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

Channel Estimation for Frequency Division Duplexing Multi-user Massive MIMO Systems via Tensor Compressive Sensing

To make full use of space multiplexing gains for the multi-user massive multiple-input multiple-output, accurate channel state information at the transmitter (CSIT) is required. However, the large number of users and antennas make CSIT a higher-order data representation. Tensor-based compressive sensing (TCS) is a promising method that is suitable for high-dimensional data processing; it can reduce training pilot and feedback overhead during channel estimation. In this paper, we consider the channel estimation in frequency division duplexing (FDD) multi-user massive MIMO system. A novel estimation framework for three dimensional CSIT is presented, in which the modes include the number of transmitting antennas, receiving antennas, and users. The TCS technique is employed to complete the reconstruction of three dimensional CSIT. The simulation results are given to demonstrate that the proposed estimation approach outperforms existing algorithms

MmWave Massive MIMO Based Wireless Backhaul for 5G Ultra-Dense Network

Ultra-dense network (UDN) has been considered as a promising candidate for future 5G network to meet the explosive data demand. To realize UDN, a reliable, Gigahertz bandwidth, and cost-effective backhaul connecting ultra-dense small-cell base stations (BSs) and macro-cell BS is prerequisite. Millimeter-wave (mmWave) can provide the potential Gbps traffic for wireless backhaul. Moreover, mmWave can be easily integrated with massive MIMO for the improved link reliability. In this article, we discuss the feasibility of mmWave massive MIMO based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. Especially, we propose a digitally-controlled phase-shifter network (DPSN) based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macro-cell BS can simultaneously support multiple small-cell BSs with multiple streams for each smallcell BS, which is essentially different from conventional hybrid precoding/combining schemes typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospect to enable the mmWave massive MIMO based wireless backhaul for 5G UDN are discussed.Comment: This paper has been accepted by IEEE Wireless Communications Magazine. This paper is related to 5G, ultra-dense network (UDN), millimeter waves (mmWave) fronthaul/backhaul, massive MIMO, sparsity/low-rank property of mmWave massive MIMO channels, sparse channel estimation, compressive sensing (CS), hybrid digital/analog precoding/combining, and hybrid beamforming. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=730653