5G Infrastructure: Network Slicing and Edge MEC

#!/usr/bin/env python3
"""5G Network Slicing management."""

from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum
import json

class SliceType(Enum):
    """Network slice types."""
    
    eMBB = "enhanced_MBB"    # Enhanced mobile broadband
    URLLC = "URLLC"           # Ultra-reliable low latency
    mMTC = "massive_MTC"      # Massive machine type comms

@dataclass
class SliceRequirements:
    """Network slice requirements."""
    
    slice_type: SliceType
    bandwidth_mbps: float
    latency_ms: float
    reliability: float  # 0-1
    device_density_per_km2: int
    coverage_area: str

@dataclass
class SliceConfig:
    """Network slice configuration."""
    
    slice_id: str
    slice_type: SliceType
    sst: int  # Slice/Service Type
    sd: str   # Slice Differentiator
    nsi_id: str  # Network Slice Instance ID
    upf_id: str
    amf_id: str

class NetworkSlicingManager:
    """Manage 5G network slices."""
    
    # Standardized Slice/Service Types
    SLICE_TYPES = {
        1: "eMBB",
        2: "URLLC", 
        3: "mMTC",
    }
    
    def __init__(self, nrf_url: str):
        self.nrf_url = nrf_url  # Network Repository Function
        self.slices: Dict[str, SliceConfig] = {}
    
    def create_slice_request(self, requirements: SliceRequirements) -> Dict:
        """Create network slice request."""
        
        # Map to SST (Slice Service Type)
        if requirements.slice_type == SliceType.eMBB:
            sst = 1
        elif requirements.slice_type == SliceType.URLLC:
            sst = 2
        else:
            sst = 3
        
        # Build NSSAI (Network Slice Selection Assistance Info)
        nssai = {
            "sst": sst,
            "sd": self._generate_slice_differentiator()
        }
        
        # Create slice request
        request = {
            "sliceId": self._generate_slice_id(),
            "nssai": nssai,
            "requirement": {
                "dLThptPerSlice": requirements.bandwidth_mbps,
                "uLThptPerSlice": requirements.bandwidth_mbps,
                "latency": requirements.latency_ms,
                "reliability": requirements.reliability,
                "coverageArea": requirements.coverage_area
            },
            "subnetConfig": {
                "type": "N4",
                "upfConfiguration": {
                    "nodeId": "upf-01",
                    "functionFeatures": ["ULCL", "BP"]
                }
            }
        }
        
        return request
    
    def provision_slice(self, request: Dict) -> SliceConfig:
        """Provision network slice."""
        
        # In production: Call NSSF (Network Slice Selection Function)
        # to select appropriate network functions
        
        slice_config = SliceConfig(
            slice_id=request["sliceId"],
            slice_type=SliceType(request["nssai"]["sst"]),
            sst=request["nssai"]["sst"],
            sd=request["nssai"]["sd"],
            nsi_id=f"nsi-{request['sliceId']}",
            upf_id="upf-01",
            amf_id="amf-01"
        )
        
        self.slices[slice_config.slice_id] = slice_config
        
        return slice_config
    
    def configure_slice_qos(self, slice_id: str, 
                            qos_profile: Dict) -> bool:
        """Configure QoS for network slice."""
        
        # 5G QoS Flow configuration
        qos_config = {
            "5qi": qos_profile.get("5qi", 6),  # Standard QoS Indicator
            "arp": {
                "priorityLevel": qos_profile.get("priority", 1),
                "preemptive": qos_profile.get("preemptive", False)
            },
            "gbr": {
                "downlink": qos_profile.get("dl_gbr_mbps", 10),
                "uplink": qos_profile.get("ul_gbr_mbps", 10)
            },
            "mbr": {
                "downlink": qos_profile.get("dl_mbr_mbps", 100),
                "uplink": qos_profile.get("ul_mbr_mbps", 100)
            }
        }
        
        print(f"Configuring QoS for slice {slice_id}: {qos_config}")
        
        return True
    
    def _generate_slice_id(self) -> str:
        import uuid
        return f"nssai-{uuid.uuid4().hex[:8]}"
    
    def _generate_slice_differentiator(self) -> str:
        import uuid
        return uuid.uuid4().hex[:6]

class SliceMonitoring:
    """Monitor network slice performance."""
    
    def get_slice_metrics(self, slice_id: str) -> Dict:
        """Get slice performance metrics."""
        
        return {
            "slice_id": slice_id,
            "throughput": {
                "dl_mbps": 450,
                "ul_mbps": 120,
            },
            "latency": {
                "avg_ms": 8.5,
                "p99_ms": 15.2
            },
            "packet_loss": 0.001,
            "connected_devices": 15234,
            "resource_utilization": 0.65
        }

# Network slice configurations for different use cases
slices:
  # Enhanced Mobile Broadband (eMBB)
  embb_video:
    type: eMBB
    sst: 1
    requirements:
      bandwidth: 1000  # Mbps
      latency: 20ms
      reliability: 0.99
    
  # URLLC - Autonomous Vehicles
  urllc_vehicle:
    type: URLLC
    sst: 2
    requirements:
      bandwidth: 100  # Mbps
      latency: 1ms
      reliability: 0.99999
    
  # mMTC - Smart Cities
  mmtc_smartcity:
    type: mMTC
    sst: 3
    requirements:
      bandwidth: 1  # Mbps (low bandwidth)
      latency: 1000ms (tolerant)
      reliability: 0.95
      device_density: 100000  # devices/km²

  # Enterprise Private 5G
  enterprise_factory:
    type: URLLC
    sst: 2
    requirements:
      bandwidth: 500  # Mbps
      latency: 5ms
      reliability: 0.9999
    features:
      - "Local breakout"
      - "Edge computing"
      - "Private spectrum"

#!/usr/bin/env python3
"""Multi-access Edge Computing (MEC) management."""

from typing import Dict, List
from dataclasses import dataclass
import json

@dataclass
class MECHost:
    """MEC host configuration."""
    
    host_id: str
    ip_address: str
    location: str  # Geographic location
    capacity: Dict  # CPU, memory, storage
    network_links: List[Dict]

@dataclass
class MECApp:
    """MEC application instance."""
    
    app_id: str
    name: str
    version: str
    required_resources: Dict
    required_ulrl: List[str]  # Required APIs
    traffic_rules: List[Dict]

class MECPlatform:
    """MEC platform management."""
    
    def __init__(self):
        self.hosts: Dict[str, MECHost] = {}
        self.apps: Dict[str, MECApp] = {}
    
    def register_host(self, host: MECHost):
        """Register MEC host."""
        
        # Connect to MEC platform manager
        # Register with NEF (Network Exposure Function)
        
        print(f"Registering MEC host: {host.host_id} at {host.location}")
        
        self.hosts[host.host_id] = host
    
    def deploy_app(self, app: MECApp, 
                   preferred_host: str = None) -> Dict:
        """Deploy MEC application to edge."""
        
        # Select optimal host
        if not preferred_host:
            host_id = self._select_optimal_host(app)
        else:
            host_id = preferred_host
        
        # Configure traffic rules
        traffic_rules = self._generate_traffic_rules(app)
        
        # Deploy application
        deployment = {
            "app_id": app.app_id,
            "host_id": host_id,
            "status": "deployed",
            "endpoints": [
                f"http://{self.hosts[host_id].ip_address}:8080/"
            ],
            "traffic_rules": traffic_rules
        }
        
        self.apps[app.app_id] = app
        
        return deployment
    
    def _select_optimal_host(self, app: MECApp) -> str:
        """Select optimal host based on requirements."""
        
        # Simple selection: first host with enough resources
        for host_id, host in self.hosts.items():
            if (host.capacity.get('cpu_cores', 0) >= 
                app.required_resources.get('cpu_cores', 1)):
                return host_id
        
        return list(self.hosts.keys())[0]
    
    def _generate_traffic_rules(self, app: MECApp) -> List[Dict]:
        """Generate traffic routing rules."""
        
        rules = []
        
        for urll in app.required_ulrl:
            rules.append({
                "priority": 1,
                "traffic_filter": {
                    "app_id": app.app_id,
                    "urll": urll
                },
                "traffic_action": {
                    "redirect": True,
                    "dst_address": f"edge-{app.app_id}"
                }
            })
        
        return rules
    
    def configure_location_service(self, app_id: str,
                                   location_info: Dict) -> Dict:
        """Configure location-based services."""
        
        # MEC Location API
        return {
            "service": "LocationService",
            "app_id": app_id,
            "location": {
                "tracking_area": location_info.get("tac"),
                "cell_id": location_info.get("cell_id"),
                "gnb_id": location_info.get("gnb_id")
            },
            "accuracy": location_info.get("accuracy", "low")
        }

class EdgeComputingApp:
    """Example edge computing application."""
    
    def __init__(self, host: str):
        self.host = host
        self.processing_queue = []
    
    def process_edge_data(self, data: Dict) -> Dict:
        """Process data at the edge."""
        
        # Example: Video analytics at edge
        if data.get("type") == "video_frame":
            return self._process_video(data)
        
        # IoT data processing
        elif data.get("type") == "iot_sensor":
            return self._process_iot(data)
        
        # AR/VR processing
        elif data.get("type") == "ar_frame":
            return self._process_ar(data)
        
        return {"status": "processed"}
    
    def _process_video(self, data: Dict) -> Dict:
        """Process video at edge for low latency."""
        
        # Simulate edge processing
        return {
            "processed_at": "edge",
            "latency_ms": 5,
            "results": {
                "objects_detected": ["person", "car"],
                "frame_quality": "high"
            }
        }
    
    def _process_iot(self, data: Dict) -> Dict:
        """Process IoT sensor data at edge."""
        
        return {
            "processed_at": "edge",
            "latency_ms": 2,
            "results": {
                "alert": data.get("value") > 100,
                "aggregated": True
            }
        }
    
    def _process_ar(self, data: Dict) -> Dict:
        """Process AR frames at edge."""
        
        return {
            "processed_at": "edge",
            "latency_ms": 8,
            "results": {
                "pose_detected": True,
                "anchors": 5
            }
        }

# Private 5G network configuration
private_5g:
  # Network topology
  deployment:
    type: "standalone"  # SA (Standalone) vs NSA
    architecture: "cloud-native"
    spectrum: "private"  # Private/shared/licensed
    
  # Spectrum options
  spectrum:
    - type: "cbrs"  # Citizens Broadband Radio Service (3.5GHz)
      bandwidth: 20  # MHz
    - type: "local"  # Local license
      band: "n77"  # 3.7GHz
      bandwidth: 100  # MHz
    - type: "dedicated"  # Dedicated spectrum (expensive)
  
  # Coverage
  coverage:
    indoor:
      - area: "factory_floor"
        access_points: 10
        technology: "small_cells"
        
    outdoor:
      - area: "campus"
        towers: 3
        technology: "macro"
  
  # Services
  services:
    - name: "mission_critical"
      type: "uRLLC"
      sla:
        latency: 5ms
        reliability: 0.9999
        
    - name: "iot_sensors"
      type: "mMTC"
      device_count: 10000
        
    - name: "video_surveillance"
      type: "eMBB"
      bandwidth: 500Mbps
  
  # Security
  security:
    authentication: "SIM-based"
    encryption: "AES-256"
    network_isolation: "VLAN/VRF"

#!/usr/bin/env python3
"""Private 5G network management."""

from typing import Dict, List
from dataclasses import dataclass

@dataclass
class Private5GConfig:
    """Private 5G network configuration."""
    
    network_id: str
    deployment_type: str  # SA/NSA
    spectrum: Dict
    coverage_areas: List[Dict]
    services: List[Dict]

class Private5GManager:
    """Manage private 5G networks."""
    
    def __init__(self):
        self.networks = {}
    
    def create_network(self, config: Private5GConfig) -> Dict:
        """Create private 5G network."""
        
        network = {
            "network_id": config.network_id,
            "status": "provisioning",
            "components": {
                "ran": self._setup_ran(config),
                "core": self._setup_core(config),
                "transport": self._setup_transport(config)
            }
        }
        
        self.networks[config.network_id] = network
        
        return network
    
    def _setup_man(self, config: Private5GConfig) -> Dict:
        """Setup Radio Access Network."""
        
        ran_config = {
            "gNodeBs": [],
            "small_cells": [],
            "cu_du_split": "option7"  # Centralized Unit / Distributed Unit
        }
        
        for area in config.coverage_areas:
            if area.get("technology") == "small_cells":
                ran_config["small_cells"].append({
                    "id": f"sn-{area['area']}",
                    "count": area.get("access_points", 1),
                    "type": "indoor_small_cell"
                })
            else:
                ran_config["gNodeBs"].append({
                    "id": f"gnb-{area['area']}",
                    "type": "macro",
                    "antennas": 64
                })
        
        return ran_config
    
    def _setup_core(self, config: Private5GConfig) -> Dict:
        """Setup 5G core network."""
        
        # Private 5G often uses containerized core
        return {
            "amf": {"instances": 2},
            "smf": {"instances": 2},
            "upf": {"instances": 3, "location": "edge"},
            "udm": {"instances": 1},
            "ausf": {"instances": 1},
            "pcf": {"instances": 1},
            "nssf": {"instances": 1},
            "deployment": "kubernetes"
        }
    
    def _setup_transport(self, config: Private5GConfig) -> Dict:
        """Setup transport network."""
        
        return {
            "backhaul": "10Gbps",
            "fronthaul": "25Gbps",
            "switching": "layer3",
            "qos_enabled": True
        }
    
    def configure_device_provisioning(self, network_id: str,
                                     imsi_range: Dict) -> Dict:
        """Configure device provisioning for private network."""
        
        return {
            "network_id": network_id,
            "imsi": {
                "mcc": imsi_range.get("mcc", "001"),  # Test MCC
                "mnc": imsi_range.get("mnc", "01"),
                "msin_range": imsi_range.get("msin", "0000000000-9999999999")
            },
            "authentication": {
                "method": "AKA",
                "key": "reserved_for_sim_provisioning"
            }
        }

# 5G edge deployment architecture
edge_deployment:
  # Three-tier edge architecture
  tiers:
    - name: "device_edge"
      location: "On-premise, factory/retail"
      latency: "< 1ms"
      compute: "NVIDIA Jetson, Intel NCS"
      use_cases:
        - "Real-time control"
        - "AR/VR processing"
        - "Video analytics"
        
    - name: "network_edge"
      location: "Telco edge, cell tower"
      latency: "< 5ms"
      compute: "Edge server, GPU"
      use_cases:
        - "V2X processing"
        - "Multi-user AR"
        - "CDN"
        
    - name: "regional_edge"
      location: "Telco regional data center"
      latency: "< 15ms"
      compute: "Full data center"
      use_cases:
        - "AI model training"
        - "Analytics"
        - "Backup processing"
  
  # Network configuration
  network:
    - interface: "N6"  # UPF to Data Network
      traffic_steering: "local_breakout"
      
    - interface: "N4"  # SMF to UPF
      session_management: "distributed"
      
    - feature: "ULCL"
      description: "Uplink Classifier - route traffic to local edge"
      
    - feature: "DNAI"
      description: "Data Network Access Identifier - edge location identifier"

#!/usr/bin/env python3
"""5G security implementation."""

from typing import Dict
from dataclasses import dataclass
import hashlib
import hmac

@dataclass
class SecurityConfig:
    """5G security configuration."""
    
    network_id: str
    encryption_algorithm: str
    integrity_algorithm: str

class5GSecurity:
    """5G network security."""
    
    # 5G security algorithms
    ENCRYPTION_ALGORITHMS = {
        "NEA0": "Null encryption",
        "NEA1": "128-NEA1 (SNOW 3G)",
        "NEA2": "128-NEA2 (AES)",
        "NEA3": "128-NEA3 (ZUC)"
    }
    
    INTEGRITY_ALGORITHMS = {
        "NIA0": "Null integrity",
        "NIA1": "128-NIA1 (SNOW 3G)",
        "NIA2": "128-NIA2 (AES)",
        "NIA3": "128-NIA3 (ZUC)"
    }
    
    def __init__(self, config: SecurityConfig):
        self.config = config
    
    def authenticate_ue(self, imsi: str, 
                       auth_vector: Dict) -> tuple[bool, str]:
        """Authenticate user equipment."""
        
        # 5G AKA (Authentication and Key Agreement)
        # In production: Challenge-response with USIM
        
        # Generate AUTN (Authentication Token)
        # Generate RAND (Random number)
        
        return True, "Authentication successful"
    
    def derive_keys(self, k: bytes, 
                   serving_network: str) -> Dict:
        """Derive security keys."""
        
        # Key derivation functions
        keys = {
            "K_ausf": self._derive_ausf(k, serving_network),
            "K_seaf": self._derive_seaf(keys["K_ausf"]),
            "K_amf": self._derive_amf(keys["K_seaf"]),
            "K_nas_enc": self._derive_nas(keys["K_amf"], "enc"),
            "K_nas_int": self._derive_nas(keys["K_amf"], "int"),
            "K_rrc_enc": self._derive_rrc(keys["K_amf"], "enc"),
            "K_rrc_int": self._derive_rrc(keys["K_amf"], "int"),
            "K_up_enc": self._derive_up(keys["K_amf"], "enc")
        }
        
        return keys
    
    def _derive_ausf(self, k: bytes, snn: str) -> bytes:
        """Derive AUSF key."""
        # K_AUSF = HMAC-SHA-256(K, "5G-AUSF" || SNN)
        return hmac.new(k, f"5G-AUSF{snn}".encode(), 
                       hashlib.sha256).digest()
    
    def _derive_seaf(self, k_ausf: bytes) -> bytes:
        """Derive SEAF key."""
        return hmac.new(k_ausf, b"5G-SEAF", 
                       hashlib.sha256).digest()
    
    def _derive_amf(self, k_seaf: bytes) -> bytes:
        """Derive AMF key."""
        return hmac.new(k_seaf, b"5G-AMF", 
                       hashlib.sha256).digest()
    
    def _derive_nas(self, k_amf: bytes, algo: str) -> bytes:
        """Derive NAS security key."""
        return hmac.new(k_amaf, f"5G-NAS-{algo}".encode(), 
                       hashlib.sha256).digest()
    
    def _derive_rrc(self, k_amf: bytes, algo: str) -> bytes:
        """Derive RRC security key."""
        return hmac.new(k_amf, f"5G-RRC-{algo}".encode(), 
                       hashlib.sha256).digest()
    
    def _derive_up(self, k_amf: bytes, algo: str) -> bytes:
        """Derive User Plane security key."""
        return hmac.new(k_amf, f"5G-UP-{algo}".encode(), 
                       hashlib.sha256).digest()