Asymptotic Performance Bounds of Low-Complexity Decoders for LDPC and Polar Codes

Core Concepts

The asymptotic error rates of LDPC and polar codes are functions of complexity T and code length N, in the form of 2^(-a2^b T/N), where a and b are constants that depend on channel and coding schemes. Polar codes asymptotically outperform (J, K)-regular LDPC codes with a code rate R ≤1 - J(J-1)/(2J+(J-1)) in the low-complexity regime (T ≤O(NlogN)).

Abstract

The paper explores the performance-complexity tradeoff of low-complexity decoders for LDPC and polar codes.
For LDPC codes:

Establishes a lower bound on the BER as a function of decoding complexity, showing a double-exponential reduction in error rate with the number of iterations.
Analyzes the average BER over the ensemble of LDPC codes, providing a tighter lower bound.
For (J, K)-regular LDPC codes, shows the BER and BLER are bounded by Ω(2^(-c1c2^(log2(J-1)(K-1)/(2J))T/N)).
For polar codes:

Characterizes the tradeoff between the complexity of the polar code SSC decoder and its BLER.
Shows the BLER is upper bounded by O(2^(-0.5T/N)) for complexity T ∈(2NloglogN, NlogN).
Demonstrates that polar codes asymptotically outperform (J, K)-regular LDPC codes with R ≤1 - J(J-1)/(2J+(J-1)) in the low-complexity regime.
The results indicate that to further enhance the decoding efficiency for LDPC codes, the key lies in how to efficiently pass messages on the factor graph. Polar codes exhibit superior decoding efficiency in the low-complexity regime due to their ability to effectively gather and utilize information.

Stats

The number of messages passed (NMP) is used as the complexity measure T.

Quotes

None

Key Insights Distilled From

On the Performance of Low-complexity Decoders of LDPC and Polar Codes

by Qingqing Pen... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19266.pdf

Deeper Inquiries

How can the scheduling policy of LDPC codes be improved to better utilize the information on the Tanner graph

To improve the scheduling policy of LDPC codes and better utilize the information on the Tanner graph, several strategies can be implemented. One approach is to optimize the message-passing algorithm by considering the dependencies between variable nodes and check nodes more efficiently. This can involve prioritizing the messages that have the most significant impact on the decoding process, ensuring that critical information is propagated effectively. Additionally, exploring adaptive scheduling policies that dynamically adjust the message-passing order based on the graph structure and the reliability of the received information can enhance decoding efficiency. By incorporating machine learning techniques, such as reinforcement learning, the scheduling policy can be optimized to adapt to varying channel conditions and code configurations, leading to improved performance in high-throughput and low-power communication scenarios.

What other metrics, beyond decoding complexity, could be explored to further enhance the performance of polar codes

Beyond decoding complexity, other metrics that could be explored to enhance the performance of polar codes include code distance, error correction capability, and convergence speed. By focusing on increasing the minimum distance of polar codes, the codes can better withstand channel noise and improve error correction capabilities. This can be achieved through advanced code construction techniques, such as puncturing and shortening, to tailor the code properties to specific communication scenarios. Additionally, optimizing the decoding algorithm to enhance convergence speed, especially in low-complexity regimes, can lead to faster and more reliable decoding. By investigating the trade-offs between code rate, error correction performance, and decoding complexity, polar codes can be further optimized for future communication systems.

How might the insights from this analysis of LDPC and polar codes inform the design of future channel coding schemes for 6G and beyond

The insights gained from the analysis of LDPC and polar codes can inform the design of future channel coding schemes for 6G and beyond in several ways. Firstly, the performance-complexity trade-offs observed in LDPC and polar codes can guide the development of hybrid coding schemes that leverage the strengths of both code families. By combining the efficient decoding of polar codes with the error correction capabilities of LDPC codes, new coding schemes can be designed to achieve higher throughput and lower power consumption. Additionally, the understanding of the fundamental limits in decoding efficiency and the impact of code construction on performance can drive the innovation of novel coding techniques tailored to the requirements of 6G applications. By incorporating adaptive coding strategies, advanced decoding algorithms, and optimized code designs, future channel coding schemes can meet the demanding requirements of 6G networks, such as ultra-high data rates and ultra-low latency.