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. IEEE Transactions on Wireless Communications, 15(5), 3492-3505. doi:10.1109/twc.2016.2522419Khan, I., Zafar, M., Jan, M., Lloret, J., Basheri, M., & Singh, D. (2018). Spectral and Energy Efficient Low-Overhead Uplink and Downlink Channel Estimation for 5G Massive MIMO Systems. Entropy, 20(2), 92. doi:10.3390/e20020092Khan, I., & Singh, D. (2018). Efficient compressive sensing based sparse channel estimation for 5G massive MIMO systems. AEU - International Journal of Electronics and Communications, 89, 181-190. doi:10.1016/j.aeue.2018.03.038Khan, I., Singh, M., & Singh, D. (2018). Compressive Sensing-based Sparsity Adaptive Channel Estimation for 5G Massive MIMO Systems. Applied Sciences, 8(5), 754. doi:10.3390/app8050754Arshad, M., Khan, I., Lloret, J., & Bosch, I. (2018). A Novel Multi-User Codebook Design for 5G in 3D-MIMO Heterogeneous Networks. Electronics, 7(8), 144. doi:10.3390/electronics7080144Shahjehan, W., Shah, S., Lloret, J., & Bosch, I. (2018). Joint Interference and Phase Alignment among Data Streams in Multicell MIMO Broadcasting. Applied Sciences, 8(8), 1237. doi:10.3390/app8081237Jose, J., Ashikhmin, A., Marzetta, T. L., & Vishwanath, S. (2011). Pilot Contamination and Precoding in Multi-Cell TDD Systems. IEEE Transactions on Wireless Communications, 10(8), 2640-2651. doi:10.1109/twc.2011.060711.101155Jose, J., Ashikhmin, A., Marzetta, T. L., & Vishwanath, S. (2009). Pilot contamination problem in multi-cell TDD systems. 2009 IEEE International Symposium on Information Theory. doi:10.1109/isit.2009.5205814Jose, J., Ashikhmin, A., Whiting, P., & Vishwanath, S. (2011). Channel Estimation and Linear Precoding in Multiuser Multiple-Antenna TDD Systems. IEEE Transactions on Vehicular Technology, 60(5), 2102-2116. doi:10.1109/tvt.2011.2146797Marzetta, T. L. (2010). Noncooperative Cellular Wireless with Unlimited Numbers of Base Station Antennas. IEEE Transactions on Wireless Communications, 9(11), 3590-3600. doi:10.1109/twc.2010.092810.091092Rusek, F., Persson, D., Buon Kiong Lau, Larsson, E. G., Marzetta, T. L., & Tufvesson, F. (2013). Scaling Up MIMO: Opportunities and Challenges with Very Large Arrays. IEEE Signal Processing Magazine, 30(1), 40-60. doi:10.1109/msp.2011.2178495Chang, Z., Wang, Z., Guo, X., Han, Z., & Ristaniemi, T. (2017). Energy-Efficient Resource Allocation for Wireless Powered Massive MIMO System With Imperfect CSI. IEEE Transactions on Green Communications and Networking, 1(2), 121-130. doi:10.1109/tgcn.2017.2696161Prasad, K. N. R. S. V., Hossain, E., & Bhargava, V. K. (2017). Energy Efficiency in Massive MIMO-Based 5G Networks: Opportunities and Challenges. IEEE Wireless Communications, 24(3), 86-94. doi:10.1109/mwc.2016.1500374wcFodor, G., Rajatheva, N., Zirwas, W., Thiele, L., Kurras, M., Guo, K., … De Carvalho, E. (2017). An Overview of Massive MIMO Technology Components in METIS. IEEE Communications Magazine, 55(6), 155-161. doi:10.1109/mcom.2017.1600802Lu, L., Li, G. Y., Swindlehurst, A. L., Ashikhmin, A., & Zhang, R. (2014). An Overview of Massive MIMO: Benefits and Challenges. IEEE Journal of Selected Topics in Signal Processing, 8(5), 742-758. doi:10.1109/jstsp.2014.2317671Larsson, E. G., Edfors, O., Tufvesson, F., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communications Magazine, 52(2), 186-195. doi:10.1109/mcom.2014.6736761Yi Xu, Guosen Yue, & Shiwen Mao. (2014). User Grouping for Massive MIMO in FDD Systems: New Design Methods and Analysis. IEEE Access, 2, 947-959. doi:10.1109/access.2014.2353297Duly, A. J., Kim, T., Love, D. J., & Krogmeier, J. V. (2014). Closed-Loop Beam Alignment for Massive MIMO Channel Estimation. IEEE Communications Letters, 18(8), 1439-1442. doi:10.1109/lcomm.2014.2316157Choi, J., Love, D. J., & Bidigare, P. (2014). Downlink Training Techniques for FDD Massive MIMO Systems: Open-Loop and Closed-Loop Training With Memory. IEEE Journal of Selected Topics in Signal Processing, 8(5), 802-814. doi:10.1109/jstsp.2014.2313020Noh, S., Zoltowski, M. D., & Love, D. J. (2016). Training Sequence Design for Feedback Assisted Hybrid Beamforming in Massive MIMO Systems. IEEE Transactions on Communications, 64(1), 187-200. doi:10.1109/tcomm.2015.2498184Jiang, Z., Molisch, A. F., Caire, G., & Niu, Z. (2015). Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation. IEEE Transactions on Wireless Communications, 14(5), 2868-2882. doi:10.1109/twc.2015.2396058Adhikary, A., Junyoung Nam, Jae-Young Ahn, & Caire, G. (2013). Joint Spatial Division and Multiplexing—The Large-Scale Array Regime. IEEE Transactions on Information Theory, 59(10), 6441-6463. doi:10.1109/tit.2013.2269476Talwar, S., Viberg, M., & Paulraj, A. (1996). Blind separation of synchronous co-channel digital signals using an antenna array. I. Algorithms. IEEE Transactions on Signal Processing, 44(5), 1184-1197. doi:10.1109/78.502331Comon, P., & Golub, G. H. (1990). Tracking a few extreme singular values and vectors in signal processing. Proceedings of the IEEE, 78(8), 1327-1343. doi:10.1109/5.5832

Robust Location-Aided Beam Alignment in Millimeter Wave Massive MIMO

Location-aided beam alignment has been proposed recently as a potential approach for fast link establishment in millimeter wave (mmWave) massive MIMO (mMIMO) communications. However, due to mobility and other imperfections in the estimation process, the spatial information obtained at the base station (BS) and the user (UE) is likely to be noisy, degrading beam alignment performance. In this paper, we introduce a robust beam alignment framework in order to exhibit resilience with respect to this problem. We first recast beam alignment as a decentralized coordination problem where BS and UE seek coordination on the basis of correlated yet individual position information. We formulate the optimum beam alignment solution as the solution of a Bayesian team decision problem. We then propose a suite of algorithms to approach optimality with reduced complexity. The effectiveness of the robust beam alignment procedure, compared with classical designs, is then verified on simulation settings with varying location information accuracies.Comment: 24 pages, 7 figures. The short version of this paper has been accepted to IEEE Globecom 201

Transmit Signal Design for MIMO Radar and Massive MIMO Channel Estimation

The widespread availability of antenna arrays and the capability to independently control signal emissions from each antenna make transmit signal design increasingly important for radar and wireless communication systems. In the rst part of this work, we develop the framework for a MIMO radar transmit scheme which trades o waveform diversity for beampattern directivity. Time-division beamforming consists of a linear precoder that provides direct control of the transmit beampattern and is able to form multiple transmit beams in a single pulse. The MIMO receive ambiguity function, which incorporates the receiver structure, reveals a space and delay-Doppler separability that emphasizes the importance of the transmit-receive beampattern and single-input single-output (SISO) ambiguity function. The second part of this work focuses on channel estimation for massive MIMO systems. As the size of arrays increase, conventional channel estimation techniques no longer remain practical. In current systems, training sequences probe wireless channels in orthogonal directions to obtain channel state information for block fading channels. The training overhead becomes signicant as the number of transmit antennas increases, thereby creating a need for alternative channel estimation techniques. In this work, we relax the orthogonal restriction on the sounding vectors and introduce a feedback channel to enable closed-loop sounding vector design. A probability of misalignment framework is introduced, which provides a measure to sequentially design sounding vectors