scholarly journals Antenna Selection Strategies for MIMO-OFDM Wireless Systems: An Energy Efficiency Perspective

2016 ◽  
Vol 65 (4) ◽  
pp. 2048-2062 ◽  
Author(s):  
Ngoc Phuc Le ◽  
Farzad Safaei ◽  
Le Chung Tran
Keyword(s):  
Energy Efficiency ◽  
Antenna Selection ◽  
Wireless Systems ◽  
Selection Strategies ◽  
Mimo Ofdm

scholarly journals Energy-Efficiency Analysis of Per-Subcarrier Antenna Selection with Peak-Power Reduction in MIMO-OFDM Wireless Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Ngoc Phuc Le ◽  
Le Chung Tran ◽  
Farzad Safaei
Keyword(s):  
Energy Efficiency ◽  
Power Efficiency ◽  
Peak Power ◽  
Antenna Selection ◽  
Average Power ◽  
Wireless Systems ◽  
Power Reduction ◽  
Cumulative Distribution ◽  
Peak Power Reduction ◽  
Mimo Ofdm

The use of per-subcarrier antenna subset selection in OFDM wireless systems offers higher system capacity and/or improved link reliability. However, the implementation of the conventional per-subcarrier selection scheme may result in significant fluctuations of the average power and peak power across antennas, which affects the potential benefits of the system. In this paper, power efficiency of high-power amplifiers and energy efficiency in per-subcarrier antenna selection MIMO-OFDM systems are investigated. To deliver the maximum overall power efficiency, we propose a two-step strategy for data-subcarrier allocation. This strategy consists of an equal allocation of data subcarriers based on linear optimization and peak-power reduction via cross-antenna permutations. For analysis, we derive the CCDF (complementary cumulative distribution function) of the power efficiency as well as the analytical expressions of the average power efficiency. It is proved from the power-efficiency perspective that the proposed allocation scheme outperforms the conventional scheme. We also show that the improvement in the power efficiency translates into an improved capacity and, in turn, increases energy efficiency of the proposed system. Simulation results are provided to validate our analyses.

scholarly journals Performance Analysis of Antenna Selection Techniques in MIMO-OFDM System with Hardware Impairments: Energy Efficiency perspective

2018 ◽  
Vol 8 (4) ◽  
pp. 2272
Author(s):  
Anuj Singal ◽  
Deepak Kedia
Keyword(s):  
Energy Efficiency ◽  
Performance Analysis ◽  
Energy Efficient ◽  
Antenna Selection ◽  
Quantization Noise ◽  
Ofdm System ◽  
Selection Scheme ◽  
Selection Technique ◽  
Hardware Impairments ◽  
Mimo Ofdm

<p>In this paper we propose a new MIMO-OFDM model in which we consider various antenna selection techniques like Bulk selection and Per-subcarrier selection etc. with hardware impairments such as non-linearties of amplifiers, quantization noise, phase noise and I-Q imbalance etc. As we know that the transceiver hardware impairments limit the channel capacity and the energy efficiency of MIMO-OFDM system, so we can not neglect the fundamental impacts of these hardware impairments {Kappa  (0.05 0.1)} on the energy efficiency in the high SNR domain. Therefore we analyze the Energy Efficiency of Bulk and Per-subcarrier antenna selection techniques with or without hardware impairments. It has been observed that the energy efficiency decreases as the value of these hardware impairments increases. As we compared the Bulk antenna selection with the Per-subcarrier antenna selection scheme, the Per-subcarrier antenna selection requires more number of RF (radio frequency) chains and transmits power in comparison to the Bulk selection. Due to this, the Bulk antenna selection technique is more energy efficient than Per-subcarrier antenna selection.</p>

scholarly journals A Low-Complexity Receive-Antenna-Selection Algorithm for MIMO–OFDM Wireless Systems

2009 ◽  
Vol 58 (6) ◽  
pp. 2793-2802 ◽  
Author(s):  
Yi Liu ◽  
Yangyang Zhang ◽  
Chunlin Ji ◽  
W.Q. Malik ◽  
D.J. Edwards
Keyword(s):  
Antenna Selection ◽  
Wireless Systems ◽  
Low Complexity ◽  
Selection Algorithm ◽  
Mimo Ofdm ◽  
Receive Antenna

scholarly journals Energy Efficiency Analysis of Antenna Selection Techniques in Massive MIMO-OFDM System with Hardware Impairments

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Anuj Singal ◽  
Deepak Kedia
Keyword(s):  
Energy Efficiency ◽  
Power Consumption ◽  
Antenna Selection ◽  
Scaling Factor ◽  
Power Scaling ◽  
Ofdm System ◽  
Selection Scheme ◽  
Hardware Impairments ◽  
Mimo Ofdm ◽  
Circuit Power

In massive multiple-input multiple-output (M-MIMO) systems, a large number of antennas increase system complexity as well as the cost of hardware. In this paper, we propose an M-MIMO-OFDM model using per-subcarrier antenna selection and bulk antenna selection schemes to mitigate these problems. Also, we derive a new uplink and downlink energy efficiency (EE) equation for the M-MIMO-OFDM system by taking into consideration the antenna selection schemes, power scaling factor (g=0.25,  0.5), and a range of hardware impairments {κBS, κUEϵ (0, 0.052, 0.12)}. In addition, we investigate a trend of EE by varying various parameters like number of base station antennas (BSAs), SNR, level of hardware impairments, total circuit power consumption, power optimization, antenna selection schemes, and power scaling factor in the proposed M-MIMO-OFDM model. The simulation results thus obtained show that the EE increases with increase in the value of SNR. Also, it increases abruptly up to 100 number of BSA. However, the increase in the EE is not significant in the range of 125 to 400 number of BSA. Further, the bulk antenna selection technique has comparatively more EE than the per-subcarrier antenna selection. Moreover, EE gaps between antenna selection schemes decrease with increase in the value of hardware impairments and power scaling factor. However, as the hardware degradation effect increases, the EE of the bulk antenna selection scheme suffers more degradation as compared to the Per-subcarrier antenna selection scheme. It has also been observed that EE performance is inversely proportional to the total circuit power consumption (λ+γ) and it increases with the power optimization.

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