5G LDPC Intel® FPGA IP User Guide

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ID 683107
Date 4/01/2024
Public
Document Table of Contents
Document Table of Contents x
1. About the 5G LDPC Intel® FPGA IP 2. Getting Started with the Intel® FPGA IP 3. Designing with the 5G LDPC Intel® FPGA IP 4. 5G LDPC Intel® FPGA IP Functional Description 5. Parameter Optimization for the 5G LDPC IP 6. 5G LDPC IP User Guide Archive 7. Document Revision History for the 5G LDPC Intel® FPGA IP User Guide
1. About the 5G LDPC Intel® FPGA IP x
1.1. 5G LDPC Intel® FPGA IP Features 1.2. 5G LDPC Intel® FPGA IP Device Family Support 1.3. 5G LDPC IP Release Information 1.4. 5G LDPC IP Performance and Resource Utilization
2. Getting Started with the Intel® FPGA IP x
2.1. Installing and Licensing Intel® FPGA IP Cores
2.1. Installing and Licensing Intel® FPGA IP Cores x
2.1.1. Intel® FPGA IP Evaluation Mode 2.1.2. 5G LDPC IP IP Timeout Behavior
3. Designing with the 5G LDPC Intel® FPGA IP x
3.1. Generating a 5G LDPC Intel® FPGA IP 3.2. Simulating the 5G LDPC IP RTL 3.3. 5G LDPC Simulation Results
4. 5G LDPC Intel® FPGA IP Functional Description x
4.1. 5G LDPC Decoder 4.2. 5G LDPC Encoder 4.3. Avalon Streaming Interfaces in DSP Intel FPGA IP
4.1. 5G LDPC Decoder x
4.1.1. 5G LDPC Decoder Signals 4.1.2. 5G LDPC Decoder Data Formats 4.1.3. 5G LDPC IP Decoder Parameters
4.2. 5G LDPC Encoder x
4.2.1. 5G LDPC Encoder Signals 4.2.2. 5G LDPC Encoder Data Formats
5. Parameter Optimization for the 5G LDPC IP x
5.1. C++ and MATLAB Software Models
5.1. C++ and MATLAB Software Models x
5.1.1. Running the 5G LDPC IP C++ Models 5.1.2. Running the 5G LDPC Decoder and Encoder MATLAB Model in the Design Example
1. About the 5G LDPC Intel® FPGA IP
2. Getting Started with the Intel® FPGA IP
3. Designing with the 5G LDPC Intel® FPGA IP
4. 5G LDPC Intel® FPGA IP Functional Description
5. Parameter Optimization for the 5G LDPC IP
6. 5G LDPC IP User Guide Archive
7. Document Revision History for the 5G LDPC Intel® FPGA IP User Guide

5. Parameter Optimization for the 5G LDPC IP

You should optimize 5G NR performance for BLER (block error rate) performance, throughput, and sizing.

For throughput vs BLER performance, you can limit the maximum number of iterations that the LDPC decoder uses before giving up on decoding the block. BLER improves with increasing the maximum number of iterations. However, you can potentially reduce the throughput if you raise this number too high. At some point, it is better to give up and request retransmission of a packet or to have the network reassign your LDPC parameters for the shared data channel. You can use the C++ or MATLAB software models, to perform this assessment and to determine which set of parameters suits the network best.

The following BLER curves shows vastly different error rate curves, by changing the parameters of the LDPC error correction scheme.
Figure 17. BLER vs. SNR Graphs Obtained with Different 5G LDPC IP Parameters (log-log scale)The BLER vs SNR curves show the difference in performance introduced by the maximum number of iterations allowed for a particular LDPC mode of the 3GPP 5G NR specification. The smaller that parameter, the worse the performance is for a particular SNR.
Figure 18. BLER vs. SNR Graphs for a Specific Set of 5G LDPC IP Parameters with Different Iteration Maxima Set for the Decoder (semi-log scale shows the transition dynamic range)
Figure 19. BLER vs SNR Graphs for Several Iteration Maxima with 5G LDPC IP Decoder with 5-Bit and 6-Bit LLRs Respectively (semi-log scale)The figure shows the difference in BLER by changing the bit precision of input LLR values, i.e. by using 5-bit LLRs vs 6-bit LLRs. The designs with 6-bit LLRs have better BLER performance, but the ones with 5-bit LLRs require fewer FPGA resources and can also operate at a higher maximum frequency

The vertical axis in both figures shows the block rate on the linear scale from 0 (no errors; that BLER is not theoretically attainable) to 1 (the error rate is 100%; the decoder fails to decode every dataword transmitted). The horizontal axis is in logarithmic units (dB) of the ratio between the power of the normalized modulated carrier and the effective power of the additive Gaussian noise in the channel, i.e. its variance.

A transmitted codeword can morph into another valid one, if it is sampled from a noisy channel and translated to LLR values. The LDPC decoder might assert source_et_pass and even close before it reaches the maximum number of iterations indicating a successful operation in those cases. However, the decoded dataword does not match the original transmission block. These occurrences are expected. The network should cure such situations by changing the LDPC parameters it is currently using to a set of more robust ones and using the hybrid automatic repeat request (HARQ) mechanism.


Section Content
C++ and MATLAB Software Models

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