TY - JOUR
T1 - Massive mimo-noma networks with imperfect sic
T2 - Design and fairness enhancement
AU - De Sena, Arthur Sousa
AU - Lima, Francisco Rafael Marques
AU - Da Costa, Daniel Benevides
AU - Ding, Zhiguo
AU - Nardelli, Pedro H.J.
AU - Dias, Ugo Silva
AU - Papadias, Constantinos B.
N1 - Funding Information:
Manuscript received December 1, 2019; revised April 14, 2020; accepted June 2, 2020. Date of publication June 11, 2020; date of current version September 10, 2020. The work of D. B. da Costa was supported in part by the Brazilian Research, Development, and Innovation Agency, CNPq, under Grant 302863/2017-6, in part by the Ceará Council of Scientific and Technological Development (FUNCAP) (Edital PRONEM) under Grant 01/2016, and in part by the Nokia Foundation through the Nokia Visiting Professors Program under Grant Project 201900134. The work of P. H. J. Nardelli was supported in part by the Academy of Finland via ee-IoT Project under Grant 319009, in part by the Framework for the Identification of Rare Events via Machine Learning and IoT Networks (FIREMAN) consortium under Grant CHIST-ERA/n.326270, and in part by the EnergyNet Research Fellowship under Grant 321265 and Grant 328869. The work of Ugo Silva Dias was supported in part by the Brazilian Research, Development, and Innovation Agency, CNPq, under Grant 311796/2018-4 and in part by the Ministry of Justice and Public Security, Government of Brazil, under Grant TED UnB-SENACON/MJSP 01/2019. The work of Constantinos B. Papadias was supported in part by the FIREMAN consortium under Grant CHISTERA/n.326270. Part of this article has been submitted at the IEEE International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC 2020), London, U.K. The associate editor coordinating the review of this article and approving it for publication was S. Buzzi. (Corresponding author: Daniel Benevides Da Costa.) Arthur Sousa de Sena and Pedro H. J. Nardelli are with the Department of Electrical Engineering, Lappeenranta University of Technology, 53850 Lappeenranta, Finland (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/9
Y1 - 2020/9
N2 - This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.
AB - This paper addresses multi-user multi-cluster massive multiple-input-multiple-output (MIMO) systems with non-orthogonal multiple access (NOMA). Assuming the downlink mode, and taking into consideration the impact of imperfect successive interference cancellation (SIC), an in-depth analytical analysis is carried out, in which closed-form expressions for the outage probability and ergodic rates are derived. Subsequently, the power allocation coefficients of users within each sub-group are optimized to maximize fairness. The considered power optimization is simplified to a convex problem, which makes it possible to obtain the optimal solution via Karush-Kuhn-Tucker (KKT) conditions. Based on the achieved solution, we propose an iterative algorithm to provide fairness also among different sub-groups. Simulation results alongside with insightful discussions are provided to investigate the impact of imperfect SIC and demonstrate the fairness superiority of the proposed dynamic power allocation policies. For example, our results show that if the residual error propagation levels are high, the employment of orthogonal multiple access (OMA) is always preferable than NOMA. It is also shown that the proposed power allocation outperforms conventional massive MIMO-NOMA setups operating with fixed power allocation strategies in terms of outage probability.
KW - Fairness maximization
KW - Imperfect SIC
KW - Massive MIMO
KW - NOMA
UR - http://www.scopus.com/inward/record.url?scp=85091289526&partnerID=8YFLogxK
U2 - 10.1109/TWC.2020.3000192
DO - 10.1109/TWC.2020.3000192
M3 - Article
AN - SCOPUS:85091289526
SN - 1536-1276
VL - 19
SP - 6100
EP - 6115
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 9
M1 - 9115309
ER -