IEICE TRANSACTIONS on Communications
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Uplink Pilot Allocation for Multi-Cell Massive MIMO Systems
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Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E102-B No.2 pp.373-380
- Publication Date
- 2019/02/01
- Publicized
- 2018/08/13
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2017EBP3312
- Type of Manuscript
- PAPER
- Category
- Wireless Communication Technologies
Authors
Wanming HAO
Zhengzhou University,the Kyushu University
Osamu MUTA
the Kyushu University
Haris GACANIN
the Nokia Bell Labs
Hiroshi FURUKAWA
the Kyushu University
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Wanming HAO, Osamu MUTA, Haris GACANIN, Hiroshi FURUKAWA, "Uplink Pilot Allocation for Multi-Cell Massive MIMO Systems" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 2, pp. 373-380, February 2019, doi: 10.1587/transcom.2017EBP3312.
Abstract: Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2017EBP3312/_p
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@ARTICLE{e102-b_2_373,
author={Wanming HAO, Osamu MUTA, Haris GACANIN, Hiroshi FURUKAWA, },
journal={IEICE TRANSACTIONS on Communications},
title={Uplink Pilot Allocation for Multi-Cell Massive MIMO Systems},
year={2019},
volume={E102-B},
number={2},
pages={373-380},
abstract={Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.},
keywords={},
doi={10.1587/transcom.2017EBP3312},
ISSN={1745-1345},
month={February},}
Copy
TY - JOUR
TI - Uplink Pilot Allocation for Multi-Cell Massive MIMO Systems
T2 - IEICE TRANSACTIONS on Communications
SP - 373
EP - 380
AU - Wanming HAO
AU - Osamu MUTA
AU - Haris GACANIN
AU - Hiroshi FURUKAWA
PY - 2019
DO - 10.1587/transcom.2017EBP3312
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 2
JA - IEICE TRANSACTIONS on Communications
Y1 - February 2019
AB - Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.
ER -
English
