Energy Efficiency Maximization Under Minimum Rate Constraints in Multi-Cell MIMO Systems With Finite Buffers

Having recognized the dramatic increase in the number of mobile devices and infrastructure nodes, standards organizations and regulatory bodies have adopted energy efficiency (EE) as a key performance metric for fifth-generation networks. Recent works on multiple input multiple output (MIMO) systems...

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Bibliographic Details
Published in:IEEE transactions on green communications and networking 2021-03, Vol.5 (1), p.174-189
Main Authors: Saraiva, Juno Vitorino, Antonioli, Roberto Pinto, Fodor, Gabor, Braga, Iran Mesquita, Freitas, Walter C., Silva, Yuri C. B., Silva, Carlos F. M. e
Format: Article
Language:English
Subjects:
Algorithms
Buffers
Dinkelbach’s algorithm
Downlink
Electronic devices
Energy efficiency
Iterative methods
Mathematical programming
Maximization
MIMO (control systems)
MIMO communication
MIMO-OFDM
Minimization
nonconvex optimization
Optimization
QoS
Quality of service
Resource allocation
Resource management
successive convex approximation
Traffic models
Transceivers
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Summary:Having recognized the dramatic increase in the number of mobile devices and infrastructure nodes, standards organizations and regulatory bodies have adopted energy efficiency (EE) as a key performance metric for fifth-generation networks. Recent works on multiple input multiple output (MIMO) systems have suggested that it is important to use finite-buffer models, because they may lead to better transceiver designs and more accurate performance analyses than full-buffer traffic models. Therefore, this article addresses the MIMO transceiver design problem for EE maximization in the downlink of finite-buffer multicell systems. Unlike previous works, our problem formulation takes into account per-user minimum rate requirements. We arrive at a nonconvex fractional optimization problem, which is hard to tackle. By exploiting the properties of fractional programming, and using Dinkelbach's method, the resulting fractional form optimization problem is transformed to an equivalent optimization problem in subtractive form. Next, the nonconvexity of this problem is handled using successive convex approximation, leading to iterative centralized and decentralized resource allocation solutions. Finally, considering a realistic channel model with space, frequency and time correlations, numerical results confirm the effectiveness of the proposed algorithms and indicate significant performance gains in terms of achieved EE over existing solutions for full and finite-buffer models.
ISSN:2473-2400
2473-2400
DOI:10.1109/TGCN.2020.3043049