Fronthaul Compression

Fronthaul compression aims to reduce the bandwidth demands of communication links connecting distributed radio units to a central processing unit in wireless networks, improving efficiency and scalability. Current research focuses on developing sophisticated compression techniques, including multivariate quantization and deep learning-based approaches, often tailored to specific network architectures like cell-free massive MIMO. These advancements are crucial for enabling the deployment of large-scale, computationally intensive applications such as federated learning and improving the performance of next-generation wireless systems by optimizing resource allocation and reducing latency. The ultimate goal is to balance compression efficiency with minimal impact on the quality of the transmitted data and overall system performance.