To advance Polar code design for 6G applications, we develop a reinforcement learning-based universal sequence design framework that is extensible and adaptable to diverse channel conditions and decoding strategies. Crucially, our method scales to code lengths up to 2048, making it suitable for use in standardization. Across all (N,K)(N, K) configurations supported in 5G, our approach achieves competitive performance relative to the NR sequence adopted in 5G and yields up to a 0.2 dB gain over the beta-expansion baseline at N=2048N = 2048. We further highlight the key elements that enabled learning at scale: (i) incorporation of physical law constrained learning grounded in the universal partial order property of Polar codes, (ii) exploitation of the weak long term influence of…
To advance Polar code design for 6G applications, we develop a reinforcement learning-based universal sequence design framework that is extensible and adaptable to diverse channel conditions and decoding strategies. Crucially, our method scales to code lengths up to 2048, making it suitable for use in standardization. Across all (N,K)(N, K) configurations supported in 5G, our approach achieves competitive performance relative to the NR sequence adopted in 5G and yields up to a 0.2 dB gain over the beta-expansion baseline at N=2048N = 2048. We further highlight the key elements that enabled learning at scale: (i) incorporation of physical law constrained learning grounded in the universal partial order property of Polar codes, (ii) exploitation of the weak long term influence of decisions to limit lookahead evaluation, and (iii) joint multi-configuration optimization to increase learning efficiency.
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This paper was accepted at IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) 2024.
Programmers frequently engage with machine learning tutorials in computational notebooks and have been adopting code generation technologies based on large language models (LLMs). However, they encounter difficulties in understanding and working with code produced by LLMs. To mitigate these challenges, we introduce a novel workflow into…
In 1991, Brenier proved a theorem that generalizes the polar decomposition for square matrices — factored as PSD ×\times unitary — to any vector field F:Rd→RdF:\mathbb{R}^d\rightarrow \mathbb{R}^d. The theorem, known as the polar factorization theorem, states that any field FF can be recovered as the composition of the gradient of a convex function uu with a measure-preserving map MM, namely F=∇u∘MF=\nabla u \circ M. We propose a practical…