TY - GEN
T1 - Pilot allocation for multi-cell TDD massive MIMO systems
AU - Hao, Wanming
AU - Muta, Osamu
AU - Gacanin, Haris
AU - Furukawa, Hiroshi
N1 - Funding Information:
This research was partially supported by JSPS KAKENHI (17K06427) and the Telecommunications Advancement Foundation.
PY - 2018/2/8
Y1 - 2018/2/8
N2 - Pilot contamination due to the pilot reuse in adjacent cells is a serious problem in time-division duplex (TDD) massive multi-input multiple-output (MIMO) system. Therefore, the pilot allocation is significant for improving the performance of the system. In this paper, we formulate the pilot allocation optimization problem for maximizing uplink sum rate of the system. To reduce the required complexity for 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 fixing the pilot allocation in other cells. 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 a 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 obtain good performance comparable to the exhaustive search algorithm, meanwhile the users' fairness is improved.
AB - Pilot contamination due to the pilot reuse in adjacent cells is a serious problem in time-division duplex (TDD) massive multi-input multiple-output (MIMO) system. Therefore, the pilot allocation is significant for improving the performance of the system. In this paper, we formulate the pilot allocation optimization problem for maximizing uplink sum rate of the system. To reduce the required complexity for 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 fixing the pilot allocation in other cells. 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 a 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 obtain good performance comparable to the exhaustive search algorithm, meanwhile the users' fairness is improved.
UR - http://www.scopus.com/inward/record.url?scp=85042475051&partnerID=8YFLogxK
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U2 - 10.1109/VTCFall.2017.8288098
DO - 10.1109/VTCFall.2017.8288098
M3 - Conference contribution
AN - SCOPUS:85042475051
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 86th IEEE Vehicular Technology Conference, VTC Fall 2017
Y2 - 24 September 2017 through 27 September 2017
ER -