Massive multiple-input multiple-output (MIMO) systems deploying a large
number of antennas at the base station considerably increase the spectrum
efficiency by serving multiple users simultaneously without causing severe
interference. However, the advantage relies on the availability of the downlink
channel state information (CSI) of multiple users, which is still a challenge
in frequency-division-duplex transmission systems. This paper aims to solve
this problem by developing a full transceiver framework that includes downlink
channel training (or estimation), CSI feedback, and channel reconstruction
schemes. Our framework provides accurate reconstruction results for multiple
users with small amounts of training and feedback overhead. Specifically, we
first develop an enhanced Newtonized orthogonal matching pursuit (eNOMP)
algorithm to extract the frequency-independent parameters (i.e., downtilts,
azimuths, and delays) from the uplink. Then, by leveraging the information from
these frequency-independent parameters, we develop an efficient downlink
training scheme to estimate the downlink channel gains for multiple users. This
training scheme offers an acceptable estimation error rate of the gains with a
limited pilot amount. Numerical results verify the precision of the eNOMP
algorithm and demonstrate that the sum-rate performance of the system using the
reconstructed downlink channel can approach that of the system using perfect
CSI
