Distributed learning systems typically assume that local data is already available at clients with fixed quality, while in practice, data is sensed through imperfect physical processes whose quality depends on modality, resolution, sensing power, and sample size. We model sensing noise as a structured, modality-dependent covariance and derive a non-convex learning convergence bound whose irreducible sensing floor is governed by the alignment between the modality noise covariance and the loss-sensitivity geometry. Thus, the optimal modality minimizes this noise-gradient alignment rather than total noise power alone. The analysis further yields a sensor-hardware achievability bound for epsilon-stationarity and a hardware-saturation threshold on the accumulated dataset size. We jointly optimize modality, resolution, power, and sample count and demonstrate the performance gain through simulations.
Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile…
Integrated sensing and communication (ISAC) and AI-and-communication (AIAC) are identified as separate usage scenarios in the ITU IMT-2030 vision for sixth-generation (6G) networks. In practice, however, these two directions are already beginning to merge. ISAC gives the network…
Future wireless networks are expected to support networked artificial intelligence (AI) services, where multiple devices transmit learned features to an edge server for distributed inference. This setting calls for task-oriented physical-layer optimization, where wireless transmi…
With the rapid development of integrated sensing and communication (ISAC) as a key enabler for future wireless networks, ensuring the security of both communication and sensing functions has become increasingly important. Current secure ISAC studies focus restrictively either on…
Physical artificial intelligence (AI) systems involve distributed sensing agents with embedded AI models that must coordinate to perceive, reason, and act in networked environments. Transmitting raw sensor data incurs significant communication overhead, latency, and redundancy. W…
Recently, large language models (LLMs) have been successfully adopted in various fields, including wireless communications, robotics, and autonomous vehicles, owing to their outstanding adaptability and reasoning abilities. Despite their huge potential, the application of LLMs fo…