A Permutation-Invariant and Variable-Dimension Data-Driven Evolutionary Algorithm for Indoor Antenna Layout Optimization

This article focuses on indoor antenna layout optimization, aiming to minimize the number of antennas while maximizing the network coverage rate by optimizing the number and locations of antennas. This optimization problem exhibits three key characteristics: 1)Expensive evaluation:Indoor scenarios often require expensive propagation models to evaluate the network coverage rate; 2)Permutation invariance:Rearranging antenna locations does not change the network coverage rate; and 3)Variable dimension:The variable number of antennas leads to variable-dimensional solutions. Although surrogate models can be adopted for existing data-driven evolutionary algorithms to replace expensive propagation models, thereby reducing evaluation costs, they overlook permutation invariance and struggle to handle variable-dimensional solutions. To this end, this article proposes a novel$d$ata-driven$e$volutionary algorithm for indoor$a$ntenna$l$ayout optimization, called DEAL. Since the indoor antenna layout consists of a set of antenna locations, DEAL leverages a neural network that operates on sets as a surrogate model. For this surrogate model, the inherent invariance of set elements to permutations is leveraged, enabling DEAL to effectively achieve permutation invariance. Furthermore, due to the variable number of set elements, DEAL can adapt to variable-dimensional solutions. DEAL also incorporates a clustering-based strategy to generate initial antenna layouts and a local search method to further improve the performance of promising solutions. Extensive experiments on eight test scenarios demonstrate that DEAL outperforms five other algorithms in terms of the network coverage rate.