Abstract

Mission-critical applications of 5G and emerging 6G networks are essential and require support for Ultra-Reliable Low-Latency Communication (URLLC), which demands high reliability and low latency. This article proposes an algorithm, Convolution-Assisted Polar Coding with Turbo-like Decoding (CA-PC-Turbo), to maximize performance in dynamic wireless channels. Convolutional coding is intertwined with polar codes, and an iterative Turbo-type decoder is used, exchanging soft information between the polar and convolutional decoders to achieve fast convergence with low computational complexity. Adaptive coding rates dynamically adjust redundancy based on channel conditions to provide reliability and optimize throughput. A global statistical analysis, presented through simulations in MATLAB version 2024a, is carried out for AWGN, Rayleigh, and Rician fading channels. Randomly selected sequences of short-block length 32, 64, and 128 bits are sent, which are typical sizes of packets in URLLC. The major performance indices, such as Bit Error Rate (BER), Frame Error Rate (FER), throughput, and decoding latency, are tested at different SNR values. Findings show that the proposed CA-PC-Turbo decoder consistently achieves lower BER and FER of 70-80%, compared to traditional decoders such as Successive Cancellation (SC), Belief Propagation (BP), and CA-SCL, especially at low-to-moderate SNR. Enhanced throughput of 25 to 30 % and decoding latency of less than 100 µs also confirm that the system is suitable for ultra-low-latency applications. The statistical results highlight the efficiency of adaptive convolution-assisted polar coding combined with Turbo-like iterative decoding in URLLC. The suggested framework strikes a balance among reliability, latency, throughput, and computational complexity, making it highly applicable to real-time, mission-critical communication in 5G and future 6G networks.