All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision,...
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MDPI AG
2025-03-01
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| author | Wittawad Pimsri Patinya Muangkammuen Puripong Suthisopapan Virasit Imtawil |
| author_facet | Wittawad Pimsri Patinya Muangkammuen Puripong Suthisopapan Virasit Imtawil |
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| description | It is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>75</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to the conventional SC decoding algorithm with infinite-precision computation. |
| format | Article |
| id | doaj-art-4886d9ed473d4df4bb08aeb013b6b70a |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-4886d9ed473d4df4bb08aeb013b6b70a2025-08-20T02:42:45ZengMDPI AGApplied Sciences2076-34172025-03-01156324110.3390/app15063241All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar DecoderWittawad Pimsri0Patinya Muangkammuen1Puripong Suthisopapan2Virasit Imtawil3Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, ThailandIt is widely acknowledged in communication theory that polar codes have been proven to achieve channel capacity across a range of communication channels. However, their exceptional performance is usually evaluated through simulations or analyses conducted under the assumption of infinite precision, i.e., floating-point arithmetic, which represents an ideal numerical computation. To address this implementation challenge, this work proposes a min-sum successive cancellation (MS-SC) polar decoder employing all-integer quantization to improve practicality in real-world scenarios. To balance the trade-off between practicality and decoding performance, we investigate whether 5-bit all-integer quantization is the optimal choice for the MS-SC polar decoder. Moreover, the simulation results over fading channels show that the proposed decoder achieves a performance almost equivalent to the high-precision successive cancellation (SC) decoder. The integer-based calculation for the MS-SC polar decoder reduces computational complexity by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>75</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared to the conventional SC decoding algorithm with infinite-precision computation.https://www.mdpi.com/2076-3417/15/6/3241polar codesSC decodingmin-sum approximationinteger quantization |
| spellingShingle | Wittawad Pimsri Patinya Muangkammuen Puripong Suthisopapan Virasit Imtawil All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder Applied Sciences polar codes SC decoding min-sum approximation integer quantization |
| title | All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder |
| title_full | All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder |
| title_fullStr | All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder |
| title_full_unstemmed | All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder |
| title_short | All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder |
| title_sort | all integer quantization for low complexity min sum successive cancellation polar decoder |
| topic | polar codes SC decoding min-sum approximation integer quantization |
| url | https://www.mdpi.com/2076-3417/15/6/3241 |
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