A Parallel Zeroth-Order Framework for Efficient Cellular Network Optimization

Network optimization plays a crucial role in wireless communications. However, the optimization of contemporary 5G networks is challenging due to its black-box nature and huge searching space. To address these challenges, this paper introduces efficient zeroth-order (ZO) algorithms and a parallel framework for optimizing large-scale networks. By leveraging the gradient-based searching strategy, the proposed algorithms, namely ZO projected gradient descent (ZO-PGD) and ZO block coordinate projected gradient descent (ZO-BCPGD), can significantly improve optimization quality and computational efficiency. Both algorithms guarantee a convergence towards stationary points under mild conditions, eliminating inherent errors in traditional ZO methods. We further propose a parallel framework for optimizing the network parameters in a simultaneous manner. By partitioning the entire network into manageable subnetworks, the original network optimization problem is reformulated as a consensus optimization problem and tackled in parallel using the penalty dual decomposition (PDD) method. We have also designed a tailored size-constrained grid clustering algorithm for network partitioning to ensure load balance among parallel working nodes. The efficacy of our proposed schemes is verified by extensive numerical results. Our ZO algorithms outperform existing methods in both computational efficiency and solution quality. Moreover, the parallel framework significantly reduces execution time without sacrificing network performance.