# Joint Base Station and IRS Deployment for Enhancing Network Coverage: A Graph-Based Modeling and Optimization Approach

arXiv: https://arxiv.org/abs/2209.09828 · HTML: https://mlsanity.com/paper/2209.09828/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2022.

## Built on by (most cited first, 5 of 5)

- [GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks](https://mlsanity.com/paper/2412.08296/) (2024)
- [Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization](https://mlsanity.com/paper/2408.03631/) (2024)
- [MobiGPT: A Foundation Model for Mobile Wireless Networks](https://mlsanity.com/paper/2509.18166/) (2025)
- [Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting](https://mlsanity.com/paper/2507.19513/) (2025)
- [Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic](https://mlsanity.com/paper/2511.17532/) (2025)

## Abstract (arXiv)

Intelligent reflecting surface (IRS) can be densely deployed in complex environment to create cascaded line-of-sight (LoS) paths between multiple base stations (BSs) and users via tunable IRS reflections, thereby significantly enhancing the coverage performance of wireless networks. To achieve this goal, it is vital to optimize the deployed locations of BSs and IRSs in the wireless network, which is investigated in this paper. Specifically, we divide the coverage area of the network into multiple non-overlapping cells and decide whether to deploy a BS/IRS in each cell given a total number of BSs/IRSs available. We show that to ensure the network coverage/communication performance, i.e., each cell has a direct/cascaded LoS path with at least one BS, as well as such LoS paths have the average number of IRS reflections less than a given threshold, there is a fundamental trade-off with the deployment cost or the number of BSs/IRSs needed. To optimally characterize this trade-off, we formulate a joint BS and IRS deployment problem based on graph theory, which, however, is difficult to be optimally solved due to the combinatorial optimization involved. To circumvent this difficulty, we first consider a simplified problem with given BS deployment and propose the optimal as well as an efficient suboptimal IRS deployment solution to it, by applying the branch-and-bound method and iteratively removing IRSs from the candidate locations, respectively. Next, an efficient sequential update algorithm is proposed for solving the joint BS and IRS deployment problem. Numerical results are provided to show the efficacy of the proposed design approach and optimization algorithms for the joint BS and IRS deployment. The trade-off between the network coverage performance and the number of deployed BSs/IRSs with different cost ratios is also unveiled.