FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

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

FDD Massive MIMO Based on Efficient Downlink Channel Reconstruction

This paper focuses on frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems and proposes a transceiver design that fully exploits the downlink spatial multiplexing gain with only a small amount of overhead. The bottleneck lies in the acquisition of downlink channel state information (CSI), which occurs when large scale antenna array is employed in FDD transmission systems. Fortunately, the spatial reciprocity between uplink and downlink inspires us to reconstruct the downlink channel based on the frequency-independent parameters (downtilts, azimuths and delays) that can be derived in the uplink. We first extract these parameters through an enhanced Newtonized orthogonal matching pursuit (e-NOMP) algorithm which is proposed in this paper to fit the massive MIMO orthogonal frequency division multiplexing (OFDM) system. After formulating the requirement to achieve an acceptable estimation error rate, we propose a low-cost downlink training scheme to estimate the downlink gains of each user channel. This scheme saves the training time resource by introducing a predefined spatial angle grid which corresponds to a beam set and by minimizing the number of selected beams which is equal to the number of OFDM symbols used for downlink training. Having obtained the reconstructed multiuser channel, the BS can maximize the spatial multiplexing gain by serving all the users simultaneously without causing severe interference. Numerical results verify the precision of the e-NOMP algorithm, and demonstrate that sum-rate performance of the reconstructionbased transceiver design approximates that of using perfect CSI

Efficient Downlink Channel Reconstruction for FDD Multi-Antenna Systems

In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems