Meta-Learning-Based Fronthaul Compression for Cloud Radio Access Networks

This work explores the application of a GRU-based meta-learning method to improve fronthaul compression efficiency in Cloud Radio Access Networks (C-RAN), a critical component of 5G. The goal is to accelerate the optimization of transformation matrices used for compressing and decompressing high-dimensional signals between remote radio heads (RRHs) and the central processor, by reducing convergence time and signaling overhead. The system sum rate is the optimization objective. The method, proposed by Ruihua Qiao, Tao Jiang, and Wei Yu at the University of Toronto, is divided into two stages: first, fully connected neural networks generate initial suboptimal matrices from local CSI at each RRH; second, GRU-blocks iteratively refine these matrices based on current and historical gradient information. By applying meta-learning with a low-dimensional gradient signaling scheme, the number of signaling rounds is significantly reduced compared to traditional gradient descent and naive global CSI transmissions. Simulations show that communication overhead is reduced while maintaining system sum rate performance.