Block-metric-optimized rate-adaptive high-dimensional reconciliation for free-space CV-QKD

In free-space continuous-variable quantum key distribution (CV-QKD) systems, atmospheric turbulence induces time-varying fading, leading to random fluctuations in the signal-to-noise ratio (SNR). Consequently, rate-adaptive reconciliation becomes essential. This paper proposes a rate-adaptive high-dimensional reconciliation method based on Raptor-like LDPC codes. The proposed scheme achieves dynamic rate adaptation by leveraging a precomputed SNR threshold table. Furthermore, before adjusting the code length, the proposed method sorts high-dimensional vector blocks based on their norms, preferentially retaining blocks with higher signal power to bolster information reliability. Simulation results demonstrate that the proposed scheme outperforms the perforation and shortening (PS) method in terms of reconciliation efficiency and frame error rate, while maintaining decoding iterations below 100. Under random transmission rate samples, the optimized scheme achieves optimal secret key rate (SKR) performance and stability, validating its robustness and practicality in time-varying channels.