wsdp 0.4.0

Wi-Fi Sensing Data Processing - A Python library for downloading, processing, analyzing and training on Wi-Fi CSI data

SDP: Sensing Data Protocol for Scalable Wireless Sensing

Published and maintained by SDP8.org — the official platform for reproducible wireless sensing.

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📖 Citation

If you use SDP in your research, please cite:

@misc{zhang2026sdpunifiedprotocolbenchmarking,
      title={SDP: A Unified Protocol and Benchmarking Framework for Reproducible Wireless Sensing}, 
      author={Di Zhang and Jiawei Huang and Yuanhao Cui and Xiaowen Cao and Tony Xiao Han and Xiaojun Jing and Christos Masouros},
      year={2026},
      eprint={2601.08463},
      archivePrefix={arXiv},
      primaryClass={eess.SP},
      url={https://arxiv.org/abs/2601.08463}, 
}

---

🇬🇧 English | 🇨🇳 中文

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🇬🇧 English

🆕 What's New in v0.4.0

• 7 critical scientific bug fixes -- corrected phase calibration, wavelet boundary handling, Doppler normalization, and more
• 7 new SOTA models -- THAT, CSITime, PA_CSI, WiFlexFormer, AttentionGRU, EI, FewSense (19 total)
• 6 new preprocessing algorithms -- conjugate_multiply, agc_compensate, pca_fusion, bandpass, hampel, decimate (26+ total)
• Benchmark leaderboard -- standardized model comparison across datasets
• CI/CD pipeline -- automated testing, linting, and release workflow
• Experiment tracking & caching -- reproducible runs with result caching
• GroupKFold cross-validation -- user-aware evaluation to prevent data leakage

---

🎯 What is SDP?

SDP is a protocol-level abstraction and unified benchmark for reproducible wireless sensing.

⚠️ SDP is not a new neural network, but a standardized protocol that unifies CSI representations for fair comparison.

🆚 Why Choose WSDP?

Capability	WSDP	SenseFi (2023)	CSIKit
Built-in Models	19 (MLP→Mamba/GNN)	11 (MLP→ViT)	❌
Preprocessing Algorithms	26+ (Wavelet, STC, etc.)	❌	Basic
Datasets	5	4	❌
Pluggable Architecture	✅ Registry	❌	❌
Protocol Abstraction	✅ Unique	❌	❌
Training Pipeline	✅	✅	❌
CLI	✅ Full	Basic	✅

Verified from official GitHub repos on 2026-03-17.

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🧠 Model Zoo (19 Models, Baseline → SOTA)

Category	Models	Use Case
Baseline	MLP, CNN1D, CNN2D, LSTM	Quick experiments, comparisons
Mainstream	ResNet1D, ResNet2D, BiLSTM+Attn, EfficientNet	Production use
SOTA	ViT, Mamba, GNN, CSIModel	Cutting-edge research
Specialized	THAT, CSITime, PA_CSI	Task-specific architectures
Lightweight	WiFlexFormer, AttentionGRU	Efficient deployment
Cross-Domain	EI, FewSense	Domain adaptation & few-shot

from wsdp.models import create_model, list_models
model = create_model("ResNet1D", num_classes=10, input_shape=(20, 30, 3))

⚠️ Baseline Model Architecture Note

Baseline models (MLP, CNN1D, CNN2D, LSTM) use a Spatial Encoder (Conv2d-based) to compress the (F, A) antenna dimension before temporal processing. This prevents parameter explosion from direct (T, F, A) flattening. See CHANGELOG.md for details.

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🧪 Algorithm Library (26+ Algorithms in 7 Categories)

Category	Algorithms	Count
Denoising	Wavelet, Butterworth, Savitzky-Golay, Bandpass, Hampel	5
Phase Calibration	Linear, Polynomial, STC, Robust	4
Amplitude	Z-Score, Min-Max, IQR Outlier, AGC Compensation	4
Interpolation	Linear, Cubic, Nearest, Anti-alias Decimate	4
Features	Doppler, Entropy, CSI Ratio, Tensor, Conjugate Multiply, PCA Fusion	6
Detection	Variance, Change Point	2
Composition	Pipeline presets, YAML config	-

from wsdp.algorithms import denoise, calibrate, normalize
denoised = denoise(csi, method='butterworth', order=5, cutoff=0.3)
calibrated = calibrate(csi, method='stc')

See Model Guide and Algorithm Guide.

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🎯 What is SDP? (Cont.)

The Problem

Wireless sensing research often suffers from:

• ❌ Hardware-specific CSI formats
• ❌ Inconsistent preprocessing pipelines
• ❌ Unstable training results
• ❌ Large performance variance across random seeds

Result: Models cannot be fairly compared.

The Solution

SDP solves this at the protocol level, not the model level:

Feature	Raw CSI	Other Tools	SDP
Standardized Format	❌ Hardware-specific	⚠️ Partial	✅ Unified CSIFrame
Multi-Dataset Support	❌ Manual parsing	⚠️ 2-3 datasets	✅ 5 datasets built-in
Preprocessing	❌ DIY	⚠️ Basic only	✅ Wavelet + Phase Calib
Reproducibility	❌ Random	⚠️ Varies	✅ 5-seed standard
Deep Learning	❌ From scratch	⚠️ Limited	✅ CNN+Transformer
CLI Interface	❌ None	⚠️ Partial	✅ Full CLI support

SDP projects raw CSI into a fixed canonical frequency grid (K=30), ensuring cross-hardware comparability.

Performance Highlights

Metric	Result
Accuracy	SOTA on 5 datasets
Reproducibility	5-seed evaluation standard
Stability	Low variance across runs

Figure 1: Accuracy comparison across datasets

Figure 2: Reproducibility and stability analysis

Figure 3: Ablation study results

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🚀 Quick Start (3 Steps, 5 Minutes)

Step 1: Install (30 seconds)

pip install wsdp

Verify installation:

wsdp --version

Step 2: Download Dataset (2 minutes)

🔑 Required: Create a free account at SDP8.org — your account credentials are needed for dataset downloads.

Option A: From CLI (Recommended for testing)

All datasets hosted on SDP8.org:

# elderAL = smallest dataset, fastest for testing
# Use your SDP8.org email/password:
wsdp download elderAL ./data --email [e-mail] --password yourpassword

# Or use a JWT token (from SDP8.org dashboard):
wsdp download elderAL ./data --token YOUR_JWT_TOKEN

# Download larger datasets:
# wsdp download widar ./data
# wsdp download gait ./data
# wsdp download xrf55 ./data
# wsdp download zte ./data --email [e-mail] --password yourpassword
# ⚠️ zte requires applying for access on the SDP platform first

Option B: From SDP8.org Web Interface

Log in at sdp8.org and download datasets manually.

Required Dataset Structure:

data/
├── elderAL/                    # Dataset name
│   ├── action0_static_new/     # Activity folder
│   │   ├── user0_position1_activity0/  # Sample folder
│   │   │   ├── sample1.csv
│   │   │   └── ...
│   │   └── ...
│   ├── action1_walk_new/
│   └── ...
├── widar/
│
├── gait/
│
├── xrf55/
│   └── WIFI/
│       └── sample.npy
└── zte/

Step 3: Train & Evaluate (2 minutes)

🐍 Python API (Recommended for research):

Create train.py:

from wsdp import pipeline

# Minimal call - uses default hyperparameters
pipeline("./data/elderAL", "./output", "elderAL")

# Or with custom hyperparameters
pipeline(
    input_path="./data/elderAL",
    output_folder="./output",
    dataset="elderAL",
    learning_rate=1e-3,
    num_epochs=50,
    batch_size=64,
)

Run:

python train.py

💻 CLI (Quick & Simple):

# Basic training
wsdp run ./data/elderAL ./output elderAL

# With hyperparameter override
wsdp run ./data/elderAL ./output elderAL --lr 0.001 --epochs 50 --batch-size 64

# With config file
wsdp run ./data/elderAL ./output elderAL --config my_config.yaml

📊 What You Get:

After training, check ./output/:

output/
├── best_model.pth              # Best model checkpoint
├── confusion_matrix.png        # Evaluation visualization
├── training_curves.png         # Loss & accuracy curves
└── output.log                  # Detailed training logs

✅ If you see these files, SDP is working correctly!

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📊 Supported Datasets

Dataset	Format	Subcarriers	Complex	Scenarios	Size
Widar	.dat (bfee)	30	✅	Gesture recognition	~2GB
Gait	.dat (bfee, Intel IWL5300)	30	✅	Gait recognition	~1GB
XRF55	.npy	30	✅	Human activity	~3GB
ElderAL	.csv	varies	❌	Elderly activity	~500MB
ZTE	.csv	512	✅	CSI with I/Q	~4GB

More datasets coming soon! See Roadmap.

---

🔬 Research & Customization

🧠 Plug in Your Own Model

Step 1: Create custom_model.py:

import torch
import torch.nn as nn

class YourCustomModel(nn.Module):
    def __init__(self, num_classes=6):
        super().__init__()
        # Your architecture here
        # Input shape: (Batch, Timestamp, Frequency, Antenna)
        
    def forward(self, x):
        # Your forward pass
        return output

# Required: expose model class
model = YourCustomModel

Step 2: Run with your model:

wsdp run ./data/elderAL ./output elderAL -m custom_model.py

📁 Use Your Own Dataset

Organize your data:

data/
└── my_dataset/
    ├── user0_pos0_action0/
    │   ├── sample1.csv
    │   └── ...
    └── user0_pos0_action1/
        └── ...

Run:

wsdp run ./data/my_dataset ./output my_dataset

🗺️ Codebase Map

Want to go deeper? Here's where to modify:

Directory	Purpose	What to Modify
models/	Architectures	Define or compare model architectures
algorithms/	Signal Processing	Denoising, calibration, etc.
datasets/	Dataset Wrappers	Add new dataset loaders
readers/	File Readers	Add new format parsers
structure/	Data Structures	Modify CSIFrame format
processors/	Protocol Logic	Adjust canonical projection

🔌 Pluggable Algorithm Architecture

WSDP features a Registry Pattern that makes algorithms pluggable:

from wsdp.algorithms import denoise, calibrate, register_algorithm

# Unified API — switch methods with one parameter
denoised = denoise(csi, method='butterworth', order=5)
calibrated = calibrate(csi, method='stc')

# Register your own algorithm
def my_denoise(csi, **kwargs):
    return my_custom_filter(csi)

register_algorithm('denoise', 'my_method', my_denoise)
result = denoise(csi, method='my_method')  # Works like built-in!

You can try this in examples/getting_started.ipynb or just in your custom pipeline!

Configuration file support:

# algorithms_config.yaml
denoise:
  method: butterworth
  params:
    order: 5
    cutoff: 0.3
calibrate:
  method: stc
normalize:
  method: z-score

from wsdp.algorithms import load_config, execute_pipeline
config = load_config('algorithms_config.yaml')
processed = execute_pipeline(csi, config)

Or:

pipeline("./data/elderAL", "./output", "elderAL", config_file='./algorithms_config.yaml')

Pipeline presets:

from wsdp.algorithms import apply_preset, execute_pipeline

# Choose a preset for your use case
steps = apply_preset('high_quality')  # or 'fast', 'robust', etc.
processed = execute_pipeline(csi, steps)

📊 Algorithm Library

Category	Algorithm	Key Function	Reference
Denoising	Wavelet	wavelet_denoise_csi()	Donoho & Johnstone, 1994
	Butterworth	butterworth_denoise()	Butterworth, 1930
	Savitzky-Golay	savgol_denoise()	Savitzky & Golay, 1964
	Bandpass	bandpass_filter()	Standard DSP
	Hampel	hampel_filter()	Hampel, 1974
Phase Calibration	Linear	phase_calibration()	Halperin et al., 2010
	Polynomial	polynomial_calibration()	Extension of linear
	STC	stc_calibration()	Xie et al., IEEE TWC 2019
	Robust	robust_phase_sanitization()	Wang et al., ICPADS 2012
Normalization	Z-Score	normalize_amplitude()	Standard statistical
	Min-Max	normalize_amplitude()	Standard statistical
	AGC Compensation	agc_compensate()	AGC gain correction
Interpolation	Linear/Cubic/Nearest	interpolate_grid()	de Boor, 1978
	Anti-alias Decimate	decimate()	Anti-alias downsampling
Features	Doppler	doppler_spectrum()	Ali et al., MobiCom 2015
	Entropy	entropy_features()	Shannon, 1948
	CSI Ratio	csi_ratio()	Halperin et al., 2011
	Tensor Decomposition	tensor_decomposition()	Kolda & Bader, SIAM 2009
	Conjugate Multiply	conjugate_multiply()	Antenna pair correlation
	PCA Fusion	pca_fusion()	Dimensionality reduction
Detection	Activity	detect_activity()	Zhou et al., 2013
	Change Point	change_point_detection()	Adams & MacKay, 2007

Built-in Presets:

Preset	Denoise	Calibrate	Use Case
high_quality	Butterworth (order=5)	STC	Maximum accuracy
fast	Savitzky-Golay	Linear	Speed-optimized
robust	Wavelet	Robust	Noisy environments
gesture_recognition	Butterworth (order=4)	STC	Gesture tasks
activity_detection	Savitzky-Golay	Polynomial	HAR tasks
localization	Wavelet	Robust	Localization tasks

---

🧪 Understanding SDP (10-Min Deep Dive)

The SDP Pipeline

Raw CSI
  ↓
[Deterministic Sanitization]
  - Phase calibration
  - Wavelet denoising
  ↓
[Canonical Tensor Construction]
  - K=30 frequency grid
  - Standardized shape
  ↓
[Deep Learning Model]
  ↓
Prediction

Canonical Tensor Format

After sanitization, SDP constructs a Canonical CSI Tensor:

$$X \in \mathbb{C}^{A \times K \times T}$$

Where:

• $A$ = Number of antennas
• $K$ = 30 (fixed frequency grid)
• $T$ = Time samples

This ensures cross-hardware comparability.

Why Deterministic?

Raw CSI contains hardware distortions:

• Phase offsets
• Sampling time offsets
• Noise fluctuations

SDP enforces deterministic calibration and denoising, guaranteeing:

• ✅ Same raw CSI → Same cleaned tensor
• ✅ Reproducibility is enforced, not optional

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📚 Documentation & Resources

• 📖 Full Documentation
• 🔧 API Reference
• 🤝 Contributing Guide
• 📝 Changelog
• 💻 Colab Tutorial

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🗺️ Roadmap

• v0.1 - Initial protocol design
• v0.2 - 5 datasets support, CLI tool
• v0.3 - More datasets (WiFi-HAR, CSI-HAR, etc.)
• v0.4 - 19 models, 26+ algorithms, leaderboard, CI/CD, scientific bug fixes
• v0.5 - PyPI official release, online demo platform
• v1.0 - Full protocol standardization

Want a specific dataset? Open an issue and let us know!

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for:

• Development setup
• Coding guidelines
• Pull request process

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📄 License

MIT License - see LICENSE file.

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🇨🇳 中文

🆕 v0.4.0 更新内容

• 7 个关键科学 bug 修复 -- 修正相位校准、小波边界处理、多普勒归一化等问题
• 7 个新 SOTA 模型 -- THAT, CSITime, PA_CSI, WiFlexFormer, AttentionGRU, EI, FewSense（共 19 个）
• 6 个新预处理算法 -- conjugate_multiply, agc_compensate, pca_fusion, bandpass, hampel, decimate（共 26+ 个）
• 基准排行榜 -- 跨数据集标准化模型对比
• CI/CD 流水线 -- 自动化测试、代码检查和发布工作流
• 实验追踪与缓存 -- 可复现运行，支持结果缓存
• GroupKFold 交叉验证 -- 用户感知评估，防止数据泄漏

---

🎯 SDP 是什么？

SDP 是一个协议级抽象框架，用于可复现的无线感知研究。

⚠️ SDP 不是一个新的神经网络，而是一个标准化协议，统一 CSI 表示以实现公平比较。

问题所在

无线感知研究常面临：

• ❌ 硬件特定的 CSI 格式
• ❌ 不一致的预处理流程
• ❌ 不稳定的训练结果
• ❌ 随机种子间性能方差大

结果：模型无法公平比较。

解决方案

SDP 在协议层面解决问题，而非模型层面：

特性	原始 CSI	其他工具	SDP
标准化格式	❌ 硬件特定	⚠️ 部分支持	✅ 统一 CSIFrame
多数据集支持	❌ 手动解析	⚠️ 2-3 个	✅ 5 个内置数据集
预处理	❌ 自行实现	⚠️ 仅基础	✅ 小波+相位校准
可复现性	❌ 随机	⚠️ 不稳定	✅ 5 种子标准
深度学习	❌ 从零开始	⚠️ 有限	✅ CNN+Transformer
CLI 接口	❌ 无	⚠️ 部分	✅ 完整 CLI 支持

SDP 将原始 CSI 投影到固定的规范频率网格 (K=30)，确保跨硬件可比性。

性能亮点

指标	结果
准确率	5 个数据集上达到 SOTA
可复现性	5 种子评估标准
稳定性	多次运行方差低

图 1：跨数据集准确率对比

图 2：可复现性与稳定性分析

图 3：消融实验结果

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🚀 快速开始（3 步，5 分钟）

第 1 步：安装（30 秒）

pip install wsdp

验证安装：

wsdp --version

第 2 步：下载数据集（2 分钟）

🔑 前提条件：在 SDP8.org 注册免费账号 — 下载数据集需要使用账号凭证。

方式 A：命令行下载（测试推荐）

所有数据集由 SDP8.org 官方托管：

# elderAL = 最小数据集，测试最快
# 使用 SDP8.org 的邮箱/密码：
wsdp download elderAL ./data --email [e-mail] --password yourpassword

# 或使用 JWT Token（从 SDP8.org 控制台获取）：
wsdp download elderAL ./data --token YOUR_JWT_TOKEN

# 下载其他数据集：
# wsdp download widar ./data
# wsdp download gait ./data
# wsdp download xrf55 ./data
# wsdp download zte ./data --email [e-mail] --password yourpassword
# ⚠️ zte requires applying for access on the SDP platform first

方式 B：从 SDP8.org 网页下载

登录 sdp8.org 后手动下载数据集。

必需的数据集结构：

data/
├── elderAL/                    # 数据集名称
│   ├── action0_static_new/     # 活动文件夹
│   │   ├── user0_position1_activity0/  # 样本文件夹
│   │   │   ├── sample1.csv
│   │   │   └── ...
│   │   └── ...
│   ├── action1_walk_new/
│   └── ...
├── widar/
├── gait/
├── xrf55/
└── zte/

第 3 步：训练与评估（2 分钟）

🐍 Python API（研究推荐）：

创建 train.py：

from wsdp import pipeline

# 最小调用 - 使用默认超参数
pipeline("./data/elderAL", "./output", "elderAL")

# 或自定义超参数
pipeline(
    input_path="./data/elderAL",
    output_folder="./output",
    dataset="elderAL",
    learning_rate=1e-3,
    num_epochs=50,
    batch_size=64,
)

运行：

python train.py

💻 命令行（快速简单）：

# 基础训练
wsdp run ./data/elderAL ./output elderAL

# 自定义超参数
wsdp run ./data/elderAL ./output elderAL --lr 0.001 --epochs 50 --batch-size 64

# 使用配置文件
wsdp run ./data/elderAL ./output elderAL --config my_config.yaml

📊 输出文件：

训练后，查看 ./output/：

output/
├── best_model.pth              # 最佳模型检查点
├── confusion_matrix.png        # 评估可视化
├── training_curves.png         # 损失和准确率曲线
└── output.log                  # 详细训练日志

✅ 如果看到这些文件，说明 SDP 运行正常！

---

📊 支持的数据集

数据集	格式	子载波	复数	场景	大小
Widar	.dat (bfee)	30	✅	手势识别	~2GB
Gait	.dat (bfee)	30	✅	步态识别	~1GB
XRF55	.npy	30	✅	人体活动	~3GB
ElderAL	.csv	varies	❌	老年人活动	~500MB
ZTE	.csv	512	✅	I/Q 格式 CSI	~4GB

更多数据集即将推出！ 查看 路线图。

---

🔬 研究与定制

🧠 接入你自己的模型

第 1 步： 创建 custom_model.py：

import torch
import torch.nn as nn

class YourCustomModel(nn.Module):
    def __init__(self, num_classes=6):
        super().__init__()
        # 你的架构代码
        # 输入形状: (Batch, Timestamp, Frequency, Antenna)
        
    def forward(self, x):
        # 你的前向传播
        return output

# 必需：暴露模型类
model = YourCustomModel

第 2 步： 使用你的模型运行：

wsdp run ./data/elderAL ./output elderAL -m custom_model.py

📁 使用你自己的数据集

组织你的数据：

data/
└── my_dataset/
    ├── user0_pos0_action0/
    │   ├── sample1.csv
    │   └── ...
    └── user0_pos0_action1/
        └── ...

运行：

wsdp run ./data/my_dataset ./output my_dataset

🗺️ 代码结构地图

想深入修改？这里是各目录功能：

目录	用途	修改内容
models/	架构	定义或比较模型架构
algorithms/	信号处理	去噪、校准等
datasets/	数据集包装	添加新数据集加载器
readers/	文件读取器	添加新格式解析器
structure/	数据结构	修改 CSIFrame 格式
processors/	协议逻辑	调整规范投影

🔌 可插拔算法架构

WSDP 采用注册表模式，让算法可以自由切换：

from wsdp.algorithms import denoise, calibrate, register_algorithm

# 统一 API — 一个参数切换方法
denoised = denoise(csi, method='butterworth', order=5)
calibrated = calibrate(csi, method='stc')

# 注册你自己的算法
def my_denoise(csi, **kwargs):
    return my_custom_filter(csi)

register_algorithm('denoise', 'my_method', my_denoise)
result = denoise(csi, method='my_method')  # 像内置算法一样使用！

配置文件支持：

# algorithms_config.yaml
denoise:
  method: butterworth
  params:
    order: 5
    cutoff: 0.3
calibrate:
  method: stc
normalize:
  method: z-score

from wsdp.algorithms import load_config, execute_pipeline
config = load_config('algorithms_config.yaml')
processed = execute_pipeline(csi, config)

Pipeline 预设：

from wsdp.algorithms import apply_preset, execute_pipeline

# 选择适合的预设
steps = apply_preset('high_quality')  # 或 'fast', 'robust' 等
processed = execute_pipeline(csi, steps)

📊 算法库

类别	算法	核心函数	参考文献
去噪	小波	wavelet_denoise_csi()	Donoho & Johnstone, 1994
	巴特沃斯	butterworth_denoise()	Butterworth, 1930
	Savitzky-Golay	savgol_denoise()	Savitzky & Golay, 1964
	带通滤波	bandpass_filter()	标准 DSP
	Hampel 滤波	hampel_filter()	Hampel, 1974
相位校准	线性	phase_calibration()	Halperin et al., 2010
	多项式	polynomial_calibration()	线性校准的扩展
	STC	stc_calibration()	Xie et al., IEEE TWC 2019
	鲁棒	robust_phase_sanitization()	Wang et al., ICPADS 2012
归一化	Z-Score	normalize_amplitude()	标准统计方法
	Min-Max	normalize_amplitude()	标准统计方法
	AGC 补偿	agc_compensate()	AGC 增益校正
插值	线性/三次/最近邻	interpolate_grid()	de Boor, 1978
	抗混叠降采样	decimate()	抗混叠降采样
特征提取	多普勒	doppler_spectrum()	Ali et al., MobiCom 2015
	熵	entropy_features()	Shannon, 1948
	CSI 比率	csi_ratio()	Halperin et al., 2011
	张量分解	tensor_decomposition()	Kolda & Bader, SIAM 2009
	共轭乘积	conjugate_multiply()	天线对相关
	PCA 融合	pca_fusion()	降维融合
检测	活动	detect_activity()	Zhou et al., 2013
	变点	change_point_detection()	Adams & MacKay, 2007

内置预设：

预设	去噪	校准	适用场景
high_quality	Butterworth (order=5)	STC	最高精度
fast	Savitzky-Golay	线性	速度优化
robust	小波	鲁棒	噪声环境
gesture_recognition	Butterworth (order=4)	STC	手势任务
activity_detection	Savitzky-Golay	多项式	人体活动识别
localization	小波	鲁棒	定位任务

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🧪 理解 SDP（10 分钟深度阅读）

SDP 流程

原始 CSI
  ↓
[确定性清洗]
  - 相位校准
  - 小波去噪
  ↓
[规范张量构建]
  - K=30 频率网格
  - 标准化形状
  ↓
[深度学习模型]
  ↓
预测

规范张量格式

清洗后，SDP 构建规范 CSI 张量：

$$X \in \mathbb{C}^{A \times K \times T}$$

其中：

• $A$ = 天线数量
• $K$ = 30（固定频率网格）
• $T$ = 时间样本

这确保了跨硬件可比性。

为什么是确定性的？

原始 CSI 包含硬件失真：

• 相位偏移
• 采样时间偏移
• 噪声波动

SDP 强制执行确定性校准和去噪，保证：

• ✅ 相同的原始 CSI → 相同的清洗后张量
• ✅ 可复现性是强制的，不是可选的

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📚 文档与资源

• 📖 完整文档
• 🔧 API 参考
• 🤝 贡献指南
• 📝 更新日志
• 💻 Colab 教程

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🗺️ 路线图

• v0.1 - 初始协议设计
• v0.2 - 5 个数据集支持，CLI 工具
• v0.3 - 更多数据集（WiFi-HAR、CSI-HAR 等）
• v0.4 - 19 个模型，26+ 算法，排行榜，CI/CD，科学 bug 修复
• v0.5 - PyPI 正式发布，在线演示平台
• v1.0 - 完整协议标准化

想要特定数据集？ 提交 issue 告诉我们！

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🤝 贡献

欢迎贡献！查看 CONTRIBUTING.md 了解：

• 开发环境搭建
• 编码规范
• Pull Request 流程

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📄 许可证

MIT 许可证 - 详见 LICENSE 文件。

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Made with ❤️ by the WSDP Team

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