Integrated Multi-Target Inference and Multiuser Communication in Active RIS-Assisted Networks

This work investigates the integration of multi-target inference and multiuser communication in an active reconfigurable intelligent surface (aRIS)-assisted network. To infer the targets’ elevation and azimuth pairs and reflection coefficients from signals transmitted by a base station and reflected by the aRIS, which form computationally intractable nonlinear models, we develop a constructive minimum mean square error (MMSE) estimator based on their probability distribution functions. The resulting MSE is expressed analytically as a deterministic function of the probing signal, enabling its optimization. We then formulate the problem of jointly designing a beamformer and the aRISs power-amplified reconfigurable elements to ensure both accurate target inference and fair user rates. A computational program using closed-form updates is developed. Numerical results demonstrate a flexible trade-off between inference accuracy and achieved user rates.