scholarly journals A Survey on VLC Based Massive MIMO-OFDM for 5G Networks

2019 ◽  
Vol Volume-3 (Issue-2) ◽  
pp. 363-366
Author(s):  
Keerti Gupta ◽  
Neetu Sikarwar ◽  
Keyword(s):  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Ajay Kumar Yadav ◽  
Pritam Keshari Sahoo ◽  
Yogendra Kumar Prajapati

Abstract Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results.


2017 ◽  
Vol 24 (3) ◽  
pp. 86-94 ◽  
Author(s):  
K. N. R. Surya Vara Prasad ◽  
Ekram Hossain ◽  
Vijay K. Bhargava

2016 ◽  
Vol 64 (11) ◽  
pp. 4607-4621 ◽  
Author(s):  
Alam Zaib ◽  
Mudassir Masood ◽  
Anum Ali ◽  
Weiyu Xu ◽  
Tareq Y. Al-Naffouri

2017 ◽  
Vol 6 (6) ◽  
pp. 778-781 ◽  
Author(s):  
Yan Liang ◽  
Hongbin Li ◽  
Fei Li ◽  
Rongfang Song ◽  
Lihua Yang
Keyword(s):  

2018 ◽  
Vol 38 (3) ◽  
pp. 1114-1136 ◽  
Author(s):  
Ricardo Kehrle Miranda ◽  
João Paulo C. L. da Costa ◽  
Binghua Guo ◽  
André L. F. de Almeida ◽  
Giovanni Del Galdo ◽  
...  

2021 ◽  
pp. 1-1
Author(s):  
Yiwei Sun ◽  
Hua Wang ◽  
Minghao Yuan ◽  
Tianye Zhu ◽  
Agnes Kawoya

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