The proposed multivariate quantization techniques, α-PMQ and neural-MQ, offer significant performance gains in terms of reduced fronthaul capacity requirements and improved signal quality. However, these gains come with increased computational complexity at the distributed unit (DU).
For α-PMQ, while the computational complexity grows exponentially only in the per-RU fronthaul rate, the need to manage parallel quantization steps introduces additional overhead in terms of scheduling and interference management. This complexity can lead to longer processing times at the DU, especially as the number of RUs increases. The performance gains in terms of reduced effective interference and improved signal quality must be weighed against the potential delays introduced by the more complex quantization process.
In the case of neural-MQ, the complexity arises from the need to train and optimize a neural network that can handle the quantization tasks. While the computational complexity grows linearly with the sum-fronthaul capacity, the training phase can be resource-intensive, requiring significant computational resources and time. Additionally, the performance of neural-MQ is highly dependent on the quality of the training data and the architecture of the neural network, which can introduce variability in performance.
Overall, the tradeoff lies in balancing the computational resources available at the DU with the desired performance improvements in fronthaul quantization. Careful consideration must be given to the operational environment and the specific requirements of the cell-free massive MIMO system to determine the optimal configuration.