Sliding Window-Based Transmit Antenna Selection Technique for Large-Scale MU-MIMO Networks

2014 ◽  
Vol E97.A (7) ◽  
pp. 1640-1641 ◽  
Author(s):  
Tae-Won BAN ◽  
Bang Chul JUNG
Keyword(s):  
Large Scale ◽  
Antenna Selection ◽  
Sliding Window ◽  
Transmit Antenna Selection ◽  
Selection Technique ◽  
Mimo Networks

scholarly journals Low-Complexity Transmit Antenna Selection and Beamforming for Large-Scale MIMO Communications

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Kun Qian ◽  
Wen-Qin Wang ◽  
Huaizong Shao
Keyword(s):  
Communication Systems ◽  
Large Scale ◽  
Antenna Selection ◽  
Low Complexity ◽  
Exhaustive Search ◽  
Computation Complexity ◽  
Transmit Antenna Selection ◽  
Mimo Communications ◽  
Mimo Communication ◽  
Minimum Variance Distortionless Response

Transmit antenna selection plays an important role in large-scale multiple-input multiple-output (MIMO) communications, but optimal large-scale MIMO antenna selection is a technical challenge. Exhaustive search is often employed in antenna selection, but it cannot be efficiently implemented in large-scale MIMO communication systems due to its prohibitive high computation complexity. This paper proposes a low-complexity interactive multiple-parameter optimization method for joint transmit antenna selection and beamforming in large-scale MIMO communication systems. The objective is to jointly maximize the channel outrage capacity and signal-to-noise (SNR) performance and minimize the mean square error in transmit antenna selection and minimum variance distortionless response (MVDR) beamforming without exhaustive search. The effectiveness of all the proposed methods is verified by extensive simulation results. It is shown that the required antenna selection processing time of the proposed method does not increase along with the increase of selected antennas, but the computation complexity of conventional exhaustive search method will significantly increase when large-scale antennas are employed in the system. This is particularly useful in antenna selection for large-scale MIMO communication systems.

scholarly journals Low-Complexity Transmit Antenna Selection in Large-Scale Spatial Modulation Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Peng Wei ◽  
Lu Yin ◽  
Yue Xiao ◽  
Xu He ◽  
Shaoqian Li
Keyword(s):  
Computational Complexity ◽  
System Performance ◽  
Large Scale ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Spatial Modulation ◽  
Transmit Antenna Selection ◽  
Input Multiple Output

Transmit antenna selection (TAS) is an efficient way for improving the system performance of spatial modulation (SM) systems. However, in the case of large-scale multiple-input multiple-output (MIMO) configuration, the computational complexity of TAS in large-scale SM will be extremely high, which prohibits the application of TAS-SM in a real large-scale MIMO system for future 5G wireless communications. For solving this problem, in this paper, two novel low-complexity TAS schemes, named as norm-angle guided subset division (NAG-SD) and threshold-based NAG-SD ones, are proposed to offer a better tradeoff between computational complexity and system performance. Simulation results show that the proposed schemes can achieve better performance than traditional TAS schemes, while effectively reducing the computational complexity in large-scale spatial modulation systems.

scholarly journals A Novel Machine Learning Aided Antenna Selection Scheme for MIMO Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2250 ◽  
Author(s):  
Wannian An ◽  
Peichang Zhang ◽  
Jiajun Xu ◽  
Huancong Luo ◽  
Lei Huang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Communication Systems ◽  
Large Scale ◽  
Antenna Selection ◽  
Multiple Input Multiple Output ◽  
Training Data ◽  
Mimo Channel ◽  
Transmit Antenna Selection ◽  
Selection Scheme ◽  
Channel Conditions

In this article, we propose a multi-label convolution neural network (MLCNN)-aided transmit antenna selection (AS) scheme for end-to-end multiple-input multiple-output (MIMO) Internet of Things (IoT) communication systems in correlated channel conditions. In contrast to the conventional single-label multi-class classification ML schemes, we opt for using the concept of multi-label in the proposed MLCNN-aided transmit AS MIMO IoT system, which may greatly reduce the length of training labels in the case of multi-antenna selection. Additionally, applying multi-label concept may significantly improve the prediction accuracy of the trained MLCNN model under correlated large-scale MIMO channel conditions with less training data. The corresponding simulation results verified that the proposed MLCNN-aided AS scheme may be capable of achieving near-optimal capacity performance in real time, and the performance is relatively insensitive to the effects of imperfect CSI.

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