Reduced Complexity Soft-Output MIMO Sphere Detectors—Part II: Architectural Optimizations

Mansour, Mohammad M.; Alex, Sam P.; Jalloul, Louay M. A.

doi:10.1109/tsp.2014.2352593

Cited by 27 publications

(18 citation statements)

References 20 publications

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“…By using NOMA combined with multiple antenna structures, not only high-rate data transmission is provided but also the destructive effects caused by fading are minimized [2][3][4][5][6][7]. However, symbol detection is a crucial process to detect symbols coherently in wireless communication systems [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Therefore, various algorithms such as zero-forcing (ZF), the vertical Bell Laboratories layered spice-time (VBLAST), and ML are used for symbol detection [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…However, symbol detection is a crucial process to detect symbols coherently in wireless communication systems [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Therefore, various algorithms such as zero-forcing (ZF), the vertical Bell Laboratories layered spice-time (VBLAST), and ML are used for symbol detection [8][9][10][11][12][13][14][15][16][17]. Although the ML algorithm is the best performing algorithm among these since the computational complexity of this algorithm increase in proportion to the increasing number of antennas, the practicality of this algorithm for the MIMO systems is very low [12].…”

Section: Introductionmentioning

confidence: 99%

“…Although the ML algorithm is the best performing algorithm among these since the computational complexity of this algorithm increase in proportion to the increasing number of antennas, the practicality of this algorithm for the MIMO systems is very low [12]. Therefore, many studies take place in the literature on reducing the computational complexity problem of ML algorithms [13][14][15][16]. In the study of [14]; a differential metrics-based ML algorithm that does not require a computational complexity such as the QR decomposition matrix is proposed.…”

Section: Introductionmentioning

confidence: 99%

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Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Seyman¹

2022

Computer Systems Science and Engineering

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One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Seyman¹

2022

Computer Systems Science and Engineering

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“…The computing complexity of SD has been shown to decrease as the performance decrease [13], but there is a 16-QAM K-best Schnorr-Euchner (KSE) [10] to reduce computational complexity with near-ML performance which modifies the K-best SD via Schnorr-Euchner strategy. To balance the link performance and computational complexity, an optimized SD algorithm and the corresponding architecture by using a state-machine is reported [14], [15]. Similar to SD, Layered ORthogonal lattice Detector (LORD) is proposed to reduce the computational complexity.…”

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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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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“…Other tree-search schemes, such as the K-Best algorithm [20][21][22][23][24][25][26], address the non-deterministic throughput aspects of sphere decoders. Practical implementation aspects have been investigated in [18,23,[25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

A low-complexity MIMO subspace detection algorithm

Mansour

2015

J Wireless Com Network

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A low-complexity multiple-input multiple-output (MIMO) subspace detection algorithm is proposed. It is based on decomposing a MIMO channel into multiple subsets of decoupled streams that can be detected separately. The new scheme employs triangular decomposition followed by elementary matrix operations to transform the channel into a generalized elementary matrix whose structure matches the subsets of streams to be detected. The proposed approach avoids matrix inversion and allows subsets to overlap, thus achieving better diversity gain. An optimized detector architecture based on a 2-by-2 ML detector core is also presented. Simulations demonstrate that the proposed algorithm performs to within a few tenths of a dB from the optimum detection algorithm.

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Reduced Complexity Soft-Output MIMO Sphere Detectors—Part II: Architectural Optimizations

Cited by 27 publications

References 20 publications

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

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

A low-complexity MIMO subspace detection algorithm

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