Low Complexity Multi-User MIMO Detection for Uplink SCMA System Using Expectation Propagation Algorithm

Sparse code multiple access (SCMA), which combines the advantages of low density signature (LDS) and code-division multiple access (CDMA), is regarded as one of the promising modulation technique candidate for the next generation of wireless systems. Conventionally, the message passing algorithm (MPA) is used for data detector at the receiver side. However, the MPA-SCMA cannot be implemented in the next generation wireless systems, because of its unacceptable complexity cost. Specifically, the complexity of MPA-SCMA grows exponentially with the number of antennas. Considering the use of high dimensional systems in the next generation of wireless systems, such as massive multi-user MIMO systems, the conventional MPA-SCMA is prohibitive. In this paper, we propose a low complexity detector algorithm named the expectation propagation algorithm (EPA) for SCMA. Mainly, the EPA-SCMA solves the complexity problem of MPA-SCMA and enables the implementation of SCMA in massive MU-MIMO systems. For instance, the EPA-SCMA also enables the implemantation of SCMA in the next generation wireless systems. We further show that the EPA can achieve the optimal detection performance as the numbers of transmit and receive antennas grow. We also demonstrate that a rotation design in SCMA codebook is unnecessary, which is quite rather different from the general assumptio

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

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

Baseband Processing for 5G and Beyond: Algorithms, VLSI Architectures, and Co-design

In recent years the number of connected devices and the demand for high data-rates have been significantly increased. This enormous growth is more pronounced by the introduction of the Internet of things (IoT) in which several devices are interconnected to exchange data for various applications like smart homes and smart cities. Moreover, new applications such as eHealth, autonomous vehicles, and connected ambulances set new demands on the reliability, latency, and data-rate of wireless communication systems, pushing forward technology developments. Massive multiple-input multiple-output (MIMO) is a technology, which is employed in the 5G standard, offering the benefits to fulfill these requirements. In massive MIMO systems, base station (BS) is equipped with a very large number of antennas, serving several users equipments (UEs) simultaneously in the same time and frequency resource. The high spatial multiplexing in massive MIMO systems, improves the data rate, energy and spectral efficiencies as well as the link reliability of wireless communication systems. The link reliability can be further improved by employing channel coding technique. Spatially coupled serially concatenated codes (SC-SCCs) are promising channel coding schemes, which can meet the high-reliability demands of wireless communication systems beyond 5G (B5G). Given the close-to-capacity error correction performance and the potential to implement a high-throughput decoder, this class of code can be a good candidate for wireless systems B5G. In order to achieve the above-mentioned advantages, sophisticated algorithms are required, which impose challenges on the baseband signal processing. In case of massive MIMO systems, the processing is much more computationally intensive and the size of required memory to store channel data is increased significantly compared to conventional MIMO systems, which are due to the large size of the channel state information (CSI) matrix. In addition to the high computational complexity, meeting latency requirements is also crucial. Similarly, the decoding-performance gain of SC-SCCs also do come at the expense of increased implementation complexity. Moreover, selecting the proper choice of design parameters, decoding algorithm, and architecture will be challenging, since spatial coupling provides new degrees of freedom in code design, and therefore the design space becomes huge. The focus of this thesis is to perform co-optimization in different design levels to address the aforementioned challenges/requirements. To this end, we employ system-level characteristics to develop efficient algorithms and architectures for the following functional blocks of digital baseband processing. First, we present a fast Fourier transform (FFT), an inverse FFT (IFFT), and corresponding reordering scheme, which can significantly reduce the latency of orthogonal frequency-division multiplexing (OFDM) demodulation and modulation as well as the size of reordering memory. The corresponding VLSI architectures along with the application specific integrated circuit (ASIC) implementation results in a 28 nm CMOS technology are introduced. In case of a 2048-point FFT/IFFT, the proposed design leads to 42% reduction in the latency and size of reordering memory. Second, we propose a low-complexity massive MIMO detection scheme. The key idea is to exploit channel sparsity to reduce the size of CSI matrix and eventually perform linear detection followed by a non-linear post-processing in angular domain using the compressed CSI matrix. The VLSI architecture for a massive MIMO with 128 BS antennas and 16 UEs along with the synthesis results in a 28 nm technology are presented. As a result, the proposed scheme reduces the complexity and required memory by 35%–73% compared to traditional detectors while it has better detection performance. Finally, we perform a comprehensive design space exploration for the SC-SCCs to investigate the effect of different design parameters on decoding performance, latency, complexity, and hardware cost. Then, we develop different decoding algorithms for the SC-SCCs and discuss the associated decoding performance and complexity. Also, several high-level VLSI architectures along with the corresponding synthesis results in a 12 nm process are presented, and various design tradeoffs are provided for these decoding schemes