Antenna Selection Strategy for Energy Efficiency Maximization in Uplink OFDMA Networks: A Multi-Objective Approach

This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional ante...

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Veröffentlicht in:IEEE transactions on wireless communications Jg. 19; H. 1; S. 595 - 609
Hauptverfasser: Khalili, Ata, Robat Mili, Mohammad, Rasti, Mehdi, Parsaeefard, Saeedeh, Ng, Derrick Wing Kwan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Antenna selection
Antennas
Chain mobility
Chains
Channels
Computational efficiency
Computer simulation
Design optimization
Energy conversion efficiency
Energy efficiency
Interference
Lower bounds
majorization minimization (MM)
Maximization
Mixed integer
multi-objective optimization problem (MOOP)
Multiple objective analysis
Nonlinear programming
Optimization
Penalty function
Power control
Power efficiency
Radio frequency
Resource allocation
Resource management
Transmitting antennas
Uplink
Wireless communication
ISSN:1536-1276, 1558-2248
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Zusammenfassung:This paper aims at investigating the problem of energy efficiency (EE) maximization for uplink multi-cell networks via a joint design of sub-channel assignment, power control, and antenna selection. We study the problem under two practical scenarios. In the first scenario, known as conventional antenna selection (CAS), there is only one radio frequency (RF) chain available at the mobile user and all the sub-channels for each user can be assigned to one of the antennas. For the second scenario, known as generalized antenna selection (GAS), the number of RF chains is equal to the number of antennas and the messages of each user can transmit over its assigned sub-channels via different antennas. The resource allocation design is formulated as a multi-objective optimization problem (MOOP) and then converted into a single objective optimization problem (SOOP) via the weighted Tchebycheff method. The considered problem is a mixed integer nonlinear programming (MINLP) which is generally intractable. To address this problem, a penalty function is introduced to handle the binary variable constraints. In order to obtain a computationally efficient suboptimal solution, the majorization minimization (MM) approach is proposed where a surrogate function serves as the lower bound of the objective function. Furthermore, we propose another low-complexity practical algorithm to further reduce the computational cost. Simulation results demonstrate the superiority of the proposed method and unveil an interesting trade-off between EE and SE for two considered scenarios.
Bibliographie:ObjectType-Article-1
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ISSN:1536-1276
1558-2248
DOI:10.1109/TWC.2019.2946832