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david1465833828
2026-05-07 11:23:07 +08:00
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Code/Python/4.Test Platform Software/Massive_Mimo_Beamforming_Testing_Platform.py
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import math
import time
import numpy as np
import tkinter as tk
from tkinter import ttk, messagebox, filedialog
import torch
import matplotlib as mpl
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from matplotlib import font_manager
# =========================================================
# 0. 全局设置
# =========================================================
def configure_matplotlib_fonts():
"""自动选择可用中文字体，修复中文显示问题。"""
candidate_fonts = [
"Microsoft YaHei",
"SimHei",
"Noto Sans CJK SC",
"Noto Serif CJK SC",
"WenQuanYi Zen Hei",
"AR PL UMing CN",
"PingFang SC",
"Heiti SC",
"STHeiti",
"Songti SC",
]
installed = {f.name for f in font_manager.fontManager.ttflist}
selected = None
for name in candidate_fonts:
if name in installed:
selected = name
break
mpl.rcParams["axes.unicode_minus"] = False
mpl.rcParams["mathtext.fontset"] = "stix"
if selected:
mpl.rcParams["font.sans-serif"] = [selected]
else:
mpl.rcParams["font.sans-serif"] = ["DejaVu Sans"]
configure_matplotlib_fonts()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch.manual_seed(2024)
np.random.seed(2024)
L_SNAPSHOTS_DEFAULT = 100
MC_DEFAULT = 100
# 颜色表：修复 METHOD_COLORS 未定义
METHOD_COLORS = {
"Analog": "#77AC30",
"Digital": "#000000",
"DFT": "#4DBEEE",
"OMP": "#D95319",
"TSC-OMP": "#0072BD",
}
# =========================================================
# 1. 算法引擎
# =========================================================
class BeamEngine:
def __init__(self):
self.device = device
self.N_ant = 24
self.N_RF = 4
self.noise_power = 1.0
self.INR_linear = 10 ** (30 / 10)
# ---------- 阵列模型 ----------
def get_positions_lambda(self, array_type: str, n_ant: int):
array_type = array_type.upper()
if array_type == "ULA":
pos = 0.5 * np.arange(-(n_ant - 1) / 2, (n_ant - 1) / 2 + 1)
return pos.astype(np.float32)
# 24 阵元 NULA：沿用现有主线代码
if array_type == "NULA" and n_ant == 24:
fc = 4.9e9
lam = 3e8 / fc
a = np.arange(0, 11 * 43 + 1, 43)
b = np.array([11 * 43 + 63])
c = 11 * 43 + 63 + np.arange(43, 11 * 43 + 1, 43)
delta_d = 1e-3 * np.concatenate((a, b, c))
pos = delta_d / lam
return pos.astype(np.float32)
# 兜底广义 NULA
base = 0.5 * np.arange(n_ant)
jitter = 0.08 * np.sin(np.linspace(0, 3 * np.pi, n_ant))
pos = base + jitter
pos = pos - np.mean(pos)
return pos.astype(np.float32)
def gen_a(self, theta_deg, array_type="ULA"):
pos = self.get_positions_lambda(array_type, self.N_ant)
pos_t = torch.tensor(pos, dtype=torch.float32, device=self.device).unsqueeze(1)
theta_rad = torch.deg2rad(torch.tensor(theta_deg, dtype=torch.float32, device=self.device))
if array_type.upper() == "ULA":
return torch.exp(1j * 2 * torch.pi * pos_t * torch.sin(theta_rad))
return torch.exp(-1j * 2 * torch.pi * pos_t * torch.sin(theta_rad))
def build_dictionary(self, angle_grid, array_type="ULA"):
return torch.cat(
[self.gen_a(float(th), array_type) / math.sqrt(self.N_ant) for th in angle_grid],
dim=1
)
# ---------- 子程序 ----------
def extract_omp(self, w_opt, a_dic):
res = w_opt.clone()
f_rf = None
for _ in range(self.N_RF):
proj = a_dic.mH @ res
idx = torch.argmax(torch.abs(proj))
atom = a_dic[:, idx:idx + 1]
f_rf = atom if f_rf is None else torch.cat((f_rf, atom), dim=1)
res = w_opt - f_rf @ (torch.linalg.pinv(f_rf) @ w_opt)
return f_rf
def make_prior_broad(self, theta_prior, window_half_width, array_type="ULA"):
a_prior_broad = torch.zeros((self.N_ant, 1), dtype=torch.complex64, device=self.device)
for tb in torch.arange(theta_prior - window_half_width,
theta_prior + window_half_width + 0.1,
0.5,
device=self.device):
a_prior_broad += self.gen_a(float(tb), array_type)
a_prior_broad /= torch.norm(a_prior_broad)
return a_prior_broad
def calc_hybrid_weight(self, f_rf, a_target, r_in):
a_eff = f_rf.mH @ a_target
r_eff = f_rf.mH @ r_in @ f_rf
r_eff = r_eff + (0.05 * torch.trace(r_eff).real / self.N_RF) * torch.eye(self.N_RF, device=self.device)
inv_r_eff = torch.linalg.inv(r_eff)
f_bb = (inv_r_eff @ a_eff) / (a_eff.mH @ inv_r_eff @ a_eff)
w = f_rf @ f_bb
return w / torch.norm(w)
def build_all_structures(self, target_angle, clutter_angle, array_type="ULA", window_half_width=5.0):
theta_prior = round(target_angle)
a_target = self.gen_a(target_angle, array_type)
a_clutter = self.gen_a(clutter_angle, array_type)
# 干扰+噪声先验协方差
r_interference_prior = (
self.noise_power * torch.eye(self.N_ant, device=self.device)
+ self.INR_linear * (a_clutter @ a_clutter.mH)
)
a_prior_broad = self.make_prior_broad(theta_prior, window_half_width, array_type)
w_digital_prior = torch.linalg.inv(r_interference_prior) @ a_prior_broad
w_digital_prior /= torch.norm(w_digital_prior)
# 传统 OMP / TSC-OMP 字典
dic_angles_full = np.arange(-90, 90.5, 0.5)
dic_angles_tsc = np.arange(
theta_prior - window_half_width,
theta_prior + window_half_width + 0.1,
0.1
)
a_dic_full = self.build_dictionary(dic_angles_full, array_type)
a_dic_tsc = self.build_dictionary(dic_angles_tsc, array_type)
# DFT 射频矩阵
angles_dft = torch.linspace(
theta_prior - window_half_width,
theta_prior + window_half_width,
self.N_RF,
device=self.device
)
f_rf_dft = torch.cat(
[self.gen_a(float(th), array_type) / math.sqrt(self.N_ant) for th in angles_dft],
dim=1
)
f_rf_omp = self.extract_omp(w_digital_prior, a_dic_full)
f_rf_tsc = self.extract_omp(w_digital_prior, a_dic_tsc)
# 静态理想协方差，用于方向图和纯数字权值
sig_power_static = 10 ** (0 / 10)
r_in_static = (
self.noise_power * torch.eye(self.N_ant, device=self.device)
+ sig_power_static * (a_target @ a_target.mH)
+ self.INR_linear * (a_clutter @ a_clutter.mH)
)
r_in_static_dl = r_in_static + (
0.01 * torch.trace(r_in_static).real / self.N_ant
) * torch.eye(self.N_ant, device=self.device)
w_analog = a_target / math.sqrt(self.N_ant)
inv_r_stat = torch.linalg.inv(r_in_static_dl)
w_digital = (inv_r_stat @ a_target) / (a_target.mH @ inv_r_stat @ a_target)
w_digital /= torch.norm(w_digital)
# DFT / OMP / TSC-OMP 混合
w_dft = self.calc_hybrid_weight(f_rf_dft, a_target, r_in_static)
w_omp = self.calc_hybrid_weight(f_rf_omp, a_target, r_in_static)
w_tsc = self.calc_hybrid_weight(f_rf_tsc, a_target, r_in_static)
return {
"theta_prior": theta_prior,
"a_target": a_target,
"a_clutter": a_clutter,
"w_analog": w_analog,
"w_digital": w_digital,
"w_dft": w_dft,
"w_omp": w_omp,
"w_tsc": w_tsc,
"f_rf_dft": f_rf_dft,
"f_rf_omp": f_rf_omp,
"f_rf_tsc": f_rf_tsc,
}
# ---------- 主功能1：波束方向图 ----------
def beam_patterns(self, target_angle, clutter_angle, array_type="ULA",
methods=None, window_half_width=5.0):
if methods is None:
methods = ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]
structures = self.build_all_structures(target_angle, clutter_angle, array_type, window_half_width)
theta_scan = torch.arange(-90, 90.1, 0.1, device=self.device)
a_scan = torch.cat([self.gen_a(float(th), array_type) for th in theta_scan], dim=1)
weights = {
"Analog": structures["w_analog"],
"Digital": structures["w_digital"],
"DFT": structures["w_dft"],
"OMP": structures["w_omp"],
"TSC-OMP": structures["w_tsc"],
}
result = {"theta_scan": theta_scan.cpu().numpy()}
for method in methods:
w = weights[method]
gain = torch.abs(a_scan.mH @ w) ** 2
bp = 10 * torch.log10((gain / torch.max(gain)).squeeze())
result[method] = bp.cpu().numpy()
return result
# ---------- 主功能2：RMSE 曲线 ----------
def rmse_curves(self, target_angle, clutter_angle, array_type="ULA",
methods=None, window_half_width=5.0,
mc=100, snapshots=100, snr_values=None):
"""
RMSE 计算逻辑参考：
- ULA: RMSE_Test_ula(2).py
- NULA: RMSE_Test_nula(2).py
其中 DFT / OMP / TSC-OMP 均采用先验宽波束 + 低维 Capon 超分辨。
"""
if methods is None:
methods = ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]
if snr_values is None:
snr_values = np.arange(-10, 22, 2)
structures = self.build_all_structures(target_angle, clutter_angle, array_type, window_half_width)
theta_prior = structures["theta_prior"]
a_target = structures["a_target"]
a_clutter = structures["a_clutter"]
scan_grid = torch.arange(
theta_prior - window_half_width,
theta_prior + window_half_width + 0.01,
0.01,
device=self.device
)
a_scan = torch.cat([self.gen_a(float(th), array_type) for th in scan_grid], dim=1)
curves = {m: [] for m in methods}
a_eff_dft = structures["f_rf_dft"].mH @ a_scan
a_eff_omp = structures["f_rf_omp"].mH @ a_scan
a_eff_tsc = structures["f_rf_tsc"].mH @ a_scan
for snr in snr_values:
sig_power = 10 ** (snr / 10)
s_t = np.sqrt(sig_power / 2) * (
torch.randn(mc, 1, snapshots, device=self.device) +
1j * torch.randn(mc, 1, snapshots, device=self.device)
)
s_c = np.sqrt(self.INR_linear / 2) * (
torch.randn(mc, 1, snapshots, device=self.device) +
1j * torch.randn(mc, 1, snapshots, device=self.device)
)
n_t = np.sqrt(self.noise_power / 2) * (
torch.randn(mc, self.N_ant, snapshots, device=self.device) +
1j * torch.randn(mc, self.N_ant, snapshots, device=self.device)
)
x = a_target.unsqueeze(0) * s_t + a_clutter.unsqueeze(0) * s_c + n_t
r_in = torch.bmm(x, x.mH) / snapshots
# 纯模拟：Bartlett
if "Analog" in methods:
p_ana = torch.einsum('si, bij, js -> bs', a_scan.conj().T, r_in, a_scan).abs()
ang_ana = scan_grid[torch.argmax(p_ana, dim=1)]
curves["Analog"].append(torch.sqrt(torch.mean((ang_ana - target_angle) ** 2)).item())
# 纯数字：全维 Capon / MVDR
if "Digital" in methods:
trace_r_in = torch.diagonal(r_in, dim1=-2, dim2=-1).sum(-1).real
r_in_dl = r_in + (
0.05 * trace_r_in / self.N_ant
).view(-1, 1, 1) * torch.eye(self.N_ant, device=self.device).unsqueeze(0)
inv_r_in_dl = torch.linalg.inv(r_in_dl)
p_dig = 1.0 / torch.abs(torch.einsum('si, bij, js -> bs', a_scan.conj().T, inv_r_in_dl, a_scan))
ang_dig = scan_grid[torch.argmax(p_dig, dim=1)]
curves["Digital"].append(torch.sqrt(torch.mean((ang_dig - target_angle) ** 2)).item())
def process_hybrid(f_rf, a_eff):
y = f_rf.mH.unsqueeze(0) @ x
r = (y @ y.mH) / snapshots
r = r + (
0.05 * torch.diagonal(r, dim1=-2, dim2=-1).sum(-1).view(-1, 1, 1) / self.N_RF
) * torch.eye(self.N_RF, device=self.device).unsqueeze(0)
inv_r = torch.linalg.inv(r)
p = 1.0 / torch.abs(torch.einsum('si, bij, js -> bs', a_eff.conj().T, inv_r, a_eff))
ang = scan_grid[torch.argmax(p, dim=1)]
return torch.sqrt(torch.mean((ang - target_angle) ** 2)).item()
if "DFT" in methods:
curves["DFT"].append(process_hybrid(structures["f_rf_dft"], a_eff_dft))
if "OMP" in methods:
curves["OMP"].append(process_hybrid(structures["f_rf_omp"], a_eff_omp))
if "TSC-OMP" in methods:
curves["TSC-OMP"].append(process_hybrid(structures["f_rf_tsc"], a_eff_tsc))
curves["snr"] = np.array(snr_values)
return curves
# =========================================================
# 2. 绘图面板
# =========================================================
class PlotPanel(ttk.Frame):
def __init__(self, master):
super().__init__(master)
self.figure = Figure(figsize=(8.6, 5.8), dpi=100)
self.canvas = FigureCanvasTkAgg(self.figure, master=self)
self.canvas_widget = self.canvas.get_tk_widget()
self.canvas_widget.pack(fill="both", expand=True)
self.toolbar = NavigationToolbar2Tk(self.canvas, self, pack_toolbar=False)
self.toolbar.update()
self.toolbar.pack(fill="x")
def redraw(self):
self.canvas.draw_idle()
def save_figure(self):
path = filedialog.asksaveasfilename(
defaultextension=".png",
filetypes=[("PNG 图片", "*.png"), ("PDF 文件", "*.pdf"), ("SVG 文件", "*.svg")]
)
if path:
self.figure.savefig(path, dpi=300, bbox_inches="tight")
return path
return None
# =========================================================
# 3. GUI 主体
# =========================================================
class HBFWorkbench(tk.Tk):
def __init__(self):
super().__init__()
self.title("大规模阵列 MIMO 波束优化综合平台")
self.geometry("1450x900")
self.minsize(1200, 760)
self.engine = BeamEngine()
self._setup_style()
self.status_var = tk.StringVar(value=f"就绪 | 设备: {device}")
self._build_ui()
def _setup_style(self):
style = ttk.Style(self)
try:
style.theme_use("clam")
except Exception:
pass
style.configure("TNotebook.Tab", padding=(14, 8), font=("Microsoft YaHei", 10))
style.configure("Header.TLabel", font=("Microsoft YaHei", 12, "bold"))
style.configure("Accent.TButton", font=("Microsoft YaHei", 10, "bold"))
def _build_ui(self):
main = ttk.Frame(self)
main.pack(fill="both", expand=True, padx=10, pady=10)
left = ttk.Frame(main)
left.pack(side="left", fill="y", padx=(0, 8))
right = ttk.Frame(main)
right.pack(side="right", fill="both", expand=True)
# ---------- 参数卡 ----------
card1 = ttk.LabelFrame(left, text="场景参数")
card1.pack(fill="x", pady=(0, 8))
self.var_target = tk.DoubleVar(value=5.0)
self.var_clutter = tk.DoubleVar(value=-15.0)
self.var_window = tk.DoubleVar(value=5.0)
self.var_mc = tk.IntVar(value=MC_DEFAULT)
self.var_snap = tk.IntVar(value=L_SNAPSHOTS_DEFAULT)
self.var_array = tk.StringVar(value="ULA")
self.var_function = tk.StringVar(value="beam")
self._add_labeled_entry(card1, "目标角度 (°)", self.var_target)
self._add_labeled_entry(card1, "干扰角度 (°)", self.var_clutter)
self._add_labeled_entry(card1, "波门半宽 (°)", self.var_window)
self._add_labeled_entry(card1, "Monte Carlo", self.var_mc)
self._add_labeled_entry(card1, "快拍数", self.var_snap)
row = ttk.Frame(card1)
row.pack(fill="x", pady=4)
ttk.Label(row, text="阵列类型", width=12).pack(side="left")
ttk.Combobox(row, textvariable=self.var_array, values=["ULA", "NULA"], state="readonly", width=12).pack(side="left")
# ---------- 功能卡 ----------
card2 = ttk.LabelFrame(left, text="功能选择")
card2.pack(fill="x", pady=(0, 8))
ttk.Radiobutton(card2, text="波束方向图显示", variable=self.var_function, value="beam").pack(anchor="w")
ttk.Radiobutton(card2, text="测角 RMSE 曲线", variable=self.var_function, value="rmse").pack(anchor="w")
# ---------- 算法卡 ----------
card3 = ttk.LabelFrame(left, text="算法选择（可多选）")
card3.pack(fill="x", pady=(0, 8))
self.alg_vars = {
"Analog": tk.BooleanVar(value=True),
"Digital": tk.BooleanVar(value=True),
"DFT": tk.BooleanVar(value=True),
"OMP": tk.BooleanVar(value=True),
"TSC-OMP": tk.BooleanVar(value=True),
}
labels = {
"Analog": "纯模拟",
"Digital": "纯数字(MVDR)",
"DFT": "DFT",
"OMP": "传统OMP",
"TSC-OMP": "TSC-OMP",
}
for k in ["Analog", "Digital", "DFT", "OMP", "TSC-OMP"]:
ttk.Checkbutton(card3, text=labels[k], variable=self.alg_vars[k]).pack(anchor="w")
# ---------- 按钮 ----------
btns = ttk.Frame(left)
btns.pack(fill="x", pady=(4, 8))
ttk.Button(btns, text="运行", style="Accent.TButton", command=self.run_main_task).pack(fill="x", pady=3)
ttk.Button(btns, text="保存当前图", command=lambda: self._save_panel(self.main_plot)).pack(fill="x", pady=3)
# ---------- 说明 ----------
note = ttk.LabelFrame(left, text="说明")
note.pack(fill="x", pady=(0, 8))
ttk.Label(
note,
text="• 设置好您所需的参数，算法可以多选\n"
"• 选择您所需要的图表，有波束方向图和测角RMSE比较图可选\n"
"• 单击“运行”即可输出所需图表，可以随时保存",
justify="left"
).pack(anchor="w", padx=4, pady=4)
# ---------- 绘图区域 ----------
self.main_plot = PlotPanel(right)
self.main_plot.pack(fill="both", expand=True)
# ---------- 状态栏 ----------
status = ttk.Label(self, textvariable=self.status_var, anchor="w")
status.pack(fill="x", padx=10, pady=(0, 8))
def _add_labeled_entry(self, master, label, var, width=12):
row = ttk.Frame(master)
row.pack(fill="x", pady=4)
ttk.Label(row, text=label, width=12).pack(side="left")
ttk.Entry(row, textvariable=var, width=width).pack(side="left")
def _save_panel(self, panel):
path = panel.save_figure()
if path:
self.status_var.set(f"已保存图像: {path}")
def get_selected_methods(self):
return [k for k, v in self.alg_vars.items() if v.get()]
def _set_busy(self, msg):
self.config(cursor="watch")
self.status_var.set(msg)
self.update_idletasks()
def _set_idle(self, msg="就绪"):
self.config(cursor="")
self.status_var.set(msg)
self.update_idletasks()
def run_main_task(self):
try:
target = float(self.var_target.get())
clutter = float(self.var_clutter.get())
array_type = self.var_array.get()
func = self.var_function.get()
window = float(self.var_window.get())
mc = int(self.var_mc.get())
snapshots = int(self.var_snap.get())
methods = self.get_selected_methods()
if not methods:
messagebox.showwarning("提示", "请至少选择一种算法。")
return
t0 = time.time()
self._set_busy("正在运行，请稍候...")
fig = self.main_plot.figure
fig.clear()
ax = fig.add_subplot(111)
if func == "beam":
data = self.engine.beam_patterns(target, clutter, array_type, methods, window)
theta = data["theta_scan"]
name_map = {
"Analog": "纯模拟",
"Digital": "纯数字(MVDR)",
"DFT": "DFT",
"OMP": "传统OMP",
"TSC-OMP": "TSC-OMP",
}
ls_map = {
"Analog": "-.",
"Digital": "-",
"DFT": "--",
"OMP": ":",
"TSC-OMP": "-",
}
for m in methods:
ax.plot(
theta,
data[m],
linestyle=ls_map[m],
color=METHOD_COLORS[m],
linewidth=2.4,
label=name_map[m]
)
ax.axvline(target, color="g", linestyle="--", linewidth=1.8, label=f"目标 ({target:.2f}°)")
ax.axvline(clutter, color="r", linestyle="--", linewidth=1.8, label=f"干扰 ({clutter:.2f}°)")
ax.set_xlim([-60, 60])
ax.set_ylim([-60, 2])
ax.set_title(f"波束方向图 ({array_type})", fontsize=13, fontweight="bold")
ax.set_xlabel("角度 θ (°)")
ax.set_ylabel("归一化增益 (dB)")
ax.grid(True, ls="--", alpha=0.6)
ax.legend(loc="lower center", ncol=min(4, len(methods) + 2), fontsize=9)
else:
curves = self.engine.rmse_curves(
target_angle=target,
clutter_angle=clutter,
array_type=array_type,
methods=methods,
window_half_width=window,
mc=mc,
snapshots=snapshots,
)
name_map = {
"Analog": "纯模拟",
"Digital": "纯数字(MVDR)",
"DFT": "DFT",
"OMP": "传统OMP",
"TSC-OMP": "TSC-OMP",
}
marker_map = {
"Analog": "v",
"Digital": "o",
"DFT": "d",
"OMP": "*",
"TSC-OMP": "s",
}
for m in methods:
ax.semilogy(
curves["snr"],
curves[m],
marker=marker_map[m],
color=METHOD_COLORS[m],
linewidth=2.2,
label=name_map[m]
)
ax.set_title(f"测角 RMSE-SNR 曲线 ({array_type})", fontsize=13, fontweight="bold")
ax.set_xlabel("输入 SNR (dB)")
ax.set_ylabel("RMSE (°)")
ax.set_ylim([1e-3, 20])
ax.grid(True, which="both", ls="--", alpha=0.6)
ax.legend()
fig.tight_layout()
self.main_plot.redraw()
self._set_idle(f"运行完成，用时 {time.time() - t0:.2f}s | 设备: {device}")
except Exception as e:
self._set_idle("运行失败")
messagebox.showerror("错误", str(e))
if __name__ == "__main__":
app = HBFWorkbench()
app.mainloop()