2D ISAC Imaging Simulation of Millimeter Wave OFDM Signals Based on MATLAB MUSIC Algorithm (Including MATLAB Source Code)

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🔊 Author Introduction: Graduate student from a top university, researcher in the field of MATLAB;

🚅 Motto: A journey of a thousand miles begins with a single step.

✅ Research Areas: Complete code, paper reproduction, program customization, journal writing, research collaboration

🏆 Code Acquisition Method: Method to obtain MATLAB code for the “Sea God’s Light”

For more MATLAB signal processing simulation content, click below👇

MATLAB Signal Processing (Advanced Version)

MATLAB Signal Processing Radar Communication (Affordable Version)

Introduction

📋📋📋 The contents of this article are as follows: 🎁🎁🎁

Table of Contents

💥1 Overview

📚2 Code and Execution Steps

🎉3 Execution Results

🌈4 MATLAB Version and References

🔎5 Code Acquisition Method

🍅6 Simulation Consultation

1 Overview

See CSDN Sea God’s Light with the same title

**1 MUSIC Algorithm Principle** The MUSIC (Multiple Signal Classification) algorithm is a high-resolution direction of arrival (DOA) estimation method based on subspace decomposition. Its core idea is to decompose the covariance matrix of the received signal into signal and noise subspaces, utilizing the orthogonality of the noise subspace and the signal direction vectors to construct the spatial spectrum function. For millimeter wave OFDM signals, the MUSIC algorithm can be extended to two-dimensional parameter estimation (joint distance-angle estimation). The signal model can be represented as: $X = A(θ)S + N$ where $A(θ)$ is the direction matrix, $S$ is the signal matrix, and $N$ is the noise matrix. **2 2D ISAC Imaging Process** **Signal Preprocessing Stage** The millimeter wave OFDM communication signal is transmitted after orthogonal modulation, and the receiving end collects the echo signal through an antenna array. The received signal undergoes carrier synchronization and symbol timing synchronization to eliminate frequency offset and timing deviation. **Distance Dimension Processing** The frequency domain characteristics of the OFDM signal are utilized for distance estimation. An FFT transformation is performed on each subcarrier to extract phase information and construct the distance dimension vector: $R(f_k) = \\sum_{n=0}^{N-1} r_n e^{-j2πkn/N}$ **Angle Dimension Processing** A uniform linear array or planar array is used to receive the signal, and the array covariance matrix is calculated: $R_{xx} = E[XX^H]$ Eigenvalue decomposition is performed on it, and the eigenvectors corresponding to the smaller eigenvalues are selected to form the noise subspace $U_n$. **Two-Dimensional Joint Estimation** A two-dimensional spatial spectrum function is constructed: $P(θ,d) = rac{1}{a^H(θ,d)U_nU_n^Ha(θ,d)}$ where $a(θ,d)$ is the joint steering vector containing distance and angle information. Two-dimensional parameter estimation is achieved through peak searching. **3 Key Points of Simulation Implementation** **Parameter Settings** – Carrier frequency: 24-30GHz millimeter wave band – OFDM parameters: Number of subcarriers 256, cyclic prefix length 64 – Antenna array: 4×4 URA configuration, element spacing half wavelength **Key Steps** Generate OFDM baseband signal, add cyclic prefix and window function Simulate target reflection echo, considering multipath delay and angle offset Calculate two-dimensional spatial spectrum and perform peak detection Improve resolution through interpolation algorithms **Performance Evaluation Metrics** – Distance resolution: $\\Delta d = c/(2B)$ – Angle resolution: $\\Deltaθ ≈ 2/(Mcosθ)$ – Mean Square Error (MSE) curve – Detection probability and false alarm probability curve **4 Typical Simulation Results** Under the condition of SNR=10dB, the algorithm can achieve: – Distance estimation accuracy better than 0.1m – Angle estimation accuracy better than 1° – Multi-target resolution capability: effectively distinguish when the target spacing is greater than the Rayleigh limit – Imaging results can clearly display the distribution of target scattering points This simulation method can be extended to MIMO-OFDM systems, further enhancing imaging performance through space-time coding. In practical implementation, RF imperfections and array calibration and other non-ideal factors must be considered.

2 Code and Execution Steps

2.1 Partial Code2.2 Execution Steps

(1) Directly run main.m to generate the figure with one click

3Execution Results

4 MATLAB 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. Modern Electronic Technology. 2019

3 Note

This section is excerpted from the internet for reference only. If there is any infringement, please contact for removal.

5 Code Acquisition Method

🏆CodeAcquisitionMethodMethod to obtain MATLAB code for the “Sea God’s Light”

6 Simulation Consultation

**🍅 Simulation Consultation**

1 Various intelligent optimization algorithms improvement and application

1.1 PID optimization

1.2 VMD optimization

1.3 Distribution network reconstruction

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 allocation

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 location 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 domain 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 Multi-Layer Perceptron 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, strip 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 lifespan 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, clear water value prediction, unemployment rate prediction, electricity consumption prediction, transportation volume prediction, manufacturing purchasing manager 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 CAPTCHA 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 Simultaneous Pickup and Delivery Vehicle Routing Problem (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 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 Cooperative Tasks

4.6.8 Drone Task Allocation

**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 Broken 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 Biological Electrical 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 Integrated Navigation

**8 Power System Aspects**

Microgrid optimization, reactive power optimization, distribution network reconstruction, energy storage configuration