Physics-Informed Neural Optimization Based Antenna Coding Design for Pixel Antenna Systems

Abstract

Pixel antennas enable highly radiation pattern reconfigurability to enhance wireless systems, but its antenna coding design, that is optimizing the states of switches embedded in pixel antennas, remains an NP-hard challenge. Conventional approaches for antenna coding design typically rely on heuristic search algorithms, which suffer from high computational complexity. To overcome this issue, we propose a novel efficient data-free optimization algorithm called physics-informed neural optimizer (PINO) for antenna coding design. By integrating a deep convolutional neural network prior and a Gumbel-Sigmoid continuous relaxation into a differentiable physics engine, the proposed algorithm transforms the binary optimization problem into a continuous differentiable problem, which enables the antenna coding optimization problem to be efficiently solved via gradient descent. Simulation results demonstrate that the proposed algorithm outperforms the heuristic search based algorithms, reducing computational time while achieving higher average channel gain.