Does Gaussian Approximation Work Well for the Long-Length Polar Code Construction?

Gaussian approximation (GA) is widely used to construct polar codes. However, when the code length is long, the subchannel selection inaccuracy due to the calculation error of conventional approximate GA (AGA), which uses a two-segment approximation function, results in a catastrophic performance loss. In this paper, new principles to design the GA approximation functions for polar codes are proposed. First, we introduce the concepts of polarization violation set (PVS) and polarization reversal set (PRS) to explain the essential reasons that the conventional AGA scheme cannot work well for the long-length polar code construction. In fact, these two sets will lead to the rank error of subsequent subchannels, which means that the orders of subchannels are misaligned, which is a severe problem for polar code construction. Second, we propose a new metric, named cumulative-logarithmic error (CLE), to quantitatively evaluate the remainder approximation error of AGA in the logarithm. We derive the upper bound of CLE to simplify its calculation. Finally, guided by PVS, PRS, and CLE bound analysis, we propose new construction rules based on a multi-segment approximation function, which obviously improve the calculation accuracy of AGA so as to ensure the excellent performance of polar codes especially for the long code lengths. Numerical and simulation results indicate that the proposed AGA schemes are critical to constructing high-performance polar codes.

[1]  Bin Li,et al.  An Adaptive Successive Cancellation List Decoder for Polar Codes with Cyclic Redundancy Check , 2012, IEEE Communications Letters.

[2]  L. Litwin,et al.  Error control coding , 2001 .

[3]  Erdal Arikan,et al.  Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels , 2008, IEEE Transactions on Information Theory.

[4]  Kai Chen,et al.  Polar codes: Primary concepts and practical decoding algorithms , 2014, IEEE Communications Magazine.

[5]  Ido Tal On the Construction of Polar Codes for Channels With Moderate Input Alphabet Sizes , 2017, IEEE Trans. Inf. Theory.

[6]  Jinhong Yuan,et al.  A practical construction method for polar codes in AWGN channels , 2013, IEEE 2013 Tencon - Spring.

[7]  Matías Toril,et al.  Optimization of the Assignment of Base Stations to Base Station Controllers in GERAN , 2008, IEEE Communications Letters.

[8]  H. Vincent Poor,et al.  Channel Coding Rate in the Finite Blocklength Regime , 2010, IEEE Transactions on Information Theory.

[9]  Anke Schmeink,et al.  Construction of Polar Codes Exploiting Channel Transformation Structure , 2015, IEEE Communications Letters.

[10]  Toshiyuki Tanaka,et al.  Performance of polar codes with the construction using density evolution , 2009, IEEE Communications Letters.

[11]  Ido Tal On the Construction of Polar Codes for Channels With Moderate Input Alphabet Sizes , 2017, IEEE Transactions on Information Theory.

[12]  Sae-Young Chung,et al.  Analysis of sum-product decoding of low-density parity-check codes using a Gaussian approximation , 2001, IEEE Trans. Inf. Theory.

[13]  Kai Chen,et al.  CRC-Aided Decoding of Polar Codes , 2012, IEEE Communications Letters.

[14]  Emre Telatar,et al.  On the construction of polar codes , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[15]  Alexander Vardy,et al.  How to Construct Polar Codes , 2011, IEEE Transactions on Information Theory.

[16]  Ying Li,et al.  Construction and Block Error Rate Analysis of Polar Codes Over AWGN Channel Based on Gaussian Approximation , 2014, IEEE Communications Letters.

[17]  Peter Trifonov,et al.  Efficient Design and Decoding of Polar Codes , 2012, IEEE Transactions on Communications.

[18]  E. Arkan,et al.  A performance comparison of polar codes and Reed-Muller codes , 2008, IEEE Communications Letters.