OPPO 6G Data Generation with an E2E Framework

Homepage of OPPO 6G Data Generation Challenge

Datasets

H1_32T4R.mat
H2_32T4R.mat
Please put the original data in data folder.

Data Augmentation Scheme

Complex number is special : a+bj has a same square similarity with -a-bj, b-aj and -b+aj
With the strategy above, you can quadruple the amount of training data compared to the raw.
We randomly scale the data with a factor between 0.8~1.2, random gaussian noise with mean equals to 0 and std equals to 1e-4 are adopted.

Architectures

Auto encoder with reconstruction loss.
ResNet18 as an Encoder.
3D Conv as a Decoder.
Position Attention Module and Channel Attention Module are important.
Normalization such as BatchNorm2d after Decoder is important.
Latent Quantization.

Pretrained Models

We provide several pretrained models in the folder of saved_models.

Sim : similarity score tested on the raw data.
Multi : multi score tested on the raw data.
Score : tested on the local raw data.
Feel free to use the pretrained weights or training from scratch.

Training

Modify the data_type in train.py, maybe you have to choose a suitable GPU id.
Online validation, only save the models with best scores so far.
Hints : smaller batch size may result in higher similarity score and higher multi score.
Epochs : we perform no ablation study on this parameter, you can just let it run.
Benchmark : data1: local score approx 0.82~0.83
Benchmark : data2: local score approx 0.76~0.77

Boost Scheme

We use adaboost weights to ensemble several models for acquiring performance gain.
Without model ensembles, you can still achieve an online score up to 0.72 easily.

Submit_pt

You can just use the single model without ensembles which is much easier.
Without deep ensembles, it is still trivial to achieve a score up to 0.72

Reference

Deep Residual Learning for Image Recognition (CVPR, 2016)
Dual Attention Network for Scene Segmentation (CVPR, 2019)
Deep Learning-based Implicit CSI Feedback in Massive MIMO (IEEE Transactions on Communications, 2021)