Hairong Lin | Searcharxiv

arXiv2026

Petabit-per-second Random Number Generation

Physical random number generators based on chaotic microcombs, with their complex nonlinear dynamics and multi-channel parallel capability, have attracted considerable research attention. However, key technical challenges for chaotic microcombs are the high correlation between symmetric teeth and the low bandwidth of single-channel teeth, which seriously affect the speed and scalability of random number generation. We experimentally demonstrate a petabit-per-second (Pbit/s) parallel random number generation system based on intensity chaotic modulation and Rayleigh scattering. Through intensity modulation, the effective bandwidth of the single-channel entropy source is increased from 440MHz to 27.6GHz. Crucially, Rayleigh scattering further contributes through the random superposition of backscattered light, which introduces unpredictable fluctuations in intensity, phase, and polarization. This randomness suppresses inter-channel correlation among parallel entropy sources to ~0.02, ensuring their orthogonality. Moreover, by employing polarization-diverse coherent detection on a single-channel, four new low correlated sub-channels are extracted: X-/Y- intensity and phase. We achieve a single-channel bit rate of 14.336 Tbit/s and a total bit rate of 1.032 Pbit/s (over 72 parallel channels) with offline post-processing, representing the highest post-processing record reported in both the single-channel and the total system. Moreover, our scheme based on a single chaotic microcomb and fiber scattering link show fundamentally scalable. The total bit rate can be significantly pushed beyond the Pbit/s level by further expanding the usable comb channel and/or by deploying multiple fiber scattering links in parallel, paving a practical path toward higher throughput regimes.

physics.optics↗

arXiv2026

Deep Joint Source-Channel Coding for Wireless Video Transmission with Asymmetric Context

In this paper, we propose a high-efficiency deep joint source-channel coding (JSCC) method for video transmission based on conditional coding with asymmetric context. The conditional coding-based neural video compression requires to predict the encoding and decoding conditions from the same context which includes the same reconstructed frames. However in JSCC schemes which fall into pseudo-analog transmission, the encoder cannot infer the same reconstructed frames as the decoder even a pipeline of the simulated transmission is constructed at the encoder. In the proposed method, without such a pipeline, we guide and design neural networks to learn encoding and decoding conditions from asymmetric contexts. Additionally, we introduce feature propagation, which allows intermediate features to be independently propagated at the encoder and decoder and help to generate conditions, enabling the framework to greatly leverage temporal correlation while mitigating the problem of error accumulation. To further exploit the performance of the proposed transmission framework, we implement content-adaptive coding which achieves variable bandwidth transmission using entropy models and masking mechanisms. Experimental results demonstrate that our method outperforms existing deep video transmission frameworks in terms of performance and effectively mitigates the error accumulation. By mitigating the error accumulation, our schemes can reduce the frequency of inserting intra-frame coding modes, further enhancing performance.

eess.IV↗