Projected majorized-correlation technique for noise filtering and accuracy in orthogonal frequency division multiplexing - Alsaadi, Alaa Abdullah Mohammed (2020)
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.