Title: - Deep Learning for Super-Resolution Channel Estimation in Reconfigurable Intelligent Surface Aided Systems
Implementation plan:
Scenario - 1 (SRDnNet with cascaded Channel):
Step 1: Initially, we construct a MIMO-OFDM network by initializing the required parameters (pilots = 36*2, NBS = 16, K = 5, NS = 64, M = 144, SNR = 5dB)
Step 2: Next, we perform the Channel Estimation process.
Step 3: Then, we design the SRDnNet Model with the estimated CSI.
Step 4: Next, we Generate synthetic noisy channel matrices and then train SRDnNet models with those noisy data.
Step 5: Next, we Simulate the RIS-Aided Multi-User MIMO-OFDM System.
Step 6: Finally, we plot the performance of the following metrics:
6.1: SNR Vs. NMSE
6.2: Number of elements Vs. NMSE
6.3: Number of antennae Vs. NMSE
6.4: Number of Pilots Vs. NMSE
6.5: NBS · N Vs. Complexity order.
6.6: MSE Vs. Doppler shift.
Scenario - 2 (LMMSE with cascaded Channel):
Step 1: Initially, we construct a MIMO-OFDM network by initializing the required parameters (pilots = 36*2, NBS = 16, K = 5, NS = 64, M = 144)
Step 2: Next, we perform the Channel Estimation process.
Step 3: Then, we design the LMMSE Model with the estimated CSI.
Step 4: Next, we Generate synthetic noisy channel matrices and then train the LMMSE models with those noisy data.
Step 5: Next, we Simulate the RIS-Aided Multi-User MIMO-OFDM System.
Step 6: Finally, we plot the performance of the following metrics:
6.1: SNR Vs. NMSE
6.2: Number of elements Vs. NMSE
6.3: Number of antennae Vs. NMSE
6.4: Number of Pilots Vs. NMSE
6.5: NBS · N Vs. Complexity order.
6.6: MSE Vs. Doppler shift.
Scenario - 6 (LS with cascaded Channel):
Step 1: Initially, we construct a MIMO-OFDM network by initializing the required parameters (pilots = 36*2, NBS = 16, K = 5, NS = 64, M = 144)
Step 2: Next, we perform the Channel Estimation process.
Step 3: Then, we design the LS Model with the estimated CSI.
Step 4: Next, we Generate synthetic noisy channel matrices and then train the LS models with those noisy data.
Step 5: Next, we Simulate the RIS-Aided Multi-User MIMO-OFDM System.
Step 6: Finally, we plot the performance of the following metrics:
6.1: SNR Vs. NMSE
6.2: Number of elements Vs. NMSE
6.3: Number of antennae Vs. NMSE
6.4: Number of Pilots Vs. NMSE
6.5: NBS · N Vs. Complexity order.
6.6: MSE Vs. Doppler shift.
Scenario -4 (DnCAE-SR with cascaded Channel):
Step 1: Initially, we construct a MIMO-OFDM network by initializing the required parameters (pilots = 36*2, NBS = 16, K = 5, NS = 64, M = 144)
Step 2: Next, we perform the Channel Estimation process.
Step 3: Then, we design the DnCAE-SR Model with the estimated CSI.
Step 4: Next, we Generate synthetic noisy channel matrices and then train DnCAE-SR models with those noisy data.
Step 5: Next, we Simulate the RIS-Aided Multi-User MIMO-OFDM System.
Step 6: Finally, we plot the performance of the following metrics:
6.1: SNR Vs. NMSE
6.2: Number of elements Vs. NMSE
6.3: Number of antennae Vs. NMSE
6.4: Number of Pilots Vs. NMSE
6.5: MSE Vs. Doppler shift.
Note: -
1. We make a simulation based process only, not a real time process.
2. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.
3. Please note that this implementation plan does not include any further steps after it is put into implementation.
4. If the above plan satisfies your requirement, please confirm us soon.
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1. #SuperResolution
2. #ChannelEstimation
3. #ReconfigurableIntelligent
4. #PerformanceAnalysis
5. #ThesisResearch
6. #SignalProcessing
7. #WirelessCommunications
8. #IntelligentSystems
9. #DataEstimation
10. #ResearchInnovation
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23 окт 2024
