All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
Wittawad Pimsri, Patinya Muangkammuen, Puripong Suthisopapan, Virasit Imtawil · Applied Sciences · 2025
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 75% compared to the conventional SC decoding algorithm with infinite-precision computation.