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In order to enable frequency-division duplex (FDD) massive multiple-input-multiple-output (MIMO) systems, a beam-grouping, precoding and feedback scheme is proposed to reduce the downlink training overhead and channel state information (CSI) feedback overhead. When the number of transmit antennas on a base station (BS) is growing large, the downlink training overhead for CSI estimation and the CSI feedback overhead are growing tremendously. In addition to the downlink training overhead, simulation results show that the proposed method is able to reduce the feedback overhead without sacrificing the downlink achievable information rate (AIR). Furthermore, the proposed method has a lower computation complexity comparing with other methods in the recent literature.

In order to estimate the location of an indoor mobile station (MS), estimated time-of-arrival (TOA) can be obtained at each of several access points (APs). These TOA estimates can then be used to solve for the location of the MS. Alternatively, it is possible to estimate the location of the MS directly by incorporating the received signals at all APs in a direct estimator of position. This article presents a deeper analysis of a previously proposed maximum likelihood (ML)-TOA estimator, including a uniqueness property and the behavior in nonline-of-sight (NLOS) situations. Then, a ML direct location estimation technique utilizing all received signals at the various APs is proposed based on the ML-TOA estimator. The Cramer-Rao lower bound (CRLB) is used as a performance reference for the ML direct location estimator.