encoder: https://uk.mathworks.com/help/comm/ref/comm.turboencoder-system-object.html

· Turbo decoder: https://uk.mathworks.com/help/comm/ref/comm.turbodecoder-system-object         

内容概要:本文介绍了一个基于MATLAB实现的无人机三维路径规划项目,采用蚁群算法(ACO)与多层感知机(MLP)相结合的混合模型(ACO-MLP)。该模型通过三维环境离散化建模,利用ACO进行全局路径搜索,并引入MLP对环境特征进行自适应学习与启发因子优化,实现路径的动态调整与多目标优化。项目解决了高维空间建模、动态障碍规避、局部最优陷阱、算法实时性及多目标权衡等关键技术难题,结合并行计算与参数自适应机制,提升了路径规划的智能性、安全性和工程适用性。文中提供了详细的模型架构、核心算法流程及MATLAB代码示例,涵盖空间建模、信息素更新、MLP训练与融合优化等关键步骤。; 适合人群:具备一定MATLAB编程基础,熟悉智能优化算法与神经网络的高校学生、科研人员及从事无人机路径规划相关工作的工程师;适合从事智能无人系统、自动驾驶、机器人导航等领域的研究人员; 使用场景及目标:①应用于复杂三维环境下的无人机路径规划,如城市物流、灾害救援、军事侦察等场景;②实现飞行安全、能耗优化、路径平滑与实时避障等多目标协同优化;③为智能无人系统的自主决策与环境适应能力提供算法支持; 阅读建议:此资源结合理论模型与MATLAB实践,建议读者在理解ACO与MLP基本原理的基础上,结合代码示例进行仿真调试,重点关注ACO-MLP融合机制、多目标优化函数设计及参数自适应策略的实现,以深入掌握混合智能算法在工程中的应用方法。
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