Robust massive MIMO Equilization for mmWave systems with low resolution ADCs

Leveraging the available millimeter wave spectrum will be important for 5G. In this work, we investigate the performance of digital beamforming with low resolution ADCs based on link level simulations including channel estimation, MIMO equalization and channel decoding. We consider the recently agreed 3GPP NR type 1 OFDM reference signals. The comparison shows sequential DCD outperforms MMSE-based MIMO equalization both in terms of detection performance and complexity. We also show that the DCD based algorithm is more robust to channel estimation errors. In contrast to the common believe we also show that the complexity of MMSE equalization for a massive MIMO system is not dominated by the matrix inversion but by the computation of the Gram matrix.Comment: submitted to WCNC 2018 Workshop

Channel Estimation for Massive MIMO Systems

Massive multiple input multiple output (MIMO) systems can significantly improve the channel capacity by deploying multiple antennas at the transmitter and receiver. Massive MIMO is considered as one of key technologies of the next generation of wireless communication systems. However, with the increase of the number of antennas at the base station, a large number of unknown channel parameters need to be dealt with, which makes the channel estimation a challenging problem. Hence, the research on the channel estimation for massive MIMO is of great importance to the development of the next generation of communication systems. The wireless multipath channel exhibits sparse characteristics, but the traditional channel estimation techniques do not make use of the sparsity. The channel estimation based on compressive sensing (CS) can make full use of the channel sparsity, while use fewer pilot symbols. In this work, CS channel estimation methods are proposed for massive MIMO systems in complex environments operating in multipath channels with static and time-varying parameters. Firstly, a CS channel estimation algorithm for massive MIMO systems with Orthogonal Frequency Division Multiplexing (OFDM) is proposed. By exploiting the spatially common sparsity in the virtual angular domain of the massive MIMO channels, a dichotomous-coordinate-decent-joint-sparse-recovery (DCD-JSR) algorithm is proposed. More specifically, by considering the channel is static over several OFDM symbols and exhibits common sparsity in the virtual angular domain, the DCD-JSR algorithm can jointly estimate multiple sparse channels with low computational complexity. The simulation results have shown that, compared to existing channel estimation algorithms such as the distributed-sparsity-adaptive-matching-pursuit (DSAMP) algorithm, the proposed DCD-JSR algorithm has significantly lower computational complexity and better performance. Secondly, these results have been extended to the case of multipath channels with time-varying parameters. This has been achieved by employing the basis expansion model to approximate the time variation of the channel, thus the modified DCD-JSR algorithm can estimate the channel in a massive MIMO OFDM system operating over frequency selective and highly mobile wireless channels. Simulation results have shown that, compared to the DCD-JSR algorithm designed for time-invariant channels, the modified DCD-JSR algorithm provides significantly better estimation performance in fast time-varying channels

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). 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Self-Defense, Defense of Others, and the State

With the wide spread of wireless technology, the time for 4G has arrived, and 5G will appear not so far in the future. However, no matter whether it is 4G or 5G, low latency is a mandatory requirement for baseband processing at base stations for modern cellular standards. In particular, in a future 5G wireless system, with massive MIMO and ultra-dense cells, the demand for low round trip latency between the mobile device and the base station requires a baseband processing delay of 1 ms. This is 10 percentage of today’s LTE-A round trip latency, while at the same time massive MIMO requires large-scale matrix computations. This is especially true for channel estimation and MIMO detection at the base station. Therefore, it is essential to ensure low latency for the user data traffic. In this master’s thesis, LTE/LTE-A uplink physical layer processing is examined, especially the process of channel estimation and MIMO detection. In order to analyze this processing we compare two conventional algorithms’ performance and complexity for channel estimation and MIMO detection. The key aspect which affects the algorithms’ speed is identified as the need for “massive complex matrix inversion”. A parallel coding scheme is proposed to implement a matrix inversion kernel algorithm on a single instruction multiple data stream (SIMD) vector processor. The major contribution of this thesis is implementation and evaluation of a parallel massive complex matrix inversion algorithm. Two aspects have been addressed: the selection of the algorithm to perform this matrix computation and the implementation of a highly parallel version of this algorithm.Med den breda spridningen av trådlös teknik, har tiden för 4G kommit, och 5G kommer inom en överskådlig framtid. Men oavsett om det gäller 4G eller 5G, låg latens är ett obligatoriskt krav för basbandsbehandling vid basstationer för moderna mobila standarder. I synnerhet i ett framtida trådlöst 5G-system, med massiva MIMO och ultratäta celler, behövs en basbandsbehandling fördröjning på 1 ms för att klara efterfrågan på en låg rundresa latens mellan den mobila enheten och basstationen. Detta är 10 procent av dagens LTE-E rundresa latens, medan massiva MIMO samtidigt kräver storskaliga matrisberäkningar. Detta är särskilt viktigt för kanaluppskattning och MIMO-detektion vid basstationen. Därför är det viktigt att se till att det är låg latens för användardatatrafik. I detta examensarbete, skall LTE/LTE-A upplänk fysiska lagret bearbetning undersökas, och då särskilt processen för kanaluppskattning och MIMO-detektion. För att analysera denna processing jämför vi två konventionella algoritmers prestationer och komplexitet för kanaluppskattning och MIMO-detektion. Den viktigaste aspekten som påverkar algoritmernas hastighet identifieras som behovet av "massiva komplex matrisinversion". Ett parallellt kodningsschema föreslås för att implementera en "matrisinversion kernel-algoritmen" på singelinstruktion multidataström (SIMD) vektorprocessor. Det största bidraget med denna avhandling är genomförande och utvärdering av en parallell massiva komplex matrisinversion kernel-algoritmen. Två aspekter har tagits upp: valet av algoritm för att utföra denna matrisberäkning och implementationen av en högst parallell version av denna algoritm

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

Two-Timescale Design for RIS-Aided Massive MIMO Systems

The emerging technology, reconfigurable intelligent surface (RIS), could support high data rate while maintaining low costs and energy consumption. Besides, it can constructively reflect the signal from the base station (BS) to users which helps solve the blockage problem in the urban area. Due to these benefits, RIS could be an energy-efficient and cost-effective complement to conventional massive multiple-input multiple-output (MIMO) systems. Focusing on the underload network in far-field outdoor scenarios with fixed users, this thesis investigates the theoretical performance and optimisation design of uplink RIS-aided massive MIMO systems under different detectors and different channel state information (CSI). A novel two-timescale transmission scheme is exploited where the BS detectors and RIS phase shifts are designed based on fast-changing instantaneous CSI and slow-changing statistical CSI, respectively, which achieves a good trade-off between the system performance and the channel estimation overhead. First, this thesis analyses the RIS-aided massive MIMO system with low-complexity maximal-ratio combination (MRC) detectors under the general Rician fading channel model. Closed-form expressions for the achievable rate are derived with blocked and unblocked direct links, based on which the power scaling laws, the rate scaling orders, and the impact of Rician factors are revealed, respectively. A genetic algorithm (GA)-based method is proposed for the design of the RIS phase shifts relying only on the statistical CSI. Simulation results demonstrate the benefit of integrating the RIS into conventional massive MIMO systems. Second, the RIS-aided massive MIMO system is investigated in the presence of the channel estimation error. Following the two-timescale strategy, a low-overhead channel estimation method is proposed to estimate the instantaneous aggregated CSI, whose quality and properties are analysed to shed light on the benefit brought by the RIS. With MRC detectors and the channel estimation results, the achievable rate is derived and a comprehensive framework for the power scaling laws with respect to the number of BS antennas and RIS elements is given. The superiority of the proposed two-timescale scheme over the instantaneous-CSI scheme is validated. Third, the more general scenario in the presence of spatial correlation and electromagnetic interference (EMI) is studied. The channel estimation result is revisited which shows that the RIS could play more roles with spatial correlation. Then, the closed-form expression of the achievable rate is derived and the negative impact of the EMI is analysed. To maximise the minimum user rate, the phase shifts of the RIS are designed based on an accelerated gradient ascent method, which has low computational complexity and relies only on the statistical CSI. Fourth, to solve the severe multi-user interference issue, a zero-forcing (ZF) detector-based design is considered for the RIS-aided massive MIMO system. After tackling the challenging matrix inversion operator, the closed-form ergodic rate expression is derived. Then, the promising properties of introducing ZF detectors into RIS-aided massive MIMO systems are revealed. Fifth and last, the RIS-aided massive MIMO system with ZF detectors and imperfect CSI is analysed. A minimum mean-squared error (MMSE) channel estimator is proposed and analysed. The closed-form expression of the ergodic rate is derived and two insightful upper and lower bounds are proposed, which unveil the rate scaling orders and prove that the considered structure is promising for enhanced mobile broadband, green communications, and the Internet of Things. Besides, both the sum user rate maximisation and the minimum user rate maximisation problems are solved based on the low-complexity majorization-minimisation (MM) algorithms

MIMOシステムにおける格子基底縮小を用いた信号検出法及びその応用に関する研究

Multiple-input multiple-output (MIMO) technology has attracted attention in wireless communications, since it provides signi cant increases in data throughput and the high spectral efficiency. MIMO systems employ multiple antennas at both ends of the wireless link, and hence can increase the data rate by transmitting multiple data streams. To exploit the potential gains o ered by MIMO, signal processing involved in a MIMO receiver requires a large computational complexity in order to achieve the optimal performance. In MIMO systems, it is usually required to detect signals jointly as multiple signals are transmitted through multiple signal paths between the transmitter and the receiver. This joint detection becomes the MIMO detection. The maximum likelihood (ML) detection (MLD) is known as the optimal detector in terms of minimizing bit error rate (BER). However, the complexity of MLD obstructs its practical implementation. The common linear detection such as zero-forcing (ZF) or minimum mean squared error (MMSE) o ers a remarkable complexity reduction with performance loss. The non-linear detection, e.g. the successive interference cancellation (SIC), detects each symbol sequentially withthe aid of cancellation operations which remove the interferences from the received signal. The BER performance is improved by using the SIC, but is still inferior to that of the ML detector with low complexity. Numerous suboptimal detection techniques have been proposed to approximately approach the ML performance with relatively lower complexity, such as sphere detection (SD) and QRM-MLD. To look for suboptimal detection algorithm with near optimal performance and a ordable complexity costs for MIMO gains faces a major challenge. Lattice-reduction (LR) is a promising technique to improve the performance of MIMO detection. The LR makes the column vectors of the channel state information (CSI) matrix close to mutually orthogonal. The following signal estimation of the transmitted signal applies the reduced lattice basis instead of the original lattice basis. The most popular LR algorithm is the well-known LLL algorithm, introduced by Lenstra, Lenstra, and Lov asz. Using this algorithm, the LR aided (LRA) detector achieves more reliable signal estimation and hence good BER performance. Combining the LLL algorithm with the conventional linear detection of ZF or MMSE can further improve the BER performance in MIMO systems, especially the LR-MMSE detection. The non-linear detection i.e. SIC based on LR (LR-SIC) is selected from many detection methods since it features the good BER performance. And ordering SIC based on LR (LR-OSIC) can further improve the BER performance with the costs of the implementation of the ordering but requires high computational complexity. In addition, list detection can also obtain much better performance but with a little high computational cost in terms of the list of candidates. However, the expected performance of the several detections isnot satis ed directly like the ML detector, in particular for the high modulation order or the large size MIMO system. This thesis presents our studies about lattice reduction aided detection and its application in MIMO system. Our studies focus on the evaluation of BER performance and the computational complexity. On the hand, we improve the detection algorithms to achieve the near-ML BER performance. On the other hand, we reduce the complexity of the useless computation, such as the exhaustive tree search. We mainly solve three problems existed in the conventional detection methods as - The MLD based on QR decomposition and M-algorithm (QRMMLD) is one solution to relatively reduce the complexity while retaining the ML performance. The number of M in the conventional QRM-MLD is de ned as the number of the survived branches in each detection layer of the tree search, which is a tradeo between complexity and performance. Furthermore, the value of M should be large enough to ensure that the correct symbols exist in the survived branches under the ill-conditioned channel, in particular for the large size MIMO system and the high modulation order. Hence the conventional QRM-MLD still has the problem of high complexity in the better-conditioned channel. - For the LRA MIMO detection, the detection errors are mainly generated from the channel noise and the quantization errors in the signal estimation stage. The quantization step applies the simple rounding operation, which often leads to the quantization error. If this error occurs in a row of the transmit signal, it has to propagate to many symbols in the subsequent signal estimation and result in degrading the BER performance. The conventional LRA MIMO detection has the quantization problem, which obtains less reliable signal estimation and leads to the BER performance loss. - Ordering the column vectors of the LR-reduced channel matrix brings large improvement on the BER performance of the LRSIC due to decreasing the error propagation. However, the improvement of the LR-OSIC is not su cient to approach the ML performance in the large size MIMO system, such as 8 8 MIMO system. Hence, the LR-OSIC detection cannot achieve the near-ML BER performance in the large size of MIMO system. The aim of our researches focuses on the detection algorithm, which provides near-ML BER performance with very low additional complexity. Therefore, we have produced various new results on low complexity MIMO detection with the ideas of lattice reduction aided detection and its application even for large size MIMO system and high modulation order. Our works are to solve the problems in the conventional MIMO detections and to improve the detection algorithms in the signal estimation. As for the future research, these detection schemes combined with the encoding technique lead to interesting and useful applications in the practical MIMO system or massive MIMO.電気通信大学201

On Low-Resolution ADCs in Practical 5G Millimeter-Wave Massive MIMO Systems

Nowadays, millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems is a favorable candidate for the fifth generation (5G) cellular systems. However, a key challenge is the high power consumption imposed by its numerous radio frequency (RF) chains, which may be mitigated by opting for low-resolution analog-to-digital converters (ADCs), whilst tolerating a moderate performance loss. In this article, we discuss several important issues based on the most recent research on mmWave massive MIMO systems relying on low-resolution ADCs. We discuss the key transceiver design challenges including channel estimation, signal detector, channel information feedback and transmit precoding. Furthermore, we introduce a mixed-ADC architecture as an alternative technique of improving the overall system performance. Finally, the associated challenges and potential implementations of the practical 5G mmWave massive MIMO system {with ADC quantizers} are discussed.Comment: to appear in IEEE Communications Magazin