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This paper investigates the hybrid precoding design for millimeter wave(mmWave) multiple-input multiple-output (MIMO) systems with finite-alphabetinputs. The mmWave MIMO system employs partially-connected hybrid precodingarchitecture with dynamic subarrays, where each radio frequency (RF) chain isconnected to a dynamic subset of antennas. We consider the design of analog anddigital precoders utilizing statistical and/or mixed channel state information(CSI), which involve solving an extremely difficult problem in theory: First,designing the optimal partition of antennas over RF chains is a combinatorialoptimization problem, whose optimal solution requires an exhaustive search overall antenna partitioning solutions; Second, the average mutual informationunder mmWave MIMO channels lacks closed-form expression and involvesprohibitive computational burden; Third, the hybrid precoding problem withgiven partition of antennas is nonconvex with respect to the analog and digitalprecoders. To address these issues, this study first presents a simplecriterion and the corresponding low complexity algorithm to design the optimalpartition of antennas using statistical CSI. Then it derives the lower boundand its approximation for the average mutual information, in which thecomputational complexity is greatly reduced compared to calculating the averagemutual information directly. In addition, it also shows that the lower boundwith a constant shift offers a very accurate approximation to the averagemutual information. This paper further proposes utilizing the lower boundapproximation as a low-complexity and accurate alternative for developing amanifold-based gradient ascent algorithm to find near optimal analog anddigital precoders. Several numerical results are provided to show that ourproposed algorithm outperforms existing hybrid precoding algorithms.

Channel-Statistics-Based Hybrid Precoding for Millimeter-Wave MIMO Systems With Dynamic Subarrays