Modeling and Simulation of Intelligent Channel Estimation in mmWave System
Step 1: Initially, we will construct a 5G mmWave MIMO network model with 10 users , 1 RIS and 1 Base stations.
Step 2: Then, we simulate the model and collect data using 5G Wave Channel Prediction Dataset
Step 3: Next, we preprocess the signal data using AI-driven Random Forest Regression Algorithm for normalization.
Step 4; Next, we perform feature extraction using segmented denoising algorithm for Channel quality and reciprocity analysis based on collected data.
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Step 5: Next, we estimate the channels using Bilinear Pattern Detection (BPD) with XLRIS-CRAMER intelligent channel estimation algorithm based on collected data.
Step 6: Next, we reduce the adaptive noise using Hybrid NLMS-NLMF adaptive filtering algorithm based on collected data.
Step 7: Then, we optimize beam foaming, passive components, and power amplifier performance using JointBeamPA framework algorithm based on collected data.
Step 8: Finally , we plot performance metrics of the following
8.1: SNR Vs. NMSE
8.2: SNR Vs. BER
8.3: SNR Vs. Spectral Efficiency
8.4: SNR Vs. Pilot Overhead
8.5: SNR Vs. Convergence Speed
Dataset:
Link : https://www.kaggle.com/
1. Development Tool: MATLAB R2023a or above
2. Operating System: Windows-10 (64-bit) or above
Note:
1) If the proposed plan does not fully align with your requirements, please provide all necessary details—including steps, parameters, models, and expected outcomes—in advance. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming.
2) If there’s no built-in solution for what the project needs, we can always turn to reference models, customize our own, different math models or write the code ourselves to fulfil the process.
3) If the plan satisfies your requirement, Please confirm with us.
4) Project based on Simulation only.
