Performance Analysis of EXIT Charts and Turbo Principle for Mobile Communication
Implementation plan:
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Scenario 1: (Using QPSK)
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Step 1: Initially import sionna and tensorflow libraries for define constellation for QPSK, then implement the three labeling strategies (Gray, Anti-Gray and Natural) using custom sionna mapping
Step 2: Next, we Generate binary input sequences and perform symbol mapping for each labeling strategy.
Step 3: Next, we implement AWGN channel for noisy symbol generation at SNR = 0 dB.
Step 4: Next, we Flip the signs of a priori LLRs from the GaussianPriorSource for transmitted bits equal to 1, keeping LLRs unchanged for bits equal to 0, and generate LLRs for Ia values from 0 to 1 .
Step 5: Next as input to the demapperpriorsource(use sionna api), we should give noisy symbols, noise variance and the calculated from apriori llrs
Step 6: Next, we subtract the apriori llrs from the output llrs of the demapper with prior, the subtracted value is called the extrinsic llrs.(llrs should be flipped or not before computing MI values)
Step 7: Calculate Bitwise MI between the transmitted bits and the extrinsic llrs using the MI (Mutual Information) formula(this value is called Ie)
Step 8:Finally, we plot performance metrics of the following
8.1: Ia (bits/s) vs Ie (bits/s)
8.2: QPSK vs. Bit Error Rate (BER)
8.3: QPSK vs. Spectral Efficiency (bps/Hz)
Scenario 2: (Using QAM)
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Step 1: Initially import sionna and tensorflow libraries for define constellation for 16 QAM, then implement the three labeling strategies (Gray, Anti-Gray and Natural) using custom sionna mapping
Step 2: Next, we Generate binary input sequences and perform symbol mapping for each labeling strategy.
Step 3: Next, we implement AWGN channel for noisy symbol generation at SNR = 0 dB.
Step 4: Next, we Flip the signs of a priori LLRs from the GaussianPriorSource for transmitted bits equal to 1, keeping LLRs unchanged for bits equal to 0, and generate LLRs for Ia values from 0 to 1 .
Step 5: Next as input to the demapperpriorsource(use sionna api), we should give noisy symbols, noise variance and the calculated from apriori llrs
Step 6: Next, we subtract the apriori llrs from the output llrs of the demapper with prior, the subtracted value is called the extrinsic llrs.(llrs should be flipped or not before computing MI values)
Step 7: Calculate Bitwise MI between the transmitted bits and the extrinsic llrs using the MI (Mutual Information) formula(this value is called Ie)
Step 8:Finally, we plot performance metrics of the following
8.1: Ia (bits/s) vs Ie (bits/s)
8.2: Number of QAM vs. Bit Error Rate (BER)
8.3: Number of QAM vs. Spectral Efficiency (bps/Hz)
Software Requirements:
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1. Development Tool: Python 3.11.4 or above
2. Operating System: Windows-10(64-bit) or above
Note:
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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.
