Abstract

Motivation: Optimization of parallel transmission (pTx) pulse design with hard constraints on SAR will benefit from faster approaches. Goal(s): We seek to incorporate hard quadratic constraints for pTx using a physics-driven deep learning (DL) approach. Approach: We unroll an extension of the log-barrier method to enforce SAR constraints, while learning the optimal gradient step sizes using a neural network. This strategy accelerates optimization with fewer steps, while not sacrificing performance. Results: Preliminary results show that our method is faster than traditional techniques like CVXPY with similar performance. Impact: Our proposed method reduces the time-consuming optimization used in conventional pTx and may lead to improvements especially for real-time UHF applications.