Adaptive Multi-Objective Beamforming and Power Allocation for MIMO-ISAC in Low-Altitude Wireless Networks

Abstract

Low-altitude wireless networks (LAWNs) require reliable multi-user communication together with accurate range and velocity sensing. Communication and sensing share the same transmit power and spatial degrees of freedom (DoF), and therefore joint beamforming is required to coordinate multi-user spectral efficiency with delay-Doppler estimation accuracy. An adaptive multi-objective beamforming and power allocation framework is developed for a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) base station. Communication performance is measured by the achievable multi-user sum spectral efficiency. Sensing performance is characterized by the Cramér–Rao lower bounds (CRLBs) for delay and Doppler frequency. A dimensionless system effectiveness integrated metric (SEIM) combines the three normalized performance components. The beamforming problem is lifted to transmit covariance matrices and treated via semidefinite relaxation (SDR) and alternating successive convex approximation (SCA) under power and per-user signal-to-interference-plus-noise ratio (SINR) constraints. An entropy-regularized weight subproblem provides a closed-form softmax update, and a damping step couples the weight update with the covariance iterations. Numerical results characterize the communication–sensing tradeoff with respect to the transmit power, array size, user loading, SINR requirements, and objective weights.