### Abstract

In this paper, complexity reduction schemes for Gibbs sampling multi-input multi-output (MIMO) detection with maximum ratio combining are proposed. In a conventional Gibbs sampling MIMO detection algorithm, the Gibbs sampling is directly applied to a received signal. Thus, a squared Euclid distance between the received signal vector and a candidate symbol vector is calculated as a metric and it requires (2 × No. of received antennas) multiplication operations. On the other hand, in a proposed algorithm, each candidate symbol is updated with a metric calculated by two multiplication operations. However, after each iteration, another metric is also need to be calculated to select the best candidate symbol vector. To reduce the number of multiplication operations, a summation and subtraction metric (SSM) is applied. Furthermore, as an initial transmitsymbol vector, a zero vector is applied in the conventional and proposed Gibbs sampling MIMO detection algorithms since the receiver can avoid to calculate the pseudo inverse of a channel matrix. The bit error rate performance and the complexities of these schemes are compared with that of QR decomposition with M-algorithm (QRM)-maximum likelihood detection (MLD). Numerical results obtained through computer simulation show that the proposed Gibbs sampling MIMO detection algorithm is less complex when the numbers of transmit signals and received antennas are more than 32x32.

Original language | English |
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Title of host publication | 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings |

Publisher | Institute of Electrical and Electronics Engineers Inc. |

Pages | 1-5 |

Number of pages | 5 |

Volume | 2018-June |

ISBN (Electronic) | 9781538663554 |

DOIs | |

Publication status | Published - 2018 Jul 20 |

Event | 87th IEEE Vehicular Technology Conference, VTC Spring 2018 - Porto, Portugal Duration: 2018 Jun 3 → 2018 Jun 6 |

### Other

Other | 87th IEEE Vehicular Technology Conference, VTC Spring 2018 |
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Country | Portugal |

City | Porto |

Period | 18/6/3 → 18/6/6 |

### Fingerprint

### ASJC Scopus subject areas

- Computer Science Applications
- Electrical and Electronic Engineering
- Applied Mathematics

### Cite this

*2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings*(Vol. 2018-June, pp. 1-5). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VTCSpring.2018.8417576

**Complexity Reduction Schemes for Gibbs Sampling MIMO Detection with Maximum Ratio Combining.** / Sanada, Yukitoshi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings.*vol. 2018-June, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 87th IEEE Vehicular Technology Conference, VTC Spring 2018, Porto, Portugal, 18/6/3. https://doi.org/10.1109/VTCSpring.2018.8417576

}

TY - GEN

T1 - Complexity Reduction Schemes for Gibbs Sampling MIMO Detection with Maximum Ratio Combining

AU - Sanada, Yukitoshi

PY - 2018/7/20

Y1 - 2018/7/20

N2 - In this paper, complexity reduction schemes for Gibbs sampling multi-input multi-output (MIMO) detection with maximum ratio combining are proposed. In a conventional Gibbs sampling MIMO detection algorithm, the Gibbs sampling is directly applied to a received signal. Thus, a squared Euclid distance between the received signal vector and a candidate symbol vector is calculated as a metric and it requires (2 × No. of received antennas) multiplication operations. On the other hand, in a proposed algorithm, each candidate symbol is updated with a metric calculated by two multiplication operations. However, after each iteration, another metric is also need to be calculated to select the best candidate symbol vector. To reduce the number of multiplication operations, a summation and subtraction metric (SSM) is applied. Furthermore, as an initial transmitsymbol vector, a zero vector is applied in the conventional and proposed Gibbs sampling MIMO detection algorithms since the receiver can avoid to calculate the pseudo inverse of a channel matrix. The bit error rate performance and the complexities of these schemes are compared with that of QR decomposition with M-algorithm (QRM)-maximum likelihood detection (MLD). Numerical results obtained through computer simulation show that the proposed Gibbs sampling MIMO detection algorithm is less complex when the numbers of transmit signals and received antennas are more than 32x32.

AB - In this paper, complexity reduction schemes for Gibbs sampling multi-input multi-output (MIMO) detection with maximum ratio combining are proposed. In a conventional Gibbs sampling MIMO detection algorithm, the Gibbs sampling is directly applied to a received signal. Thus, a squared Euclid distance between the received signal vector and a candidate symbol vector is calculated as a metric and it requires (2 × No. of received antennas) multiplication operations. On the other hand, in a proposed algorithm, each candidate symbol is updated with a metric calculated by two multiplication operations. However, after each iteration, another metric is also need to be calculated to select the best candidate symbol vector. To reduce the number of multiplication operations, a summation and subtraction metric (SSM) is applied. Furthermore, as an initial transmitsymbol vector, a zero vector is applied in the conventional and proposed Gibbs sampling MIMO detection algorithms since the receiver can avoid to calculate the pseudo inverse of a channel matrix. The bit error rate performance and the complexities of these schemes are compared with that of QR decomposition with M-algorithm (QRM)-maximum likelihood detection (MLD). Numerical results obtained through computer simulation show that the proposed Gibbs sampling MIMO detection algorithm is less complex when the numbers of transmit signals and received antennas are more than 32x32.

UR - http://www.scopus.com/inward/record.url?scp=85050990512&partnerID=8YFLogxK

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U2 - 10.1109/VTCSpring.2018.8417576

DO - 10.1109/VTCSpring.2018.8417576

M3 - Conference contribution

VL - 2018-June

SP - 1

EP - 5

BT - 2018 IEEE 87th Vehicular Technology Conference, VTC Spring 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

ER -