6G networks promise AI-native RAN architectures that autonomously coordinate applications and infrastructure. Yet a critical gap remains: applications know their constraints (battery-critical mission, safety deadlines) but not network state (cell load, coverage); the RAN knows wireless conditions but not application needs. Static policies ignore context, autonomous ML lacks explainability, manual coordination does not scale. We introduce semantic negotiation: applications and RAN expose constraints via MCP servers, an LLM reasons about tradeoffs asynchronously, and human operators supervise critical decisions. The LLM queries live wireless state, proposes changes, and validates outcomes via query-feedback loops. Operators see why decisions were made: tool rationale, constraint validation, tradeoff reasoning, not just what changed. Our OpenAirInterface 5G proof-of-concept demonstrates explainable coordination for proactive planning in private enterprise networks where human oversight is essential and regulatory compliance requires full auditability.
Dysarthria detection from speech enables scalable neurological health monitoring but raises serious privacy concerns due to leakage of spoken content. We explore whether dysarthria can be detected and classified without intelligible speech using privacy-aware signal processing. Using MFCC features and lightweight classifiers, we show that dysarthria-relevant cues persist under strong privacy filtering, enabling a favorable privacy-utility trade-off.
This paper proposes MetaPower, a design of a magnetic resonance-based wireless power transfer system that integrates capacitive electrodes and metasurface, capable of locating devices and focusing energy towards them for smart charging. Our design builds on the fact that magnetic resonators generate strong localized magnetic fields with lensing effects. We leverage this principle to control the energy distribution by applying different equivalent permeability and phase profiles on our metasurface arrays on each cell. We show how our surface offers improved features and safety guarantees over conventional wireless charging methods, enabling programmability in the transferred power distribution based on the target device's location. Consider, for example, a metasurface-enabled table that automatically detects a tagged device and then actively focuses energy in the near-field based on the object location. We present a detailed design of MetaPower's target localization and near-field magnetic focusing through HFSS and MATLAB simulations. We also present preliminary results from a proof-of-concept implementation of the localization electrodes, and discuss the system-level surface implementation for future work.
Neural Architecture Search (NAS) automatically searches a neural architectural space for onek that achieves high accuracy on a target task, typically image analysis. While current NAS frameworks excel at maximizing accuracy, they have largely overlooked inference latency as a design constraint. In this paper, a novel NAS framework is developed that effectively exploits kernel tuning using the TVM auto-tuning ML compiler to synthesize Latency-Constrained DNNs (NASLCD). We demonstrate the superiority of NAS-LCD over state-of-the-art NAS approaches, including Once-For-All (OFA) and proxy-based ZEN-NAS.
