Lattice-Reduction-Aided Partial Marginalization for Soft Output MIMO Detector with Fixed and Reduced Complexity

Kim, Hyunsub; Kim, Minjoon; Lee, Hyukyeon; Kim, Jaeseok

doi:10.1109/lcomm.2016.2543700

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…If the restriction is applied, the noise power e z (σ 2 p ) can be derived theoretically as shown in (11).…”

Section: Equivalent Noise Variancementioning

confidence: 99%

“…The LLR calculation still requires high computational complexity even if the MAX-log approximation is used, because the calculation is implemented with the brute force search. Some techniques have been proposed to reduce the computational cost of the LLR calculation with assistance of lattice reduction-aided linear receivers [6]- [11]. They shrink the vector space searched by the brute force search, which reduces the computational cost.…”

Section: Introductionmentioning

confidence: 99%

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Superposition Signal Input Decoding for Lattice Reduction-Aided MIMO Receivers

Denno

KASHIHARA

Hou

2023

IEICE Trans. Commun.

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This paper proposes a novel approach to low complexity soft input decoding for lattice reduction-aided MIMO receivers. The proposed approach feeds a soft input decoder with soft signals made from hard decision signals generated by using a lattice reduction-aided linear detector. The soft signal is a weighted-sum of some candidate vectors that are near by the hard decision signal coming out from the lattice reduction-aided linear detector. This paper proposes a technique to adjust the weight adapt to the channel for the higher transmission performance. Furthermore, we propose to introduce a coefficient that is used for the weights in order to enhance the transmission performance. The transmission performance is evaluated in a 4 × 4 MIMO channel. When a linear MMSE filter or a serial interference canceller is used as the linear detector, the proposed technique achieves about 1.0 dB better transmission performance at the BER of 10 −5 than the decoder fed with the hard decision signals. In addition, the low computational complexity of the proposed technique is quantitatively evaluated.

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“…If the restriction is applied, the noise power e z (σ 2 p ) can be derived theoretically as shown in (11).…”

Section: Equivalent Noise Variancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%