Successive interference cancellation aided sphere decoder for multi-input multi-output systems (original) (raw)
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Soft-output sphere decoding: algorithms and VLSI implementation
IEEE Journal on Selected Areas in Communications, 2000
Multiple-input multiple-output (MIMO) detection algorithms providing soft information for a subsequent channel decoder pose significant implementation challenges due to their high computational complexity. In this paper, we show how sphere decoding can be used as an efficient tool to implement soft-output MIMO detection with flexible trade-offs between computational complexity and (error rate) performance. In particular, we provide VLSI implementation results which demonstrate that single tree-search, sorted QR-decomposition, channel matrix regularization, log-likelihood ratio clipping, and imposing run-time constraints are the key ingredients for realizing soft-output MIMO detectors with near max-log performance at a chip area that is only 50% higher than that of the best-known hard-output sphere decoder VLSI implementation.
Soft iterative decoding techniques have shown to a great extent result in terms of Bit Error Rate performance in wireless communication. By using MIMO system we attain high multiplexing gain. However, this is only possible if an expedient detection technique is used. ML detection technique allows soft decisions for each received bit along with good error performance. In this paper we propose new technique for decoding Multiple Input -Multiple Output (MIMO) system, which combine Sphere Decoding (SD) with Zero Forcing (ZF) ,MMSE and V-BLAST techniques to make accessible near optimal low complexity and high performance modified sphere decoding algorithm. Simulation results on a QPSK Modulation with 4 transmit and 4 receive antennas show that the propose sphere decoder can achieve the near-optimal performance.
Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems
Signal Processing, 2010
A list sphere decoder (LSD) can be used to approximate the optimal maximum a posteriori (MAP) detector for the detection of multiple-input multiple-output (MIMO) signals. In this paper, we consider two LSD algorithms with different search methods and study some algorithm design choices which relate to the performance and computational complexity of the algorithm. We show that by limiting the dynamic range of log-likelihood ratio, the required LSD list size can be lowered, and, thus, the complexity of the LSD algorithm is decreased. We compare the real and the complexvalued signal models and their impact on the complexity of the algorithms. We show that the real-valued signal model is clearly the less complex choice and a better alternative for implementation. We also show the complexity of the sequential search LSD algorithm can be reduced by limiting the maximum number of checked nodes without sacrificing the performance of the system. Finally, we study the complexity and performance of an iterative receiver, analyze the tradeoff choices between complexity and performance, and show that the additional computational cost in LSD is justified to get better soft-output approximation.
Complexity assessment of sphere decoding methods for MIMO detection
2009 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), 2009
Sphere Decoding (SD) algorithms have been shown to provide maximum likelihood (ML) detection over Gaussian multiple input-multiple output (MIMO) channels with lower complexity than the exhaustive search. These methods are based on a closest lattice point search over a limited search space (hypersphere). There exist several implementations of these algorithms pursuiting different search strategies and working either within a set of real numbers, thus called Real Sphere Decoders (RSD), or performing the search directly within a set of complex numbers, commonly known as Complex Sphere Decoders (CSD). In this paper, a performance comparison between the real and the complex version of the Schnorr-Euchner (SE) sphere decoder has been carried out in order to find out which algorithm is the most suitable depending on the application. Furthermore a recently appeared fixed-complexity version of the SE decoder (FSD) has been evaluated both in terms of complexity and performance and the results have been compared with the original version. In contrast to yet existing complexity analyses, not only the number of visited nodes has been investigated but also the total number of operations.
Soft-Output Sphere Decoding: Performance and Implementation Aspects
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Multiple-input multiple-output (MIMO) detection algorithms providing soft information for a subsequent channel decoder pose significant implementation challenges due to their high computational complexity. In this paper, we show how sphere decoding can be used as an efficient tool to implement soft-output MIMO detection with flexible trade-offs between computational complexity and (error rate) performance. In particular, we demonstrate that single tree search, ordered QR decomposition, channel matrix regularization, and log-likelihood ratio clipping are the key ingredients for realizing soft-output MIMO detectors with near max-log performance at a computational complexity that is reasonably close to that of hard-output sphere decoding.
A Novel Approach for Sphere Decoder MIMO System
In sphere decoding techniques, it was seen that the generalized sphere decoder algorithms have been applied to decode the MIMO systems. The transmitted vector is determined by decoding a sequence of determined sub problems.In this paper, the proposed sphere decoder has promised considerable performance as compared to the generalized sphere decoder. The proposed sphere decoding algorithm follows an adaptive radius selection approach for reducing the computational complexity as compared to the generalized sphere decoding algorithm and with the ideal ML decoder which applies on the QPSK signaling. Also it has proven that the proposed sphere decoder has given the performance near optimal like the ML decoder. The performance comparison is done under the Flat Rayleigh fading environment. We substantiate our new proposed reduced complexity near optimal sphere decoding algorithm with simulations.
Analysis of a fixed-complexity sphere decoding method for spatial multiplexing MIMO
complexity, 2009
A new detection algorithm for decoding multiple-input multiple-output (MIMO) transmission is proposed and analyzed. The method is based on combining sphere decoding (SD) and zero forcing (ZF) techniques. The proposed method performs a fixed number of operations to detect the signal, independent of the noise level, and hence provides a fixed complexity near optimal performance. The algorithm is especially suited for systems with a large number of transmit antennas and allows efficient implementation in hardware. The high efficiency of this algorithm is obtained by limiting the number of overall SD iterations. Moreover, in the proposed method matrices with high condition number are more likely to undergo SD.
International Journal of Antennas and Propagation, 2012
In order to apply sphere decoding algorithm in multiple-input multiple-output communication systems and to make it feasible for real-time applications, its computational complexity should be decreased. To achieve this goal, this paper provides some useful insights into the effect of initial and the final sphere radii and estimating them effortlessly. It also discusses practical ways of initiating the algorithm properly and terminating it before the normal end of the process as well as the cost of these methods. Besides, a novel algorithm is introduced which utilizes the presented techniques according to a threshold factor which is defined in terms of the number of transmit antennas and the noise variance. Simulation results show that the proposed algorithm offers a desirable performance and reasonable complexity satisfying practical constraints.
IRJET-A Sphere Decoding Algorithm for MIMO
Modern wireless communication system demanding high data rate operating in bandwidth deficient world is using Multiple Input Multiple Output (MIMO) antenna arrangement. MIMO transmission has become a popular technique to increase spectral efficiency. MIMO supports greater data rate and higher reliability in wireless communication. The receivers employing linear receivers or decision feedback detectors in detection use less hardware are of suboptimal. Optimal detectors are realized by Maximum Likelihood detector can achieve superb performance, yet the computational complexity is enormously high. Therefore suboptimal detectors such as Viterbi Decoding, Sphere Decoding, Genetic Algorithm based Decoder are reach the performance of ML detectors, and potentially a great deal of computational cost can be saved. In this paper, a practical sphere-Decoding algorithm is proposed. It utilizes a simple and effective way to set the initial radius which plays a decisive role in determining the computational complexity. The complexity and SER rate of the sphere decoder is good when compare with other decoders used in MIMO receiver design. The performance of sphere decoder and maximum likelihood decoder is same but the complexity is reduced in sphere decoder.
Complexity and Analysis of Sphere Decoding and QRD-M Detection for MIMO Communication System
2013
MIMO seems to be the ultimate solution to increase the system capacity without requiring the need to additional spectral resources. In MIMO, multiple signals are transmitted instantaneously via enough spaced antennas. At the receiver side, the main challenge resides in designing signal processing techniques, i.e., detection techniques, capable of separating those Transmitted signals with acceptable complexity and achieved performance. Motivated by the importance of the detection techniques as an important factor in determining both the feasibility and performance of the MIMO systems, this study only considers the receiver structure for the MIMO techniques. The study includes a detailed performance analysis of detection algorithms. Also, deep understandings of the affecting factors on the MIMO performance are covered including, the number of transmit and receive antennas, constellation size. The tree-search based detection techniques; i.e., SD and QRD-M with the two promising approac...