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Intelligent reflecting surface (IRS) can be densely deployed in complexenvironment to create cascaded line-of-sight (LoS) paths between multiple basestations (BSs) and users via tunable IRS reflections, thereby significantlyenhancing the coverage performance of wireless networks. To achieve this goal,it is vital to optimize the deployed locations of BSs and IRSs in the wirelessnetwork, which is investigated in this paper. Specifically, we divide thecoverage area of the network into multiple non-overlapping cells and decidewhether to deploy a BS/IRS in each cell given a total number of BSs/IRSsavailable. We show that to ensure the network coverage/communicationperformance, i.e., each cell has a direct/cascaded LoS path with at least oneBS, as well as such LoS paths have the average number of IRS reflections lessthan a given threshold, there is a fundamental trade-off with the deploymentcost or the number of BSs/IRSs needed. To optimally characterize thistrade-off, we formulate a joint BS and IRS deployment problem based on graphtheory, which, however, is difficult to be optimally solved due to thecombinatorial optimization involved. To circumvent this difficulty, we firstconsider a simplified problem with given BS deployment and propose the optimalas well as an efficient suboptimal IRS deployment solution to it, by applyingthe branch-and-bound method and iteratively removing IRSs from the candidatelocations, respectively. Next, an efficient sequential update algorithm isproposed for solving the joint BS and IRS deployment problem. Numerical resultsare provided to show the efficacy of the proposed design approach andoptimization algorithms for the joint BS and IRS deployment. The trade-offbetween the network coverage performance and the number of deployed BSs/IRSswith different cost ratios is also unveiled.

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