A Joint Optimization Method for Radio Antenna Arrays Under Tri-Domain Errors and Atmospheric Effects Based on Improved Dueling DQN

This paper presents a co-optimization framework for sparse concentric ring arrays based on an improved Dueling Deep Q-Network (DDQN) with a two-tier adaptive step-size strategy. The method aims at joint sidelobe suppression and structural optimization under non-ideal conditions. A tri-domain stochastic error model is introduced to characterize position, phase, and amplitude perturbations, and atmospheric-effect-aware evaluation is incorporated for high-frequency propagation scenarios. For a six-ring sparse array, radius-only optimization achieves a PSLL of −24.4810 dB, corresponding to an average improvement of 5.809 dB over the initial array and an additional reduction compared with the baseline DDQN method. Extending the design to joint optimization of ring radii and element counts further reduces the PSLL to −30.629 dB, demonstrating the effectiveness of combined geometric and sparsity control. Monte Carlo simulations show that the optimized array maintains stable sidelobe performance under tri-domain stochastic perturbations, with an average PSLL of −26.758 dB. Further analysis using real meteorological data indicates that atmospheric effects introduce moderate variations in the normalized beam pattern, while the overall performance remains primarily influenced by stochastic perturbations under the considered modeling conditions. The proposed framework provides an effective and robust optimization approach for sparse concentric ring arrays in practical high-frequency scenarios.