Lab056: compare 20 and 500 ksymbol/s in controlled RF session
This commit is contained in:
LittleSam129
312
experiments/lab056_abab_energy.py
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312
experiments/lab056_abab_energy.py
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"""Lab056: программная подготовка ABAB и диагностика сохранённого RX."""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import sys
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if __package__ in (None, ""):
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import numpy as np
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from scipy import signal
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from experiments import lab043_pluto_to_rtlsdr as shaping
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from experiments import lab055_energy_audit as energy
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SAMPLE_RATE = 8_000_000
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CARRIER = 50_000
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GAP_SAMPLES = 800_000
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RX_SAMPLES = 24_000_000
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RX_LEAD_SECONDS = .5
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RAW_DIRECTORY = energy.PROJECT_ROOT / "data/raw/lab056"
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OUTPUT = energy.PROJECT_ROOT / "data/processed/lab056"
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def save_new(path: Path, value: dict) -> None:
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text = json.dumps(value, ensure_ascii=False, indent=2, allow_nan=False) + "\n"
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("x", encoding="utf-8") as stream:
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stream.write(text)
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def build_burst(symbol_rate: int) -> np.ndarray:
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"""Прежние PRBS7/BPSK/RRC, 204,8 мс символов, RMS всего burst=44."""
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if symbol_rate not in (20000, 500000):
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raise ValueError("Разрешены только два исследуемых режима")
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symbols = 2. * shaping.pilot_prbs7(4096 * (symbol_rate // 20000)) - 1.
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samples_per_symbol = SAMPLE_RATE // symbol_rate
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waveform = shaping.shape_frame_like_lab042(symbols, samples_per_symbol)
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waveform *= np.exp(2j * np.pi * CARRIER * np.arange(len(waveform)) / SAMPLE_RATE)
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waveform *= 44 / np.sqrt(np.mean(abs(waveform)**2))
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values = np.empty(2 * len(waveform), dtype=np.int8)
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values[::2] = np.clip(np.rint(waveform.real), -128, 127).astype(np.int8)
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values[1::2] = np.clip(np.rint(waveform.imag), -128, 127).astype(np.int8)
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return values
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def prepare(directory: Path = RAW_DIRECTORY) -> dict:
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"""Создать единственный конечный TX-файл; аппаратная частота не назначена."""
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directory.mkdir(parents=True, exist_ok=True)
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tx_path, metadata_path = directory / "tx_abab.cs8", directory / "tx_metadata.json"
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if tx_path.exists() or metadata_path.exists():
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raise FileExistsError("Артефакты Lab056 уже существуют")
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bursts = {rate: build_burst(rate) for rate in (20000, 500000)}
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segments = []
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cursor = GAP_SAMPLES
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with tx_path.open("xb") as stream:
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stream.write(bytes(2 * GAP_SAMPLES))
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for label, rate in (("A1", 20000), ("B1", 500000), ("A2", 20000), ("B2", 500000)):
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values = bursts[rate]
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count = len(values) // 2
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complex_values = values[::2].astype(np.float32) + 1j * values[1::2].astype(np.float32)
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whole, active = energy.Moments(), energy.Moments()
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whole.add(complex_values)
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trim = 21 * (SAMPLE_RATE // rate)
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active.add(complex_values[trim:-trim])
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segments.append({"label": label, "symbol_rate": rate, "start": cursor,
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"samples": count, "active_start": cursor + trim, "active_samples": count - 2 * trim,
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"whole": whole.result(rate), "active": active.result(rate)})
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stream.write(values.tobytes())
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stream.write(bytes(2 * GAP_SAMPLES))
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cursor += count + GAP_SAMPLES
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result = {"stage": "PREPARATION_ONLY", "rf_tx": 0, "rf_frequency_hz": None,
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"physical_path": "OTA_REQUESTED_FREQUENCY_AND_GEOMETRY_PENDING", "sample_rate_hz": SAMPLE_RATE,
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"carrier_hz": CARRIER, "tx_samples": cursor, "tx_bytes": 2 * cursor,
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"tx_seconds": cursor / SAMPLE_RATE, "rx_samples": RX_SAMPLES,
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"rx_seconds": RX_SAMPLES / SAMPLE_RATE, "rx_lead_seconds": RX_LEAD_SECONDS,
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"gap_seconds": GAP_SAMPLES / SAMPLE_RATE, "gap_is_zero_digital_iq_not_rf_off": True,
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"tx_sha256": energy.sha256(tx_path), "segments": segments,
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"whole_power_difference_db": float(10 * np.log10(segments[1]["whole"]["power_code2"] / segments[0]["whole"]["power_code2"])),
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"active_power_difference_db": float(10 * np.log10(segments[1]["active"]["power_code2"] / segments[0]["active"]["power_code2"]))}
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if abs(result["whole_power_difference_db"]) > .01 or any(s["whole"]["clipped_samples_fraction"] for s in segments):
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raise RuntimeError("Цифровая подготовка не прошла проверку мощности/клиппинга")
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save_new(metadata_path, result)
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return result
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def locate_sequence(tx: energy.Cs8File, rx: energy.Cs8File) -> dict:
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"""Диагностическое согласование полной ABAB; TX reference разрешён.
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Грубая сетка 32 отсчёта использует всю последовательность, затем начало
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каждого burst уточняется прежним методом Lab055 на обучающей плитке.
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Прореживание без антиалиасинга здесь только для корреляционного поиска,
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а не для измерений мощности или спектра.
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"""
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stride = 32
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def decimated(source):
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chunks = [source.read(start, min(262144, source.size - start))[::stride]
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for start in range(0, source.size, 262144)]
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return np.concatenate(chunks).astype(np.complex128)
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reference, received = decimated(tx), decimated(rx)
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if len(received) < len(reference):
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raise ValueError("RX короче TX")
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reference -= reference.mean()
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received -= received.mean()
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cumulative = np.concatenate(([0.], np.cumsum(abs(received)**2)))
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window_energy = cumulative[len(reference):] - cumulative[:-len(reference)]
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reference_energy = np.sum(abs(reference)**2)
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time = np.arange(len(reference)) * stride / SAMPLE_RATE
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best = (-1., 0, 0.)
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for frequency in np.arange(-2., 2.01, .25):
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rotated = reference * np.exp(2j * np.pi * frequency * time)
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correlation = signal.fftconvolve(received, np.conj(rotated[::-1]), mode="valid")
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scores = abs(correlation) / np.sqrt(np.maximum(reference_energy * window_energy, 1e-30))
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index = int(np.argmax(scores))
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if scores[index] > best[0]:
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best = (float(scores[index]), index * stride, float(frequency))
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# Это качество диагностического совпадения, не критерий PHY/CRC.
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if best[0] < .05:
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raise ValueError("ABAB не локализована достаточно уверенно; измерение UNKNOWN")
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return {"start": best[1], "correlation": best[0], "coarse_cfo_hz": best[2], "stride": stride}
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class OffsetSource:
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def __init__(self, source: energy.Cs8File, offset: int):
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self.source, self.offset = source, offset
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def read(self, start: int, count: int) -> np.ndarray:
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return self.source.read(self.offset + start, count)
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def window_power(values: np.ndarray, symbol_rate: int) -> dict:
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moments = energy.Moments()
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moments.add(values)
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result = moments.result(symbol_rate)
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centered = values - values.mean()
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frequencies, density = energy.spectrum(centered)
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for width in (27000, 675000):
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result[f"band_{width}_power"] = energy.band_power(frequencies, density, CARRIER - width / 2, CARRIER + width / 2)
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size = 32000 # Неперекрывающиеся блоки по 4 мс для оценки разброса мощности.
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powers = [float(np.mean(abs(centered[start:start + size])**2)) for start in range(0, len(centered) - size + 1, size)]
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result["power_standard_error"] = float(np.std(powers, ddof=1) / np.sqrt(len(powers))) if len(powers) > 1 else None
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return result
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def difference_db(new: float | None, old: float | None) -> float | None:
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if new is None or old is None or new <= 0 or old <= 0:
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return None
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return float(10 * np.log10(new / old))
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def complex_gain(reference: np.ndarray, received: np.ndarray, cfo_hz: float) -> dict:
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"""Вывести комплексный h прежней модели; те же чётные обучающие плитки."""
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boundaries = np.linspace(0, len(reference), 9, dtype=int)
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train = np.zeros(len(reference), dtype=bool)
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for tile in range(0, 8, 2):
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train[boundaries[tile]:boundaries[tile + 1]] = True
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rotated = reference * np.exp(2j * np.pi * cfo_hz * np.arange(len(reference)) / SAMPLE_RATE)
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x, y = rotated[train], received[train].astype(np.complex128)
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x, y = x - x.mean(), y - y.mean()
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gain = np.vdot(x, y) / max(float(np.sum(abs(x)**2)), 1e-30)
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return {"real": float(gain.real), "imag": float(gain.imag),
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"phase_rad_at_active_start": float(np.angle(gain))}
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def comparison_summary(rows: list[dict]) -> dict:
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"""Сравнить повторы отдельно; X/Z используют средние линейные мощности."""
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by_label = {row["label"]: row for row in rows}
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repeatability = []
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for mode in ("A", "B"):
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first, second = by_label[mode + "1"], by_label[mode + "2"]
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repeatability.append({"comparison": mode + "2/" + mode + "1",
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"raw_rx_power_difference_db": difference_db(second["raw_rx"]["power_code2"], first["raw_rx"]["power_code2"]),
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"background_difference_db": difference_db(second["background_power"], first["background_power"]),
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"received_power_difference_db": difference_db(second["background_subtracted_signal_power"], first["background_subtracted_signal_power"]),
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"channel_gain_difference_db": difference_db(second["channel_gain_power"], first["channel_gain_power"]),
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"matched_component_difference_db": difference_db(second["measurement"]["heldout_signal_power"], first["measurement"]["heldout_signal_power"])})
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def mean(mode, key):
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values = [by_label[mode + str(number)][key] for number in (1, 2)]
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return None if any(value is None or value <= 0 for value in values) else float(np.mean(values))
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a_power, b_power = mean("A", "background_subtracted_signal_power"), mean("B", "background_subtracted_signal_power")
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return {"repeatability": repeatability,
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"averaging": "RATIO_OF_MEAN_LINEAR_POWERS_B1_B2_OVER_A1_A2",
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"received_power_difference_db": difference_db(b_power, a_power),
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"channel_gain_difference_db": difference_db(mean("B", "channel_gain_power"), mean("A", "channel_gain_power")),
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"energy_per_symbol_difference_db": difference_db(None if b_power is None else b_power / 500000,
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None if a_power is None else a_power / 20000),
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"energy_basis": "BACKGROUND_SUBTRACTED_RECEIVED_POWER_DIVIDED_BY_SYMBOL_RATE"}
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def analyze(directory: Path, rx_path: Path) -> dict:
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metadata = energy.read_json(directory / "tx_metadata.json")
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tx_path = directory / "tx_abab.cs8"
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if energy.sha256(tx_path) != metadata["tx_sha256"]:
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raise ValueError("TX SHA не совпал")
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tx, rx = energy.Cs8File(tx_path), energy.Cs8File(rx_path)
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alignment = locate_sequence(tx, rx)
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rows = []
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for segment in metadata["segments"]:
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rate, count = segment["symbol_rate"], segment["samples"]
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start = alignment["start"] + segment["start"]
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measurement = energy.analyze_rx(OffsetSource(tx, segment["start"]), rx, [start], count,
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SAMPLE_RATE // rate)["frames"][0]
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start = measurement["start"]
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trim = 21 * (SAMPLE_RATE // rate)
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received_active = rx.read(start + trim, count - 2 * trim)
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raw = window_power(received_active, rate)
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gain = complex_gain(tx.read(segment["start"] + trim, count - 2 * trim), received_active, measurement["cfo_hz"])
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if not np.isclose(gain["real"]**2 + gain["imag"]**2, measurement["gain_abs"]**2, rtol=1e-10, atol=1e-15):
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raise ValueError("Вывод комплексного h расходится с прежней моделью Lab055")
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windows = [window_power(rx.read(begin, 480000), rate) for begin in
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(start - 640000, start + count + 160000)]
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background = float(np.mean([w["ac_power_code2"] for w in windows]))
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excess = raw["ac_power_code2"] - background
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uncertainty = float(np.sqrt(raw["power_standard_error"]**2 +
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sum(w["power_standard_error"]**2 for w in windows) / 4 +
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((windows[0]["ac_power_code2"] - windows[1]["ac_power_code2"]) / 2)**2))
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stable = excess > 3 * uncertainty
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rows.append({"label": segment["label"], "symbol_rate": rate, "raw_rx": raw,
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"local_zero_iq_background_windows": windows, "background_power": background,
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"background_subtracted_raw": excess, "background_subtracted_uncertainty": uncertainty,
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"background_subtracted_stable": stable, "background_subtracted_signal_power": excess if stable else None,
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"channel_gain_complex": gain, "channel_gain_power": measurement["gain_abs"]**2, "measurement": measurement})
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by_label = {r["label"]: r for r in rows}
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pairs = []
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for number in (1, 2):
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a, b = by_label[f"A{number}"], by_label[f"B{number}"]
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pairs.append({"pair": number,
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"received_power_difference_db": difference_db(b["background_subtracted_signal_power"], a["background_subtracted_signal_power"]),
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"channel_gain_difference_db": difference_db(b["channel_gain_power"], a["channel_gain_power"]),
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"matched_component_difference_db": difference_db(b["measurement"]["heldout_signal_power"], a["measurement"]["heldout_signal_power"]),
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"matched_component_symbol_energy_difference_db": difference_db(b["measurement"]["heldout_signal_power"] / b["symbol_rate"], a["measurement"]["heldout_signal_power"] / a["symbol_rate"])})
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# До начала и после конца всего цифрового файла: отдельные внешние окна.
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outside = []
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for begin in (alignment["start"] - 800000, alignment["start"] + tx.size + 400000):
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if 0 <= begin and begin + 400000 <= rx.size:
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outside.append({"start": begin, **window_power(rx.read(begin, 400000), 20000)})
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return {"scope": "TX_REFERENCE_DIAGNOSTICS_NOT_INDEPENDENT_PACKET_RX", "rf_tx": 0,
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"tx_sha256": metadata["tx_sha256"], "rx_sha256": energy.sha256(rx_path),
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"alignment": alignment, "segments": rows, "pairs": pairs, "comparison_summary": comparison_summary(rows),
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"outside_waveform_background": outside,
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"background_note": "Внутренние gaps: нулевой цифровой IQ при работающем TX; это не RF OFF. Внешние окна также требуют сверки времён запуска/остановки по журналам.",
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"power_units": "RX code squared, not calibrated watts/dBm",
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"residual_note": "Шум и неописанные моделью искажения; RX не нормируется для выравнивания A/B"}
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def software_capture(directory: Path, b_gain: float, name: str) -> dict:
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"""Синтетический CS8 с известным усилением, DC и CFO; не аппаратная запись."""
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metadata = energy.read_json(directory / "tx_metadata.json")
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tx = energy.Cs8File(directory / "tx_abab.cs8")
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path = directory / f"synthetic_{name}.cs8"
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random_generator = np.random.default_rng(56056)
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lead = 4_160_013
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with path.open("xb") as stream:
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for low in range(0, RX_SAMPLES, 262144):
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count = min(262144, RX_SAMPLES - low)
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values = (1 + 2j + .6 * (random_generator.normal(size=count) + 1j * random_generator.normal(size=count)))
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first, last = max(low, lead), min(low + count, lead + tx.size)
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if first < last:
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source = tx.read(first - lead, last - first)
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gains = np.full(len(source), .03)
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indexes = np.arange(first - lead, last - lead)
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for segment in metadata["segments"]:
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if segment["label"].startswith("B"):
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gains[(indexes >= segment["start"]) & (indexes < segment["start"] + segment["samples"])] = b_gain
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values[first - low:last - low] += source * gains * np.exp(1j * (.7 + 2 * np.pi * .2 * np.arange(first, last) / SAMPLE_RATE))
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packed = np.empty(2 * count, dtype=np.int8)
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packed[::2] = np.clip(np.rint(values.real), -128, 127)
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packed[1::2] = np.clip(np.rint(values.imag), -128, 127)
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stream.write(packed.tobytes())
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result = analyze(directory, path)
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expected = 20 * np.log10(b_gain / .03)
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errors = [abs(p["channel_gain_difference_db"] - expected) for p in result["pairs"]]
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result.update(synthetic=True, expected_gain_difference_db=float(expected),
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known_sequence_start=lead, passed=bool(max(errors) < .3))
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if not result["passed"]:
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raise RuntimeError(f"Синтетическая проверка не прошла: {errors}")
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return result
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def run_functional_tests() -> None:
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assert difference_db(.25, 1) == 10 * np.log10(.25)
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assert difference_db(None, 1) is None
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def main() -> None:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument("stage", choices=("prepare", "software", "analyze"))
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parser.add_argument("--rx", type=Path)
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parser.add_argument("--output", type=Path)
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args = parser.parse_args()
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if args.stage == "prepare":
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result = prepare()
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save_new(OUTPUT / "preparation.json", result)
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elif args.stage == "software":
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for name, gain in (("equal", .03), ("minus6db", .015)):
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result = software_capture(RAW_DIRECTORY, gain, name)
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save_new(OUTPUT / f"software_{name}.json", result)
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print(name, result["pairs"], flush=True)
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return
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else:
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if args.rx is None or args.output is None:
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parser.error("Для сохранённого RX нужны --rx и новый --output")
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result = analyze(RAW_DIRECTORY, args.rx)
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save_new(args.output, result)
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print(json.dumps(result, ensure_ascii=False, indent=2))
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if __name__ == "__main__":
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main()
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