Joint Beamforming Optimization for Pinching-Antenna Systems (PASS)-assisted Symbiotic Radio

This paper investigates a novel downlink symbiotic radio (SR) framework empowered by the pinching antenna system (PASS), aiming to enhance both primary and secondary transmissions through reconfigurable antenna positioning. PASS consists of multiple waveguides equipped with numerous low-cost pinching antennas (PAs), whose positions can be flexibly adjusted to simultaneously manipulate large-scale path loss and signal phases.We formulate a joint transmit and pinching beamforming optimization problem to maximize the achievable sum rate while satisfying the detection error probability constraint for the IR and the feasible deployment region constraints for the PAs. This problem is inherently nonconvex and highly coupled. To address it, two solution strategies are developed. 1) A learning-aided gradient descent (LGD) algorithm is proposed, where the constrained problem is reformulated into a differentiable form and solved through end-to-end learning based on the principle of gradient descent. The PA position matrix is reparameterized to inherently satisfy minimum spacing constraints, while transmit power and waveguide length limits are enforced via projection and normalization. 2) A two-stage optimization-based approach is designed, in which the transmit beamforming is first optimized via successive convex approximation (SCA), followed by pinching beamforming optimization using a particle swarm optimization (PSO) search over candidate PA placements. The SCA-PSO algorithm achieves performance close to that of the element-wise method while significantly reducing computational complexity by exploring a randomly generated effective solution subspace, while further improving upon the LGD method by avoiding undesirable local optima.