Ergodic capacity and symbol error rate of distributed massive MIMO systems over Rayleigh-inverse Gaussian fading channels using ZF detectors

2020 ◽  
Vol 38 ◽  
pp. 100906
Author(s):  
Bibhuti Bhusan Pradhan ◽  
Lakshi Prosad Roy
2021 ◽  
Author(s):  
Fereshteh salimian rizi ◽  
Abolfazl Falahati

Abstract A composite α-µ/Lognormal fading channel is proposed with several channel performance criteria. This model considers the most effective occurrences in a fading channel, mainly non-linearity, multi-cluster nature of propagation medium, and shadowing effects. The new generation of communication systems is moving towards the use of millimetre waves (mmW). In this type of propagation, large-scale effects of fading channel on the received signal are significant, so in the proposed composite model, the lognormal distribution is considered to model large-scale effects of fading, which is the most accurate distribution to model shadowing. The Gaussian-Hermite quadrature sum is used to approximate the probability distribution function (PDF) of the proposed model. After calculating the statistics, the symbol error rate (SER) and ergodic capacity are computed. The Mellin transform technique is used to calculate the SER expression of different modulation schemes; then, ergodic capacity is computed for a diverse frequency spectrum. Finally, the Monte Carlo method is used to evaluate the analyses.


2021 ◽  
Vol 16 (3) ◽  
pp. 24-27
Author(s):  
E. Obi ◽  
B.O. Sadiq ◽  
O.S . Zakariyya ◽  
A. Theresa

Multiple-input multiple-output (MIMO) systems are increasingly becoming popular due to their ability to multiply data rates without any expansion in the bandwidth. This is critical in this era of high-data rate applications but limited bandwidth. MIMO detectors play an important role in ensuring effective communication in such systems and as such the performance of the following are compared in this paper with respect to symbol error rate (SER) versus signal-to-noise ratio (SNR): maximum likelihood (ML), zero forcing (ZF), minimum mean square error (MMSE) and vertical Bell laboratories layered space time (VBLAST). Results showed that the ML has the best performance as it has the least Symbol Error Rate (SER) for all values of Signal to Noise Ratio (SNR) as it was 91.9% better than MMSE, 99.6% better than VBLAST and 99.8% better than ZF at 20db for a 2x2 antenna configuration., it can also be deduced that the performance increased with increase in number of antenna for all detectors except the V-BLAST detector.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 83615-83627 ◽  
Author(s):  
Yuanxue Xin ◽  
Pengfei Shi ◽  
Wenrui Tang ◽  
Dongming Wang ◽  
Xuewu Zhang ◽  
...  

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