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Introduction
The evolution of RAN planning in the 5G era RAN planning used to be an art. Senior engineers tuned empirical models, adjusted constants based on experience, ran a These pressures expose the cracks in legacy approaches such as manual calibration, fragmented tools per band and per
limited set of coverage plots, and validated assumptions with drive tests. That approach was manageable in a world of a few bands, predominantly outdoor macro sites and best- effort mobile broadband. 5G has changed the game. Operators are now planning and operating networks that are simultaneously: • Multi-band : sub-1 GHz, 1.8–2.6 GHz, 3.5 GHz and mmWave • Multi-layer : macro, micro, pico, indoor systems, neutral host, repeaters • Multi-domain : public networks, dedicated and hybrid private 5G, campus networks, mission-critical use cases • Multi-business-model : consumer eMBB, FWA, RedCap IoT, industrial automation, network slicing At the same time, expectations have hardened. Enterprise customers demand connectivity SLAs for business-critical applications. Regulators expect operators’ coverage commitments to be met precisely, with the real threat of sanction and penalties if they are missed. Finance teams expect tight linkage between models, investments and outcomes. And planning teams are expected to do all of this faster, with fewer resources.
environment, spreadsheets, desktop-bound simulations, slow iteration, and a lack of unified, defensible KPIs.
Or to put it more starkly, if you’re still manually calibrating empirical models in a desktop tool, you’re leaving money on the table. To future-proof your RAN planning capability, three shifts are essential: 1. From manual calibration to AI-driven accuracy that continuously learns from real data 2. From disconnected, environment-specific models to advanced propagation architectures that span all scenarios 3. From siloed, offline workflows to streamlined, cloud-native processes that integrate planning with business decision-making In the following chapters we explore each building block in turn, with practical recommendations and illustrative use cases that set out how operators can de-risk and accelerate their 5G strategies. If you’re still manually calibrating empirical models in a desktop tool, you’re leaving money on the table.
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