6G Networks: Next Generation Wireless Technology 2026

class TerahertzChannel:
    """
    Model THz frequency channel characteristics.
    """
    
    FREQUENCY_BANDS = {
        'subTHz': (100, 300),      # GHz
        'THz': (300, 3000),        # GHz
        'Far-THz': (3000, 10000),  # GHz
    }
    
    def __init__(self, frequency_ghz, distance_meters):
        self.frequency = frequency_ghz
        self.distance = distance_meters
        self.atmospheric_attenuation = self.calculate_attenuation()
    
    def calculate_attenuation(self):
        """
        THz frequencies experience atmospheric absorption.
        """
        # Water vapor absorption peaks
        absorption_peaks = [557, 752, 1190, 1833]  # GHz
        
        base_attenuation = 0.01  # dB/km at 140 GHz
        for peak in absorption_peaks:
            if abs(self.frequency - peak) < 50:
                base_attenuation *= 5  # Peak absorption
        
        return base_attenuation * self.distance
    
    def path_loss_db(self):
        """
        Free-space path loss at THz frequencies.
        """
        c = 3e8  # speed of light
        wavelength = c / (self.frequency * 1e9)
        
        # FSPL = 20*log10(4*pi*d/lambda)
        return 20 * np.log10(4 * np.pi * self.distance / wavelength)
    
    def effective_data_rate(self, tx_power_dbm=10, bandwidth_ghz=10):
        """
        Calculate achievable data rate with Shannon capacity.
        """
        noise_power = -174 + 10 * np.log10(bandwidth_ghz * 1e9)
        snr = tx_power_dbm - noise_power - self.path_loss_db()
        
        capacity = bandwidth_ghz * 1e9 * np.log2(1 + 10**(snr/10))
        return capacity / 1e9  # Gbps

class IntelligentReflectingSurface:
    """
    Simulate IRS beamforming optimization.
    """
    
    def __init__(self, num_elements, carrier_freq):
        self.num_elements = num_elements  # e.g., 256, 1024 elements
        self.frequency = carrier_freq
        self.phase_shifts = np.zeros(num_elements)
    
    def optimize_phases(self, bs_position, user_position, channel):
        """
        Optimize phase shifts to maximize received signal.
        """
        # Direct channel from BS to user
        h_direct = channel.gain(bs_position, user_position)
        
        # Channel from BS to IRS
        h_bs_irs = channel.gain(bs_position, self.position)
        
        # Channel from IRS to user
        h_irs_user = channel.gain(self.position, user_position)
        
        # Combined channel with IRS
        h_reflected = h_bs_irs * self.phase_shifts * h_irs_user
        
        # Optimize for maximum SNR
        target_phase = np.angle(h_direct) - np.angle(h_reflected)
        
        for i in range(self.num_elements):
            self.phase_shifts[i] = np.exp(1j * target_phase)
        
        return self.phase_shifts
    
    def calculate_beamforming_gain(self, phase_shifts, channels):
        """
        Calculate beamforming gain from IRS.
        """
        # Array factor
        array_gain = np.abs(np.sum(np.exp(1j * phase_shifts)))
        
        # Convert to dB
        return 20 * np.log10(array_gain / self.num_elements)

class SemanticCommunicator:
    """
    Implement semantic communication for 6G.
    """
    
    def __init__(self, task_model, knowledge_base):
        self.task_model = task_model  # e.g., object detection, speech
        self.knowledge_base = knowledge_base
        self.semantic_encoder = SemanticEncoder(task_model)
        self.goal_extractor = GoalExtractor()
    
    def encode_semantic(self, source_data, communication_goal):
        """
        Extract and encode semantic information based on goal.
        """
        # Extract semantic features relevant to goal
        semantic_info = self.semantic_encoder.extract(
            source_data,
            task=communication_goal
        )
        
        # Remove redundant information
        relevant_semantics = self.filter_irrelevant(
            semantic_info,
            communication_goal
        )
        
        # Compress to minimal semantic representation
        return self.compress_semantics(relevant_semantics)
    
    def decode_semantic(self, encoded_semantics, receiver_context):
        """
        Reconstruct semantic information at receiver.
        """
        # Use knowledge base to fill gaps
        completed_semantics = self.knowledge_base.complete(
            encoded_semantics,
            context=receiver_context
        )
        
        # Generate output based on task
        return self.task_model.generate(completed_semantics)
    
    def calculate_semantic_efficiency(self, bits_transmitted, goal_achieved):
        """
        Measure semantic efficiency vs traditional bitrate.
        """
        # Traditional: bits per second
        bit_efficiency = bits_transmitted / self.time
        
        # Semantic: goal achievement per bit
        semantic_efficiency = goal_achievement_score / bits_transmitted
        
        return {
            'bit_rate': bit_efficiency,
            'semantic_efficiency': semantic_efficiency,
            'improvement_factor': semantic_efficiency / bit_efficiency
        }

class NativeAI6G:
    """
    AI-native 6G network architecture.
    """
    
    def __init__(self):
        self.radio_intelligence = AIController('radio')
        self.network_intelligence = AIController('network')
        self.application_intelligence = AIController('application')
    
    def intelligent_resource_allocation(self, user_demands, network_state):
        """
        AI-driven dynamic resource allocation.
        """
        # Predict user demands
        predicted_demands = self.predict_demand(user_demands)
        
        # Optimize resource allocation
        allocation = self.optimize_resources(
            demands=predicted_demands,
            state=network_state,
            objectives=['throughput', 'latency', 'energy']
        )
        
        return allocation
    
    def predict_demand(self, historical_demands):
        """
        Predict future resource demands using neural networks.
        """
        # Simple LSTM-based prediction
        model = self.build_demand_predictor()
        
        # Train on historical data
        model.train(historical_demands)
        
        # Predict next time slot
        return model.predict(steps_ahead=10)
    
    def joint_communication_sensing(self):
        """
        Integrated sensing and communication (ISAC).
        """
        return {
            'radar_function': self.radar_beamforming,
            'communication_function': self.comm_beamforming,
            'shared_hardware': self.hybrid_antenna_array,
            'joint_processing': self.unified_signal_processor
        }

class HolographicMIMO:
    """
    Implement holographic beamforming.
    """
    
    def __init__(self, antenna_elements_x, antenna_elements_y):
        self.N_x = antenna_elements_x
        self.N_y = antenna_elements_y
        self.total_elements = N_x * N_y
        
        # Sub-wavelength spacing for holographic imaging
        self.spacing_lambda = 0.5  # Half-wavelength
        self.array_aperture = self.calculate_aperture()
    
    def holographic_beamforming(self, target_position):
        """
        Create holographic beam for precise focusing.
        """
        # Calculate wavefront
        wavefront = self.calculate_wavefront(target_position)
        
        # Generate holographic pattern
        holographic_weights = np.conj(wavefront) / np.abs(wavefront)
        
        return holographic_weights
    
    def near_field_focusing(self, focus_distance, target_position):
        """
        Focus energy at specific 3D point.
        """
        # Near-field spherical wave model
        distances = self.calculate_distances(target_position)
        
        # Phase shifts for focusing
        k = 2 * np.pi / self.wavelength
        focus_phases = np.exp(1j * k * distances)
        
        return focus_phases
    
    def calculate_spatial_resolution(self):
        """
        HMIMO provides super-resolution spatial beamforming.
        """
        # Angular resolution
        theta_3db = 0.886 / self.N_x
        
        # Range resolution (near-field)
        range_resolution = self.wavelength * self.array_aperture / self.N_x
        
        return {
            'angular_resolution_deg': theta_3db,
            'range_resolution_m': range_resolution,
            'doppler_resolution': self.calculate_doppler()
        }

class DynamicSpectrumAccess:
    """
    Intelligent spectrum sharing for 6G.
    """
    
    def __init__(self, sensing_capability=True):
        self.sensing = sensing_capability
        self.spectrum_database = SpectrumDB()
        self.policy_engine = PolicyEngine()
    
    def query_available_spectrum(self, location, requirements):
        """
        Query available spectrum at location.
        """
        # Get incumbent users
        incumbents = self.spectrum_database.get_incumbents(location)
        
        # RF sensing if available
        if self.sensing:
            sensed_occupancy = self.perform_sensing(location)
        else:
            sensed_occupancy = {}
        
        # Calculate available bands
        available = self.calculate_available(
            incumbents=incumbents,
            sensed=sensed_occupancy,
            requirements=requirements
        )
        
        return available
    
    def execute_quantum_key_distribution(self):
        """
        QKD for ultra-secure 6G communications.
        """
        return {
            'protocol': 'BB84',
            'key_rate': '1 Mbps @ 100 km',
            'trusted_nodes': 'Satellite-to-ground',
            'integration': '6G key management'
        }

class AutonomousVehicle6G:
    """
    Vehicle-to-everything (V2X) with 6G.
    """
    
    def __init__(self):
        self.sensor_fusion = SensorFusion()
        self.prediction_model = PredictionModel()
        self.communication = V2XCommunication()
    
    def coordinated_planning(self, nearby_vehicles):
        """
        Use 6G for coordinated autonomous driving.
        """
        # Share intention and trajectory
        my_intention = self.predict_my_trajectory()
        nearby_intentions = self.communication.exchange(
            vehicles=nearby_vehicles,
            data={
                'trajectory': my_intention,
                'sensors': self.get_brief_sensor_data(),
                'planned_actions': self.get_planned_maneuvers()
            }
        )
        
        # Joint optimization
        coordinated_plan = self.optimize_joint_plan(
            my_intention,
            nearby_intentions
        )
        
        return coordinated_plan
    
    def remote_driving(self, remote_operator):
        """
        Teleoperation with 6G (sub-millisecond latency).
        """
        # High-fidelity video stream
        video = self.get_stereo_video()
        
        # Force feedback
        haptic_data = self.get_tactile_sensors()
        
        # Send to remote operator
        self.communication.send_stream(
            destination=remote_operator,
            video=video,
            audio=self.get_audio(),
            haptic=haptic_data,
            vehicle_state=self.get_state()
        )
        
        # Receive commands
        commands = self.communication.receive_commands(remote_operator)
        return self.execute_commands(commands)

class DigitalTwin6G:
    """
    Real-time digital twin over 6G.
    """
    
    def create_twin(self, physical_system):
        """
        Create real-time digital twin.
        """
        # Initial model
        twin_model = self.build_model(physical_system)
        
        # Continuous synchronization via 6G
        self.sync_interval = 0.001  # 1ms sync (6G capable)
        
        # Run simulation
        while True:
            sensor_data = self.collect_sensors(physical_system)
            
            # Update twin
            twin_model.update(sensor_data)
            
            # Predict
            predictions = twin_model.predict(horizon_ms=100)
            
            # Anomaly detection
            if predictions.has_anomaly():
                self.alert(predictions.anomaly_details)
            
            await asyncio.sleep(self.sync_interval)
    
    def remote_control(self, twin_model):
        """
        Control physical system via digital twin.
        """
        # Commands to physical system
        commands = twin_model.optimize_control()
        
        # 6G enables real-time closed-loop control
        return commands

class SAGINController:
    """
    Space-air-ground integrated network.
    """
    
    def __init__(self):
        self.satellite_network = SatelliteConstellation()
        self.hap_network = HighAltitudePlatforms()
        self.terrestrial = Terrestrial6G()
    
    def seamless_offload(self, user_position, requirements):
        """
        Intelligent multi-layer offloading.
        """
        # Determine best connectivity path
        layers = [
            self.terrestrial,
            self.hap_network,
            self.satellite_network
        ]
        
        best_path = self.select_path(
            layers=layers,
            position=user_position,
            requirements=requirements,
            criteria=['latency', 'bandwidth', 'cost']
        )
        
        return best_path

class Green6G:
    """
    Energy-efficient 6G design.
    """
    
    def calculate_energy_per_bit(self, data_rate_gbps):
        """
        Target: 1 pJ/bit (vs 100 pJ/bit in 5G).
        """
        # Advanced MIMO
        energy_mimo = 0.3  # pJ/bit
        
        # Edge AI processing
        energy_ai = 0.1  # pJ/bit
        
        # THz circuits
        energy_thz = 0.2  # pJ/bit
        
        # Radio
        energy_radio = 0.2  # pJ/bit
        
        total = energy_mimo + energy_ai + energy_thz + energy_radio
        
        return {
            'total_pj_per_bit': total,
            'target_met': total <= 1.0,
            'reduction_vs_5g': 100 / total
        }