The proposed α-parallel multivariate quantization (α-PMQ) and neural-multivariate quantization (neural-MQ) schemes can be extended to handle multiple-antenna user equipment (UEs) and radio units (RUs) by adapting the quantization processes to account for the increased dimensionality of the signals involved. In the context of multiple antennas, the quantization function Q(·) must be designed to operate on vectors of size N_tx × 1 for each RU and N_rx × 1 for each UE, rather than scalar values as in the single-antenna case.
For α-PMQ, the extension involves modifying the interference graph to reflect the interactions between multiple antennas at both UEs and RUs. This means that the disturbance vector ∆m→n must be computed for each antenna pair, leading to a more complex representation of interference. The parallel local quantization steps can still be employed, but the selection of RUs for simultaneous updates must consider the interference contributions from all antennas, potentially leading to a more intricate scheduling algorithm.
In the case of neural-MQ, the neural network architecture can be adapted to accommodate the multi-dimensional input and output signals. This may involve using convolutional layers or recurrent structures that can effectively learn the relationships between the multiple antennas' signals. The training of the neural network would also need to incorporate a larger dataset that reflects the multi-antenna scenarios, ensuring that the learned quantization strategies are robust across various channel conditions and configurations.