This repo contains the code to implement the site-specific deep learning (DL) pipeline for mmWave beam alignment proposed in the paper:
Here's the Arxiv Version.
The conference version of this work won the Best Paper Award at the 2022 IEEE IEEE Global Communications Conference (GLOBECOM) in the SAC-MLC track:
2. Download the DeepMIMO dataset and place the scenario files in the in your desired directory.
3. Train models: run the DLGF_train.py file to train grid-free (GF) models, and run the DLCB_train_grid.py file to train codebook-based (CB) models.
4. Compile and plot results: use the compile_results.ipynb notebook to compile and plot the results, use the visualize_leanred_beams.ipynb notebook to plot the learned probing/sensing beams as well as the predicted beams.
Train GF or CB models: run the DLGF_train.py/DLCB_train.py file to train models. The following parameters can be adjusted in the argument:
- scenario: DeepMIMO Ray-tracing scenario: O1_28, I3_60, O1_28B, Boston_5G, etc.
- activated_BS: index of the activated BS in the DeepMIMO scenario
- num_probing_beam: number of probing beams
- array_type: BS and UE array type, ULA or UPA
- BW: bandwidth in MHz
- noise_PSD_dB: noise PSD in dBm/Hz
- measurement_gain: spreading gain of the probing measurements
- h_NMSE_dB: channel estimation error in dB, to generate noisy training data
- IA_threshold: IA threshold in dB, UE need to achieve highe SNR than this threshold with one of the probing beams for initial access
- UE_rotation: randomly rotate the UE antenna array
- use_specific_Tx_power: use specific Tx power instead of the default value
- Tx_power_dBm: Tx power in dBm
- num_feedback: number of probing beam measuements to feed back to the BS, None means all the measurements are fed back
- feedback_mode: feedback mode for the probing measurements: diagonal, full or max. Consider measurements of all Tx/Rx beam pairs as a square matrix, feed back diagonal elements (each probing beam is matched to a sensing beam), the full matrix (measure all combinations), or the max element for each transmit beam (feedback the best sensing beam for each probing beam)
- beam_synthesizer: beam prediction function architecture, MLP or CNN
- learned_probing: TxRx: learn Tx and Rx probing beams jointly; Tx: learn Tx probing beams only; Rx: learn Rx probing beams only. If only the probing/sensing beams are learned, the sensing/probing beams on the other side are sub-sampled DFT beams.
- gamma: γ is the weight of the normalized BF loss, 1-gamma is the weight of the IA loss
- loss_fn: loss function for training the GF models: BF_IA_loss, SPE_loss, BF_loss. BF_IA_loss is the proposed loss function in the paper that combines the normalized BF gain of the predicted beams and the gain of the probing beams. SPE_loss is based on the achievable rate with the predicted Tx and Rx beams. BF_loss is the unnormalized BF gain of the predicted beams.
- nepoch: number of epochs to train for
- batch_size: batch size
- dataset_split_seed: random seed for partitioning the dataset into training and testing
For example, to train a GF model for the O1_28 scenario with random UE rotations, using 16 probing beams, noisy training data with NMSE -11 dB and optimized for the BF loss, run the following command:
python DLGF_train.py --scenario O1_28 --num_probing_beam 16 --h_NMSE_dB -11 --UE_rotation --feedback_mode diagonal --beam_synthesizer MLP --learned_probing TxRx --loss_fn BF_loss --nepoch 2000 --batch_size 256python DLCB_train.py --scenario O1_28 --num_probing_beam 16 --h_NMSE_dB -11 --UE_rotation --feedback_mode diagonal --beam_synthesizer MLP --learned_probing TxRx --nepoch 2000 --batch_size 256Note: The latest O1_28 dataset seems to be slight different from the version I downloaded in 2022. You may need to tune the Tx power and the spreding gain (--measurement_gain) accordingly to get a reasonable SNR range. The version of dataset that I used has been uploaded here.