Infovista | RAN planning best practice | eBook

Explore best practices we see delivering results with leading operators worldwide, structured into a practical, operator-focused framework for modern RAN Planning.

RAN planning best practice: The three building blocks to future- proof your network planning

Contents

Foreword: Why future-proof RAN planning matters now

Introduction: The evolution of RAN planning in the 5G era

Building Block 1: AI-driven accuracy in RAN planning

5 5 6 6 7 7 8 9

From static equations to learning models

Key architectural elements of AI-driven accuracy What AI-driven accuracy enables in practice Building Block 2: Advanced propagation models Why the propagation architecture must evolve Elements of an advanced propagation framework The shift to pre-calibrated propagation models and curated geodata as-a-service Building Block 3: Streamlined and intuitive processes in RAN planning From siloed expert-only workflows to planning-as-a-service Cloud-native planning as an enabler of process modernization The business case for a modern planning approach Geodata – when you need it, where you need it Propagation – pre-configured for accuracy IT overheads – cloud collaboration and scale

11 11

13 13 13 13 14

Support and maintenance – SaaS efficiencies

RAN planning best practice – 10 key KPIs

Conclusion: A roadmap to future-proof RAN planning

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The role of the radio network planner has never been more pivotal, or more complex. 5G rollouts, spectrum auctions, urban densification, private networks, FWA, Open RAN and cloud-native cores are converging into a single, relentless question for every operator: are we planning our networks in a way that will still make sense three to five years from now? Why future-proof RAN planning matters now Foreword

For planning leaders, the challenge is no longer simply “where do we build next?”, but “how do we plan smarter?” How do we make decisions that anticipate continuous evolution, withstand internal scrutiny, support enterprise business models and protect every dollar of CAPEX and OPEX? At Infovista, we see a clear pattern across the most advanced operators. The ones who are genuinely future-proofing their RAN have mastered three interconnected building blocks: 1. AI-driven accuracy – making RF predictions continuously trustworthy 2. Advanced propagation models – unifying all environments, technologies and use cases in one architectural framework 3. Streamlined and intuitive processes –

operationalizing planning as an agile, collaborative, cloud-native service

This eBook explores each of these building blocks in depth. It is written for those with responsibility for RAN and radio planning strategy in both public and private 5G networks. Our goal is simple: to share a practical blueprint for modernizing your planning capability so your next wave of 5G investments is not just deployed, but delivers against your business objectives.

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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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AI-driven accuracy in RAN planning Building Block 1

Only AI/ML-based RAN planning and design optimization can enable operators to reduce of the number of base stations without impacting their user experience. From static equations to learning models Legacy models such as Okumura-Hata, COST or Walfisch-Ikegami remain useful references, but they share constraints: • They assume environments can be sufficiently abstracted into a few tuned parameters • They require per-market, per-morphology calibration campaigns • They are poor at capturing complex 3D effects in dense urban or indoor/outdoor transitions • They struggle to absorb new data sources — crowdsourced data, call traces, rich GIS — at scale An AI-driven approach turns this problem on its head. Instead of a fixed formula with a few tweakable constants, machine learning trained on large volumes of real RF measurements and high-resolution 3D geodata means RAN planning processes become smarter, learning how radio behaves in different environments. The result is a modern AI-powered RF planning and optimization approach that is not static, but adaptive.

Accuracy has always been part of the fundamentals of RF planning. But where in relatively simple legacy macro networks “accurate enough” sufficed, in the more complex world of planning and deploying 5G, accuracy has become both more complex and more valuable. When designing dense urban C-band layers, industrial private networks, ports, airports or campus networks, small systematic errors in prediction rapidly translate into very real impacts. From unnecessary sites and uncovered indoor areas to missed SLAs and expensive post-deployment optimization, the costs of inaccurate RF planning are all too real. For example, independent research by analyst firm Mobile Experts reveals an improvement of 1 dB precision in RSRP leads to roughly 0.4 bps/Hz overall improvement in spectral efficiency; with the same numbers of radios and the same antennas, this is the equivalent of a 24% increase of capacity . For a nationwide U.S. mobile network, this potentially equates to approximately $2 billion over a 10-year period through a reduction in the number of base stations and smalls cells deployed.

Accurate RAN planning saves billions By Mobile Experts

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Key architectural elements of AI-driven accuracy AI-driven RF planning therefore changes not only what we model but how we think about accuracy itself. Precision needs to be scalable, repeatable and self-improving. 1. Pre-calibrated intelligence. This means planners no longer start from a blank slate but instead can draw on vast, diverse datasets gathered from real-world morphologies. By beginning with a propagation engine that already understands the fundamentals of RF behavior across urban, suburban and rural environments, the model “knows” how signals behave around city blocks or forested valleys before a single local calibration point is added. 2. Unified 3D context. Rather than relying on simplified clutter categories, a modern network planning solution reads the environment in full three-dimensional detail: terrain elevation, building geometry, rooftop levels, bridges, vegetation density and even foliage states. This matters especially for 3.5 GHz, C-band and mmWave frequencies where reflections, diffraction and obstructions dominate. 3. Continuous learning loop. The model is not frozen in time, it evolves. Every new drive test, crowdsourced measurement, walk-test trace or call-record KPI is an opportunity for refinement. Over time, prediction errors shrink and the model becomes more representative of each operator’s unique network DNA. 4. Cloud-native performance. Instead of being constrained by local compute or desktop software, planners can execute nationwide simulations, multiple “what-if” scenarios, and frequency overlays in parallel. This elasticity is what makes AI-driven accuracy operationally viable.

What AI-driven accuracy enables in practice

SLA confidence: When serving industrial or mission-critical customers deploying private 5G networks, they expect SLAs to be met on first activation, not after weeks of optimization. AI- driven models give operators the assurance to commit to performance guarantees and deliver against them. Accelerated deployment: When every simulation starts closer to reality, planners spend less time tuning and more time exploring scenarios. They can test multiple hardware or spectrum configurations in the same window that previously allowed only one. Team productivity increases, deployment time decreases.

AI-driven accuracy, therefore, will have a tangible and measurable impact on both operational and financial performance, through faster planning cycles, fewer errors and a tighter link between investment and outcome. But what does this mean in practice? Smarter macro and densification decisions: With more reliable signal and throughput predictions, planners can design networks that are right-sized from day one. The days of “just in case” overbuilding are replaced by data- backed certainty, reducing both site count and rollout cost.

THE TAKEAWAY

AI-driven accuracy transforms planning from a reactive, manually tuned exercise into a proactive, continuously learning process that supports measurable business outcomes. This delivers both engineering confidence and financial efficiency: fewer sites, faster calibration and closer alignment between predicted and delivered performance. Across a national network, that can mean millions in avoided CAPEX and a more sustainable, energy-efficient footprint.

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Advanced propagation models Building Block 2

Once the foundation of AI-driven accuracy is laid, the next question is: can your propagation architecture keep up with the full diversity of your network strategy? Advanced propagation modeling is about more than raw accuracy. It is about creating a unified, scalable modeling fabric that can handle every frequency, topology and use case without fragmentation. Why the propagation architecture must evolve In the early LTE era, propagation modeling could afford to be simple. A single calibrated model, perhaps with a few clutter categories and terrain corrections, could serve most needs. But today’s 5G RAN is deliberately heterogeneous by design, spanning frequencies, form factors and use-cases that behave in fundamentally different ways. Operators now find themselves simultaneously: • Rolling out 3.5 GHz capacity layers across dense urban cores • Evaluating or deploying mmWave hotspots for ultra-high throughput • Extending coverage with sub-1 GHz low- band layers • Designing FWA overlays to deliver fixed broadband without fiber • Building private 5G networks across ports, campuses, and logistics hubs • Preparing for RedCap-based IoT and new device categories that blend mobility with efficiency

Each of these environments follows different physical laws. Mid-band propagation is dominated by diffraction and reflection from clutter; mmWave depends on line-of-sight (LoS) and suffers rapid attenuation; indoor and campus networks introduce metal-rich multipath environments that challenge even the best ray-tracers. As a result, project-based desktop planning and their traditional propagation workflows have struggled to keep up. Many organizations still rely on fragmented toolchains: one model for macro, another for small cells, a different one for indoor systems, and ad-hoc spreadsheets or CAD overlays for private network design. Each of these silos demands separate calibration, mapping and validation. However, this fragmentation breeds inconsistency, with teams debating whose model is “right,” planning cycles stretch and cross-market comparisons become impossible. More critically, it erodes confidence at executive level. When CFOs or enterprise clients ask, “How do you know this design will perform as promised?”, the answer shouldn’t depend on which tool or team produced it. The solution is architectural. To meet the complexity of modern networks, operators need an advanced propagation framework – one cohesive engine that scales across all frequencies, morphologies and use-cases.

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Elements of an advanced propagation framework To understand in more detail, we need to first understand four key pillars that together form a framework for advanced propagation. 1. Native 3D modeling

True accuracy begins with the environment — buildings, rooftops, bridges, tree canopies, even in-leaf/out-leaf vegetation. 3D realism is essential for higher- frequency planning (C-band, mmWave) where line-of-sight and multipath interactions dominate. Up-to-date, high-resolution geodata sources must enrich this modeling with global consistency and automated refresh cycles.

2. API-enabled scalability

Through elastic, cloud-native infrastructure, propagation can now operate at global scale. Large-area simulations that once required overnight processing can run in parallel across multiple compute nodes. Operators can model nationwide networks, entire cities or industrial sites at high precision without overloading local infrastructure. This scalability supports rapid “what-if” scenario analysis, portfolio- wide densification studies or enterprise network tenders.

3. Multi-use-case versatility

A single propagation fabric should support macro, small cell, indoor, FWA, private/ campus and RedCap IoT networks. Planners no longer need bespoke setups; instead, they need predefined templates optimized for their use case and frequency band, running them instantly through a cloud-native environment. The result: faster reuse, consistent accuracy and simplified governance.

4. Consistent data model and governance

Because the same cloud-based propagation API can underpin all simulation workloads, every scenario should draw from a shared data model and common parameters. This means when two regions or teams compare coverage maps, they are comparing like-for-like. It also enables automation, allowing propagation to feed directly into cost modeling, optimization and assurance platforms.

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The shift to pre-calibrated propagation models and curated geodata as-a-service

High-quality geodata and well-calibrated propagation models are the foundation of

accurate network design. Yet across the industry, sourcing, validating and tuning these datasets remains one of the most time-consuming — and costly — steps in the planning process. Cloud- based propagation modeling is now changing that equation. This is why Infovista and Google Cloud have partnered to transform wireless network planning and deliver a game-changing SaaS solution. Through Google Cloud’s Propagation API, planners and service providers can access an advanced, pre-calibrated propagation model paired with high-quality, frequently updated global geodata. Planners no longer need to assemble their own datasets or run extensive local calibration campaigns before achieving production-grade accuracy.

What does the Infovista + Google Cloud partnership deliver?

The model is pre-tuned using large-scale measurement datasets and refined with Google’s geospatial information — terrain, buildings, vegetation and other features — updated regularly across regions. Because the API is delivered as a service, organizations can subscribe for ongoing use or activate it on demand for time-limited or data-sensitive projects, adding flexibility and cost control to what was once a fixed, capital- intensive process.

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This approach represents a clear shift in how RF planning can be done: • From costly geodata procurement to on- demand access via a subscription that includes curated, high-resolution datasets • From complex, site-specific calibration to pre-calibrated models that are ready for immediate use across multiple frequency bands • From highly specialized tools to intuitive, accessible cloud interfaces that enable broader collaboration between radio engineers, systems integrators and enterprise teams

Technically, the Propagation API delivers radio- signal path-loss calculations with optimized accuracy by combining: • Comprehensive global geodata including precise representations of terrain, buildings and vegetation • Specialized wireless propagation algorithms refined for regional and spectrum-specific environments, including CBRS and mid-band 5G • Pre-calibration across multiple frequency bands , removing the need for repetitive local tuning

This makes advanced modeling achievable even for organizations without deep technical internal resources, for example smaller MNOs or enterprises and private network owners. Instead of building and maintaining their own calibration pipelines, planners can use a globally maintained, continuously improving model as their baseline and focus their expertise where it adds most value: scenario design, business case analysis and rollout strategy. This lowers the barrier to entry for accurate network design, whether for a national rollout, a private 5G campus or a single FWA coverage analysis.

THE TAKEAWAY

The RF propagation architecture is the backbone of planning intelligence. Evolve it from a patchwork of models into a single, learning-based framework and every design decision — across bands, markets and teams — becomes faster, more reliable and easier to defend. Cloud-based propagation services represent a new paradigm for RF planning: always current, instantly scalable and accessible to organizations of every size. What once required months of manual data preparation and calibration can now be achieved within hours, empowering the entire ecosystem, from operators to enterprises, to plan with confidence and speed.

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The third building block, streamlined and intuitive processes, is about transforming RAN planning from a specialist, desktop-bound function into a cloud-native, collaborative service that underpins fast, confident decision-making. From siloed expert-only workflows to planning-as-a-service In many organizations today, planning is still siloed and linear. Project files exchanged by email, manual imports of geodata, locally installed tools that only a handful of experts can operate, and long cycles to answer seemingly simple questions such as “What if we densify this district?” or “What would it take to serve this new campus deal?”. This model cannot keep pace with dynamic 5G deployment, enterprise sales cycles or cross- functional governance. What’s needed is planning-as-a-service: centrally governed, accessible to distributed teams, automated where possible, and directly connected to execution systems. Streamlined and intuitive processes in RAN planning Building Block 3

Cloud-native planning as an enabler of process modernization As networks evolve, so too must the tools and workflows used to design them. Traditional, desktop-based planning systems, often managed by small, specialized teams, are increasingly giving way to cloud-native planning environments that enable collaboration, automation and scale. In this new cloud-native operating model, RAN planning is transformed into a continuous, connected service rather than a series of isolated engineering tasks. It brings together data, models, teams and decisions into shared workflows.

THE TAKEAWAY

Cloud-native planning modernizes not only the tools, but the culture of RAN design. By centralizing data, automating workflows and opening collaboration to wider teams, it transforms network planning from a specialized engineering function into a transparent, agile process that keeps pace with the speed of 5G and beyond.

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Delivered as software-as-a-service (SaaS), cloud-native planning solutions such as Planet Cloud provide a consistent experience for all stakeholders, including RF engineers, enterprise teams and senior decision-makers, wherever they are. Planners no longer need to manage software versions, local installs or hardware limitations. Instead, they log into a shared environment that is always current, always aligned and always ready for collaboration. This model of cloud-native collaboration allows planning to move at the pace of the business. Scenarios can be developed, reviewed and approved in days rather than weeks, and teams across multiple regions or organizations can work in parallel rather than sequentially.

The evolution of 5G network planning Streamlining 5G planning with use case-centric workflows built on a cloud-native architecture

THE TAKEAWAY

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The business case for a modern planning approach As discussed above, modernizing RAN planning with fit-for-purpose solutions can drive CAPEX savings. AI-driven RAN planning can improve accuracy, saving potentially millions on network buildout. But the business rationale is about more than reducing CAPEX. Cloud-native, AI-driven RAN planning, with its pre-calibrated propagation models and on-demand geodata can also drive significant savings in both time and money in the RAN planning process itself. These OPEX savings can be broadly bucketed as follows:

Geodata – when you need it, where you need it Geodata delivered as a service through APIs integrated directly into the RAN planning software means organizations can access the geodata they need, when they need it. What was once a fixed, capital-intensive process, now becomes a flexible, cost-controlled process. Geodata is no longer purchased, it is subscribed to. In a time-limited project – for example, the planning of a single network cluster for a mobile network operator by a 3rd party – the economics of acquiring, installing and validating geodata simply don’t stack up. But subscribing to validated, accurate geodata for just that specific area, for only as long as the project requires, changes the OPEX equation. Propagation – pre-configured for accuracy Alongside geodata, well-calibrated propagation models are the foundation of accurate network design. But complex calibration, often spanning multiple frequencies, requires dedicated time from valuable RF engineering resources.

Cloud-based propagation modeling, with its specialized wireless propagation algorithms and pre-calibration using massive datasets across multiple frequency bands, means time- consuming and costly manual propagation model tuning is no longer required. IT overheads – cloud collaboration and scale Planning today’s complex networks is by its very nature a compute-intensive process, with its vast datasets and AI modeling. Legacy desktop planning tools are increasingly not fit-for-purpose. For example, in a scenario where a small regional operator is planning and optimizing a nationwide network, that could mean a team of 10+ RF engineers, each bringing their own IT overhead and siloed workflows. Intensive RF modeling can take advantage of cloud compute power, be it in a private, hybrid or public cloud, significantly reducing planning time. Improved collaboration between teams, all working in parallel with the same pre-calibrated propagation models and validated data sets, improves the efficiency of the end-to-end planning process.

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Support and maintenance – SaaS efficiencies The benefits of the scale and efficiency of the cloud-native planning architecture compared to a desktop-based planning approach is similarly visible in reduced time and cost of support and maintenance. Legacy desktop-based planning approaches come with the need for IT Admin support to ensure security patches are installed, software is updated and systems are running smoothly, not to mention the added cost of recurring annual software licenses. In contrast, with a cloud-based approach, the IT time and cost spent installing, upgrading and maintaining a software solution is no longer required, and there is no annual software support and maintenance costs to be paid. Taken together, the migration from a legacy desktop-based RAN planning product to a cloud-native, SaaS-based solution including geodata and tuned models, can directly translate into healthy ROI due to its simplicity and requirement for no internal IT administration.

Time savings of between 17-19% are typical across use cases. For a network planning services company engaged in a short-term project for the planning of a single network cluster, this 19% time saving could shave days and even weeks off the delivery, creating a significant competitive advantage. For a company such as chemical producer planning a private mobile network across multiple plant locations, on-demand access to validated 3D and 2D geodata and pre-calibrated propagation models can drive OPEX savings in excess of $100,000 over the lifecycle of the network. Similar is true for RAN planning teams in small regional mobile operators through to network planning services companies working on large- scale projects. The numbers will be different but the results the same – faster, more accurate, more efficient network planning that delivers against clear business KPIs.

Build your own business case for modern RAN planning Infovista has developed a business case calculator to enable you to see the efficiency savings and ROI you can realize from migrating to a modern, cloud-native RAN planning approach. Ask Infovista for your ROI calculation today.

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RAN planning best practice — 10 key KPIs The performance of RAN planning workflows should be measured with the same discipline applied to network testing processes, tracking efficiency, agility and alignment. Tracking these KPIs helps planning leaders quantify progress as they move toward cloud-native operations. Over time, improvements in these measures translate into faster rollout, reduced OPEX and a stronger alignment between technical planning and business decision-making.

Key KPIs to consider include:

Accuracy indicators such as prediction error for RSRP, RSRQ and throughput reveal the direct technical gain from the AI model. Improving accuracy indicators can directly translate into CAPEX savings simply by deploying fewer — but better placed — sites. Improved accuracy in RAN planning can deliver more capacity and reduce CAPEX spending, with operators able to choose a position that takes advantage of both benefits to some degree. In a manual world, measuring time-to-calibrate, simulate and validate new environments or frequency bands might take several weeks; with an AI-pretrained model, it can fall to a matter of days or even hours. A shorter turnaround time from initial request to delivery of a validated design indicates faster response to commercial or operational needs. Reducing this cycle allows planning teams to support more parallel initiatives, accelerating new site rollouts, enterprise proposals and network optimization programs. As accuracy improves, the operational footprint of measurement campaigns should decline. Field validation volume and cost become a tell-tale efficiency metric, creating savings in site visits and drive testing as fewer field verifications are needed. A unified propagation engine supports a higher scenario reuse rate, with standard templates and calibrated models able to be applied across markets or use cases. This both increases productivity and ensures consistent assumptions across planning teams, markets, and vendor ecosystems. Lower engineering hours reflect automation, process maturity and effective use of templates. It demonstrates that planners are focusing on higher-value analysis rather than repetitive manual setup, directly improving productivity and reducing OPEX. As the use of automation collaboration and cloud scalability increases, this tracks the ability of teams to handle a greater diversity of projects, without expanding headcount or compromising quality. Advanced frameworks enforce a single data model and calibration baseline across all environments. Variance between regional models (i.e. how much propagation accuracy or performance diverges between teams or territories) is a key metric. A low variance builds trust at executive level, assuring CFOs and enterprise clients that coverage forecasts are comparable and reliable across regions. Cloud-native propagation enables scaling up or down compute resources based on workload. Track cost per completed simulation and compute utilization rate to measure elasticity and ensure planners can deliver more insight per dollar spent, aligning planning performance with financial accountability. Metrics such as model version control adherence or traceable parameter changes reflect process maturity. Transparent, auditable models reinforce cross-departmental trust and enable confident engagement with regulators, partners and enterprise customers. Measuring how quickly planning outputs move from engineering to executive sign-off can indicate better collaboration, transparency and trust between engineering, finance and leadership — ensuring that well- informed designs move rapidly into execution. This KPI bridges planning and reality. When forecasts align closely with delivered results, it validates the integrity of the planning process and strengthens stakeholder confidence in future investment decisions. The metric includes correlation between predicted and actual deployment outcomes, covering both financial (CAPEX/OPEX) and technical (coverage, capacity, QoE) dimensions.

Accuracy indicators

Time-to-scenario

Field test volume and cost

Scenario reuse and template efficiency

Engineering hours per scenario

Cross-market consistency

Cloud efficiency and cost elasticity

Governance and auditability

Stakeholder approval cycle time

Accuracy of cost and performance forecasts

These 10 KPIs can form the backbone of a vendor evaluation and RFP, with potential suppliers asked to demonstrate how their solutions improve each metric, and to provide references or benchmarks where these benefits have already been realized.

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The networks you are planning today must support far more than traditional mobile broadband. They are expected to underpin industrial automation, smart cities, critical communications, immersive applications and entirely new business models. That reality demands a new standard for how planning is done. A roadmap to future-proof RAN planning Conclusion

This eBook has outlined three building blocks that, together, define that standard:

AI-driven accuracy – so your RF predictions and investment cases are grounded in continuously learning models that reflect real-world performance

Advanced propagation models – so one architectural framework can support every band, environment and use case, enabling consistency and speed

Streamlined and intuitive processes – so planning becomes a cloud-native, collaborative, automated service that accelerates decisions and aligns with your commercial strategy

Infovista’s VistaPlan — Planet AIM, Planet Cloud and the surrounding ecosystem — has been designed with exactly these principles in mind. By adopting this approach, operators can de-risk their 5G and private network investments, reduce time-to-market and build networks that perform as promised, not just on paper but in the field. Even if your current planning environment is based on established desktop tools, these three building blocks provide a roadmap for evolution — whether through co-existence, phased migration or greenfield deployments.

To begin this journey to future-proof RAN planning requires the right tools and processes to ensure a smooth low-risk process. Infovista has performed many successful migrations for operators from legacy desktop-first, manual, expert-reliant RAN planning to a fit-for-purpose approach built on AI-driven modeling, cloud-native workflows, Google geodata and propagation services, and KPI- driven processes. Beginning with digital map conversion and propagation model migration and culminating in project migration, operators can take the first steps to modernizing their RAN planning capabilities, fit for the increasingly AI-driven era.

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SNAPSHOT CASE STUDY TIER-1 OPERATOR ALIGNS RAN PLANNING WITH AUTOMATION STRATEGY A leading Tier-1 Asian operator set a clear strategic goal: make automation central to its network planning and optimization. However, its incumbent RF planning tool could not provide the open APIs and integration hooks needed to support this vision. By introducing Planet alongside Planet Engine, the operator established a modern planning backbone with rich APIs that could plug directly into its existing automation and orchestration platforms. This enabled:

End-to-end automation of key planning workflows that previously required manual intervention

Programmatic access to propagation, optimization and design functions for use in internal toolchains and workflows

A defined evolution path to Planet Cloud, fully aligned with the operator’s broader cloud strategy

THE RESULT Planning is no longer a standalone desktop activity, but an integrated, automated capability within the operator’s wider network lifecycle — something that simply wasn’t achievable with their legacy tools.

Go further with additional resources

AI-powered network planning and design

What’s new in cloud- based RF planning

AI-enabled network & CX intelligence

Discover VistaPlan, with ML- based propagation modeling, 3D simulations, and automation to invest smarter and design high-performance networks.

Watch this webinar to discover the enhancements across automated optimization, workflows, technology support, and collaboration that your team can leverage with Planet Cloud.

Discover VistaOne, an open, AI‑enabled platform unifying

network and customer experience intelligence.

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