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.

Polar codes are fundamental to the control channels of fifth-generation (5G) systems. Owing to their excellent block error rate (BLER) performance, polar coding is considered a strong candidate for sixth-generation (6G) networks. In scenarios where both computational complexity and ultra-high data rates are critical, we investigate integer quantization in successive cancellation (SC) decoding as a replacement for double-precision calculations. A 4096-quadrature amplitude modulation (QAM) system with orthogonal frequency-division multiplexing (OFDM) is employed to emulate high-data-rate transmission conditions. Our work shows that integer quantization influences decoding performance across multiple dimensions, depending on system parameters such as code length, rate, and required precision. Our evaluation indicates that a 5-bit integer representation can closely match the ideal BLER performance. The proposed integer-based SC decoding achieves a 14–16% reduction in computational complexity compared to fixed-point implementations with fractional precision, while maintaining decoding performance. This offers a simple and effective solution for polar-coded 6G communications.