Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems

Myllyla, M.; Juntti, Markku; Cavallaro, Joseph R.

doi:10.1016/j.sigpro.2010.04.014

Cited by 13 publications

(13 citation statements)

References 32 publications

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“…In this equivalent real system model, the QAM constellation can be viewed as two PAM constellations, and the matrix dimension is hence doubled. In [29], the real model was revealed to be more efficient for the implementation of the sphere decoder and it will be used for the system model in this paper.…”

Section: Turbo Principlementioning

confidence: 99%

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Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Chall

Nouvel

Hélard

et al. 2015

J Wireless Com Network

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Recently, iterative receiver combining multiple-input multiple-output (MIMO) detection with channel decoding has been widely considered to achieve near-capacity performance and reliable high data rate transmission, for future wireless communication systems. However, such iterative processing increases the computational complexity at the receiver. In this paper, the computational complexity of MIMO detection algorithms combined with turbo decoding is investigated. We first present an overview of the family of MIMO detection algorithms based on sphere decoding, K-Best decoding, and interference cancellation. A recently proposed low-complexity K-Best decoder (LC-K-Best) is also presented. Moreover, we analyze the convergence of combining these detection algorithms with the turbo decoder using the extrinsic information transfer (EXIT) chart. Consequently, a new scheduling order of the number of iterations for the iterative process is proposed. Several system configurations are developed and compared in terms of performance and complexity. Simulations and analytical results show that the new scheduling provides good performance with a large saving in the complexity. Additionally, the LC-K-Best decoder shows a good performance-complexity tradeoff, and it is therefore suitable for parallel and pipeline architectures that can meet high throughput requirements.

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Section: Turbo Principlementioning

confidence: 99%

“…In [26], the tuple search detector (TSD) was introduced to improve the trade-off between STS-SD and LSD. Furthermore, soft versions of K-Best decoder and FSD decoder for iterative receiver were proposed in [27][28][29] and [30], respectively. The implementation of MIMO detectors have also been widely discussed in the literature.…”

Section: Introductionmentioning

confidence: 99%

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Chall

Nouvel

Hélard

et al. 2015

J Wireless Com Network

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“…For a few good examples, refer to [24][25][26][27][28]. For a few good examples, refer to [24][25][26][27][28].…”

Section: The Sphere Detector Algorithmmentioning

confidence: 99%

“…The complexity analysis of the SD algorithm has been thoroughly investigated by several researchers. For a few good examples, refer to [24][25][26][27][28]. It has been shown that the complexity of the SD algorithm is directly proportional to the number of lattice points explored.…”

Section: The Sphere Detector Algorithmmentioning

confidence: 99%

Parallel sphere detector algorithm providing optimal MIMO detection on massively parallel architectures

Jozsa

Vidal

Martínez-Zaldívar

et al. 2015

Concurrency and Computation

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Multiple-input multiple-output (MIMO) systems have attracted considerable attention in wireless communications because they offer a significant increase in data throughput and link coverage without additional bandwidth requirement or increased transmit power. The price that has to be paid is the increased complexity of hardware components and algorithms. The sphere detector (SD) algorithm solves the problem of maximum likelihood (ML) detection for MIMO channels by significantly reducing the search space of possible solutions. The main drawback of the SD algorithm is in its sequential nature, consequently, running it on massively parallel architectures (MPAs) is very inefficient. In order to overcome the drawbacks of the SD algorithm, a new parallel sphere detector (PSD) algorithm is proposed. It implements a novel hybrid tree search method, where the algorithm parallelism is assured by the efficient combination of depth-first search and breadth-first search algorithms. A path metric-based parallel sorting is employed at each intermediate stage. The PSD algorithm is able to adjust its memory requirements and extent of parallelism to fit a wide range of parallel architectures. Mapping details for MPAs are proposed by giving the details of thread dependent, highly parallel building blocks of the algorithm. Based on the building blocks proposed, a mapping to general-purpose graphics processing unit is provided, and its performance is evaluated. In order to achieve high-throughput, several levels of parallelism are introduced, and different scheduling strategies are considered.In the first approach the robustness of MIMO is maximized, that is, the probability of error is minimized with the use of space-time codes (STCs). STCs rely on transmitting different representations of the same data stream on different parallel transmit branches, that is, it introduces controlled redundancy in both space and time.Spatial Multiplexing (SM), the second approach, focuses on maximizing the capacity of a radio link by transmitting independent data streams on different transmit branches simultaneously and within the same frequency band. The price that has to be paid is the increased complexity of detection hardware components and algorithms. The complexity of detection algorithms depends on many factors, such as antenna configuration, modulation order, channel, and coding.With regard to the bit error rate (BER) performance, the maximum likelihood (ML) detector offers the best BER performance; however, its exponential complexity is not suitable for real-time applications. The SD algorithm has been proposed in the literature to significantly reduce the search space of possible solutions while still providing the ML solution. For a few good examples, refer to [2-4] and [5].In non-optimal detectors, the complexity of the sphere detector (SD) algorithm is reduced by introducing some approximations such as (i) early termination of the search, (ii) introducing constraints on the maximum number of nodes that the detector algorithm is allowed ...

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“…The scheme adjusts the processing requirements of the SD to the target-error-rate (TER) performance by changing the log-likelihood ratio (LLR) clipping value after evaluating (i.e., tracking) the provided BER performance. In [1], [2] the fundamental idea of tuning the SD complexity and the performance via LLR clipping is demonstrated via extensive simulations. However, no discussion has been made on how to practically set the LLR clipping value in order to adjust on-the-fly the receiver's processing requirements to the TER.…”

mentioning

confidence: 99%

Complexity Adjusted Soft-Output Sphere Decoding by Adaptive LLR Clipping

Nikitopoulos

Ascheid

2011

IEEE Commun. Lett.

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A-posteriori probability (APP) receivers operating over multiple-input, multiple-output channels provide enhanced bit error rate (BER) performance at the cost of increased complexity. However, employing full APP processing over favorable transmission environments, where less efficient approaches may already provide the required performance at a reduced complexity, results in unnecessary processing.For slowly varying channel statistics substantial complexity savings can be achieved by simple adaptive schemes. Such schemes track the BER performance and adjust the complexity of the soft output sphere decoder by adaptively setting the related log-likelihood ratio (LLR) clipping value.

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Implementation aspects of list sphere decoder algorithms for MIMO-OFDM systems

Cited by 13 publications

References 32 publications

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Iterative receivers combining MIMO detection with turbo decoding: performance-complexity trade-offs

Parallel sphere detector algorithm providing optimal MIMO detection on massively parallel architectures

Complexity Adjusted Soft-Output Sphere Decoding by Adaptive LLR Clipping

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