# An Integrated Access and Backhaul Approach to Sustainable Dense Small Cell Network Planning
## 1 Introduction
cell planning problem, emphasizing the trade-offs between deployment costs, SINR/rate coverage, and the versatility of algorithms like NSGA-II in tackling these challenges. The benefits of multi-hop backhauling over its single-hop counterpart are highlighted through optimizing association and resource allocation for IABs, as discussed by [10]. Ref. [11] illustrates the efficacy of resource allocation for mmWave multi-hop backhaul networks by modeling it as a matching game, while [12] stresses the challenges and strategies for network planning, specifically in the face of obstacle blockages in mmWave access networks. The authors of [13] introduce the complexities tied to the densification of the 5G RAN, emphasising the significance of effective backhaul planning for the small cell. The quandary between fiber-optic backhaul (which demands a higher CAPEX) and self-backhauled stations (leading to increased interference) further deepens the intricacies. The authors present a solution in the form of a GA-based hybrid backhauling technique, which offers superior performance in terms of the total cost of ownership (TCO) compared to wholly wired or unwired approaches. Concluding the analysis with [14], this paper highlights the issues associated with the placement of IAB nodes, especially in geographically constrained or interference-prone areas. By introducing mmWave blocking-aware constrained deployment optimization techniques, the paper establishes that despite the limitations inherent in deployment optimization, the diligient planning of networks can substantially enhance the coverage of IAB systems. Most of the studies referred to above, however, simplify and relax the requirements and practical constraints for where IAB nodes and donors are deployed in practice. The placement of IAB nodes and donors is typically chosen from a set of given potential locations that are deemed feasible based on various factors, including physical constraints, government regulations, and the optimization of network performance. Also, sufficient bandwidth needs to be provided for wireless backhaul. Therefore, the motivation for this research emanates from the existing gaps in the current models of IAB network planning. High frequency mmwave bands increase the network's backhaul performance but introduce location and operational constraints due to significant pathloss and the necessity for precise IAB-node placements. Furthermore, the challenges are intensified as the IAB nodes must be selectively deployed from pre-defined potential locations, and the utilization of high-frequency mmWave necessitates stringent line of sight (LoS) conditions to mitigate the effects of pathloss. Traditional linear programming approaches with wireless backhaul, although effective, often overlook the intricate geographical constraints and the impacts of the significant mmWave pathloss. Our work aspires to solve these challenges by introducing an innovative, geographically aware optimization methodology that incorporates potential node locations, offering a comprehensive solution to the existing challenges in IAB deployments. In this study, we address the challenges in IAB network planning, particularly in light of the location and number constraints necessitated by the IAB donors' wired connectivity to the core network. Recognizing the pivotal role of donor quantity and potential node locations, our research makes the following contributions:  
* We use a mixed-integer linear programming (MILP) formulation to an architect-optimized single-hop network in scenarios characterized by a dense distribution of donors. This provides a robust framework to adepthly navigate IAB node deployment challenges. * We formulate a non-convex multi-hop network problem in scenarios with sparse donor distribution. To overcome the inherent complexities of this formulation, we introduce a modified greedy algorithm, demonstrating its efficacy in the face of the non-trivial, NP-hard nature of multi-hop IAB deployments.