User-Heterogeneous Cell-Free Massive MIMO Downlink and Uplink Beamforming via Tensor Decomposition

Abstract

We consider a cell-free massive MIMO (CF-mMIMO) system in which multiple access points (APs), connected to a common central processing unit (CPU) through unbounded fronthaul, collaboratively serve multiple users in a heterogeneous scenario in which each user equipment (UE) has a different number of antennas, and therefore is capable of communicating via distinct numbers of digital streams. For such a user-heterogeneous system, new joint transmit (TX)/receive (RX) beamforming (BF) algorithms are then proposed, both for downlink and uplink modes and integrated with two alternative transmit (TX) power and spatial resource allocation strategies, which enable interference-free communications. To that end, a novel tensor decomposition scheme is presented, based on an orthogonality-enforcing modification of the recently-proposed multilinear generalized singular value decomposition (ML-GSVD). Simulation results show both that the new orthogonality-enforcing ML-GSVD (OEML-GSVD) achieves greater accuracy than the previous multilinear generalized singular value decomposition (ML-GSVD) without sacrificing convergence speed, and that the corresponding OEML-GSVD-based proposed beamformers outperform state-of-the-art (SotA) techniques, as well as an equivalent beamformer based on the previous ML-GSVD alternative.

Keywords

• Cell-free massive MIMO (CF-mMIMO)
• beamforming design
• tensor factorization
• multilinear generalized singular value decomposition (ML-GSVD)