A performance comparison of polar codes and Reed-Muller codes

By E. Arkan. IEEE Communications Letters, 2008

474 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/30f6e7715c1b808c38edee543796be257338979b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Peter Vladimirovich Trifonov
2. Kai Niu
3. Kai Chen
4. Toshiyuki TANAKA
5. Ryuhei Mori
6. E. Arıkan
7. Rüdiger L. Urbanke
8. Satish Babu Korada

In Electrical and Electronic Engineering

1. Kai Niu
2. Kai Chen
3. E. Arıkan
4. Peter Vladimirovich Trifonov
5. Toshiyuki TANAKA
6. Ryuhei Mori
7. Satish Babu Korada
8. Rüdiger L. Urbanke

Citations in the corpus

• Performance of polar codes with the construction using density evolution (2009, methodology): “The block error probabilities of codes optimized for the BEC via Arıkan’s heuristic method [2] are also shown for comparison.”
• Efficient Design and Decoding of Polar Codes (2012, background): “Consider a(8, 5, 2) polar code with generator matrix [16]”
• Channel Polarization: A Method for Constructing Capacity-Achieving Codes for Symmetric Binary-Input Memoryless Channels (2008, methodology): “g a factor-graph of F⊗n, as shown in Fig. 12 for N = 8. We carried out experimental studies to assess the performance of polar codes under BP decoding, using RM codes under BP decoding as a benchmark [17]. The results showed significantly better performance for polar codes. Also, the performance of p”
• Systematic Polar Coding (2011, background): “An alternative to SC decoding of polar codes is belief propagation (BP) decoding, as discussed in [10].”
• CRC-Aided Decoding of Polar Codes (2012, background): “Belief propagation (BP) [3], [4] and linear programming (LP) [5] decoders are reported to have a significant improvement over SC.”