The paper introduces Memory-Augmented Source Coding (MASC), a scheme that embeds contextual patterns into a source model to improve robustness in low‑SNR communications. MASC uses a shared Parameterized Contextual Memory (PCM) for multi‑order n‑gram patterns and a Mixture‑of‑Memory‑Experts Router (MMER) to selectively activate memory experts based on hidden states, thereby refining probability estimates and shortening code length. Experiments on Rayleigh fading and AWGN channels show that MASC reduces decoding sensitivity to residual channel errors compared to traditional SSCC with autoregressive decoding and LLM‑based Arithmetic Coding.
arXiv:2601. 10267v2 Announce Type: replace Abstract: Separate Source-Channel Coding (SSCC) remains attractive for text transmission due to its modularity and compatibility with mature entropy coders and powerful channel codes.
Learned image compression (LIC) has achieved impressive rate-distortion performance. However, existing methods remain highly vulnerable to packet loss, a common challenge in satellite and emergency communications.
arXiv:2605.10681v2 Announce Type: replace-cross Abstract: Forward error correction is essential for reliable communication over noisy channels. Attention-based model-free neural decoders have shown s...
arXiv:2606. 16607v1 Announce Type: cross Abstract: This paper considers neural channel state information (CSI) compression for time-varying massive multiple-input multiple-output (MIMO) channels in frequency division duplex (FDD) systems with limited feedback resources.
Ada-TokenCom is a rate‑adaptive token communication framework that uses large autoregressive models to compress and transmit tokens efficiently. It combines next‑token prediction with arithmetic coding, sending only the most informative tokens and letting the receiver generate the rest. A Lyapunov‑based algorithm dynamically adjusts compression and modulation to match changing network conditions, and simulations show it outperforms existing digital and deep joint source‑channel coding baselines.
arXiv:2606. 06273v1 Announce Type: cross Abstract: Lossless pixel-level image transmission is a fundamental regime beyond semantic communications, because exact recovery requires both accurate symbol probability modeling and reliable delivery over noisy channels.
arXiv:2607. 01660v1 Announce Type: new Abstract: Hardware impairments in massive multiple-input multiple-output (MIMO) receivers introduce inter-symbol memory and inter-element coupling, severely degrading channel estimation.
arXiv:2608. 02172v1 Announce Type: cross Abstract: Neural receivers have been recognized as a promising paradigm for the next-generation (NextG) communications.
arXiv:2608. 05303v1 Announce Type: cross Abstract: On-device deployment of Large Language Models (LLMs) has become essential for personalized edge applications.
arXiv:2606. 12858v1 Announce Type: cross Abstract: Conventional communication systems, including both separation-based coding and learning-based joint source-channel coding (JSCC), are typically designed under Shannon's rate-distortion theory.
OMP-MoE is a training‑free compression framework that prunes redundant experts in Mixture‑of‑Experts large language models by framing the problem as sparse signal reconstruction solved with Orthogonal Matching Pursuit. The method greedily selects expert contributions as dictionary atoms to minimize reconstruction error, then optimizes cross‑layer expert allocation via a water‑filling strategy, and finally introduces an adaptive inference mechanism (OMP‑MoE†) that dynamically adjusts expert activation based on energy prediction. Experiments on Qwen, DeepSeek‑V2, GPT‑OSS, and Mixtral MoE show consistent performance gains at 25‑50% pruning ratios, with Qwen3‑30B‑A3B retaining 93.3% of original performance at 50% compression while achieving significant speedups.
arXiv:2605. 22863v2 Announce Type: replace Abstract: LLM agents today communicate via text, which incurs considerable latency and information loss due to the need to autoregressively decode the sharer model's state and encode at the receiver model.