Symbol error rate of amplify-and-forward distributed space–time codes over Nakagami-m fading channel

2009 ◽  
Vol 45 (3) ◽  
pp. 174 ◽  
Author(s):  
B. Maham ◽  
A. Hjørungnes
Keyword(s):  
Error Rate ◽  
Fading Channel ◽  
Symbol Error Rate ◽  
Space Time ◽  
Amplify And Forward ◽  
Space Time Codes

Performance Analysis of OQPSK Signals with Bi-Kappa Noise for Wireless Sensor Networks

2013 ◽  
Vol 330 ◽  
pp. 957-960
Author(s):  
Qiao Ling Du ◽  
Zhi Rui Wang ◽  
Yu Pei ◽  
Yi Ding Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Error Rate ◽  
Fading Channel ◽  
Rayleigh Fading ◽  
Symbol Error Rate ◽  
Wireless Sensor ◽  
Channel Simulation ◽  
Simulation Results

This paper investigates the performance analysis of OQPSK in HF band for wireless sensor networks. An analytical model for getting symbol error rate (SER) is given in presence of Bi-Kappa noise in HF band. And the SER of OQPSK is given in AWGN and Rayleigh fading channel. Simulation results HF noise as Bi-Kappa noise should be investigated in HF band for WSN.

Ergodic Capacity and Symbol Error Rate Analysis of a Wireless System with α-μ Composite Fading Channel

2021 ◽  
Author(s):  
Fereshteh salimian rizi ◽  
Abolfazl Falahati
Keyword(s):  
Error Rate ◽  
Fading Channel ◽  
Communication Systems ◽  
Large Scale ◽  
Scale Effects ◽  
Ergodic Capacity ◽  
Symbol Error Rate ◽  
Performance Criteria ◽  
Rate Analysis ◽  
New Generation

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.

scholarly journals Performance Analysis of Grouped Multilevel Space-Time Trellis Coding Technique Using Cognitive Radio in Different Deployment Models

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Shakti Raj Chopra ◽  
Akhil Gupta ◽  
Rakesh Kumar Jha
Keyword(s):  
Cognitive Radio ◽  
Error Rate ◽  
Cooperative Diversity ◽  
Symbol Error Rate ◽  
Space Time ◽  
Link Quality ◽  
Trellis Coding ◽  
Space Time Coding ◽  
Proposed Model

In this era, the number of users in a network is increasing tremendously at a faster rate; as a consequence, quality of service (QoS) is drastically deteriorating. To compensate such kinds of problems, we attempted to enhance the QoS of the network, which leads to an improvement in throughput, link quality, spectral efficiency, and many more. To meet the requirements mentioned above, many researchers intervene to advance and propose different techniques with an appropriate design methodology. In this work, we try to emphasize symbol error rate (SER) and frame error rate (FER) by implementing some of the existing space-time coding techniques like Space-Time Trellis Coding (STTC), multilevel space-time trellis coding (MLSTTC), and grouped multilevel space-time trellis coding (GMLSTTC). Though all these techniques are proved to be efficient enough, we explicitly included a powerful method of cooperative diversity-based spectrum sensing in cognitive radio scenario. From this analysis, we landed on to the conclusion that this technique is far better to deal with all these parameters, which can improve the QoS of the network. This paper has also analyzed the effect of the proposed model of GMLSTTC with cognitive radio on various deployment setups such as urban, suburban, and rural macrodeployment setup of the ITU-R M.2135 standard.

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