AI Telecommunications Industry System Development

Development of an AI system for telecommunications

Telecom networks are among the largest sources of structured data in the world: millions of events per second from equipment, calls, and sessions. AI analyzes these streams in real time, proactively eliminating network degradation and optimizing the user experience.

Predictive Network Maintenance

Prediction of equipment failures

A telecom network consists of tens of thousands of pieces of equipment: base stations, switches, DWDM systems, routers. Scheduled maintenance means ill-timed replacement. AI is shifting the approach to predictive.

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

class NetworkEquipmentPredictor:
    """
    Прогноз отказа оборудования за 3–7 дней на основе SNMP/Netflow метрик
    """

    def build_features(self, equipment_metrics_df):
        """
        equipment_metrics_df: SNMP polling каждые 5 минут
        Метрики: CPU, memory, temperature, interface_errors, optical_power
        """
        df = equipment_metrics_df.copy()

        # Временные признаки по каждой метрике
        for col in ['cpu_util', 'memory_util', 'temp_celsius', 'rx_optical_power_dbm']:
            # Скользящие статистики за 1 час, 4 часа, 24 часа
            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()

            # Тренд: растёт или падает
            df[f'{col}_trend_24H'] = df[col].diff(periods=288)  # 288 = 24ч × 12 интервалов/час

        # Счётчики ошибок
        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):
        """Вероятность отказа в ближайшие N дней"""
        X_scaled = self.scaler.transform(features)
        proba = self.model.predict_proba(X_scaled)[:, 1]
        return proba

Optical degradation:

Critical Parameter: Rx Optical Power. Power declining trend → connector or transceiver contamination/degradation: - Normal range: -18 to -8 dBm (depending on transceiver type) - 3 dBm decrease in 2 weeks → replacement before signal loss

Network Quality of Service (QoS/QoE)

Predicting user experience degradation:

ML links network metrics to service quality: - Video call: for good QoE you need RTT <150ms, packet loss <1%, jitter <30ms - Streaming: for 4K — bandwidth >25 Mbps, re-buffering <1% - Online gaming: RTT <50ms is critically important

The model predicts MOS (Mean Opinion Score) based on network metrics: XGBoost on ITU-T P.1203 features. In case of predicted degradation, QoS policies (Traffic Shaping, Priority Queuing) are adjusted.

Anomaly Detection and Cybersecurity

Network Anomaly Detection:

Unsupervised + supervised approach: - Baseline traffic profile for each node (hourly, daily, weekly pattern) - LSTM Autoencoder: reconstruction of normal pattern → reconstruction error = anomaly score - Typical anomalies: DDoS (spike volume), port scanning (fan-out topology), data exfiltration (unusual large transfer)

import torch
import torch.nn as nn

class TrafficAnomalyDetector(nn.Module):
    """LSTM Autoencoder для детекции аномалий в трафике"""

    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)
        # Декодируем из 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 planning and RF optimization

Radio Frequency (RF) Optimization:

For cellular networks (4G/5G): automatic configuration of base station parameters: - Transmitter power: balancing coverage and interference - Antenna tilt: vertical - affects cell size - Frequency plan: minimizing co-channel interference

SON (Self-Organizing Networks) — automatic optimization: - Self-Configuration: when installing a new base station — automatic selection of parameters - Self-Optimization: MLB (Mobility Load Balancing), MRO (Mobility Robustness Optimization) - Self-Healing: detection of faulty base stations, automatic load redistribution

Load forecast for capacity planning:

LSTM on traffic for each BS → load forecast for 1–12 months → network expansion planning.

Customer Experience Management

Churn Prediction:

Telecom is one of the first sectors to apply ML to churn forecasting: - Indicators: consumption changes, support requests, credit history, competitive offers - LightGBM: AUC 0.82–0.87 on a 30-day churn forecast - Targeted retention: personalized offer for the risk segment

Development time: 5–9 months for a comprehensive Telecom AI platform with predictive maintenance, anomaly detection, and churn prediction.