Design of An Iterative Adaptive and Energy- Efficient Polar Code Architectures for Low- Latency 5G Communication Systems

Abstract

Newgen 5G networks need enhanced polar code decoding to balance throughput, energy economy, and error resilience
for ultra-reliable low-latency communication. Although theoretically valid, polar code decoding algorithms have high latencies,
energy consumption, and low channel and hardware flexibility, making them unsuitable for real-time edge-based 5G applications.
This study proposes a performance analysis and algorithmic framework with five novel polar code processing optimization methods
for 5G system constraints to overcome these limitations. C-ADTP prunes the decoding tree to minimize latency by 25-35% and
energy use by 18-22% without sacrificing FER using real-time CSI and QoS criteria. The Sparse Instruction-Level Polar
Vectorization Engine (SIL-PVE) enhances performance by 1.8 to 2.4 times and reduces decoding time by 30% using sparse bitwise
vectorization and SIMD/VLIW architectures. RLO-PGG's dynamic bit-channel assignment technique decreases latency by 20-30%
in high-speed applications and increases mobile flexibility by using user mobility patterns and delay spread characteristics.
Thermodynamic Core Balancer for Polar Decoding (TCB-PD) saves 28–32% of energy without affecting decoding quality using thermal-aware job scheduling. Finally, the Hybrid Interleaved Polar Puncturing Optimizer (HIPPO) improves bandwidth-constrained performance by 15% and provides coding rate flexibility using a bit-difficulty predictor and hybrid interlea These approaches provide scalable real-time foundations for next-generation wireless systems and improve polar code decoding.