A WorstCase Performance Optimization Based Design Approach to Robust SymbolLevel Precoding for Downlink MUMIMO This paper addresses the optimization problem of symbollevel precoding (SLP) in the downlink of a multiuser multipleinput multipleoutput (MUMIMO) wireless system while the precoderxe2x80x99s output is subject to partiallyknown distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signaltointerferenceplusnoise ratio (SINR) constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We then adopt a worstcase design approach to protect the usersxe2x80x99 intended symbols and the targeted constructive interference with a desired level of confidence. Due to the nonconvexity of the relaxed robust formulation, we propose an iterative algorithm based on the block coordinate ascentdescent method. We show through simulation results that the proposed robust design is flexible in the sense that the CI constraints can be relaxed so as to keep a desirable balance between achievable rate and power consumption. Remarkably, the new formulation yields more energyefficient solutions for appropriate choices of the penalty parameter, compared to the original SLP problem.

EnergyEfficient MixedTimescale Hybrid Precoding for Multiuser Massive MIMO Systems Considering the huge training cost and feedback overhead of wireless communication system where base station is equipped with largescale antenna array, an energyefficient mixedtimescale hybrid precoding is proposed in this paper. The design of hybrid precoding is decoupled, where analog precoder and digital precoder are determined by secondorder statistical channel state information (CSI) and instantaneous CSI respectively. Moreover, for analog precoding, a fixed subconnected architecture is considered to further reduce the hardware complexity and power consumption. It transforms the optimization of analog precoding matrix into lowdimensional problems. Meanwhile, a twostep heuristic method is proposed to obtain nearoptimal solution. Simulation results show that our proposed scheme has a good tradeoff between performance and hardware complexity, power consumption for massive MIMO.