Cluster-Wise Processing in Fronthaul-Aware Cell-Free Massive MIMO Systems

We exploit a general cluster-based network architecture for a fronthaul-limited user-centric cell-free massive multiple-input multiple-output (CF-mMIMO) system under different degrees of cooperation among the access points (APs) to achieve scalable implementation. In particular, we consider a CF-mMIMO system wherein the available APs are grouped into multiple processing clusters (PCs) to share channel state information (CSI), ensuring that they have knowledge of the CSI for all users assigned to the given cluster for the purposes of designing resource allocation and precoding. We utilize the sum pseudo-SE metric, which accounts for intra-cluster interference and inter-cluster-leakage, providing a close approximation to the true sum achievable SE. For a given PC, we formulate two optimization problems to maximize the cluster-wise weighted sum pseudo-SE under fronthaul constraints, relying solely on local CSI. These optimization problems are associated with different computational complexity requirements. The first optimization problem jointly designs precoding, user association, and power allocation, and is performed at the small-scale fading time scale. The second optimization problem optimizes user association and power allocation at the large-scale fading time scale. Accordingly, we develop a novel application of modified weighted minimum mean square error (WMMSE)-based approach to solve the challenging formulated non-convex mixed-integer problems. Numerical results show that (a) the proposed cluster-wise processing solutions significantly outperform the heuristic approaches under both the statistical and instantaneous CSI-based designs, while statistical CSI-based design is good enough in some network configurations since it provides a better performance/implementation complexity trade-off; (b) under limited fronthaul, our proposed cluster-wise processing frameworks achieve sum-spectral efficiency (SE) that is competitive with state-of-the-art network-wide processing solutions, while avoiding the high computational complexity of processing and the heavy overhead of CSI acquisition.