| 6G networks will rely on highly heterogeneous and disaggregated infrastruc-tures, while access networks must meet stringent coverage and performance requirements while incorporating Integrated Sensing and Communication (ISAC) as a native capability, introducing challenges in sensing performance, coverage, and resource requirements. AI/ML techniques are expected to play a key role in enabling adaptive and efficient network operation. To this end, transport networks will need to support diverse deployments, including mac-ro and dense micro radio units as well as distributed and cloud-based RAN architectures (D-RAN, O-RAN), while converging fronthaul, backhaul, and sensing traffic over multi-technology wireless and optical infrastructures. Such complex deployments depend on geography, feasibility, traffic de-mand, technologies’ availability and scalability, and cost. Therefore, early-stage techno-economic evaluation of alternative deployment options is es-sential to identify cost drivers, optimize design choices, and assess sensing-related architectural options, enabling sustainable and economically viable 6G network planning. This paper provides proposes a methodology to per-form technoeconomic analysis of ISAC-enabled 6G networks and provides insights on technoeconomic factors affecting deployment options of network sensing in 6G networks. |
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