# Integrated Sensing and Communication

Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.

Beamforming for near-field extreme massive MIMO integrated sensing and communication usually requires repeatedly solving nonconvex constrained optimization, and online latency grows with array size. PATBO fixes the communication and sensing beam directions geometrically and lets a Transformer predict only the normalized stream powers, trained with Cramér–Rao bound (CRB) sensitivity weighting. On a retained test set with 1024 antennas, 4 users and 2 targets, direct inference reaches 19.54% joint feasibility, physics-constrained power repair raises it to 87.79%, and optimizer fallback for the remainder brings it to 100%, at 1.11 ms per sample.

A UAV ISAC synthetic aperture radar imager casts imaging as an exact linear inverse problem, using a pretrained diffusion model as prior and the echoes as likelihood guidance to reconstruct super-resolution images under arbitrary known trajectories and unknown position errors.

A UAV ISAC synthetic aperture radar imager casts imaging as an exact linear inverse problem, using a pretrained diffusion model as prior and the echoes as likelihood guidance to reconstruct super-resolution images under arbitrary known trajectories and unknown position errors.

A multistatic terahertz link treats ordinary communication traffic scattered off an object as the observation, identifying shape at orientations never seen in training, with real gains from more transmitters and accuracy that holds when three quarters of the surface is hidden.

A multistatic terahertz link treats ordinary communication traffic scattered off an object as the observation, identifying shape at orientations never seen in training, with real gains from more transmitters and accuracy that holds when three quarters of the surface is hidden.

The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.

The authors propose AIMS, an agentic AI framework for sim-to-real multi-modal integrated sensing and communication (ISAC): given a natural-language deployment request specifying the target task, deployment conditions, and real-data budget, it derives a deployment-specific sim-to-real configuration and coordinates its execution to produce a deployment-specific task model, using a two-agent architecture for scene construction and task learning; on the real-world DeepSense 6G dataset it improves vehicle detection and beam prediction over the considered simulation and fusion baselines, and a separate orchestration benchmark shows improved plan correctness with structured domain knowledge and validation feedback.