VLSI Implementation of Hard- and Soft-Output Sphere Decoding for Wide-Band MIMO Systems

Studer, Christoph; Wenk, M.; Burg, Andreas

doi:10.1007/978-3-642-28566-0_6

Cited by 8 publications

(16 citation statements)

References 20 publications

(68 reference statements)

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“…The comparison presented in Table VI shows that the mapping of the PSD algorithm on the GP-GPU achieves the best throughput among the true-ML algorithms and outperforms many of the nonoptimal GP-GPU implementations. The detection throughput of the GP-GPU mapping is similar to the non-optimal implementations presented in [49] and [44] but is outperformed by the FPGA implementation presented in [51] and the very large scale integration (VLSI) system implementation published in [52]. However, those solutions implement non-optimal detection.…”

Section: Comparison Of Detection Throughput and Bit Error Rate Performentioning

confidence: 75%

“…In the first group, only the algorithm presented in [48] exploits two levels of parallelism such as: (i) a system level parallelism that implements the concurrent execution of the preprocessing, decoding and the simultaneous detection of symbol vectors and (ii) a data dependency based low- [52] non-ML hard 1x1 j j D 2 672-2143 VLSI multiple SD to 4 4 to j j D 8 130 nm CMOS based cores BER, bit error rate; ML, maximum likelihood; SNR, signal to noise ratio; SD, sphere detector; PSD, parallel SD; FSD, fixed-complexity SD; APP, a posteriori probability.…”

Section: Comparison Of Detection Throughput and Bit Error Rate Performentioning

confidence: 99%

“…To our knowledge, the best implementations were published in [52]. Significant speedups were achieved by executing several SD cores in parallel.…”

Section: Performance Of Non-optimal Detection Algorithms Mapped On Fimentioning

confidence: 99%

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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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Section: Comparison Of Detection Throughput and Bit Error Rate Performentioning

confidence: 75%

Section: Comparison Of Detection Throughput and Bit Error Rate Performentioning

confidence: 99%

See 1 more Smart Citation

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

Jozsa

Vidal

Martínez-Zaldívar

et al. 2015

Concurrency and Computation

View full text Add to dashboard Cite

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“…Spheredecoding (SD) algorithms have been proposed to perform exact maximum-likelihood (ML) JED in SIMO and MIMO systems that use a small number of time slots [9]- [12], [20], [21]. Unfortunately, the complexity of SD methods quickly becomes prohibitive for larger dimensional problems [22], [23] and approximate linear methods, which are widely used for coherent data detection in massive MIMO systems [8], cannot be used for JED (see Section II-B for the reasons). Very recently, a handful of approximate JED algorithms have been proposed for large wireless systems [17], [24]- [26].…”

Section: Related Relevant Resultsmentioning

confidence: 99%

“…Remark 5. While a plethora of VLSI designs exist for data detection in coherent small-scale and massive (multi-user) MIMO systems that separate channel estimation from data detection (see [8], [19], [22], [23], [39]- [46] and the references therein), TASER is the only other SIMO-JED VLSI design that has been described in the literature [17]. Furthermore, low-complexity linear methods that are commonly used for conventional (multiuser) MIMO data detection cannot be used in the JED scenario (as briefly discussed in Section II-B).…”

Section: B Fpga and Asic Implementation Resultsmentioning

confidence: 99%

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

Castañeda

Goldstein

Studer

2018

IEEE Trans. Circuits Syst. I

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Abstract-Channel estimation errors have a critical impact on the reliability of wireless communication systems. While virtually all existing wireless receivers separate channel estimation from data detection, it is well known that joint channel estimation and data detection (JED) significantly outperforms conventional methods at the cost of high computational complexity. In this paper, we propose a novel JED algorithm and corresponding VLSI designs for large single-input multiple-output (SIMO) wireless systems that use constant-modulus constellations. The proposed algorithm is referred to as PRojection Onto conveX hull (PrOX) and relies on biconvex relaxation (BCR), which enables us to efficiently compute an approximate solution of the maximumlikelihood JED problem. Since BCR solves a biconvex problem via alternating optimization, we provide a theoretical convergence analysis for PrOX. We design a scalable, high-throughput VLSI architecture that uses a linear array of processing elements to minimize hardware complexity. We develop corresponding field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, and we demonstrate that PrOX significantly outperforms the only other existing JED design in terms of throughput, hardware-efficiency, and energy-efficiency.Index Terms-FPGA and ASIC designs, joint channel estimation and data detection (JED), large single-input multiple-output (SIMO) wireless systems, biconvex relaxation (BCR).

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A Full-Pipelined Architecture of the Schnorr-Euchner MIMO Sphere Decoder

Guo

Zeng

Dou

et al. 2014

Lecture Notes in Electrical Engineering

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Abstract. Multiple-input and multiple-output (MIMO) is an important approach in high-rate wireless communications. The Schnorr-Euchner (SE) spheredecoding algorithm enables fast detection for receivers by recursive tree searching with optimized expanding order. In this paper, we propose a loopedpipeline architecture for an SE MIMO detector that processes multiple groups of data in parallel. The full-pipelined design has a short critical path that provides a maximum working frequency of 229 MHz on FPGA. High throughput is achieved at a relatively low cost.

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VLSI Implementation of Hard- and Soft-Output Sphere Decoding for Wide-Band MIMO Systems

Cited by 8 publications

References 20 publications

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

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

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

A Full-Pipelined Architecture of the Schnorr-Euchner MIMO Sphere Decoder

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