Energy Efficiency Maximization for IRS-Assisted Uplink Systems: Joint Resource Allocation and Beamforming Design
被引:19
作者:
Forouzanmehr, Maliheh
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机构:
Shahed Univ, Elect Engn Dept, Tehran 3319118651, IranShahed Univ, Elect Engn Dept, Tehran 3319118651, Iran
Forouzanmehr, Maliheh
[1
]
Akhlaghi, Soroush
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h-index: 0
机构:
Shahed Univ, Elect Engn Dept, Tehran 3319118651, IranShahed Univ, Elect Engn Dept, Tehran 3319118651, Iran
Akhlaghi, Soroush
[1
]
论文数: 引用数:
h-index:
机构:
Khalili, Ata
[1
]
Wu, Qingqing
论文数: 0引用数: 0
h-index: 0
机构:
Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China
Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R ChinaShahed Univ, Elect Engn Dept, Tehran 3319118651, Iran
Wu, Qingqing
[2
,3
]
机构:
[1] Shahed Univ, Elect Engn Dept, Tehran 3319118651, Iran
[2] Univ Macau, State Key Lab Internet Things Smart City, Macau, Peoples R China
[3] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Peoples R China
Array signal processing;
Optimization;
Antennas;
Resource management;
Uplink;
Matrix decomposition;
MISO communication;
Intelligent reflecting surface (IRS);
energy efficiency (EE);
and antenna selection (AS);
INTELLIGENT REFLECTING SURFACE;
WIRELESS NETWORK;
D O I:
10.1109/LCOMM.2021.3115812
中图分类号:
TN [电子技术、通信技术];
学科分类号:
0809 ;
摘要:
This letter investigates the design of resource allocation to maximize the energy efficiency (EE) in a multiuser multiple-input multiple-output (MIMO) intelligent reflecting surface (IRS)-assisted uplink network. The design is formulated as a mixed-integer non-convex optimization problem which jointly optimizes the antenna selection (AS) and power allocation at user sides and the beamforming matrices adopted at the BS and IRS. To facilitate the design of a suboptimal solution, we first decompose the original problem into two sub-problems via the alternative optimization (AO) method. In particular, for the first sub-problem, we propose an iterative algorithm based on the majorization minimization (MM) approach to make the numerator of the fractional problem into a concave form and then we employed the Dinkelbach algorithm. For the second sub-problem, we adopt the inner approximation (IA) method to optimize the beamforming matrices at the BS and IRS. Simulation results demonstrate the superiority of the proposed method over benchmark schemes for the case of a single antenna case and also provide considerable performance gains due to the use of an antenna selection strategy.