MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection

Kim, Hyunsub; Lee, Hyukyeon; Kim, Jaeseok

doi:10.1109/lanman.2015.7114716

Cited by 3 publications

(4 citation statements)

References 18 publications

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“…Compared to 4 × 4 MIMO detections, the proposed algorithm takes 71k FLOPs to maintain SNR performance with 64 QAM in frequency-selective fading channel (with 100 ns RMS delay and 15 taps delay spread). If this AOC-based MIMO detection is worked in AWGN only, the required FLOPs, which is more than LRA-MMSE FSD [39] (7937), is around 3.97k in 64-QAM. Compared to KSE method [10], the AOC algorithm takes about 16k if 9 constellations are chosen in each layer with 16 QAM and K is set to 12.…”

Section: A Algorithm Levelmentioning

confidence: 99%

“…Hence, several studies have proposed different strategies to optimize the LR-aided algorithm [28]- [37], a LR-Aided K-best detection [38] combines hardware-optimized LLL (HOLLL) and K-best algorithm to approach ML performance. And a 64-QAM lattice-reduction-aided minimum-mean-squared-error-based fixed-complexity sphere decoder (LRA-MMSE-FSD) [39] makes FSD low computational cost via reducing the number of full expansion (FD) stage. In 8 × 8 MIMO, a 64-QAM MIMO detection base on fixed-complexity Effective Lenstra-Lenstra-LovÃąsz (fcELLL) algorithm [37] is introduced to improve the computational efficiency of Effective Lenstra-Lenstra-Lovász (ELLL).…”

Section: Introductionmentioning

confidence: 99%

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A Cost-Effective Adaptive Overlapped Cluster-Based MIMO Detector in a Frequency Domain Reconfigurable Modem

Liao

Hsu

2019

IEEE Access

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Not only to reduce the candidates but also to maintain detection performance in multiple-input multiple-output (MIMO) detection, an adaptive overlapped cluster (AOC) scheme to balance detection error and computing cost is built for 4 × 4 and 8 × 8 MIMO-OFDM systems with up to 256 quadrature amplitude modulation (QAM). The constellations are partitioned into several clusters. A cluster with size decided by channel status is chosen for signal decoding. Different partition schemes are combined to minimize the numbers of clusters required to cover a candidate symbol as the pre-estimated signal falls at cluster edges, namely overlapped clustering. The simulations of a 4 × 4 MIMO OFDM with 64 QAM and 8 × 8 MIMO OFDM with 256 QAM hint that the AOC-based detection requires an additional 0.57 dB and 1.02 dB compared to maximum likelihood (ML). Compared with K-best sphere decoding (SD), it is reduced the computing complexity to 24.50% ∼ 56.25% in 4 × 4 MIMO OFDM and, 35.00% ∼ 56.25% in 8 × 8 MIMO OFDM. In addition, the proposed scheme is ported to a reconfigurable frequency-domain (FD) modem, which is designed and implemented via TSMC 45-nm technology, with multi-rate clocking and processing elements (PEs) upgrading for supporting the proposed MIMIO detection. The results show that the throughput is 1077.8 Mbps with 4 × 4 64-QAM modulations. INDEX TERMS Low complexity, MIMO detection, multi-rate clock, cluster, reconfigurable modem.

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Section: A Algorithm Levelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Cost-Effective Adaptive Overlapped Cluster-Based MIMO Detector in a Frequency Domain Reconfigurable Modem

Liao

Hsu

2019

IEEE Access

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“…Therefore, to obtain a channel capacity close to the Shannon limit, a new kind of MIMO system, known as the Turbo-MIMO system that is based on bit-interleaved coded modulation, was investigated by Sellathurai and Haykin [5]. Endowed with turbo learning principle [6], these iterative receivers make detection and decoding by exchanging soft bits information mutually, which lets them approach approximately optimal performance with in a computationally feasible manner [5] [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, the potential performance degradation is essential to most of iterative receivers that choose minimum mean square error (MMSE) based algorithm [9][10][11] for its inner decoder, which makes the extrinsic information transferred to the decoder (outer decoder) less reliable. This in turn lets designing near-optimal inner decoders with more reliable extrinsic estimates [6], an open research topic to deal with this problem, be attracted the most attention.…”

Section: Introductionmentioning

confidence: 99%

An Iterative SISO Improved Complex Sphere Detection and Decoder for Turbo-MIMO Systems

Ni¹,

Hirata²,

Li³

et al. 2019

ijSciences

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An iterative soft-input soft-output (SISO) improved complex sphere detection and decoder algorithm is proposed for signal detection in Turbo-MIMO system. It forms candidate points set Θ in terms of an accumulated cost function based on a search arc constrained by the received signals. Then, the candidate points subset, the lower cost bound of which is not smaller than upper bound, is fathomed and dropped from further consideration. Meanwhile, once a new feasible candidate point is turned up, the path closest to completion is casted upon to generate the set Θ with optimal candidate vectors, aiming to determining the extrinsic information for a Turbo coded bit with most likelihood. Bridged by de-multiplexing and multiplexing, an SISO improved complex sphere detection is concatenated with a SISO Log maximum a posteriori Turbo decoder as if a principal Turbo detection is embedded with a subordinate Turbo decoder, exchanging each other's detection and decoding soft-decision information iteratively. As a result, the proposed algorithm converges rapidly, which results in lower computational complexity. The transfer curves that relate the input mutual information to the output mutual information is achieved through simulations. Thus, an asymptotic interval of the input SNR threshold for the proposed scheme to converge has been observed. Finally, an upper bound of the diversity has been obtained based on the intuitional deduction and theoretical analyses. The simulation results also show that the proposed scheme has a strong ability of anti-multi-stream interference, and its performance is close to that of the iterative soft-input soft-output list complex sphere detection and decoder algorithm, but with a shorter time delay.

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An Enhanced Transceiver Structure for Higher Performances in MIMO-OFDM Systems

Song

2018

IEEE Access

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MMSE-based lattice-reduction-aided fixed-complexity sphere decoder for low-complexity near-ML MIMO detection

Cited by 3 publications

References 18 publications

A Cost-Effective Adaptive Overlapped Cluster-Based MIMO Detector in a Frequency Domain Reconfigurable Modem

A Cost-Effective Adaptive Overlapped Cluster-Based MIMO Detector in a Frequency Domain Reconfigurable Modem

An Iterative SISO Improved Complex Sphere Detection and Decoder for Turbo-MIMO Systems

An Enhanced Transceiver Structure for Higher Performances in MIMO-OFDM Systems

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