Pre-trained models and datasets for “Fairness-Aware Low-Rank BD-RIS Design via Curriculum Self-Supervised Learning”
This dataset contains channel realizations and pre-trained neural network checkpoints used to reproduce the figures of the paper "Fairness-Aware Low-Rank BD-RIS Design via Curriculum Self-Supervised Learning" submitted to IEEE Wireless Communications Letters. Con
This dataset contains channel realizations and pre-trained neural network
checkpoints used to reproduce the figures of the paper “Fairness-Aware
Low-Rank BD-RIS Design via Curriculum Self-Supervised Learning” submitted
to IEEE Wireless Communications Letters.
Contents:
– PaperWCL_sim.mat: 1000 channel realizations (3GPP TR 38.901 UMa, 2.6 GHz)
used for evaluating Figures 4 and 5.
– paperWCL.mat: 100,000 channel realizations used to generate the training
dataset.
– TrainingDataRepo.rar: archive containing the PyTorch dataset
(Dataset_M_10_N_30_K_5_Ntx_2_realiz_100000_self_supervised.pt) and the
pre-trained checkpoints (full-rank, low-rank, no-curriculum, and
fairness-aware variants with λ ∈ 0.1, 0.3, 0.5, 0.7).
System configuration: M=10 BS antennas, N=30 BD-RIS elements, K=5 users,
Ntx=2 transmit antennas per user.
Companion code: https://github.com/DarielPereira/Low-Rank-BDRIS
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Files are hosted on the source repository. Click download to access the full dataset.