Chaeriah Bin Ali Wael

Spectrum sensing for low SNR environment using maximum-minimum eigenvalue (MME) detection

The rapid progression in wireless networking has suggested dynamic spectrum allocation to increase spectrum utilization. Cognitive radio (CR) is a key technology to enable dynamic spectrum access. Spectrum sensing provide spectrum awareness to CR system. Although energy detection (ED) is commonly used for spectrum sensing method, it gives poor performance under low noise environment and also suffers from noise uncertainty. Eigenvalue based detection is used to overcome ED drawbacks in this paper. Simulation results are presented for evaluating detection performance with respect to the received SNR and threshold parameters such as desired probability of false alarm (Pf), number of samples (N) and smoothing factor (L). It is shown in all cases that higher SNR value and threshold parameters setting give better probability of detection. In addition, the effect of different pulse shaping filters on probability of detection are investigated.

Sidelobe suppression on pulse compression using curve-shaped nonlinear frequency modulation

Pulse compression is a method for improving the range resolution of the radar systems. Linear Frequency Modulation (LFM) is a type of pulse compression which is commonly used in the radar applications because of its simplicity and Doppler tolerance. However this method provides relatively high first peak sidelobe level within around -13.3 dB. This paper presents a developed curve-shaped Nonlinear Frequency Modulation (NLFM) using second degree polynomial approach for radar pulse compression in order to reduce the sidelobe level. The radar signal is assumed has bandwidth 30 MHz while the length of pulse signal as long as 10e-6 s. The curve-shaped chirp waveform is formed by the polynomial equation utilizing the LFM waveform as the base line. According to the simulation results and analysis, using a certain polynomial equation, the curve-shaped NLFM with negative orientation provides lower sidelobe level than the LFM. The best first peak sidelobe level achieved by this proposed NLFM is approximately -18.6 dB.

Adaptively compensated multiband spectral subtraction for robust noise reduction

An adaptively compensated multiband spectral subtraction (MBSS) is presented in this paper. In this research, the adaptive compensation in the MBSS utilizes artificial neural network. The purpose of this compensation is to improve the quality of speech signal after denoising of MBSS step. This compensation is calculated adaptively depend on the MBSS parameters, estimated noise, and difference between input and estimated speech signal. The neural network used was Multi-Layer Perceptron consisted of three hidden layers. The proposed neural network was trained by three speech signals contaminated by white gaussian noises with SNR 0dB and 30dB. For investigating the performance, the proposed method was tested by five noised speech signals with SNR 0dB to 10dB. The result of experiment is examined and evaluated by SNR and PESQ scores. Based on the examination, the proposed speech enhancement method exposed the better performance than the origin MBSS algorithm.

Wideband spectrum sensing using Welch periodogram in cognitive radio

Spectrum sensing is a key function of cognitive radio (CR) to identify the vacant frequency bands (which is also referred to as spectrum holes). The future technology of CRs should be capable to scan wideband frequencies to increase spectrum utilization. This paper investigates a wideband spectrum sensing technique based on energy detection in frequency domain using Welch periodogram. The signal of primary user is modeled as multiband signal with 3 pairs of active band in each simulation. To reduce high sampling rate due to sensing of a wide bandwidth, CR receiver operates at sub-Nyquist sampling rate using Modulated Wideband Converter (MWC). Performance metrics like probability of false alarm (PF), probability of detection (PD) are evaluated by analyzing receiver operating characteristics (ROC) curve. The simulation shows that ROC curve of Welch periodogram gives better result then periodogram.

Performance analysis of sub-Nyquist sampling for Wideband spectrum sensing in cognitive radio

Spectrum sensing is a key function of cognitive radio (CR) to identify the vacant frequency bands (which is also referred to as spectrum holes). The future technology of CR networks should be capable to scan wideband frequencies to increase spectrum utilization. To reduce high sampling rate for sampling wideband signal, Modulated Wideband Converter (MWC) is used for data acquisition. In this paper, MWC is also used to detect vacant spectrum of wideband signal modeled as multiband signal. Performance of the system is evaluated through simulation using Monte Carlo method. Performance metrics such as probability of detection (Pd), probability of false alarm (Pf) are examined in various SNR with sparsity level is 6/195 and 10/195. Numerical results show that spectrum sensing with sub-Nyquist sampling using MWC gives good detection performance. Other then SNR value, Sparsity level of the signal also have contribution signal detection performance. Perfect recovered support is achieved at SNR = 1 dB for the case with sparsity level = 6/195. When sparsity level = 10/195, Pd = 1 is never achieved.

Performance analysis of curve-shaped NLFM against Doppler effect and background noise

Linear Frequency Modulation (LFM) is a pulse compression method which is largely implemented in the radar applications. The high peak sidelobe is its main problem instead of its superiority on the simplicity and Doppler tolerance characteristics. The curve-shaped Nonlinear Frequency Modulation (NLFM) has been developed for overcoming this lack and prior investigation showed that this method provide lower sidelobe suppression than LFM. For obtaining the overall performance to be compared with LFM method, this research will investigate the curve-shaped NLFM in the case of Doppler effects and high level background noise. The Doppler effects considered in this research consist of two including center frequency shift and time dilation. Then, the background noise investigated is Gaussian distributed noise with signal to noise ratio (SNR) levels in range 0dB to −20dB. According to the simulation results, in the Doppler shift case, the curve-shaped NLFM is more sensitive than LFM as other NLFM drawbacks. However, this method is better than LFM in the time dilation scenario. Then, this pulse compression also has better detectability than LFM in the certain case of high level background noise condition.

PTS-based PAPR reduction in fixed WiMAX system with Grouping Phase Weighting (GPW)

WiMAX is a wireless technology that was developed to overcome the limitations of wireline networks to meet the needs of Broadband Wireless Access (BWA) services to customers. As standardized, fixed WiMAX uses OFDM as its physical air interface. Therefore, it also suffers from high PAPR. To solve this problem, Partial Transmit Sequence (PTS) is used in this paper due to its better performance among other PAPR reduction techniques. Unfortunately, in conventional PTS, an exhaustive search over all combinations of allowed phase weighting factors is needed. This process leads to high computational complexity. Hence, Grouping Phase Weighting (GPW) is used to simplify search complexity and still maintain to provide effective PAPR reduction as conventional PTS. The simulation is conducted to IEEE 802.16d system with various mandatory modulation types and channel coding rates. The derived results show that the choice of modulation type does not give significant effect on the PAPR reduction. The higher channel coding rate gives higher PAPR reduction.