arXiv subjects

Minh Dat Nguyen

Publications and source records attributed to Minh Dat Nguyen.

4 recordsLinked to original sources

arXiv2026

Agentic RDZ: Autonomous Zone Management with AI Agents and an FR3 Coexistence Use Case

Radio Dynamic Zones (RDZs) allow wireless experiments to operate outside conventional spectrum regulations while continuously guaranteeing protection for incumbent users. Existing RDZ prototypes automate this task procedurally, through handcrafted rules and predefined workflows, and become brittle when experiments encounter hardware impairments, user workflows and devices, or interference mechanisms not anticipated at design time. This paper introduces the agentic RDZ (A-RDZ), which, to the best of our knowledge, is the first RDZ realization in which agents use Large Language Models (LLMs) to perform spectrum management, experiment management, policy interpretation, and zone orchestration. Built on the GENESIS agentic framework, the architecture pairs autonomous reasoning with a deterministic policy gate and near-real-time (near-RT) reflexes, so that agents can improve outcomes but never weaken the zone's protection guarantee. We validate the A-RDZ on a hardware-in-the-loop Frequency Range 3 (FR3) (7.125-24.25 GHz) Open Radio Access Network (O-RAN) testbed in which a 5G New Radio (NR) experiment coexists with an emulated Fixed Satellite Service (FSS) earth-station incumbent. In an end-to-end use case, the monitoring agent detects an emission violation from live spectrum evidence, the orchestrator selects a mitigation that restores the interference budget while keeping the experiment running, and the action is applied and verified through the O-RAN control plane. We report the detection-to-mitigation latency decomposition and discuss the practical limits of agentic operation, including non-deterministic reasoning and decision-to-action translation.

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arXiv2026

From Open RAN to Open Spectrum: A Programmable, Intelligent Architecture for Multi-Service Spectrum Coexistence

Considering sharing or coexistence from the perspective of spectrum alone fails to recognize that any spectrum-enabled service also requires (i) radio and processing infrastructure and (ii) a protocol stack, including waveforms and signal processing pipelines. The efficiency of spectrum coexistence frameworks such as Citizen Broadband Radio Service (CBRS) is thus limited to optimizing resource allocation across a single dimension. How to address this limitation, however, remains an open challenge, especially considering the diversity of requirements and operational modes across spectrum services (e.g., sensing, communications, navigation, or positioning). This article introduces Open Spectrum, an architecture that brings softwarization, programmability, and open interfaces to heterogeneous spectrum services, extending the open Radio Access Network (RAN) principles beyond wireless networking. We propose to combine spectrum, services, and infrastructure in a common pool. Its resources are shared and orchestrated by a Spectrum Intelligent Controller (SIC), with plug-and-play spectrum applications, i.e., Spectrum Applications (sApps), and data-driven Radio-Frequency Interference (RFI) modeling using Digital Twins (DTs). We describe the Open Spectrum architecture, shared infrastructure pool, and operational workflows for tenant onboarding and incentives, conflict resolution, and service sharing across sensing, radionavigation, radiolocation, and cellular systems. System-level simulations using the BostonTwin urban DT and Sionna ray tracing show that there exist performance-driven incentives in sharing infrastructure and sharing across multiple services, enabling increased access to spectrum and improvement in median Signal to Interference plus Noise Ratio (SINR) of up to 12 dB.

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arXiv2026

Coordinated Spectrum Coexistence Across Heterogeneous Commercial and Federal Services

Future wireless networks are expected to support the coexistence of cellular communications, radio frequency (RF) sensing, radionavigation, and radiolocation radar-among others-over congested federal sub-6 GHz spectrum under heterogeneous Quality of Service (QoS) requirements, driven by escalating cellular traffic demand, the proliferation of 6G sensing and positioning services, and regulatory pressure to repurpose federal bands without displacing incumbents. We develop a unified framework in which all four services dynamically share a common Physical Resource Block (PRB) pool under centralized coordination, formulating weighted cellular sum-rate maximization subject to duty-cycle, orthogonality, sensing signal-to-noise ratio (SNR), and Cramer-Rao-based positioning constraints. The resulting Mixed-Integer Nonlinear Program (MINLP) is solved by alternating optimization across PRB assignment, scheduling, and successive convex approximation for power allocation, complemented by a low-complexity QoS-aware greedy heuristic. Site-specific ray-tracing simulations on the BostonTwin urban digital twin show substantial gains in spectrum efficiency and cellular throughput while strictly meeting sensing and positioning QoS, establishing coordinated multi-service sharing as a viable architecture for cellular-federal-radar coexistence in next-generation military and civilian networks.

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arXiv2026

GENESIS: Harnessing AI Agents for Autonomous 6G RAN Synthesis, Research, and Testing

Cellular research and development (R&D) is throttled by six structural processes that each consume months of manual engineering work per iteration: (i) synthesizing new features from standards or research papers into production code; (ii) conformance and interoperability testing; (iii) hardening against field anomalies and diverse deployment environments; (iv) data-driven optimization of network functionalities; (v) discovering and prototyping novel waveforms, functionalities, and capabilities for future standards; and (vi) securing the stack against vulnerabilities. Although Large Language Models (LLMs) have compressed comparable R&D work in general software engineering from days to minutes, their known pitfalls worsen on Radio Access Network (RAN) use cases: they hallucinate Application Programming Interfaces (APIs) and mis-read specifications, which kills interoperability of RAN components at the first mistake, and they heavily rely on simulations for designing algorithms, which is notorious for breaking when transferred to real hardware. To address these challenges, we present GENESIS, an agentic Artificial Intelligence (AI) framework that converts intents (e.g., a specification clause, a telemetry anomaly, or a research hypothesis) into solutions validated with over-the-air experiments, fed back into a persistent knowledge base. GENESIS is built on three composable primitives (agents, skills, hooks) and a knowledge layer (SYNAPSE) that doubles as the source of ground truth and the recipient of every artifact the framework produces, making capabilities compound across runs.

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