Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Sun, Yang; Cavallaro, Joseph R.

doi:10.1109/tsp.2012.2187646

Cited by 23 publications

(11 citation statements)

References 34 publications

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“…In 4×4 mode, the core achieves a throughput of 733 Mbps and consumes 320.56 mW of power. This compares favorably with other detectors in the literature with throughput ranging from 757 Mbps at 410 kGE [11]; 772 Mbps at 212 kGE [33]; 1.2 Gbps at 1097 kGE for 16-QAM [35]; and 2.2 Gbps at 555 kGE [36] for up to 64-QAM only. Table V provides a comparative summary of our implemented detector and the detectors in [11], [33], [35], [36].…”

Section: A Performance Resultssupporting

confidence: 66%

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Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

Mansour

Jalloul

2015

IEEE Trans. Signal Process.

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This paper presents an optimized low-complexity and high-throughput multiple-input multiple-output ( MIMO) signal detector core for detecting spatially-multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, as well as configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori (MAP) MIMO detector are optimized to eliminate the use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied as well to design an optimal multi-user MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90 nm CMOS.

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Section: A Performance Resultssupporting

confidence: 66%

“…To avoid confusion, we refer to the quantities in (35), (38), and (39) A similar minimization is required as well to adjust the LLR values Λ …”

Section: Post Llr Processingmentioning

confidence: 99%

Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

Mansour

Jalloul

2015

IEEE Trans. Signal Process.

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“…The search space of a MIMO signal can be represented with a compact trellis diagram [16]. We propose an efficient trellis model shown in Fig.…”

Section: Proposed Bit-clusters Trellis Modelmentioning

confidence: 99%

Bit-Clusters Trellis Search Based Iterative MIMO Detection Algorithm

Lou

Peng

Zhou

et al. 2014

Wireless Pers Commun

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A bit-clusters trellis-search (BCTS) based soft-input soft-output detection algorithm is proposed for iterative Multiple-Input Multiple-Output (MIMO) receivers. Compared to the symbol nodes trellis-search (SNTS) based MIMO detection algorithm, which significantly reduces the search complexity by limiting the number of candidates in each trellis stage compared to sphere decoding algorithm, the BCTS algorithm proposed in this article further reduces the number of candidates in the trellis. In the BCTS algorithm, we replace the symbol node in SNTS algorithm with bit-cluster that represents several relative symbols whose corresponding bit is 0 or 1 while the other bits are not concerned. Because the bit-cluster number is fewer than the symbol node number, the search complexity of proposed BCTS algorithm is much less than that of the SNTS algorithm. The simulation results show, by transforming symbol nodes to bit-clusters in trellis-search detection, the performance is improved nearly 0.5 dB when the bit-error-ratio reaches 10 −4 ; the complexity analysis shows the number of partial-Euclidian-distance (PED) comparisons and PED computations are approximately three and one times fewer than that of the SNTS algorithm respectively when constellation size is beyond 16. And this tendency will be more obvious with the transmit antenna number and constellation size increasing.

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“…Their main issue is their prohibitive worst-case complexity. In [33], a trellis-search-based SISO decoder and its VLSI architecture have been proposed. Such trellis-based decoder provides a high throughput at the cost of a large hardware area.…”

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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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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Trellis-Search Based Soft-Input Soft-Output MIMO Detector: Algorithm and VLSI Architecture

Cited by 23 publications

References 34 publications

Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors With 256-QAM

Bit-Clusters Trellis Search Based Iterative MIMO Detection Algorithm

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

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