Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-009-0354-x[EN] This paper describes a pipelined parallel algorithm for the MMSE-OSIC decoding procedure proposed in V-BLAST wireless MIMO systems, for heterogeneous networks of processors. It is based on a block version of the square-root Kalman Filter algorithm that was initially devised to solve the RLS problem. It has been parallelized in a pipelined way obtaining a good efficiency and scalability. The optimum load balancing for this parallel algorithm is dynamic, but we derive a static load balancing scheme with good performance. © 2009 Springer Science+Business Media, LLC.This work has been supported by the Generalitat Valenciana, project 20080811, by the Universidad Politecnica de Valencia, project 20080009, by the Conserjería de Educacion de la Región de Murcia (Fundacion Séneca, 08763/PI/08), and by the Ministerio de Ciencia e Innovacion (TIN2008-06570-C04-02).Martínez Zaldívar, FJ.; Vidal Maciá, AM.; Giménez Cánovas, D. (2011). Heterogeneous pipelined square-root Kalman Filter algorithm for the MMSE-OSIC problem. Journal of Supercomputing. 58(2):235-243. https://doi.org/10.1007/s11227-009-0354-xS235243582Foschini GJ (1996) Layered space-time architecture for wireless communications in a fading environment when using multiple antennas. Bell Labs Techn J 1:41–59Hassibi B (2000) An efficient square-root algorithm for BLAST. In: IEEE international conference on acoustics, speech and signal processing 2000, vol 2, pp II737–II740Zhu H, Lei Z, Chin FPS (2004) An improved square-root algorithm for BLAST. IEEE Signal Process Lett 11(9)Choi Y-S, Voltz PJ, Cassara FA (2001) On channel estimation and detection for multicarrier signals in fast and selective Rayleigh fading channels. IEEE Trans Commun 49(8)Burg A, Haene S, Perels D, Luethi P, Felber N, Fichtner W (2006) Algorithm and VLSI architecture for linear MMSE detection in MIMO-OFDM systems. In: Proceedings of the IEEE int symp on circuits and systems, May 2006Martínez Zaldívar FJ (2007) Algoritmos paralelos segmentados para los problemas de Mínimos Cuadrados Recursivos (RLS) y de Detección por Cancelación Ordenada y Sucesiva de Interferencia (OSIC). PhD thesis, Facultad de Informática, Universidad Politécnica de Valencia, SpainSayed AH, Kailath T (1994) A state-space approach to adaptive RLS filtering. IEEE Signal Process Mag 11(3):18–60Kumar V, Gram A, Gupta A, Karypis G (2003) An introduction to parallel computing: design and analysis of algorithms, Chap 4, 2nd edn. Addison-Wesley, Harlow

MIMO Detection for High-Order QAM Based on a Gaussian Tree Approximation

This paper proposes a new detection algorithm for MIMO communication systems employing high order QAM constellations. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, a straightforward application of the Belief Propagation (BP) algorithm yields very poor results. Our algorithm is based on an optimal tree approximation of the Gaussian density of the unconstrained linear system. The finite-set constraint is then applied to obtain a loop-free discrete distribution. It is shown that even though the approximation is not directly applied to the exact discrete distribution, applying the BP algorithm to the loop-free factor graph outperforms current methods in terms of both performance and complexity. The improved performance of the proposed algorithm is demonstrated on the problem of MIMO detection

Dual-lattice ordering and partial lattice reduction for SIC-based MIMO detection

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we propose low-complexity lattice detection algorithms for successive interference cancelation (SIC) in multi-input multi-output (MIMO) communications. First, we present a dual-lattice view of the vertical Bell Labs Layered Space-Time (V-BLAST) detection. We show that V-BLAST ordering is equivalent to applying sorted QR decomposition to the dual basis, or equivalently, applying sorted Cholesky decomposition to the associated Gram matrix. This new view results in lower detection complexity and allows simultaneous ordering and detection. Second, we propose a partial reduction algorithm that only performs lattice reduction for the last several, weak substreams, whose implementation is also facilitated by the dual-lattice view. By tuning the block size of the partial reduction (hence the complexity), it can achieve a variable diversity order, hence offering a graceful tradeoff between performance and complexity for SIC-based MIMO detection. Numerical results are presented to compare the computational costs and to verify the achieved diversity order

Characterization of the seismic environment at the Sanford Underground Laboratory, South Dakota

An array of seismometers is being developed at the Sanford Underground Laboratory, the former Homestake mine, in South Dakota to study the properties of underground seismic fields and Newtonian noise, and to investigate the possible advantages of constructing a third-generation gravitational-wave detector underground. Seismic data were analyzed to characterize seismic noise and disturbances. External databases were used to identify sources of seismic waves: ocean-wave data to identify sources of oceanic microseisms, and surface wind-speed data to investigate correlations with seismic motion as a function of depth. In addition, sources of events contributing to the spectrum at higher frequencies are characterized by studying the variation of event rates over the course of a day. Long-term observations of spectral variations provide further insight into the nature of seismic sources. Seismic spectra at three different depths are compared, establishing the 4100-ft level as a world-class low seismic-noise environment.Comment: 29 pages, 16 figure

Generalized feedback detection for spatial multiplexing multi-antenna systems

We present a unified detection framework for spatial multiplexing multiple-input multiple-output (MIMO) systems by generalizing Heller’s classical feedback decoding algorithm for convolutional codes. The resulting generalized feedback detector (GFD) is characterized by three parameters: window size, step size and branch factor. Many existing MIMO detectors are turned out to be special cases of the GFD. Moreover, different parameter choices can provide various performance-complexity tradeoffs. The connection between MIMO detectors and tree search algorithms is also established. To reduce redundant computations in the GFD, a shared computation technique is proposed by using a tree data structure. Using a union bound based analysis of the symbol error rates, the diversity order and signal-to-noise ratio (SNR) gain are derived analytically as functions of the three parameters; for example, the diversity order of the GFD varies between 1 and N. The complexity of the GFD varies between those of the maximum-likelihood (ML) detector and the zero-forcing decision feedback detector (ZFDFD). Extensive computer simulation results are also provided

Adaptive and Iterative Multi-Branch MMSE Decision Feedback Detection Algorithms for MIMO Systems

In this work, decision feedback (DF) detection algorithms based on multiple processing branches for multi-input multi-output (MIMO) spatial multiplexing systems are proposed. The proposed detector employs multiple cancellation branches with receive filters that are obtained from a common matrix inverse and achieves a performance close to the maximum likelihood detector (MLD). Constrained minimum mean-squared error (MMSE) receive filters designed with constraints on the shape and magnitude of the feedback filters for the multi-branch MMSE DF (MB-MMSE-DF) receivers are presented. An adaptive implementation of the proposed MB-MMSE-DF detector is developed along with a recursive least squares-type algorithm for estimating the parameters of the receive filters when the channel is time-varying. A soft-output version of the MB-MMSE-DF detector is also proposed as a component of an iterative detection and decoding receiver structure. A computational complexity analysis shows that the MB-MMSE-DF detector does not require a significant additional complexity over the conventional MMSE-DF detector, whereas a diversity analysis discusses the diversity order achieved by the MB-MMSE-DF detector. Simulation results show that the MB-MMSE-DF detector achieves a performance superior to existing suboptimal detectors and close to the MLD, while requiring significantly lower complexity.Comment: 10 figures, 3 tables; IEEE Transactions on Wireless Communications, 201