Reduced Complexity Soft-Output MIMO Sphere Detectors—Part I: Algorithmic Optimizations

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

doi:10.1109/tsp.2014.2352595

Cited by 44 publications

(20 citation statements)

References 33 publications

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“…The effective channel matrices are related by H c = R 1/2 r HR 1/2 t , where R t and R r are the transmit and receive antenna correlation matrices, respectively, with correlation factors α = β = 0.9. We assume, for 8 convenience, the generic exponential model [40]. Hence, in the case of 4×4 MIMO, we have,…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Note that we revert back to the original space of H to compute the true Euclidean distance metrics in (8). The gain achieved by VSSD compared to PCD is limited, since eachR (t) generates an independent space, and hence we end up taking the best output from N independent trials.…”

Section: Vector-based Sub-space Detector (Vssd)mentioning

confidence: 99%

“…For HO computations, PN/C saves θ 1 flops compared to N/C, PCD saves |M| × θ 1 flops compared to CD, SSSD saves N × |M| × θ 1 flops compared to SLORD, and SSD saves N × (|M|×θ 1 −(4N 2 +4N −2) RAD−(4N 2 +4N) RML) flops compared to LORD, where the subtracted terms in the latter account for distance computations in (8). These results are summarized in Table I.…”

Section: Detector Architecture and Complexity Analysismentioning

confidence: 99%

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Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

Sarieddeen

Mansour

Chehab

2018

IEEE Trans. Commun.

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A family of low-complexity detection schemes based on channel matrix puncturing targeted for large multiple-input multiple-output (MIMO) systems is proposed. It is well-known that the computational cost of MIMO detection based on QR decomposition is directly proportional to the number of nonzero entries involved in back-substitution and slicing operations in the triangularized channel matrix, which can be too high for low-latency applications involving large MIMO dimensions. By systematically puncturing the channel to have a specific structure, it is demonstrated that the detection process can be accelerated by employing standard schemes such as chase detection, list detection, nulling-andcancellation detection, and sub-space detection on the transformed matrix. The performance of these schemes is characterized and analyzed mathematically, and bounds on the achievable diversity gain and probability of bit error are derived. Surprisingly, it is shown that puncturing does not negatively impact the receive diversity gain in hard-output detectors. The analysis is extended to soft-output detection when computing per-layer bit log-likelihood ratios; it is shown that significant performance gains are attainable by ordering the layer of interest to be at the root when puncturing the channel. Simulations of coded and uncoded scenarios certify that the proposed schemes scale up efficiently both in the number of antennas and constellation size, as well as in the presence of correlated channels. In particular, softoutput per-layer sub-space detection is shown to achieve a 2.5 dB SNR gain at 10 −4 bit error rate in 256-QAM 16×16 MIMO, while saving 77% of nulling-and-cancellation computations.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Vector-based Sub-space Detector (Vssd)mentioning

confidence: 99%

Section: Detector Architecture and Complexity Analysismentioning

confidence: 99%

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Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

Sarieddeen

Mansour

Chehab

2018

IEEE Trans. Commun.

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“…Sphere decoder (SD) emerges as a suboptimal decoder capable of solving the ML detection problem with less complexity. Although SD [3] - [6] meets out the ML performance, it still requires considerable computation. The drawback of SD lies in the estimation of the SNR to determine the initial radius of the sphere, which results in variable throughput.…”

Section: Introductionmentioning

confidence: 99%

Reduced Complexity by Combining Norm Based Ordering MMSE-BSIDE Detection in MIMO Systems

Jothikumar¹,

Nakkeeran

2017

International Journal of Electronics and Telecommunications

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Abstract-The breadth first signal decoder (BSIDE) is wellknown for its optimal maximum likelihood (ML) performance with lesser complexity. In this paper, we analyze a multiple-input multiple-output (MIMO) detection scheme that combines; column norm based ordering minimum mean square error (MMSE) and BSIDE detection methods. The investigation is carried out with a breadth first tree traversal technique, where the computational complexity encountered at the lower layers of the tree is high. This can be eliminated by carrying detection in the lower half of the tree structure using MMSE and upper half using BSIDE, after rearranging the column of the channel using norm calculation. The simulation results show that this approach achieves 22% of complexity reduction for 2x2 and 50% for 4x4 MIMO systems without any degradation in the performance.

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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 I: Algorithmic Optimizations

Cited by 44 publications

References 33 publications

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

Large MIMO Detection Schemes Based on Channel Puncturing: Performance and Complexity Analysis

Reduced Complexity by Combining Norm Based Ordering MMSE-BSIDE Detection in MIMO Systems

A low-complexity MIMO subspace detection algorithm

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