Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition - Ni, Weiheng; Dong, Xiaodai; Lu, Wu-Sheng (IEEE transactions on signal processing, 2017)
For practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high implementation cost and power consumption rendered by large number of radio frequency (RF) chains of the traditional processing structure. The hybrid processing is realized through low-dimensional digital baseband processing combined with analog RF processing enabled by phase shifters. We propose to design hybrid RF and baseband precoders/combiners for multistream transmission in massive MIMO systems, by directly decomposing the predesigned unconstrained digital precoder/combiner of a large dimension. This approach is fundamental and general in the sense that any conventional full RF chain precoding solution of a MIMO system configuration can be converted to a hybrid processing structure by matrix decomposition. The constant amplitude constraint of analog RF processing results in the matrix decomposition problem nonconvex. Based on an alternate optimization technique, the nonconvex matrix decomposition problem can be decoupled into a series of convex subproblems and effectively solved by restricting the phase increment of each entry in the RF precoder/combiner within a small vicinity of its preceding iterate. A singular value decomposition-based technique is proposed to secure an initial point sufficiently close to the global solution of the original nonconvex problem. Through simulation, the convergence of the alternate optimization for such a matrix decomposition-based hybrid processing (MD-HP) scheme is examined, and the performance of the MD-HP scheme is demonstrated to be near-optimal.