Guocheng Lv

Lugaoze Feng, Guocheng Lv, Xunan Li. et al.

We study finite-blocklength bounds for noisy permutation channels whose reachable output polytope may be lower-dimensional than the output simplex. Existing Gaussian achievability analyses focus on strictly positive full-rank square DMC transition matrices. The capacity result for arbitrary strictly positive DMCs is established through a weak converse, while available strong converse bounds in the lower-dimensional setting can scale with the dimension of the output simplex rather than with that of the reachable output polytope. On the achievability side, messages are placed on a simplex lattice in affine coordinates, and decoding is performed by projecting the empirical output distribution onto the reachable affine hull followed by Euclidean nearest-neighbor decoding. Writing $d$ for the affine dimension of the reachable output polytope, a geometric reduction converts decoding errors into $d(d+1)$ one-dimensional transfer events, yielding a refined Gaussian achievability lower bound based on averaged local coordinate variances and a relative volume ratio. On the converse side, a modified meta-converse, a Kullback--Leibler divergence covering, and a local binary-testing bound yield a strong converse whose blocklength-dependent term is $d\log\sqrt n$, up to a bounded additive remainder.

Yun Tian, Zhijin Qin, Guocheng Lv et al.

Existing speech semantic communication systems mainly based on Joint Source-Channel Coding (JSCC) architectures have demonstrated impressive performance, but their effectiveness remains limited by model structures specifically designed for particular tasks and datasets. Recent advances indicate that generative large models pre-trained on massive datasets, can achieve outstanding performance arexhibit exceptional performance across diverse downstream tasks with minimal fine-tuning. To exploit the rich semantic knowledge embedded in large models and enable adaptive transmission over lossy channels, we propose a Large Speech Model enabled Semantic Communication (LargeSC) system. Simultaneously achieving adaptive compression and robust transmission over lossy channels remains challenging, requiring trade-offs among compression efficiency, speech quality, and latency. In this work, we employ the Mimi as a speech codec, converting speech into discrete tokens compatible with existing network architectures. We propose an adaptive controller module that enables adaptive transmission and in-band Unequal Error Protection (UEP), dynamically adjusting to both speech content and packet loss probability under bandwidth constraints. Additionally, we employ Low-Rank Adaptation (LoRA) to finetune the Moshi foundation model for generative recovery of lost speech tokens. Simulation results show that the proposed system supports bandwidths ranging from 550 bps to 2.06 kbps, outperforms conventional baselines in speech quality under high packet loss rates and achieves an end-to-end latency of approximately 460 ms, thereby demonstrating its potential for real-time deployment.