f3c77b1750
- Implemented the WiFi DensePose model in PyTorch, including CSI phase processing, modality translation, and DensePose prediction heads. - Added a comprehensive training utility for the model, including loss functions and training steps. - Created a CSV file to document hardware specifications, architecture details, training parameters, performance metrics, and advantages of the model.
97 lines
3.5 KiB
Python
97 lines
3.5 KiB
Python
# WiFi DensePose Implementation - Core Neural Network Architecture
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# Based on "DensePose From WiFi" by Carnegie Mellon University
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import numpy as np
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import math
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from typing import Dict, List, Tuple, Optional
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from collections import OrderedDict
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# CSI Phase Sanitization Module
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class CSIPhaseProcessor:
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"""
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Processes raw CSI phase data through unwrapping, filtering, and linear fitting
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Based on the phase sanitization methodology from the paper
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"""
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def __init__(self, num_subcarriers: int = 30):
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self.num_subcarriers = num_subcarriers
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def unwrap_phase(self, phase_data: np.ndarray) -> np.ndarray:
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"""
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Unwrap phase values to handle discontinuities
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Args:
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phase_data: Raw phase data of shape (samples, frequencies, antennas, antennas)
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Returns:
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Unwrapped phase data
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"""
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unwrapped = np.copy(phase_data)
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for i in range(1, phase_data.shape[1]): # Along frequency dimension
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diff = unwrapped[:, i] - unwrapped[:, i-1]
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# Apply unwrapping logic
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unwrapped[:, i] = np.where(diff > np.pi,
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unwrapped[:, i-1] + diff - 2*np.pi,
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unwrapped[:, i])
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unwrapped[:, i] = np.where(diff < -np.pi,
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unwrapped[:, i-1] + diff + 2*np.pi,
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unwrapped[:, i])
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return unwrapped
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def apply_filters(self, phase_data: np.ndarray) -> np.ndarray:
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"""
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Apply median and uniform filters to eliminate outliers
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"""
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from scipy.ndimage import median_filter, uniform_filter
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# Apply median filter in time domain
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filtered = median_filter(phase_data, size=(3, 1, 1, 1))
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# Apply uniform filter in frequency domain
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filtered = uniform_filter(filtered, size=(1, 3, 1, 1))
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return filtered
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def linear_fitting(self, phase_data: np.ndarray) -> np.ndarray:
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"""
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Apply linear fitting to remove systematic phase drift
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"""
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fitted_data = np.copy(phase_data)
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F = self.num_subcarriers
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for sample_idx in range(phase_data.shape[0]):
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for ant_i in range(phase_data.shape[2]):
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for ant_j in range(phase_data.shape[3]):
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phase_seq = phase_data[sample_idx, :, ant_i, ant_j]
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# Calculate linear coefficients
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alpha1 = (phase_seq[-1] - phase_seq[0]) / (2 * np.pi * F)
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alpha0 = np.mean(phase_seq)
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# Apply linear fitting
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frequencies = np.arange(1, F + 1)
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linear_trend = alpha1 * frequencies + alpha0
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fitted_data[sample_idx, :, ant_i, ant_j] = phase_seq - linear_trend
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return fitted_data
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def sanitize_phase(self, raw_phase: np.ndarray) -> np.ndarray:
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"""
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Complete phase sanitization pipeline
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"""
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# Step 1: Unwrap phase
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unwrapped = self.unwrap_phase(raw_phase)
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# Step 2: Apply filters
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filtered = self.apply_filters(unwrapped)
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# Step 3: Linear fitting
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sanitized = self.linear_fitting(filtered)
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return sanitized
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print("CSI Phase Processor implementation completed!") |