Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems

En la última década, uno de los avances tecnológicos más importantes que han hecho culminar la nueva generación de banda ancha inalámbrica es la comunicación mediante sistemas de múltiples entradas y múltiples salidas (MIMO). Las tecnologías MIMO han sido adoptadas por muchos estándares inalámbricos tales como LTE, WiMAS y WLAN. Esto se debe principalmente a su capacidad de aumentar la máxima velocidad de transmisión , junto con la fiabilidad alcanzada y la cobertura de las comunicaciones inalámbricas actuales sin la necesidad de ancho de banda extra ni de potencia de transmisión adicional. Sin embargo, las ventajas proporcionadas por los sistemas MIMO se producen a expensas de un aumento sustancial del coste de implementación de múltiples antenas y de la complejidad del receptor, la cual tiene un gran impacto sobre el consumo de energía. Por esta razón, el diseño de receptores de baja complejidad es un tema importante que se abordará a lo largo de esta tesis. En primer lugar, se investiga el uso de técnicas de preprocesado de la matriz de canal MIMO bien para disminuir el coste computacional de decodificadores óptimos o bien para mejorar las prestaciones de detectores subóptimos lineales, SIC o de búsqueda en árbol. Se presenta una descripción detallada de dos técnicas de preprocesado ampliamente utilizadas: el método de Lenstra, Lenstra, Lovasz (LLL) para lattice reduction (LR) y el algorimo VBLAST ZF-DFE. Tanto la complejidad como las prestaciones de ambos métodos se han evaluado y comparado entre sí. Además, se propone una implementación de bajo coste del algoritmo VBLAST ZF-DFE, la cual se incluye en la evaluación. En segundo lugar, se ha desarrollado un detector MIMO basado en búsqueda en árbol de baja complejidad, denominado detector K-Best de amplitud variable (VB K-Best). La idea principal de este método es aprovechar el impacto del número de condición de la matriz de canal sobre la detección de datos con el fin de disminuir la complejidad de los sistemasRoger Varea, S. (2012). Design and Implementation of Efficient Algorithms for Wireless MIMO Communication Systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16562Palanci

High performance lattice reduction on heterogeneous computing platform

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1201-2The lattice reduction (LR) technique has become very important in many engineering fields. However, its high complexity makes difficult its use in real-time applications, especially in applications that deal with large matrices. As a solution, the modified block LLL (MB-LLL) algorithm was introduced, where several levels of parallelism were exploited: (a) fine-grained parallelism was achieved through the cost-reduced all-swap LLL (CR-AS-LLL) algorithm introduced together with the MB-LLL by Jzsa et al. (Proceedings of the tenth international symposium on wireless communication systems, 2013) and (b) coarse-grained parallelism was achieved by applying the block-reduction concept presented by Wetzel (Algorithmic number theory. Springer, New York, pp 323-337, 1998). In this paper, we present the cost-reduced MB-LLL (CR-MB-LLL) algorithm, which allows to significantly reduce the computational complexity of the MB-LLL by allowing the relaxation of the first LLL condition while executing the LR of submatrices, resulting in the delay of the Gram-Schmidt coefficients update and by using less costly procedures during the boundary checks. The effects of complexity reduction and implementation details are analyzed and discussed for several architectures. A mapping of the CR-MB-LLL on a heterogeneous platform is proposed and it is compared with implementations running on a dynamic parallelism enabled GPU and a multi-core CPU. The mapping on the architecture proposed allows a dynamic scheduling of kernels where the overhead introduced is hidden by the use of several CUDA streams. Results show that the execution time of the CR-MB-LLL algorithm on the heterogeneous platform outperforms the multi-core CPU and it is more efficient than the CR-AS-LLL algorithm in case of large matrices.Financial support for this study was provided by grants TAMOP-4.2.1./B-11/2/KMR-2011-0002, TAMOP-4.2.2/B-10/1-2010-0014 from the Pazmany Peter Catholic University, European Union ERDF, Spanish Government through TEC2012-38142-C04-01 project and Generalitat Valenciana through PROMETEO/2009/013 project.Jozsa, CM.; Domene Oltra, F.; Vidal Maciá, AM.; Piñero Sipán, MG.; González Salvador, A. (2014). High performance lattice reduction on heterogeneous computing platform. Journal of Supercomputing. 70(2):772-785. https://doi.org/10.1007/s11227-014-1201-2S772785702Józsa CM, Domene F, Piñero G, González A, Vidal AM (2013) Efficient GPU implementation of lattice-reduction-aided multiuser precoding. In: Proceedings of the tenth international symposium on wireless communication systems (ISWCS 2013)Wetzel S (1998) An efficient parallel block-reduction algorithm. In: Buhler JP (ed) Algorithmic number theory. Lecture notes in computer science, vol 1423. Springer, Berlin, Heidelberg, pp 323–337Wubben D, Seethaler D, Jaldén J, Matz G (2011) Lattice reduction. Signal Process Mag IEEE 28(3):70–91Lenstra AK, Lenstra HW, Lovász L (1982) Factoring polynomials with rational coefficients. Math Ann 261(4):515–534Bremner MR (2012) Lattice basis reduction: an introduction to the LLL algorithm and its applications. CRC Press, USAWu D, Eilert J, Liu D (2008) A programmable lattice-reduction aided detector for MIMO-OFDMA. In: 4th IEEE international conference on circuits and systems for communications (ICCSC 2008), pp 293–297Barbero LG, Milliner DL, Ratnarajah T, Barry JR, Cowan C (2009) Rapid prototyping of Clarkson’s lattice reduction for MIMO detection. In: IEEE international conference on communications (ICC’09), pp 1–5Gestner B, Zhang W, Ma X, Anderson D (2011) Lattice reduction for MIMO detection: from theoretical analysis to hardware realization. IEEE Trans Circ Syst I Regul Pap 58(4):813–826Shabany M, Youssef A, Gulak G (2013) High-throughput 0.13- \upmu μ m CMOS lattice reduction core supporting 880 Mb/s detection. IEEE Trans Very Large Scale Integr (VLSI) Syst 21(5):848–861Luo Y, Qiao S (2011) A parallel LLL algorithm. In: Proceedings of the fourth international C* conference on computer science and software engineering, pp 93–101Backes W, Wetzel S (2011) Parallel lattice basis reduction—the road to many-core. In: IEEE 13th international conference on high performance computing and communications (HPCC)Ahmad U, Amin A, Li M, Pollin S, Van der Perre L, Catthoor F (2011) Scalable block-based parallel lattice reduction algorithm for an SDR baseband processor. In: 2011 IEEE international conference on communications (ICC)Villard G (1992) Parallel lattice basis reduction. In: Papers from the international symposium on symbolic and algebraic computation (ISSAC’92). ACM, New YorkDomene F, Józsa CM, Vidal AM, Piñero G, Gonzalez A (2013) Performance analysis of a parallel lattice reduction algorithm on many-core architectures. In: Proceedings of the 13th international conference on computational and mathematical methods in science and engineeringGestner B, Zhang W, Ma X, Anderson DV (2008) VLSI implementation of a lattice reduction algorithm for low-complexity equalization. In: 4th IEEE international conference on circuits and systems for communications (ICCSC 2008), pp 643–647Burg A, Seethaler D, Matz G (2007) VLSI implementation of a lattice-reduction algorithm for multi-antenna broadcast precoding. In: IEEE international symposium on circuits and systems (ISCAS 2007), pp 673–676Bruderer L, Studer C, Wenk M, Seethaler D, Burg A (2010) VLSI implementation of a low-complexity LLL lattice reduction algorithm for MIMO detection. In: Proceedings of 2010 IEEE international symposium on circuits and systems (ISCAS

From Linear Equalization to Lattice-Reduction-Aided Sphere-Detector as an Answer to the MIMO Detection Problematic in Spatial Multiplexing Systems

ISBN 978-953-307-223-4International audienc

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