Telecom AI System Development: Predictive Maintenance & ML

A typical telecom network generates petabytes of telemetry daily. Without ML analytics, this data remains unused. We implement pipelines that process SNMP, Netflow, CDR, and XDR in real time, identifying degradation patterns hours before failure. Through predictive maintenance, operators reduce OPEX by 30% and CAPEX by 20%, shifting from reactive repairs to planned replacements. For an operator with 5,000 base stations, annual savings amount to tens of millions of rubles.

How ML predicts equipment failures

A typical network includes tens of thousands of units: base stations, switches, DWDM systems. Scheduled maintenance often misses real risks, replacing healthy blocks or missing degraded ones. Our predictive approach uses ML to estimate failure probability, optimizing replacements.

import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.preprocessing import StandardScaler

class NetworkEquipmentPredictor:
    """
    Predicts equipment failure 3–7 days ahead based on SNMP/Netflow metrics
    """

    def build_features(self, equipment_metrics_df):
        """
        equipment_metrics_df: SNMP polling every 5 minutes
        Metrics: CPU, memory, temperature, interface_errors, optical_power
        """
        df = equipment_metrics_df.copy()

        # Temporal features for each metric
        for col in ['cpu_util', 'memory_util', 'temp_celsius', 'rx_optical_power_dbm']:
            # Rolling statistics over 1 hour, 4 hours, 24 hours
            for window in ['1H', '4H', '24H']:
                df[f'{col}_mean_{window}'] = df[col].rolling(window).mean()
                df[f'{col}_std_{window}'] = df[col].rolling(window).std()
                df[f'{col}_max_{window}'] = df[col].rolling(window).max()

            # Trend: rising or falling
            df[f'{col}_trend_24H'] = df[col].diff(periods=288)  # 288 = 24h × 12 intervals/hour

        # Error counters
        for error_col in ['crc_errors', 'input_drops', 'output_drops']:
            df[f'{error_col}_rate_1H'] = df[error_col].diff().rolling('1H').sum()

        return df.dropna()

    def predict_failure_risk(self, features, horizon_days=7):
        """Probability of failure within the next N days"""
        X_scaled = self.scaler.transform(features)
        proba = self.model.predict_proba(X_scaled)[:, 1]
        return proba

A critical parameter is Rx Optical Power. A decreasing trend of 3 dBm over two weeks signals contamination or connector degradation. Replacement before signal loss eliminates downtime.

Compare: reactive approach — 8 hours downtime per failure. Predictive — 30 minutes downtime. A 16x difference saves millions for a large operator. Gradient boosting failure prediction accuracy is 25% higher than threshold-based methods.

Network QoS/QoE management and anomaly detection

ML links network metrics to service quality. For video calls: RTT <150ms, packet loss <1%, jitter <30ms. For 4K streaming: bandwidth >25 Mbps, rebuffering <1%. Online gaming requires RTT <50ms.

Our model predicts Mean Opinion Score (MOS) from these metrics. We use XGBoost on features recommended by ITU-T P.1203. When degradation is detected, we apply QoS policies: Traffic Shaping, Priority Queuing.

Anomaly detection

We combine unsupervised and supervised approaches. We build a baseline traffic profile for each node (hourly, daily, weekly patterns). An LSTM Autoencoder reconstructs the normal pattern; the reconstruction error is the anomaly score. LSTM autoencoder detects anomalies 3x faster than statistical methods. Typical anomalies: DDoS (volume spike), port scanning (fan-out topology), data exfiltration (unusually large transfer). More on the architecture can be found on Wikipedia.

import torch
import torch.nn as nn

class TrafficAnomalyDetector(nn.Module):
    """LSTM Autoencoder for traffic anomaly detection"""

    def __init__(self, input_dim=32, hidden_dim=64, seq_len=24):
        super().__init__()
        # Encoder
        self.encoder = nn.LSTM(input_dim, hidden_dim, num_layers=2,
                               batch_first=True, dropout=0.2)
        # Decoder
        self.decoder = nn.LSTM(hidden_dim, hidden_dim, num_layers=2,
                               batch_first=True, dropout=0.2)
        self.output_layer = nn.Linear(hidden_dim, input_dim)

    def forward(self, x):
        # x: (batch, seq_len, input_dim)
        _, (h, c) = self.encoder(x)
        # Decode from last hidden state
        dec_input = h[-1].unsqueeze(1).repeat(1, x.shape[1], 1)
        decoded, _ = self.decoder(dec_input)
        reconstruction = self.output_layer(decoded)
        return reconstruction

    def anomaly_score(self, x):
        reconstruction = self.forward(x)
        mse = ((x - reconstruction) ** 2).mean(dim=-1).mean(dim=-1)
        return mse

Network optimization and customer experience

How AI optimizes network planning?

For cellular networks (4G/5G), we automatically tune base station parameters: transmit power (coverage vs interference balance), antenna tilt, frequency plan (minimizing co-channel interference). For RF optimization, we apply ML algorithms that automatically configure BS parameters.

Self-Organizing Networks (SON) — automatic optimization. Self-Configuration when installing a new BS, Self-Optimization (MLB, MRO), Self-Healing — identifying faulty BSs and automatically redistributing load. Load forecasting 1–12 months ahead using LSTM allows network expansion planning.

Customer experience management

Churn prediction is a classic telecom use case. Features: consumption changes, support calls, credit history, competitor offers. LightGBM achieves AUC 0.82–0.87 on a 30-day forecast. Targeted retention offers personalized propositions for risk segments. In one project, this reduced churn by 18% in a quarter, saving tens of millions of rubles in revenue.

Comparison of maintenance approaches

What's included in the work

During the analytics phase, we collect data from OSS/BSS, data centers, and network devices. Feature engineering includes generating rolling statistics and Fourier transforms for periodicity. In prototyping, we use Optuna for hyperparameter tuning. MLOps includes setting up drift detection, A/B testing, and model versioning via MLflow.

Process

1. Analytics: audit data sources, infrastructure, business metrics.
2. Design: choose model architecture, feature store, vector DB (if RAG).
3. Implementation: training, hyperparameter optimization, quantization (INT8) for inference.
4. Testing: A/B on shadow traffic, validation on historical data.
5. Deployment: containerization, deployment on Triton Inference Server or SageMaker.

Estimated timelines

A comprehensive platform takes 5 to 9 months. We'll evaluate your project in 2 days — just contact us. For operators with existing infrastructure, timelines can be reduced to 3-4 months. Request a pre-project survey — we'll study your network and propose the optimal solution.

Our experience

10+ years of AI system development for telecom (telecom AI system development), 20+ completed projects. Certified specialists in AWS, PyTorch, TensorFlow. We offer turnkey solutions: from requirements gathering to production support. Want to evaluate potential savings for your network? Get a free consultation — we'll conduct a data audit. Contact us for a detailed discussion of your project.