import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import time

# ==========================================
# 0. 全局设置
# ==========================================
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"========== 启动 AI 动态追踪生成器 ==========\n使用设备: {device}")

N_ant, N_RF, d_lambda = 24, 4, 0.5
noise_power = 1.0
INR_linear = 10**(30/10) # 30dB 强杂波

def gen_a_batch(theta_deg_tensor):
    theta_rad = torch.deg2rad(theta_deg_tensor)
    n_idx = torch.arange(-(N_ant-1)/2, (N_ant-1)/2 + 0.1, device=device).unsqueeze(0)
    phases = 2 * torch.pi * d_lambda * n_idx * torch.sin(theta_rad.unsqueeze(1))
    return torch.exp(1j * phases).unsqueeze(2)

# ==========================================
# 1. 广域追踪网络 (彻底解决相位死锁)
# ==========================================
class DynamicHBFNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(48, 512), nn.GELU(),
            nn.Linear(512, 512), nn.GELU(),
            nn.Linear(512, N_ant * N_RF * 2) 
        )

    def forward(self, w_opt):
        w_flat = w_opt.squeeze(2)
        x = torch.cat([w_flat.real, w_flat.imag], dim=1)
        out = self.net(x).view(-1, N_ant, N_RF, 2)
        complex_out = out[..., 0] + 1j * out[..., 1]
        # 严格恒模约束
        F_RF = complex_out / (torch.abs(complex_out) + 1e-8) / np.sqrt(N_ant)
        return F_RF

# ==========================================
# 2. 广域动态特训
# ==========================================
model = DynamicHBFNet().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.002)

Batch_Size, Epochs = 500, 10000      

print("正在对 AI 进行大范围目标与杂波抗干扰特训...")
for epoch in range(Epochs):
    model.train()
    optimizer.zero_grad()
    
    # 【动态大扫掠特训】：目标 0~40°，杂波 -20~-3°
    theta_t = torch.rand(Batch_Size, device=device) * 40.0
    theta_c = -20.0 + torch.rand(Batch_Size, device=device) * 17.0
    
    a_t, a_c = gen_a_batch(theta_t), gen_a_batch(theta_c)
    
    R_interf = noise_power * torch.eye(N_ant, device=device).unsqueeze(0) + INR_linear * (a_c @ a_c.mH)
    w_opt = torch.linalg.pinv(R_interf) @ a_t
    w_opt = w_opt / torch.norm(w_opt, dim=1, keepdim=True)
    
    F_RF = model(w_opt)
    F_BB = torch.linalg.pinv(F_RF.mH @ F_RF + 1e-3*torch.eye(N_RF, device=device).unsqueeze(0)) @ (F_RF.mH @ w_opt)
    w_ai = F_RF @ F_BB
    w_ai = w_ai / torch.norm(w_ai, dim=1, keepdim=True)
    
    sim = torch.abs(torch.sum(w_opt.conj() * w_ai, dim=(1,2)))**2
    loss_sim = torch.mean(1.0 - sim)
    loss_null = torch.mean(torch.abs(torch.sum(a_c.conj() * w_ai, dim=(1,2)))**2)
    
    loss = loss_sim + 150.0 * loss_null 
    loss.backward()
    optimizer.step()

print("特训完成！正在生成实时追踪 GIF 动图...")

# ==========================================
# 3. 生成精美动态 GIF
# ==========================================
model.eval()
theta_scan = torch.arange(-60, 60.1, 0.5, device=device) # 限制扫描角度让图像更聚焦
A_scan = gen_a_batch(theta_scan).squeeze(2).T 

fig, ax = plt.subplots(figsize=(10, 6))
ax.set_xlim(-60, 60)
ax.set_ylim(-60, 0)
ax.set_xlabel('角度 θ (°)', fontsize=12, fontweight='bold')
ax.set_ylabel('归一化增益 (dB)', fontsize=12, fontweight='bold')
ax.grid(True, ls='--')

line_opt, = ax.plot([], [], 'k-', lw=2, alpha=0.5, label='纯数字最优下限')
line_ai,  = ax.plot([], [], color='#D95319', ls='--', lw=2.5, label='AI 瞬时感知混合波束')
vline_t   = ax.axvline(0, color='g', ls='-.', lw=2, label='机动目标轨迹')
vline_c   = ax.axvline(0, color='r', ls='-.', lw=2, label='强杂波轨迹')

title_text = ax.set_title('', fontsize=14, fontweight='bold')
ax.legend(loc='lower center', fontsize=10)

frames_count = 60 # 60帧平滑过渡

def update(frame):
    # 目标从 0 匀速扫到 40
    t_ang = 0.0 + frame * (40.0 / (frames_count - 1))
    # 杂波从 -3 匀速扫到 -20
    c_ang = -3.0 - frame * (17.0 / (frames_count - 1))
    
    a_t = gen_a_batch(torch.tensor([t_ang], device=device))
    a_c = gen_a_batch(torch.tensor([c_ang], device=device))
    R_interf = noise_power * torch.eye(N_ant, device=device).unsqueeze(0) + INR_linear * (a_c @ a_c.mH)
    
    w_opt = torch.linalg.pinv(R_interf) @ a_t
    w_opt /= torch.norm(w_opt, dim=1, keepdim=True)
    
    # AI 仅凭一次前向传播，瞬间生成波束！
    with torch.no_grad():
        F_RF = model(w_opt)
        F_BB = torch.linalg.pinv(F_RF.mH @ F_RF + 1e-3*torch.eye(N_RF, device=device).unsqueeze(0)) @ (F_RF.mH @ w_opt)
        w_ai = F_RF @ F_BB
        w_ai /= torch.norm(w_ai, dim=1, keepdim=True)
        
    gain_opt = torch.abs(torch.sum(w_opt.conj() * A_scan.unsqueeze(0), dim=1))**2
    bp_opt = 10 * torch.log10(gain_opt).cpu().numpy().flatten()
    bp_opt -= np.max(bp_opt)
    
    gain_ai = torch.abs(torch.sum(w_ai.conj() * A_scan.unsqueeze(0), dim=1))**2
    bp_ai = 10 * torch.log10(gain_ai).cpu().numpy().flatten()
    bp_ai -= np.max(bp_ai)
    
    line_opt.set_data(theta_scan.cpu().numpy(), bp_opt)
    line_ai.set_data(theta_scan.cpu().numpy(), bp_ai)
    vline_t.set_xdata([t_ang, t_ang])
    vline_c.set_xdata([c_ang, c_ang])
    
    title_text.set_text(f'AI 端到端波束实时追踪: 目标 {t_ang:.1f}° | 强杂波 {c_ang:.1f}°')
    return line_opt, line_ai, vline_t, vline_c, title_text

# 渲染动图
anim = animation.FuncAnimation(fig, update, frames=frames_count, interval=100, blit=False)
anim.save('E:\\Coding\\graduation design\\MIMO_Beamforming_Simulation_Platform\\final\\python\\Deeplearning\\AI_Beam_Tracking.gif', writer='pillow', fps=10)