Environment-to-Link ISAC with Space-Weather Sensing for Ka-Band LEO Downlinks

Ka-band low-Earth-orbit (LEO) downlinks can suffer second-scale reliability collapses during flare-driven ionospheric disturbances, where fixed fade margins and reactive adaptive coding and modulation (ACM) are either overly conservative or too slow. This paper presents a GNSS-free, link-internal predictive controller that senses the same downlink via a geometry-free dual-carrier phase observable at 10~Hz: a high-pass filter and template-based onset detector, followed by a four-state nearly-constant-velocity Kalman filter, estimate $Δ$VTEC and its rate, and a short look-ahead (60~s) yields an endpoint outage probability used as a risk gate to trigger one-step discrete MCS down-switch and pilot-time update with hysteresis. Evaluation uses physics-informed log replay driven by real GOES X-ray flare morphologies under a disjoint-day frozen-calibration protocol, with uncertainty reported via paired moving-block bootstrap. Across stressed 60~s windows, the controller reduces peak BLER by 25--30\% and increases goodput by 0.10--0.15~bps/Hz versus no-adaptation baselines under a unified link-level abstraction. The loop runs in $\mathcal{O}(1)$ per 0.1~s epoch (about 0.042~ms measured), making on-board implementation feasible, and scope and deployment considerations for dispersion-dominated events are discussed.

Graph Representation-based Model Poisoning on the Heterogeneous Internet of Agents

Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this paradigm, federated learning (FL) serves as a key enabler that allows distributed LLM agents to co-train global models without centralizing data. However, the FL-enabled IoA system remains vulnerable to model poisoning attacks, and the prevailing distance and similarity-based defenses become fragile at billion-parameter scale and under heterogeneous data distributions. This paper proposes a graph representation-based model poisoning (GRMP) attack, which passively exploits observed benign local models to construct a parameter correlation graph and extends an adversarial variational graph autoencoder to capture and reshape higher-order dependencies. The GRMP attack synthesizes malicious local models that preserve benign-like statistics while embedding adversarial objectives, remaining elusive to detection at the server. Experiments demonstrate a gradual drop in system accuracy under the proposed attack and the ineffectiveness of the prevailing defense mechanism in detecting the attack, underscoring a severe threat to the ambitious IoA paradigm.

Fundamental Limits of Cooperative Integrated Sensing and Communications over Low-Earth Orbit THz Satellite Channels

Terahertz inter-satellite links enable unprecedented sensing precision for Low Earth Orbit (LEO) constellations, yet face fundamental bounds from hardware impairments, pointing errors, and network interference. We develop a Network Cram\'er-Rao Lower Bound (N-CRLB) framework incorporating dynamic topology, hardware quality factor $\Gamma_{\text{eff}}$, phase noise $\sigma^2_\phi$, and cooperative effects through recursive Fisher Information analysis. Our analysis reveals three key insights: (i) hardware and phase noise create power-independent performance ceilings ($\sigma_{\text{ceiling}} \propto \sqrt{\Gamma_{\text{eff}}}$) and floors ($\sigma_{\text{floor}} \propto \sqrt{\sigma^2_\phi}/f_c$), with power-only scaling saturating above $\text{SNR}_{\text{crit}}=1/\Gamma_{\text{eff}}$; (ii) interference coefficients $\alpha_{\ell m}$ enable opportunistic sensing with demonstrated gains of 5.5~dB under specific conditions (65~dB processing gain, 50~dBi antennas); (iii) measurement correlations from shared timing references, when properly modeled, do not degrade performance and can provide common-mode rejection benefits compared to mismodeled independent-noise baselines. Sub-millimeter ranging requires co-optimized hardware ($\Gamma_{\text{eff}}<0.01$), oscillators ($\sigma^2_\phi<10^{-2}$), and appropriate 3D geometry configurations.

Semantic Learning for Molecular Communication in Internet of Bio-Nano Things

Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and inter-symbol interference (ISI), limit their ability to support complex data transmission. This paper proposes an end-to-end semantic learning framework designed to optimize task-oriented molecular communication, with a focus on biomedical diagnostic tasks under resource-constrained conditions. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features, prioritizing task-relevant semantic information to enhance diagnostic classification performance. Additionally, a probabilistic channel network is introduced to approximate molecular propagation dynamics, enabling gradient-based optimization for end-to-end learning. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression with LDPC coding methods under resource-constrained communication scenarios.