Wireless communication has become more popular over the last decade. Although
there are many advantages to use wireless over fixed line systems, there is a major
disadvantage, which is a high transmission error rate in a temporal and permanent nois
y
environment. There are techniques to reduce effectiveness off transmission errors in
wireless communications. We have discussed in this paper. Many methods and techniques
have been implementing to deal and resist errors (by increasing transmission power,
switching modulation scheme) as well as evaluation the wireless connection performance
taking into account many factors.
Although Multi-Input-Multi-Output MIMO improve reliability of wireless
transmission system and increase bit rate, but this improvement relay on higher cost in
Hardware and increase in size as well as complex structure, so the cost-effective challen
ge
still exists to implement these technique.
Performance can be improved without increasing the cost significantly by using
virtual MIMO systems where the spacing is achieved by using pre-existing devices within
the network infrastructure, these systems called cooperative communication systems,
which are the subject of this research.
The simulation is based on standard software for modeling MATLAB and open
sourced Network simulator-3 (NS-3) to characterize problems in wireless communications
systems and propose appropriate solutions.
This work aims to analyze the performance of Orthogonal Frequency Division
Multiplexing (OFDM) applied in the fourth generation mobile networks and WiFi. Fuzzy
logic technique is used in this study to analyze the problem of OFDM, taking into
consi
deration the modulation techniques applied in OFDM. Three input parameters in the
fuzzy logic system are mainly considered: signal-to-noise ratio, the modulation degree and
the number of sub-carriers. The output parameters are selected to be the bandwidth and bit
error rate. This requires an analytical study to determine the optimal values of the input
parameters used in this study. This means studying the membership of functions of each
input and output parameter using fuzzy logic.