arXiv subjects

Baoquan Ren

Publications and source records attributed to Baoquan Ren.

4 recordsLinked to original sources

arXiv2026

Shared Sky, Shared Spectrum: Coordinated Satellite-5G Networks for Low-Altitude Economy

Driven by both technological development and practical demands, the low-altitude economy relying on low-altitude aircrafts (LAAs) is booming. However, neither satellites nor terrestrial fifth-generation (5G) networks alone can effectively satisfy the communication requirements for ubiquitous lowaltitude coverage. While full integration of satellites and 5G networks offers theoretical benefits, the associated overhead and complexity pose significant challenges for rapid deployment. As a more economical and immediately viable alternative, this paper investigates partially-integrated networks where satellites and 5G systems operate with coarse synchronization yet achieve coordinated spectrum sharing, pooling their capabilities to jointly serve LAAs. Leveraging the inherent position-awareness of LAAs, we propose a framework for joint time-frequency spectrum sharing with an adaptive synchronization time scale, where only large-scale channel state information (CSI) is required. To avoid solving the NP-hard optimization problem directly, link-feature-aided clustering is employed following a divide-andconquer strategy. The proposed framework achieves substantial performance gains with low overhead and complexity, enabling swift advancement of low-altitude applications while paving the way for future integrated satellite-terrestrial network evolution.

cs.IT↗

arXiv2026

From Intent to Infrastructure: LLM-Driven Agent Compilers for ISAC Networks

Integrated sensing and communications (ISAC) is moving from proof-of-concept demonstrations to system-level deployment in sixth-generation (6G) networks. Because sensing and communication share hardware, spectrum, and waveform resources, ISAC design now involves many tightly coupled choices, including waveform selection, sensing algorithm setup, resource scheduling, and deployment planning. This design space is already too large to manage well through manual tuning or isolated optimizers. This article introduces the \textit{Agent Compiler}, a large language model (LLM)-enabled compilation layer that translates high-level engineering intent into complete and executable ISAC system configurations. The Agent Compiler works in four stages: intent parsing, task decomposition, policy graph synthesis, and infrastructure mapping. It produces a verifiable intermediate representation called the ISAC Policy Graph (IPG). A runtime engine then deploys the compiled configuration and supports closed-loop adaptation at three levels: fast parameter updates, partial recompilation of affected subgraphs, and full workflow recompilation. The core design principle is strict time-scale separation: the LLM handles slow-loop strategic decisions, while proven algorithms retain real-time control in the fast loop. A UAV-assisted disaster rescue example illustrates the full compilation process. We also discuss open issues, including compilation latency, output reliability, constraint verification, and pipeline security, to guide future research.

eess.SP↗

arXiv2026

Discovery of unobservable parameters via physical embedding

Recovering a source signal from indirect measurements often requires estimating latent parameters, such as wireless channel states or MRI coil sensitivities, that cannot be directly observed. Here, we introduce Physics-Embedded Inverse Learning (PEIL), in which a learned estimator predicts these parameters and a fixed, physics-based inverse operator uses them to reconstruct the signal, so that training requires only the source signal as supervision. In systems where multiple parameter combinations can reconstruct the signal equally well, the estimator exploits this freedom to coordinate parameters that compensate for residual modelling errors rather than match ground-truth parameters. In high-mobility wireless communications, PEIL discovers task-optimal configurations that outperform baselines given access to ground-truth parameters, enabling zero-shot generalisation and over 20-fold reduction in training data relative to supervised baselines. To test whether these properties extend across physical domains, we apply PEIL to parallel MRI, where it discovers physically interpretable coil sensitivity maps without calibration scans, yielding reconstructions grounded purely in acquired measurements. These results demonstrate that non-identifiability, conventionally a liability, becomes a resource when the learning objective targets reconstruction quality rather than parameter accuracy.

eess.SP↗

arXiv2026

Joint Task Orchestration and Resource Optimization for SC3 Closed Loop in 6G Networks

In hazardous environments, sensors and actuators can be deployed to see and operate on behalf of humans, enabling safe and efficient task execution. Functioning as a neural center, the edge information hub (EIH), which integrates communication and computing capabilities, coordinates these sensors and actuators into sensing-communication-computing-control (SC3) closed loops to enable autonomous operations. From a system-level optimization perspective, this paper addresses the problem of joint sensor-actuator pairing and resource allocation across multiple SC3 closed loops. To tackle the resulting mixed-integer nonlinear programming problem, we develop a learning-optimization-integrated actor-critic (LOAC) framework. In this framework, a deep neural network-based actor generates pairing candidates, while an optimization-based critic subsequently allocates communication and computing resources. The actor is then iteratively refined through feedback from the critic. Simulation results demonstrate that the LOAC framework achieves near-optimal solutions with low computational complexity, offering significant performance gains in reducing control cost.

cs.IT↗