TY - GEN
T1 - A Weighted MMSE Approach to Amorphous Cell for Mixed-ADC Distributed Massive MIMO
AU - Yuan, Jide
AU - He, Qi
AU - Matthaiou, Michail
AU - Wang, Yuyang
AU - Quek, Tony Q.S.
AU - Jin, Shi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Distributed massive multi-input-multi-output (mMIMO) is a promising architecture which has potential to satisfy the strick latency requirement in Internet of Things (IoT). To further meet the low-cost and low-latency demand in IoT, this paper provides a low-complexity scheme to the access phase for mixed analog-to-digital convertors (ADC) distributed mMIMO. which consists of two steps. In the first step, the clustering behavior among users is detected using large scale fading information, which aims to reduce the complexity. In the second step, with the number of clusters as a priori, a weighted minimum mean square error (WMMSE) clustering algorithm that can provide stable and robust results is proposed. The clustering algorithm aims to maximize the achievable sum rate, in which the nonconvex objective function and constraints are modeled using ell1-norm approximation. Numerical results show that the proposed algorithm has strong convergence, and significant gain can be obtained in various scenarios.
AB - Distributed massive multi-input-multi-output (mMIMO) is a promising architecture which has potential to satisfy the strick latency requirement in Internet of Things (IoT). To further meet the low-cost and low-latency demand in IoT, this paper provides a low-complexity scheme to the access phase for mixed analog-to-digital convertors (ADC) distributed mMIMO. which consists of two steps. In the first step, the clustering behavior among users is detected using large scale fading information, which aims to reduce the complexity. In the second step, with the number of clusters as a priori, a weighted minimum mean square error (WMMSE) clustering algorithm that can provide stable and robust results is proposed. The clustering algorithm aims to maximize the achievable sum rate, in which the nonconvex objective function and constraints are modeled using ell1-norm approximation. Numerical results show that the proposed algorithm has strong convergence, and significant gain can be obtained in various scenarios.
UR - http://www.scopus.com/inward/record.url?scp=85062973318&partnerID=8YFLogxK
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U2 - 10.1109/ACSSC.2018.8645097
DO - 10.1109/ACSSC.2018.8645097
M3 - Conference contribution
AN - SCOPUS:85062973318
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 969
EP - 974
BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
Y2 - 28 October 2018 through 31 October 2018
ER -