Approximated Symbol Error Rate for Cross QAM Constellations

2015 ◽  
pp. 179-184
Keyword(s):  
Error Rate ◽  
Symbol Error Rate

Symbol error rate analysis of non-orthogonal multiple access systems

2021 ◽  
Vol 46 ◽  
pp. 101295
Author(s):  
Ali Mohammed A. Alkhazzar ◽  
Hassan Aghaeinia
Keyword(s):  
Error Rate ◽  
Multiple Access ◽  
Symbol Error Rate ◽  
Rate Analysis

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.

MIMO Detectors: A Comprehensive Performance Analysis

2021 ◽  
Vol 16 (3) ◽  
pp. 24-27
Author(s):  
E. Obi ◽  
B.O. Sadiq ◽  
O.S . Zakariyya ◽  
A. Theresa
Keyword(s):  
Error Rate ◽  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Symbol Error Rate ◽  
Signal To Noise ◽  
High Data ◽  
Noise Ratio ◽  
Better Than

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.

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