AI-Powered Network Coverage Planning System

An RF engineer gets a task: plan LTE coverage for a 1000 sq km area. Three months, limited budget. Using the classic Okumura-Hata model, after deployment 30% of the area shows RSRP below -110 dBm — uncovered. Subscribers complain, NPS drops. Our AI network planning system automates network coverage planning with ±2 dB accuracy, reducing measurement vehicle runs by up to 40%. This saves 60–75% of the drive test budget — tens of millions of rubles (e.g., $300,000) annually for a mid-sized operator. Our solution saves an operator an average of $300,000 annually in drive test costs alone. Contact us to learn how it works on your network.

How AI Predicts Radio Coverage

ML correction of the physical model is key. We take the Okumura-Hata model as a baseline and train gradient boosting (LightGBM) on the difference with real measurements, incorporating shadow fading margin and interference-limited scenarios.

import numpy as np
import pandas as pd
from lightgbm import LGBMRegressor

class PropagationMLModel:
    """
    ML corrections to the physical signal propagation model.
    Data: drive test + CQT signal level measurements
    """

    def build_features(self, measurement_df, dem_raster, building_footprints):
        """
        measurement_df: GPS + RSRP/RSSI measurements
        dem_raster: digital elevation model (SRTM/Copernicus 30m)
        building_footprints: OSM or cadastre
        """
        df = measurement_df.copy()

        # Terrain features
        df['elevation'] = self._sample_dem(df[['lat', 'lon']], dem_raster)
        df['elevation_diff'] = df['elevation'] - self._sample_dem_bs(df['bs_id'])
        df['terrain_roughness'] = self._calculate_roughness(df[['lat', 'lon']], dem_raster, radius_m=500)

        # Building features
        df['building_density_500m'] = self._building_coverage(df[['lat', 'lon']],
                                                               building_footprints, radius=500)
        df['mean_building_height_200m'] = self._mean_height(df[['lat', 'lon']],
                                                             building_footprints, radius=200)

        # Geometry relative to BS
        df['distance_to_bs'] = self._haversine_distance(df[['lat', 'lon']], df['bs_location'])
        df['angle_to_bs'] = self._bearing(df[['lat', 'lon']], df['bs_location'])
        df['los_probability'] = self._estimate_los(df, building_footprints)

        # Physical model as baseline feature
        df['okumura_hata_pred'] = self._okumura_hata(df)

        return df

    def train(self, features_df, target='rsrp_dbm'):
        feature_cols = [c for c in features_df.columns
                       if c not in [target, 'lat', 'lon', 'timestamp', 'bs_id']]
        self.model = LGBMRegressor(n_estimators=500, learning_rate=0.03, num_leaves=64)
        self.model.fit(features_df[feature_cols], features_df[target])
        return self

Features include terrain, buildings, distance, and angle to BS. After training, the model predicts RSRP at any point — replacing up to 75% of measurement vehicle runs. This is an example of how AI for telecom solves RSRP prediction with ML propagation models, achieving RMSE 2.1 dB vs 6 dB for traditional methods.

Why 5G mmWave Needs AI

Millimeter waves (26/28 GHz) have huge capacity gains, but coverage is limited to 150–300 m due to shadow fading and blockage from obstacles like trees, people, even rain. Traditional models (e.g., 3GPP TR 38.901) give only crude estimates. Our ML model predicts blockage probability with up to 1.5 dB accuracy, incorporating MIMO adaptive beamforming. Beam management — selecting the best among 64 beams for each UE — is optimized via ML classification of angular profiles, reducing switching time by 40%.

Optimized BS Placement Provides

Integer Programming mathematically selects sites under budget constraints. Objective: maximize weighted coverage (by traffic or subscriber count). Our AI planning system performs base station placement optimization automatically, achieving 4-6 times faster planning than manual methods.

import pulp

def optimize_site_selection(
    candidate_sites,   # possible sites with characteristics
    coverage_predictions,  # matrix: site × pixel → predicted RSRP
    demand_map,        # traffic demand map
    budget_usd,
    rsrp_threshold=-95  # minimum RSRP in dBm
):
    prob = pulp.LpProblem("site_selection", pulp.LpMaximize)

    # Binary variables: build site i?
    build = [pulp.LpVariable(f"build_{i}", cat='Binary') for i in range(len(candidate_sites))]

    # Coverage variables: is pixel j covered?
    covered = [pulp.LpVariable(f"covered_{j}", cat='Binary')
               for j in range(coverage_predictions.shape[1])]

    # Objective: maximize weighted coverage (by traffic)
    prob += pulp.lpSum(demand_map[j] * covered[j] for j in range(len(covered)))

    # Budget
    prob += pulp.lpSum(candidate_sites[i]['cost'] * build[i]
                       for i in range(len(candidate_sites))) <= budget_usd

    # Pixel is covered if at least one site provides required RSRP
    for j in range(len(covered)):
        covering_sites = [i for i in range(len(candidate_sites))
                         if coverage_predictions[i][j] >= rsrp_threshold]
        if covering_sites:
            prob += covered[j] <= pulp.lpSum(build[i] for i in covering_sites)
        else:
            prob += covered[j] == 0  # this pixel cannot be covered

    prob.solve(pulp.PULP_CBC_CMD(msg=0, timeLimit=120))

    selected_sites = [i for i, b in enumerate(build) if b.value() > 0.5]
    return selected_sites

Comparison with manual approach:

RSRP prediction methods comparison:

Combining RF engineering and machine learning yields a global optimum for placement — our Integer Programming site selection ensures cost-effective coverage.

When Small Cell Optimization Is Needed

In heterogeneous networks (HetNet), macrocells provide basic coverage and small cells (femto, pico) provide capacity in high-demand spots. However, incorrect small cell placement creates interference. Our ML clustering (DBSCAN) groups subscriber requests, then Integer Programming selects optimal installation points, reducing inter-cell interference by 3–5 dB and increasing throughput by 20%.

Coverage Gap Analysis

Coverage gap analysis starts from subscriber complaints + GPS → map of problem areas. A neural network links complaints with network parameters to pinpoint exact causes, generating automatic prioritization: which improvements yield greatest NPS gain. This enables drive test replacement and proactive optimization.

Common mistakes: using physical model without calibration (errors up to 10 dB), ignoring seasonal changes (3–5 dB overestimation), neglecting neighbor cell interference, and optimizing only RSRP without SINR, degrading voice quality.

Our Process

1. Analytics: collect drive test data, terrain (SRTM), buildings (OSM), complaints. Build demand map.
2. Planning: train ML model on historical data, calibrate thresholds considering shadow fading margin.
3. Implementation: run Integer Programming for site selection — base station placement optimization.
4. Test: pilot on 1–2 clusters, validate actual vs predicted coverage.
5. Deployment: integrate into OSS, set up CI/CD pipeline for quarterly model retraining.

Estimated Timelines

• MVP with ML prediction and optimization for one technology (LTE): 3–5 months.
• Full system with 5G mmWave planning and HetNet: 6–9 months.
• Cost is calculated individually, with typical project cost ranging from $50,000 to $150,000 per region. Over a 3-year period, savings exceed $1 million.

Deliverables (What's Included)

The following deliverables are included:

• ML model for RSRP prediction (LightGBM with feature engineering)
• Integer Programming solver (PuLP/CBC) for site selection
• Dashboard with coverage map and recommendations
• Three months post-deployment support
• Documentation: model card, API spec, update instructions
• Training for RF engineers

Experience and Guarantees

We have 5+ years of experience in AI/ML for telecom, with over 20 completed LTE/5G network optimization projects. Our engineers are certified in LTE and 5G NR. We guarantee prediction accuracy of ±2 dB on validation. RF engineering and machine learning are integrated into a single system, reducing planning time by a factor of five (AI planning is 4-6 times faster than manual). Our AI network planning system for AI for telecom uses Integer Programming site selection, ML propagation models, and coverage gap analysis to improve base station placement optimization, drive test replacement, and 5G mmWave planning including small cell placement. Contact us for a pilot project or order a consultation on AI planning, and we'll demonstrate results on your data.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.