A Multiobjective Evolutionary Algorithm for Network Planning in In-Building Distributed Antenna Systems

Deploying in-building distributed antenna systems (IB-DAS) is a crucial step towards providing ubiquitous wireless services. In this paper, we study the multiobjective network planning problem, aiming to minimize both construction costs and average power loss. The main challenge in solving this problem is efficiently representing the network structure. To address this, we encode the network structure as a spanning tree, with the root node connecting to the signal source, and leaf and non-leaf nodes representing all floors and power devices, respectively. Compared to existing encodings, this tree encoding offers several advantages, including improved locality and the ability to easily generate valid solutions. Additionally, we propose a tree-encoded evolutionary algorithm called TMOEA. Since the standard operators cannot be applied, we devise problem-specific crossover and mutation operators to produce tree-encoded solutions. Moreover, the Tchebycheff approach is employed to update solutions. Comprehensive experiments on 11 test instances with up to 30 floors demonstrate that the proposed algorithm outperforms four compared algorithms in terms of both the hypervolume indicator and the inverted generational distance indicator for each test instance.