Projected majorized-correlation technique for noise filtering and accuracy in orthogonal frequency division multiplexing
Phase noise is a random unwanted variation interfered with Orthogonal Frequency Division Multiplexing (OFDM) signal according to many factors. One of the important factor is related to oscillator itself which generates the carrier signals and causes Inter Channel Interference Noise (ICI). The sec...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/90780/1/FSKTM%202020%2018%20IR.pdf |
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| Summary: | Phase noise is a random unwanted variation interfered with Orthogonal
Frequency Division Multiplexing (OFDM) signal according to many factors. One
of the important factor is related to oscillator itself which generates the carrier
signals and causes Inter Channel Interference Noise (ICI). The second main
factor is a multipath fading channel which causes a delay in OFDM signal and
results for Inter Symbol Interference Noise (ISI).Basically, phase noise is
considered as main problem that causes significant degradation in detecting
packet-based OFDM signals. Therefore, its estimation is essential to reduce the
interference among other subcarrier signals.
The main objective of this thesis is to develop a new technique for phase noise,
accuracy and complexity in OFDM signal. This technique is called Projected
Quadratic Majorized Covariance Correlation (PQMCC) technique. PQMCC
technique is proposed to reduce the power of noise in OFDM signal, arise the
accuracy of received signal and decrease the complexity. Precisely, by
proposing the projected signal (py>) in PQMCC technique has solved the three
main issues: power of noise, accuracy in received random signal (y>) and
complexity in Tight Quadratic Majorization algorithm (TQM) for Phase Noise
Estimation Technique.
PQMCC Technique is simulated in MATLAB. The simulation results shows that
the Wiener Process Phase Noise (PHN) has no effect over the proposed signal
(py>) since it utilizes the properties of orthogonal projection matrix which leads
to preserve data [theta (θ) and vectors (h)] from destruction of noise. Literally,
the power of noise is reduced from 69dB (7.8458e+06Hz) to 67.2dB
(5.2069e+06Hz) when signal to noise ratio (snr) is 15dB. Moreover, the accuracy of the proposed projected signal (py>) is proven when the sinusoidal signal
shows right angle (θ=90°) and the area of recovered projected signal (py>) is
reduced by around 46.2891% in cm2 comparing with random signal (y>) in TQM
algorithm. In addition to that, by proposing the projected signal (py>) in PQMCC
technique, complexity of TQM algorithm is reduced from second order of big
notation O(Nc2) to first order O(Nc).In summary, the outcome of PQMCC
technique based on noise attenuation, accuracy, and complexity reduction has
achieved and proven in this thesis. |
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