Modeling and Simulink of Millimeter Wave Channel Modeling Estimation and Prediction
Implementation Plan:
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Step 1: Initially we design the mmWave network which consists of 20- User Equipment (UEs), 2- Base stations (BS), 1- MIMO-BS, 1- RIS and customized channel environment and parameters.
Step 2: Next, we perform data preprocessing to reduce noise and enhance accuracy by applying high-SNR MIMO-MRC modeling technique, clustering algorithms, and filtering techniques to enable efficient feature extraction and improved data quality.
Step 3: Then, we perform Fast Alignment to quickly identify strong near-field regions, then apply HCSSP (Hierarchical Compressed Sensing with Sub-Partitioning) technique on those sub-regions to recover sparse channel paths.
Step 4: We perform Federated Learning (FL)-based training for hybrid beamforming, where a Generative Adversarial Network (GAN) is trained on ray-tracing (RT) simulation data, and the base station (BS) aggregates user gradients in a decentralized manner without accessing raw data.
Step 5: Next, we estimate the channel by using SCOVEM (Score-Based Generative Model-Enhanced Support Vector Machine with Bayesian Optimization for MmWave Fault Diagnosis) technique.
Step 6: Then, we enhance the data distribution, channel estimation and prediction by using the Score based Diffusion model technique.
Step 7: Finally, we plot performance metrics for the following:
7.1: BER vs. SNR (dB)
7.2: SNR (dB) vs. Spectral Efficiency (bits/s/Hz)
7.3: Number of RIS elements vs. Transmission Rate (Mbps)
7.4: Number of RIS elements vs. Spatial Correlation
7.5: SNR (dB) vs. MSE (Mean Squared Error)
Software Requirements:
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1. Development Tool: Matlab-R2023a or above version
2. Operating System: Windows-10 (64-bit)
Note:
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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. Once implementation begins, modifications will not be feasible without prior input. Kindly ensure that any missing configurations or specifications are clearly outlined in the plan before confirming, as post-implementation changes will not be accommodated.
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, not a real time project.
We perform with an Existing Approach Reference 1: Title:- Virtual Antenna Array with Directional Antennas for Millimeter-Wave Channel Characterization
