telecommunications developers

Abstract: Large language models (LLMs) are entering network-management control loops, yet their plans can fall outside the executable action space. Whether hard deterministic bounds around such a planner have real effect, or merely restate a definition, is an empirical question. We measure it on a live 5G-core testbed, where an LLM planner decides when to decompose control-plane procedures into stateless handlers inside a bounded loop: a JSON schema constrains the output, a projection-repair validator re-checks it, a deterministic fallback guarantees liveness, and every decision is persisted. Three measurements anchor the paper. First, the two tested models, run unbounded with the allowed action sets stated in the prompt, still violated intent-handler rules in 13 of 44 standard calls. Every violation broke an intent-handler combination rule that the deployed flat schema cannot encode (schema enforcement as deployed caught 0/13); a union schema encoding the rule removes the class by construction. Second, with the bounds in place, all 33 decoded live plans were feasible without repair, and a matched-condition probe put the in-distribution unbounded rate at one violation in 20 windows. Third, exploratory: on the design set the planner matched an author-specified selection reference that a threshold policy and three a priori codings each missed. Transfer to held-out scenarios remained partial (5-6 of 10), and the planner's two-clause distillate could replace the runtime planner itself. Together, the results suggest elicitation and distillation, not runtime control, as the LLM's role.

Abstract: Blind Source Separation(BSS) is a fundamental problem in signal processing, aiming to separate multiple source signals from their mixtures without prior knowledge of the sources or the mixing process. Traditional approaches, such as Independent Vector Analysis (IVA) exploits statistical independence of sources. Recently, diffusion-based approaches have emerged as a promising alternative by leveraging powerful generative priors. Among them, ArrayDPS formulates BSS problem as a posterior sampling problem, and utilizes a pretrained speech diffusion model to guide the recovery of clean source signals. A key factor behind its separation capability is the multi-channel consistency (MC) objective, which enforces the estimated source signals to reconstruct the observed microphone mixtures through the estimated acoustic transfer functions. However, the number of microphones in the array is often limited, which constrains the performance of ArrayDPS. To address this issue, we propose VM-ArrayDPS, a novel method that augments the microphone array with virtual microphones with higher-SNR, these microphones can offer extra MC constraints to enhance the separation performance. Experimental results demonstrate that VM-ArrayDPS significantly outperforms ArrayDPS on both 2-speaker and 3-speaker datasets, showcasing the effectiveness of virtual microphone augmentation in improving BSS performance. We also did ablation studies to show the influence of the number of virtual microphones and weight of the MC objective brought by virtual microphones.