Telecommunications 5G Network Optimization

AI in Telecommunications & 5G Network Optimization with Chinese LLMs

Published August 27, 2026 · 12 min read · TokenEase Telecom Team

The telecommunications industry manages the world's most complex infrastructure—billions of connected devices, petabytes of traffic data, and networks that must operate at 99.999% uptime. Chinese LLMs like DeepSeek-V4, GLM-4, and Qwen3 are transforming how telecom operators plan, optimize, and maintain these critical systems.

Through TokenEase's unified API, telecom engineering teams can access advanced AI capabilities for network operations without building custom ML infrastructure or managing multiple provider relationships.

Why TokenEase for Telecom?

Process massive network logs with Qwen3's 128K context window, generate Chinese customer communications with GLM-4, and build complex network optimization logic with DeepSeek-V4's reasoning—all through a single API endpoint at 40% lower cost.

1. Intelligent Network Fault Diagnosis

Network outages can affect millions of users and cost operators $100K+ per minute. LLMs can analyze alarm logs, performance metrics, and historical incidents to identify root causes faster than traditional rule-based systems.

Example: Diagnose Network Fault with DeepSeek-V4

import requests

alarm_data = """
Alarms (last 15 minutes):
- CRITICAL: Core Router CR-Beijing-03 - BGP peer down (peer: AS4837)
- MAJOR: Link utilization Beijing-Shanghai > 95% (normal: 60%)
- MINOR: 5G gNodeB Beijing-Haidian-128 - handover failures +40%
- INFO: DNS query latency +200ms for region North-China

Topology Context:
- CR-Beijing-03 handles 40% of North China traffic
- Backup path via CR-Beijing-02 (capacity: 50% of primary)
- Affected services: Mobile data, enterprise VPN, IoT backbone
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "You are a senior network operations engineer. Analyze network alarms, identify root causes, assess business impact, and recommend remediation steps with priority. Consider cascade failures and backup path limitations."},
            {"role": "user", "content": f"Diagnose this network incident:\n{alarm_data}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2000
    }
)

diagnosis = response.json()["choices"][0]["message"]["content"]
print(diagnosis)
# Expected: Root cause (BGP peer issue), impact assessment, remediation steps

2. 5G Network Slice Configuration

5G network slicing allows operators to create virtual networks with different characteristics for diverse use cases—eMBB for consumers, uRLLC for industrial automation, and mMTC for IoT. LLMs can generate optimal slice configurations based on service requirements.

Example: Generate Slice Config with Qwen3

import requests

service_requirements = """
Service: Smart Factory Automation
Use Case: Real-time robot control
Requirements:
- Latency: < 10ms end-to-end
- Reliability: 99.9999%
- Bandwidth: 50 Mbps per robot (100 robots)
- Mobility: Stationary
- Isolation: Critical (no sharing with other services)
- Coverage: Factory floor (200m x 150m)
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen3-235b",
        "messages": [
            {"role": "system", "content": "Generate 5G network slice configurations in 3GPP-compliant format. Specify slice type (eMBB/uRLLC/mMTC), QoS parameters, resource allocation, RAN configuration, and core network functions. Include validation checks."},
            {"role": "user", "content": f"Generate slice config for:\n{service_requirements}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2500
    }
)

slice_config = response.json()["choices"][0]["message"]["content"]
print(slice_config)

3. Predictive Traffic Forecasting

Accurate traffic prediction enables proactive capacity planning, energy optimization, and quality of service management. LLMs can analyze historical patterns, event calendars, and external factors to forecast network load.

Example: Traffic Forecast with DeepSeek-V4

import requests

historical_data = """
Cell Tower: Shanghai-Lujiazui-5G-01
Traffic Pattern (GB/hour):
- Weekday average: 850 GB (peak 18:00-20:00: 1,200 GB)
- Weekend average: 1,100 GB (peak 14:00-17:00: 1,500 GB)
- Holiday multiplier: 2.5x (last National Day: 2,100 GB peak)

Upcoming Events:
- Aug 28: Concert at Oriental Pearl (expected 50K attendees)
- Aug 30-31: Weekend
- Sep 1: National holiday (expected 3x traffic)

Current Capacity: 2,500 GB/hour peak
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "Analyze network traffic patterns and generate forecasts with capacity recommendations. Identify potential congestion periods, suggest load balancing, and recommend temporary capacity additions."},
            {"role": "user", "content": f"Forecast traffic for:\n{historical_data}"}
        ],
        "temperature": 0.3,
        "max_tokens": 2000
    }
)

forecast = response.json()["choices"][0]["message"]["content"]
print(forecast)

4. Telecom Fraud Detection & Prevention

Telecom fraud costs the industry $39 billion annually. LLMs can analyze call detail records, signaling patterns, and customer behavior to detect SIM box fraud, Wangiri scams, subscription fraud, and international revenue share fraud.

Example: Fraud Pattern Analysis with Qwen3

import requests

cdr_sample = """
Suspicious Activity:
- SIM: 86138****1234 (activated 3 days ago)
- Call pattern: 2,400 calls in 24 hours to premium-rate numbers
- Call duration: Average 3 seconds (premium numbers charge on connection)
- Geography: All calls routed to +44 090x numbers (UK premium)
- IMEI: Same device used for 50 different SIMs in past week
- Top-up: All via stolen credit cards (3 chargebacks already)
- Customer profile: No biometric verification, prepaid account
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "qwen3-235b",
        "messages": [
            {"role": "system", "content": "Analyze telecom fraud patterns from CDR data. Identify fraud type, assess financial risk, recommend immediate actions, and suggest prevention measures. Consider regulatory reporting requirements."},
            {"role": "user", "content": f"Analyze this suspicious activity:\n{cdr_sample}"}
        ],
        "temperature": 0.2,
        "max_tokens": 2000
    }
)

fraud_analysis = response.json()["choices"][0]["message"]["content"]
print(fraud_analysis)
# Expected: Fraud type (IRSF/Wangiri), risk score, recommended actions

5. Multilingual Customer Service Automation

Telecom operators serve diverse populations across regions and languages. LLMs can handle customer inquiries in Chinese, English, and other languages while maintaining context across billing, technical, and service issues.

Example: Chinese Customer Support with GLM-4

import requests

customer_inquiry = """
客户投诉：
"我上个月办理了5G套餐升级，但账单显示还是按4G套餐收费。
而且我家附近的5G信号非常差，下载速度只有20Mbps。
我已经打了3次客服电话，问题都没解决。要求退差价并优化信号！"

账户信息：
- 手机号：138****5678
- 套餐：5G尊享版 (承诺下行1Gbps)
- 所在区域：北京朝阳区望京
- 投诉次数：本月第3次
- 客户等级：钻石会员 (5年网龄)
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "glm-4-plus",
        "messages": [
            {"role": "system", "content": "你是一位资深电信客服经理。分析客户投诉，生成专业的中文回复。包含问题诊断、解决方案、补偿建议、和后续跟进安排。语气要诚恳、专业，体现对高价值客户的重视。"},
            {"role": "user", "content": f"处理这个客户投诉：\n{customer_inquiry}"}
        ],
        "temperature": 0.4,
        "max_tokens": 2000
    }
)

response_text = response.json()["choices"][0]["message"]["content"]
print(response_text)

6. Network Planning & Site Selection

Deploying new cell towers and 5G small cells requires analyzing coverage gaps, population density, traffic demand, and regulatory constraints. LLMs can synthesize geographic, demographic, and technical data to recommend optimal site locations.

Example: Site Selection Analysis with DeepSeek-V4

import requests

planning_context = """
Area: Shenzhen Nanshan District (Tech Park vicinity)
Current Coverage: 4G only, average throughput 15 Mbps
Population: 850K residents + 400K daily commuters
Businesses: 2,000+ tech companies, 50+ data centers
Existing Infrastructure: 12 macro towers (4G), 0 5G
Regulatory: Max tower height 45m, must avoid schools within 100m
Budget: 8 new sites, $2M total CAPEX
Target: 5G coverage >95%, throughput >300 Mbps
"""

response = requests.post(
    "https://tokenease.io/v1/chat/completions",
    headers={
        "Authorization": "Bearer YOUR_TOKENEASE_API_KEY",
        "Content-Type": "application/json"
    },
    json={
        "model": "deepseek-v4",
        "messages": [
            {"role": "system", "content": "Generate network planning recommendations including site selection criteria, tower specifications, coverage modeling, ROI analysis, and deployment timeline. Consider technical, regulatory, and financial constraints."},
            {"role": "user", "content": f"Plan 5G deployment for:\n{planning_context}"}
        ],
        "temperature": 0.3,
        "max_tokens": 2500
    }
)

plan = response.json()["choices"][0]["message"]["content"]
print(plan)

Power Your Telecom Operations with TokenEase

Integrate DeepSeek-V4, GLM-4, and Qwen3 into your network operations center. Get started today →

Implementation Checklist

Fault Diagnosis: Feed LLMs structured alarm data with topology context for accurate root cause analysis
Slice Config: Validate generated configurations against 3GPP standards before deployment
Traffic Forecast: Combine LLM predictions with traditional time-series models for hybrid accuracy
Fraud Detection: Use LLMs for pattern analysis but implement deterministic rules for blocking decisions
Customer Service: Maintain human escalation paths for complex billing disputes and network complaints
Network Planning: Use LLM recommendations as input to RF planning tools, not as final site selections

Model Selection for Telecom

DeepSeek-V4: Best for network fault diagnosis, traffic forecasting, and technical planning
GLM-4: Ideal for Chinese customer service, local regulatory compliance, and bilingual documentation
Qwen3: Excellent for fraud pattern analysis, slice configuration, and complex reasoning tasks