A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems

[EN] Traditional Minimum Mean Square Error (MMSE) detection is widely used in wireless communications, however, it introduces matrix inversion and has a higher computational complexity. For massive Multiple-input Multiple-output (MIMO) systems, this detection complexity is very high due to its huge channel matrix dimension. Therefore, low-complexity detection technology has become a hot topic in the industry. Aiming at the problem of high computational complexity of the massive MIMO channel estimation, this paper presents a low-complexity algorithm for efficient channel estimation. The proposed algorithm is based on joint Singular Value Decomposition (SVD) and Iterative Least Square with Projection (SVD-ILSP) which overcomes the drawback of finite sample data assumption of the covariance matrix in the existing SVD-based semi-blind channel estimation scheme. Simulation results show that the proposed scheme can effectively reduce the deviation, improve the channel estimation accuracy, mitigate the impact of pilot contamination and obtain accurate CSI with low overhead and computational complexity.This research was funded by Ministerio de Economia, Industria y Competitividad, Gobierno de Espana grant number BIA2017-87573-C2-2-P.Bangash, K.; Khan, I.; Lloret, J.; León Fernández, A. (2018). A Joint Approach for Low-Complexity Channel Estimation in 5G Massive MIMO Systems. Electronics. 7(10). https://doi.org/10.3390/electronics7100218S710Gao, Z., Dai, L., Lu, Z., Yuen, C., & Wang, Z. (2014). Super-Resolution Sparse MIMO-OFDM Channel Estimation Based on Spatial and Temporal Correlations. IEEE Communications Letters, 18(7), 1266-1269. doi:10.1109/lcomm.2014.2325027Biswas, S., Masouros, C., & Ratnarajah, T. (2016). Performance Analysis of Large Multiuser MIMO Systems With Space-Constrained 2-D Antenna Arrays. 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Joint Angle and Delay Estimation for 3D Massive MIMO Systems Based on Parametric Channel Modeling

Mobile data traffic is predicted to have an exponential growth in the future. In order to meet the challenge as well as the form factor limitation on the base station, 3D massive MIMO has been proposed as one of the enabling technologies to significantly increase the spectral efficiency of a wireless system. In massive MIMO systems, a base station will rely on the uplink sounding signals from mobile stations to figure out the spatial information to perform MIMO beam-forming. Accordingly, multi-dimensional parameter estimation of a MIMO wireless channel becomes crucial for such systems to realize the predicted capacity gains. In this thesis, we study separated and joint angle and delay estimation for 3D massive MIMO systems in mobile wireless communications. To be specific, we first introduce a separated low complexity time delay and angle estimation algorithm based on unitary transformation and derive the mean square error (MSE) for delay and angle estimation in the millimeter wave massive MIMO system. Furthermore, a matrix-based ESPRIT-type algorithm is applied to jointly estimate delay and angle, the mean square error (MSE) of which is also analyzed. Finally, we found that azimuth estimation is more vulnerable compared to elevation estimation. Simulation results suggest that the dimension of the underlying antenna array at the base station plays a critical role in determining the estimation performance. These insights will be useful for designing practical massive MIMO systems in future mobile wireless communications

Achieving Low-Complexity Maximum-Likelihood Detection for the 3D MIMO Code

The 3D MIMO code is a robust and efficient space-time block code (STBC) for the distributed MIMO broadcasting but suffers from high maximum-likelihood (ML) decoding complexity. In this paper, we first analyze some properties of the 3D MIMO code to show that the 3D MIMO code is fast-decodable. It is proved that the ML decoding performance can be achieved with a complexity of O(M^{4.5}) instead of O(M^8) in quasi static channel with M-ary square QAM modulations. Consequently, we propose a simplified ML decoder exploiting the unique properties of 3D MIMO code. Simulation results show that the proposed simplified ML decoder can achieve much lower processing time latency compared to the classical sphere decoder with Schnorr-Euchner enumeration

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

Optimization of Massive Full-Dimensional MIMO for Positioning and Communication

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