Cross-Attentive Pareto Transformer for Integrated Sensing and Communication Beamforming Design

Integrated sensing and communication (ISAC) enables simultaneous wireless communication and environmental sensing, where joint beamforming design is key to balancing both tasks. To address the complexity and trade-off challenges of joint beamforming design in multi-user multi-target ISAC systems, this letter proposes the cross-attentive Pareto transformer (CAPT), an end-to-end deep learning framework that integrates enhanced spatial embeddings and cross-attention to jointly optimize beamforming. By leveraging Pareto multi-task learning, CAPT efficiently generates the Pareto front of solutions in a single inference. Simulation results show that CAPT achieves better Pareto front quality and generalization than weighted minimum mean square error (WMMSE) and convolutional neural network (CNN)-based baselines.