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Author’s Note
🔊 Author Introduction: 985 graduate student, researcher in the field of MATLAB;
🚅SeatRightInscription:Those who travel a hundred miles are half of those who travel ninety.
✅Research Scope: Complete Code, Paper Reproduction, Program Customization, Journal Writing, Research Collaboration
🏆CodeAcquisitionMethod:Method to Obtain MATLAB Poseidon Code
For more MATLAB signal processing simulation content, click 👇
MATLAB Signal Processing (Advanced Version)
MATLAB Signal Processing Radar Communication (Milk Tea Price Version)
Introduction
📋📋📋 The table of contents is as follows: 🎁🎁🎁
Table of Contents
💥1 Overview
📚2 Partial Code and Running Steps
🎉3 Running Results
🌈4 MATLAB Version and References
🔎5 Code Acquisition Method
🍅6 Simulation Consultation
1 Overview
See CSDN Poseidon with the same title
**1 GAMP Channel Estimation Principle** The Generalized Approximate Message Passing (GAMP) algorithm is a low-complexity iterative algorithm based on factor graphs and message passing, suitable for solving sparse signal recovery problems in large-scale linear systems. In millimeter-wave hybrid massive MIMO systems, GAMP achieves efficient channel estimation by leveraging the sparsity of the channel (determined by the characteristics of millimeter-wave propagation). The core idea of GAMP is to model the channel estimation problem as a sparse signal recovery problem, iteratively updating the signal estimates and noise parameters by approximating the posterior probability distribution. Its mathematical foundation is: – Assume the channel vector $ extbf{h}$ follows a sparse prior distribution, such as Bernoulli-Gaussian distribution. – The observation model is $ extbf{y} = extbf{A} extbf{h} + extbf{n}$, where $ extbf{A}$ is the measurement matrix and $ extbf{n}$ is the noise. – By iteratively calculating the approximate values of the marginal distribution, the true channel is gradually approached. **2 System Model Construction** The frequency domain received signal of the millimeter-wave hybrid MIMO system can be expressed as: $$ extbf{Y} = extbf{W}^H extbf{H} extbf{F} extbf{S} + extbf{N}$$ where $ extbf{H}$ is the sparse channel matrix, $ extbf{F}$ and $ extbf{W}$ are the analog precoding and combining matrices at the RF end, respectively, and $ extbf{S}$ is the pilot matrix. Transforming the problem into vector form: $$ extbf{y} = extbf{ } extbf{h} + extbf{n}$$ where $ extbf{F} = ( extbf{S}^T extbf{F}^T) igotimes extbf{W}^H$, $ extbf{h} = ext{vec}( extbf{H})$. **3 GAMP Implementation Process** **Initialization Phase** Set initial parameters: noise variance $ extsigma_n^2$, sparsity rate $
ho$, signal variance $ extsigma_h^2$. Initialize channel estimate $ extbf{h}^{(0)} = extbf{0}$, residual $ extbf{s}^{(0)} = extbf{0}$. **Nonlinear Estimate Update** Calculate equivalent observation: $$ extbf{z}^{(t)} = extbf{F} extbf{h}^{(t)} + extbf{s}^{(t)}$$ Update marginal posterior mean and variance: $$ ext{h}_i^{(t+1)} = Eig ext{h}_i | z_i^{(t)}, au_i^{(t)}ig ext{h}$$ $$ au_i^{(t+1)} = ext{Var}ig ext{h}_i | z_i^{(t)}, au_i^{(t)}ig ext{h}$$ **Linear Message Update** Calculate residual and equivalent noise: $$s_j^{(t)} = rac{y_j – z_j^{(t)}}{1 + au_j^{(t)}/ extsigma_n^2}$$ $$ au_j^{(t)} = igg( extsum_{i} | extbf{F}_{ji}|^2 au_i^{(t)} igg)^{-1}$$ **Stopping Condition Judgment** Terminate when the relative change of the estimated values between two adjacent iterations is less than the threshold $ extepsilon$ or the maximum number of iterations is reached: $$ rac{ig ext{h}^{(t+1)} – ig ext{h}^{(t)}ig ext{h}_2}{ig ext{h}^{(t)}ig ext{h}_2} < extepsilon$$ **4 Performance Optimization Techniques** **Adaptive Parameter Adjustment** Dynamically update sparsity rate $
ho$ and noise variance $ extsigma_n^2$: $$
ho^{(t)} = rac{ig ext{h}^{(t)}ig ext{h}_0}{N}$$ $$ extsigma_n^{2(t)} = rac{ig ext{y} – extbf{F}ig ext{h}^{(t)}ig ext{h}_2^2}{M}$$ **Hybrid Precoding Design** Jointly optimize analog precoding $ extbf{F}$ and combining matrix $ extbf{W}$ to enhance the RIP properties of the measurement matrix $ extbf{F}$, typically using DFT matrices or random phase matrices. **Complexity Analysis** The complexity of GAMP per iteration is $O(MN)$, which is much lower than the $O(MN^2)$ of traditional CS algorithms, making it suitable for large-scale antenna array scenarios. By utilizing the block diagonal structure of the matrix $ extbf{F}$, it can be further reduced to $O(N extlog N)$.
2 Partial Code and Running Steps
2.1 Partial Code2.2 Running Steps
(1) Directly run main.m to generate the graph with one click
3Running Results
4MATLAB Version and References
1 MATLAB Version
2019b
2 References
[1] Ni Yiyang, Zhu Hongbo, Wang Yuxi. Overview of Millimeter-Wave D2D Communication Technology for 5G [J]. Modern Electronic Technology. 2019
[2] Wan Yuan, Li Xingguo, Wang Hong. Application of Channel Coding Technology in Millimeter-Wave Communication [J]. Journal of Ordnance and Guidance. 2006
3 Note
This section is excerpted from the internet for reference only. If there is any infringement, please contact for deletion.
5 Code Acquisition Method
🏆CodeAcquisitionMethod:Method to Obtain MATLAB Poseidon Code
6 Simulation Consultation
**🍅 Simulation Consultation
1 Various Intelligent Optimization Algorithm Improvements and Applications**
1.1 PID Optimization
1.2 VMD Optimization
1.3 Distribution Network Restructuring
1.4 Three-Dimensional Packing
1.5 Microgrid Optimization
1.6 Optimization Layout
1.7 Optimization Parameters
1.8 Optimization Costs
1.9 Optimization Charging
1.10 Optimization Scheduling
1.11 Optimization Pricing
1.12 Optimization Dispatching
1.13 Optimization Distribution
1.14 Optimization Coverage
1.15 Optimization Control
1.16 Optimization Inventory
1.17 Optimization Routing
1.18 Optimization Design
1.19 Optimization Location
1.20 Optimization Absorption
1.21 Optimization Site Selection
1.22 Optimization Operation
1.23 Optimization Assignment
1.24 Optimization Combination
1.25 Workshop Scheduling
1.26 Production Scheduling
1.27 Economic Scheduling
1.28 Assembly Line Scheduling
1.29 Reservoir Scheduling
1.30 Cargo Position Optimization
1.31 Bus Scheduling Optimization
1.32 Container Ship Loading Optimization
1.33 Pump Combination Optimization
1.34 Medical Resource Allocation Optimization
1.35 Visual Base Station and Drone Site Selection Optimization
**2 Machine Learning and Deep Learning Classification and Prediction**
**2.1 Machine Learning and Deep Learning Classification**
2.1.1 BiLSTM Bidirectional Long Short-Term Memory Neural Network Classification
2.1.2 BP Neural Network Classification
2.1.3 CNN Convolutional Neural Network Classification
2.1.4 DBN Deep Belief Network Classification
2.1.5 DELM Deep Learning Extreme Learning Machine Classification
2.1.6 ELMAN Recurrent Neural Network Classification
2.1.7 ELM Extreme Learning Machine Classification
2.1.8 GRNN General Regression Neural Network Classification
2.1.9 GRU Gated Recurrent Unit Classification
2.1.10 KELM Hybrid Kernel Extreme Learning Machine Classification
2.1.11 KNN Classification
2.1.12 LSSVM Least Squares Support Vector Machine Classification
2.1.13 LSTM Long Short-Term Memory Network Classification
2.1.14 MLP Fully Connected Neural Network Classification
2.1.15 PNN Probabilistic Neural Network Classification
2.1.16 RELM Robust Extreme Learning Machine Classification
2.1.17 RF Random Forest Classification
2.1.18 SCN Stochastic Configuration Network Model Classification
2.1.19 SVM Support Vector Machine Classification
2.1.20 XGBOOST Classification
**2.2 Machine Learning and Deep Learning Prediction**
2.2.1 ANFIS Adaptive Neuro-Fuzzy Inference System Prediction
2.2.2 ANN Artificial Neural Network Prediction
2.2.3 ARMA Autoregressive Moving Average Model Prediction
2.2.4 BF Particle Filter Prediction
2.2.5 BiLSTM Bidirectional Long Short-Term Memory Neural Network Prediction
2.2.6 BLS Broad Learning System Prediction
2.2.7 BP Neural Network Prediction
2.2.8 CNN Convolutional Neural Network Prediction
2.2.9 DBN Deep Belief Network Prediction
2.2.10 DELM Deep Learning Extreme Learning Machine Prediction
2.2.11 DKELM Regression Prediction
2.2.12 ELMAN Recurrent Neural Network Prediction
2.2.13 ELM Extreme Learning Machine Prediction
2.2.14 ESN Echo State Network Prediction
2.2.15 FNN Feedforward Neural Network Prediction
2.2.16 GMDN Prediction
2.2.17 GMM Gaussian Mixture Model Prediction
2.2.18 GRNN General Regression Neural Network Prediction
2.2.19 GRU Gated Recurrent Unit Prediction
2.2.20 KELM Hybrid Kernel Extreme Learning Machine Prediction
2.2.21 LMS Least Mean Squares Algorithm Prediction
2.2.22 LSSVM Least Squares Support Vector Machine Prediction
2.2.23 LSTM Long Short-Term Memory Network Prediction
2.2.24 RBF Radial Basis Function Neural Network Prediction
2.2.25 RELM Robust Extreme Learning Machine Prediction
2.2.26 RF Random Forest Prediction
2.2.27 RNN Recurrent Neural Network Prediction
2.2.28 RVM Relevance Vector Machine Prediction
2.2.29 SVM Support Vector Machine Prediction
2.2.30 TCN Temporal Convolutional Network Prediction
2.2.31 XGBoost Regression Prediction
2.2.32 Fuzzy Prediction
2.2.33 Singular Spectrum Analysis Method SSA Time Series Prediction
**2.3 Machine Learning and Deep Learning Practical Application Prediction**
CPI Index Prediction, PM2.5 Concentration Prediction, SOC Prediction, Financial Warning Prediction, Yield Prediction, Parking Space Prediction, Pest Prediction, Steel Thickness Prediction, Battery Health Status Prediction, Power Load Prediction, Housing Price Prediction, Corrosion Rate Prediction, Fault Diagnosis Prediction, Photovoltaic Power Prediction, Trajectory Prediction, Aircraft Engine Life Prediction, Exchange Rate Prediction, Concrete Strength Prediction, Heating Furnace Temperature Prediction, Price Prediction, Traffic Flow Prediction, Resident Consumption Index Prediction, Air Quality Prediction, Grain Temperature Prediction, Temperature Prediction, Clean Water Value Prediction, Unemployment Rate Prediction, Electricity Consumption Prediction, Transportation Volume Prediction, Manufacturing Purchasing Managers Index Prediction
**3 Image Processing Aspects**
**3.1 Image Edge Detection**
**3.2 Image Processing**
**3.3 Image Segmentation**
**3.4 Image Classification**
**3.5 Image Tracking**
**3.6 Image Encryption and Decryption**
**3.7 Image Retrieval**
**3.8 Image Registration**
**3.9 Image Stitching**
**3.10 Image Evaluation**
**3.11 Image Denoising**
**3.12 Image Fusion**
**3.13 Image Recognition**
3.13.1 Dial Recognition
3.13.2 Lane Line Recognition
3.13.3 Vehicle Counting
3.13.4 Vehicle Recognition
3.13.5 License Plate Recognition
3.13.6 Parking Space Recognition
3.13.7 Size Detection
3.13.8 Answer Sheet Recognition
3.13.9 Appliance Recognition
3.13.10 Fall Detection
3.13.11 Animal Recognition
3.13.12 QR Code Recognition
3.13.13 Invoice Recognition
3.13.14 Clothing Recognition
3.13.15 Chinese Character Recognition
3.13.16 Traffic Light Recognition
3.13.17 Iris Recognition
3.13.18 Fire Detection
3.13.19 Disease Classification
3.13.20 Traffic Sign Recognition
3.13.21 Card Number Recognition
3.13.22 Mask Recognition
3.13.23 Crack Detection
3.13.24 Target Tracking
3.13.25 Fatigue Detection
3.13.26 Flag Recognition
3.13.27 Grass Recognition
3.13.28 Face Recognition
3.13.29 Renminbi Recognition
3.13.30 ID Card Recognition
3.13.31 Gesture Recognition
3.13.32 Digit and Letter Recognition
3.13.33 Palm Recognition
3.13.34 Leaf Recognition
3.13.35 Fruit Recognition
3.13.36 Barcode Recognition
3.13.37 Temperature Detection
3.13.38 Defect Detection
3.13.39 Chip Detection
3.13.40 Behavior Recognition
3.13.41 Verification Code Recognition
3.13.42 Medicinal Material Recognition
3.13.43 Coin Recognition
3.13.44 Postal Code Recognition
3.13.45 Playing Card Recognition
3.13.46 Fingerprint Recognition
**3.14 Image Restoration**
**3.15 Image Compression**
**3.16 Image Steganography**
**3.17 Image Enhancement**
**3.18 Image Reconstruction**
**4 Path Planning Aspects**
**4.1 Traveling Salesman Problem (TSP)**
4.1.1 Single Traveling Salesman Problem (TSP)
4.1.2 Multiple Traveling Salesman Problem (MTSP)
**4.2 Vehicle Routing Problem (VRP)**
4.2.1 Vehicle Routing Problem (VRP)
4.2.2 Capacitated Vehicle Routing Problem (CVRP)
4.2.3 Capacitated + Time Window + Distance Vehicle Routing Problem (DCTWVRP)
4.2.4 Capacitated + Distance Vehicle Routing Problem (DCVRP)
4.2.5 Distance Vehicle Routing Problem (DVRP)
4.2.6 Vehicle Routing Problem with Charging Stations + Time Windows (ETWVRP)
4.2.3 Vehicle Routing Problem with Multiple Capacities (MCVRP)
4.2.4 Distance Multi-Vehicle Routing Problem (MDVRP)
4.2.5 Vehicle Routing Problem with Simultaneous Pickup and Delivery (SDVRP)
4.2.6 Vehicle Routing Problem with Time Windows + Capacities (TWCVRP)
4.2.6 Vehicle Routing Problem with Time Windows (TWVRP)
**4.3 Multimodal Transport Problem**
**4.4 Robot Path Planning**
4.4.1 Obstacle Avoidance Path Planning
4.4.2 Maze Path Planning
4.4.3 Grid Map Path Planning
**4.5 Delivery Path Planning**
4.5.1 Cold Chain Delivery Path Planning
4.5.2 Takeout Delivery Path Planning
4.5.3 Mask Delivery Path Planning
4.5.4 Medicine Delivery Path Planning
4.5.5 Delivery Path Planning with Charging Stations
4.5.6 Chain Supermarket Delivery Path Planning
4.5.7 Vehicle Collaborative Drone Delivery Path Planning
**4.6 Drone Path Planning**
4.6.1 Aircraft Simulation
4.6.2 Drone Flight Operations
4.6.3 Drone Trajectory Tracking
4.6.4 Drone Swarm Simulation
4.6.5 Three-Dimensional Path Planning for Drones
4.6.6 Drone Formation
4.6.7 Drone Collaborative Tasks
4.6.8 Drone Task Assignment
**5 Speech Processing**
**5.1 Speech Emotion Recognition**
**5.2 Sound Source Localization**
**5.3 Feature Extraction**
**5.4 Speech Coding**
**5.5 Speech Processing**
**5.6 Speech Separation**
**5.7 Speech Analysis**
**5.8 Speech Synthesis**
**5.9 Speech Encryption**
**5.10 Speech Denoising**
**5.11 Speech Recognition**
**5.12 Speech Compression**
**5.13 Speech Hiding**
**6 Cellular Automata Aspects**
**6.1 Cellular Automata Virus Simulation**
**6.2 Cellular Automata Urban Planning**
**6.3 Cellular Automata Traffic Flow**
**6.4 Cellular Automata Gas**
**6.5 Cellular Automata Personnel Evacuation**
**6.6 Cellular Automata Forest Fire**
**6.7 Cellular Automata Game of Life**
**7 Signal Processing Aspects**
**7.1 Fault Signal Diagnosis Analysis**
7.1.1 Gear Damage Identification
7.1.2 Asynchronous Motor Rotor Bar Fault Diagnosis
7.1.3 Rolling Element Inner and Outer Ring Fault Diagnosis Analysis
7.1.4 Motor Fault Diagnosis Analysis
7.1.5 Bearing Fault Diagnosis Analysis
7.1.6 Gearbox Fault Diagnosis Analysis
7.1.7 Three-Phase Inverter Fault Diagnosis Analysis
7.1.8 Diesel Engine Fault Diagnosis
**7.2 Radar Communication**
7.2.1 FMCW Simulation
7.2.2 GPS Anti-Jamming
7.2.3 Radar LFM
7.2.4 Radar MIMO
7.2.5 Radar Angle Measurement
7.2.6 Radar Imaging
7.2.7 Radar Positioning
7.2.8 Radar Echo
7.2.9 Radar Detection
7.2.10 Radar Digital Signal Processing
7.2.11 Radar Communication
7.2.12 Radar Phased Array
7.2.13 Radar Signal Analysis
7.2.14 Radar Early Warning
7.2.15 Radar Pulse Compression
7.2.16 Antenna Radiation Pattern
7.2.17 Radar Clutter Simulation
**7.3 Bioelectric Signals**
7.3.1 Electromyography (EMG)
7.3.2 Electroencephalography (EEG)
7.3.3 Electrocardiography (ECG)
7.3.4 Heart Simulation
**7.4 Communication Systems**
7.4.1 DOA Estimation
7.4.2 LEACH Protocol
7.4.3 Encoding and Decoding
7.4.4 Variational Mode Decomposition
7.4.5 Ultra-Wideband Simulation
7.4.6 Multipath Fading Simulation
7.4.7 Cellular Networks
7.4.8 Pipeline Leakage
7.4.9 Empirical Mode Decomposition
7.4.10 Filter Design
7.4.11 Analog Signal Transmission
7.4.12 Analog Signal Modulation
7.4.13 Digital Baseband Signal
7.4.14 Digital Channel
7.4.15 Digital Signal Processing
7.4.16 Digital Signal Transmission
7.4.17 Digital Signal Denoising
7.4.18 Underwater Acoustic Communication
7.4.19 Communication Simulation
7.4.20 Wireless Transmission
7.4.21 Bit Error Rate Simulation
7.4.22 Modern Communication
7.4.23 Channel Estimation
7.4.24 Signal Detection
7.4.25 Signal Fusion
7.4.26 Signal Recognition
7.4.27 Compressed Sensing
7.4.28 Noise Simulation
7.4.29 Noise Interference
**7.5 Drone Communication**
**7.6 Wireless Sensor Localization and Layout**
7.6.1 WSN Localization
7.6.2 Height Estimation
7.6.3 Filter Tracking
7.6.4 Target Localization
7.6.4.1 Dv-Hop Localization
7.6.4.2 RSSI Localization
7.6.4.3 Intelligent Algorithm Optimization Localization
7.6.5 Combined Navigation
**8 Power System Aspects**
Microgrid Optimization, Reactive Power Optimization, Distribution Network Restructuring, Energy Storage Configuration