Residential College | false |
Status | 即將出版Forthcoming |
Energy-Efficient Resource Allocation and Antenna Selection for IRS-assisted Multi-Cell Downlink Networks | |
Atefeh Rezaei1; Ata Khalili2; Jalal Jalali3; Hossein Shafiei4; Qingqing Wu5 | |
2022-06 | |
Source Publication | IEEE Wireless Communications Letters |
ISSN | 2162-2337 |
Volume | 11Issue:6Pages:1229-1233 |
Abstract | This letter considers a network-assisted intelligent reflecting surface (IRS) technology. We aim to adopt an energy-efficient strategy via an antenna selection (AS) framework that determines which base station (BS) antennas transmit the data to the user equipment. In particular, we select the best set of antennas to increase energy efficiency (EE) while reducing power consumption. Also, the network takes advantage of the IRS system to increase the coverage and overall throughput of the network. We first propose an efficient algorithm for the considered scenario based on the successive convex approximation (SCA). Then we employ the Dinkelbach method that jointly selects the best set of antennas and optimizes their beamforming. Second, by introducing the slack variable and SCA method, we propose a tight approximation to solve the passive beamforming at the IRS. Simulation results unveil the performance of the proposed method and its influence on the power consumption at each antenna’s RF chain. |
Keyword | Antenna Selection (As) Antennas Array Signal Processing Energy Efficiency (Ee). Intelligent Reflecting Surface (Irs) Interference Miso Communication Optimization Power Demand Transmitting Antennas |
DOI | 10.1109/LWC.2022.3161410 |
URL | View the original |
Indexed By | SCIE |
Language | 英語English |
WOS Research Area | Computer Science ; Engineering ; Telecommunications |
WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
WOS ID | WOS:000808068800028 |
Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 |
Scopus ID | 2-s2.0-85127028095 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | THE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU) |
Corresponding Author | Atefeh Rezaei |
Affiliation | 1.Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran. 2.Institute for Digital Communications, Friedrich-Alexander-University Erlangen-Nurnberg, Erlangen 91054, Germany. 3.IDLab-imec Research Group, University of Antwerp, 2000 Antwerp, Belgium. 4.Faculty of Computer Engineering, K. N. Toosi University, Tehran, Iran. 5.State Key Laboratory of Internet of Things for Smart City, University of Macau, Macau, 999078, and also with the National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China. |
Recommended Citation GB/T 7714 | Atefeh Rezaei,Ata Khalili,Jalal Jalali,et al. Energy-Efficient Resource Allocation and Antenna Selection for IRS-assisted Multi-Cell Downlink Networks[J]. IEEE Wireless Communications Letters, 2022, 11(6), 1229-1233. |
APA | Atefeh Rezaei., Ata Khalili., Jalal Jalali., Hossein Shafiei., & Qingqing Wu (2022). Energy-Efficient Resource Allocation and Antenna Selection for IRS-assisted Multi-Cell Downlink Networks. IEEE Wireless Communications Letters, 11(6), 1229-1233. |
MLA | Atefeh Rezaei,et al."Energy-Efficient Resource Allocation and Antenna Selection for IRS-assisted Multi-Cell Downlink Networks".IEEE Wireless Communications Letters 11.6(2022):1229-1233. |
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