Rate Adaptive Precoder Design for Massive MIMO Cognitive Radio System Massive Multiple Input Multiple Output (MIMO) is considered a promising technology to get high multiplexing gain in the cognitive network. However, the precoder design at the cognitive base station becomes complex with large number of antennas. In this paper, the design of a twostage precoder is modified to accommodate Transmit Antenna Selection (TAS) while achieving the required secondary rate. Moreover, fast Antenna Selection (AS) algorithm based on norm selection schemes is proposed for better convergence rate. Simulation results show that the proposed algorithm reduces the computational time by 88%, the number of antennas by 40%, while the capacity reaches 90% relative to the full array without selection scenario.

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.