mmWave RF Frontend Design

# GaN PA Design Parameters
import numpy as np
import matplotlib.pyplot as plt

class GaN_PA_Design:
    def __init__(self):
        self.freq = 28e9  # 28 GHz
        self.Vds = 28     # Drain voltage
        self.Ids = 500    # Drain current (mA)
        self.gm = 0.35    # Transconductance (S)
        
    def calculate_load_impedance(self):
        """Calculate optimal load impedance for maximum power"""
        Pout_target = 35  # dBm
        Pout_W = 10**(Pout_target/10) / 1000
        
        # Load-pull analysis
        RL_opt = (self.Vds**2) / (2 * Pout_W)
        
        # Smith chart matching
        Z0 = 50
        gamma_L = (RL_opt - Z0) / (RL_opt + Z0)
        
        return RL_opt, gamma_L
    
    def design_output_match(self):
        """Design output matching network"""
        RL_opt, gamma_L = self.calculate_load_impedance()
        
        # Quarter-wave transformer
        lambda_4 = 3e8 / (4 * self.freq * np.sqrt(2.2))  # Er = 2.2
        Z_transform = np.sqrt(50 * RL_opt)
        
        return {
            'transformer_Z': Z_transform,
            'length_mm': lambda_4 * 1000,
            'substrate': 'Rogers RO3003',
            'loss_dB': 0.15
        }
                            

# Beamforming Array Calculations
class BeamformingArray:
    def __init__(self):
        self.N = 8  # 8x8 array
        self.freq = 28e9
        self.c = 3e8
        self.lambda_0 = self.c / self.freq
        self.d = 0.5 * self.lambda_0  # Element spacing
        
    def calculate_array_factor(self, theta, phi, weights):
        """Calculate 3D array factor"""
        k = 2 * np.pi / self.lambda_0
        
        AF = 0
        for m in range(self.N):
            for n in range(self.N):
                phase = k * self.d * (m * np.sin(theta) * np.cos(phi) + 
                                      n * np.sin(theta) * np.sin(phi))
                AF += weights[m, n] * np.exp(1j * phase)
        
        return np.abs(AF)**2
    
    def beam_steering(self, theta_steer, phi_steer):
        """Calculate phase shifts for beam steering"""
        phases = np.zeros((self.N, self.N))
        k = 2 * np.pi / self.lambda_0
        
        for m in range(self.N):
            for n in range(self.N):
                phases[m, n] = -k * self.d * (
                    m * np.sin(theta_steer) * np.cos(phi_steer) +
                    n * np.sin(theta_steer) * np.sin(phi_steer)
                )
        
        return np.exp(1j * phases)
                            

# PCB Stack-up Configuration
stackup = {
    'layers': [
        {'name': 'Top', 'type': 'signal', 'thickness': 35, 'material': 'copper'},
        {'name': 'Prepreg-1', 'type': 'dielectric', 'thickness': 100, 'Er': 3.5},
        {'name': 'GND-1', 'type': 'plane', 'thickness': 35, 'material': 'copper'},
        {'name': 'Core-1', 'type': 'dielectric', 'thickness': 200, 'Er': 3.5},
        {'name': 'Signal-2', 'type': 'signal', 'thickness': 35, 'material': 'copper'},
        {'name': 'Prepreg-2', 'type': 'dielectric', 'thickness': 100, 'Er': 3.5},
        {'name': 'Signal-3', 'type': 'signal', 'thickness': 35, 'material': 'copper'},
        {'name': 'Core-2', 'type': 'dielectric', 'thickness': 200, 'Er': 3.5},
        {'name': 'GND-2', 'type': 'plane', 'thickness': 35, 'material': 'copper'},
        {'name': 'Prepreg-3', 'type': 'dielectric', 'thickness': 100, 'Er': 3.5},
        {'name': 'Bottom', 'type': 'signal', 'thickness': 35, 'material': 'copper'}
    ],
    'via_types': {
        'through': {'drill': 0.2, 'pad': 0.35, 'antipad': 0.5},
        'blind': {'drill': 0.1, 'pad': 0.2, 'antipad': 0.3},
        'buried': {'drill': 0.15, 'pad': 0.25, 'antipad': 0.35}
    }
}

# Transmission line calculator
def calculate_microstrip(w, h, er):
    """Calculate characteristic impedance of microstrip"""
    from numpy import log, sqrt, pi
    
    # Effective dielectric constant
    w_h = w / h
    if w_h <= 1:
        er_eff = (er + 1)/2 + (er - 1)/2 * (1/sqrt(1 + 12/w_h) + 0.04*(1 - w_h)**2)
    else:
        er_eff = (er + 1)/2 + (er - 1)/2 / sqrt(1 + 12/w_h)
    
    # Characteristic impedance
    if w_h <= 1:
        Z0 = 60/sqrt(er_eff) * log(8/w_h + w_h/4)
    else:
        Z0 = 120*pi / (sqrt(er_eff) * (w_h + 1.393 + 0.667*log(w_h + 1.444)))
    
    return Z0, er_eff
                            

# Digital Predistortion Implementation
import numpy as np
from scipy.signal import lfilter

class DPD_System:
    def __init__(self, order=5, memory=3):
        self.order = order
        self.memory = memory
        self.coefficients = np.zeros((order, memory), dtype=complex)
        
    def adapt_coefficients(self, input_signal, output_signal):
        """Indirect learning architecture for DPD adaptation"""
        # Build basis functions matrix
        X = self.build_basis_matrix(output_signal)
        
        # Least squares solution
        self.coefficients = np.linalg.lstsq(X, input_signal, rcond=None)[0]
        
    def predistort(self, signal):
        """Apply predistortion to input signal"""
        predistorted = np.zeros_like(signal)
        
        for k in range(self.order):
            for m in range(self.memory):
                if m == 0:
                    basis = signal * np.abs(signal)**(2*k)
                else:
                    basis = np.roll(signal, m) * np.abs(np.roll(signal, m))**(2*k)
                
                predistorted += self.coefficients[k, m] * basis
        
        return predistorted