使用Matlab实现通信信道仿真与编码实验报告

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资源摘要信息:"无线通信与MATLAB实验报告——通信信道仿真" 本实验报告主要介绍了在MATLAB环境下,如何利用信息论与编码的知识进行通信信道的仿真研究。在现代通信系统中,信道仿真是一种重要的技术手段,它可以在不实际搭建物理通信系统的情况下,对通信链路的性能进行分析和预测。信道仿真通常涉及信号的发送、传输过程中的干扰、噪声以及接收端的信号恢复过程。 1. 信息论与编码基础 信息论是由克劳德·香农(Claude Shannon)在1948年提出的,它主要研究信息的度量、信息的传输、信息的处理等领域。信息论的核心是信息熵的概念,它度量了一个信息源的不确定性和信息量的大小。在通信系统中,信息论用来指导如何高效、可靠地传输信息。 编码技术是通信系统中为了提高传输效率和传输质量所采用的技术之一。常见的编码方式有Huffman编码、汉明码、卷积码等。Huffman编码是一种无损压缩编码技术,它根据信息中各个符号出现的概率来构建最优的前缀码,使得整体传输的信息更为高效。汉明码是一种线性纠错码,它可以检测并纠正单比特错误,从而提高数据传输的可靠性。 2. MATLAB语言实现 MATLAB是一种广泛应用于工程计算及数据分析的高级编程语言和交互式环境。在信道仿真中,MATLAB可以模拟各种信道模型,如高斯信道、瑞利衰落信道、莱斯衰落信道等。通过MATLAB编程,可以方便地实现信号的调制解调、编码解码、信道干扰和噪声的添加、信道容量的计算等功能。 在MATLAB中进行仿真时,可以利用其内置函数或工具箱来模拟复杂的通信系统组件。例如,使用通信工具箱(Communications System Toolbox)中的函数,可以方便地进行信号处理和通信系统的分析。此外,MATLAB还支持用户自定义函数,这为更深层次的研究提供了可能。 3. 实验内容概述 在本实验报告中,重点讨论了以下内容: - Huffman编码的原理及其在MATLAB中的实现; - 汉明码的原理及其在MATLAB中的实现; - 信道模型的选择和构建; - 信道干扰和噪声的模拟; - 信号调制解调技术的应用; - 通信链路的性能分析,如误码率(BER)的计算。 4. 实验步骤和结果分析 实验报告应该详细记录了从信源生成信号,到信号经过编码、调制、通过信道模型(包括添加噪声和干扰)、最后进行解调、解码并评估传输性能的全过程。每个步骤都应该有相应的MATLAB代码实现,并在报告中详细说明每段代码的功能和作用。 通过实验,可以观察到不同编码技术在特定信道条件下的性能表现。例如,Huffman编码能够在无损压缩方面减少传输数据的比特数,而汉明码则能够有效检测并纠正传输过程中出现的错误。实验结果需要通过图表和分析来展示,如BER随信噪比变化的曲线、信道容量的计算结果等。 总结来说,本次实验通过MATLAB仿真实现了多种编码技术在通信信道中的应用,并分析了这些技术在通信系统性能提升方面的作用。通过这种仿真方法,可以为进一步的通信系统设计和优化提供理论依据和实践指导。
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作者: Yong Soo Cho 目录 Preface. Limits of Liability and Disclaimer of Warranty of Software. 1 The Wireless Channel: Propagation and Fading. 1.1 Large-Scale Fading. 1.1.1 General Path Loss Model. 1.1.2 Okumura/Hata Model. 1.1.3 IEEE 802.16d Model. 1.2 Small-Scale Fading. 1.2.1 Parameters for Small-Scale Fading. 1.2.2 Time-Dispersive vs. Frequency-Dispersive Fading. 1.2.3 Statistical Characterization and Generation of Fading Channel. 2 SISO Channel Models. 2.1 Indoor Channel Models. 2.1.1 General Indoor Channel Models. 2.1.2 IEEE 802.11 Channel Model. 2.1.3 Saleh-Valenzuela (S-V) Channel Model. 2.1.4 UWB Channel Model. 2.2 Outdoor Channel Models. 2.2.1 FWGN Model. 2.2.2 Jakes Model. 2.2.3 Ray-Based Channel Model. 2.2.4 Frequency-Selective Fading Channel Model. 2.2.5 SUI Channel Model. 3 MIMO Channel Models. 3.1 Statistical MIMO Model. 3.1.1 Spatial Correlation. 3.1.2 PAS Model. 3.2 I-METRA MIMO Channel Model. 3.2.1 Statistical Model of Correlated MIMO Fading Channel. 3.2.2 Generation of Correlated MIMO Channel Coefficients. 3.2.3 I-METRA MIMO Channel Model. 3.2.4 3GPP MIMO Channel Model. 3.3 SCM MIMO Channel Model. 3.3.1 SCM Link-Level Channel Parameters. 3.3.2 SCM Link-Level Channel Modeling. 3.3.3 Spatial Correlation of Ray-Based Channel Model. 4 Introduction to OFDM. 4.1 Single-Carrier vs. Multi-Carrier Transmission. 4.1.1 Single-Carrier Transmission. 4.1.2 Multi-Carrier Transmission. 4.1.3 Single-Carrier vs. Multi-Carrier Transmission. 4.2 Basic Principle of OFDM. 4.2.1 OFDM Modulation and Demodulation. 4.2.2 OFDM Guard Interval. 4.2.3 OFDM Guard Band. 4.2.4 BER of OFDM Scheme. 4.2.5 Water-Filling Algorithm for Frequency-Domain Link Adaptation. 4.3 Coded OFDM. 4.4 OFDMA: Multiple Access Extensions of OFDM. 4.4.1 Resource Allocation – Subchannel Allocation Types. 4.4.2 Resource Allocation – Subchannelization. 4.5 Duplexing. 5 Synchronization for OFDM. 5.1 Effect of STO. 5.2 Effect of CFO. 5.2.1 Effect of Integer Carrier Frequency Offset (IFO). 5.2.2 Effect of Fractional Carrier Frequency Offset (FFO). 5.3 Estimation Techniques for STO. 5.3.1 Time-Domain Estimation Techniques for STO. 5.3.2 Frequency-Domain Estimation Techniques for STO. 5.4 Estimation Techniques for CFO. 5.4.1 Time-Domain Estimation Techniques for CFO. 5.4.2 Frequency-Domain Estimation Techniques for CFO. 5.5 Effect of Sampling Clock Offset. 5.5.1 Effect of Phase Offset in Sampling Clocks. 5.5.2 Effect of Frequency Offset in Sampling Clocks. 5.6 Compensation for Sampling Clock Offset. 5.7 Synchronization in Cellular Systems. 5.7.1 Downlink Synchronization. 5.7.2 Uplink Synchronization. 6 Channel Estimation. 6.1 Pilot Structure. 6.1.1 Block Type. 6.1.2 Comb Type. 6.1.3 Lattice Type. 6.2 Training Symbol-Based Channel Estimation. 6.2.1 LS Channel Estimation. 6.2.2 MMSE Channel Estimation. 6.3 DFT-Based Channel Estimation. 6.4 Decision-Directed Channel Estimation. 6.5 Advanced Channel Estimation Techniques. 6.5.1 Channel Estimation Using a Superimposed Signal. 6.5.2 Channel Estimation in Fast Time-Varying Channels. 6.5.3 EM Algorithm-Based Channel Estimation. 6.5.4 Blind Channel Estimation. 7 PAPR Reduction. 7.1 Introduction to PAPR. 7.1.1 Definition of PAPR. 7.1.2 Distribution of OFDM Signal. 7.1.3 PAPR and Oversampling. 7.1.4 Clipping and SQNR. 7.2 PAPR Reduction Techniques. 7.2.1 Clipping and Filtering. 7.2.2 PAPR Reduction Code. 7.2.3 Selective Mapping. 7.2.4 Partial Transmit Sequence. 7.2.5 Tone Reservation. 7.2.6 Tone Injection. 7.2.7 DFT Spreading. 8 Inter-Cell Interference Mitigation Techniques. 8.1 Inter-Cell Interference Coordination Technique. 8.1.1 Fractional Frequency Reuse. 8.1.2 Soft Frequency Reuse. 8.1.3 Flexible Fractional Frequency Reuse. 8.1.4 Dynamic Channel Allocation. 8.2 Inter-Cell Interference Randomization Technique. 8.2.1 Cell-Specific Scrambling. 8.2.2 Cell-Specific Interleaving. 8.2.3 Frequency-Hopping OFDMA. 8.2.4 Random Subcarrier Allocation. 8.3 Inter-Cell Interference Cancellation Technique. 8.3.1 Interference Rejection Combining Technique. 8.3.2 IDMA Multiuser Detection. 9 MIMO: Channel Capacity. 9.1 Useful Matrix Theory. 9.2 Deterministic MIMO Channel Capacity. 9.2.1 Channel Capacity when CSI is Known to the Transmitter Side. 9.2.2 Channel Capacity when CSI is Not Available at the Transmitter Side. 9.2.3 Channel Capacity of SIMO and MISO Channels. 9.3 Channel Capacity of Random MIMO Channels. 10 Antenna Diversity and Space-Time Coding Techniques. 10.1 Antenna Diversity. 10.1.1 Receive Diversity. 10.1.2 Transmit Diversity. 10.2 Space-Time Coding (STC): Overview. 10.2.1 System Model. 10.2.2 Pairwise Error Probability. 10.2.3 Space-Time Code Design. 10.3 Space-Time Block Code (STBC). 10.3.1 Alamouti Space-Time Code. 10.3.2 Generalization of Space-Time Block Coding. 10.3.3 Decoding for Space-Time Block Codes. 10.3.4 Space-Time Trellis Code. 11 Signal Detection for Spatially Multiplexed MIMO Systems. 11.1 Linear Signal Detection. 11.1.1 ZF Signal Detection. 11.1.2 MMSE Signal Detection. 11.2 OSIC Signal Detection. 11.3 ML Signal Detection. 11.4 Sphere Decoding Method. 11.5 QRM-MLD Method. 11.6 Lattice Reduction-Aided Detection. 11.6.1 Lenstra-Lenstra-Lovasz (LLL) Algorithm. 11.6.2 Application of Lattice Reduction. 11.7 Soft Decision for MIMO Systems. 11.7.1 Log-Likelihood-Ratio (LLR) for SISO Systems. 11.7.2 LLR for Linear Detector-Based MIMO System. 11.7.3 LLR for MIMO System with a Candidate Vector Set. 11.7.4 LLR for MIMO System Using a Limited Candidate Vector Set. Appendix 11.A Derivation of Equation (11.23). 12 Exploiting Channel State Information at the Transmitter Side. 12.1 Channel Estimation on the Transmitter Side. 12.1.1 Using Channel Reciprocity. 12.1.2 CSI Feedback. 12.2 Precoded OSTBC. 12.3 Precoded Spatial-Multiplexing System. 12.4 Antenna Selection Techniques. 12.4.1 Optimum Antenna Selection Technique. 12.4.2 Complexity-Reduced Antenna Selection. 12.4.3 Antenna Selection for OSTBC. 13 Multi-User MIMO. 13.1 Mathematical Model for Multi-User MIMO System. 13.2 Channel Capacity of Multi-User MIMO System. 13.2.1 Capacity of MAC. 13.2.2 Capacity of BC. 13.3 Transmission Methods for Broadcast Channel. 13.3.1 Channel Inversion. 13.3.2 Block Diagonalization. 13.3.3 Dirty Paper Coding (DPC). 13.3.4 Tomlinson-Harashima Precoding. References. Index.