M. El-Kordy

Optimal Power Allocation for Sensing-Based Spectrum Sharing in MIMO Cognitive Relay Networks

With most of the radio spectrum already allocated, cognitive radio acts as a promising solution to the spectrum scarcity problem. Increasing the secondary throughput without making interference to the primary user (PU) is a major challenge. In this paper, the secondary throughput is maximized under power constraints by obtaining an optimum value of the relay amplification factor. Comparison between using single antenna, double antennas at all the nodes of the cognitive relay network (CRN), and double antennas at the relay node only is performed. The mathematical analysis of the system is investigated for two cases: (1) The opportunistic access case, where the secondary user (SU) can transmit data only in the vacant bands of the allocated spectrum to the PU; and (2) The sensing-based spectrum sharing case, where the SU can transmit data all the time but with different transmit powers depending on the sensing information. Simulation results show that the achieved secondary throughput can be maximized at a given value of the relay amplification factor. Moreover, using double antennas at all the nodes of the CRN increases the maximum achievable throughput and improves the detection capabilities compared with using single antenna or using double antennas at the relay node only. Finally, results show that the SU can achieve more throughput under the sensing-based spectrum sharing case compared with that achieved under the opportunistic access case.

Power density spectrum for the identification of residence time distribution signals

One of the most important applications of radioisotopes in industry is the residence time distribution (RTD) measurement. RTD can be used for optimizing the design of industrial systems and determining their malfunctions. The RTD signal may be subject to different sorts of noise. This leads to errors in the RTD calculations, and hence leads to wrong analysis in the determination of system malfunctions. This paper presents a proposed approach for RTD signal identification based on power density spectrum (PDS). The cepstral features are extracted from the signal or/and its PDS. The PDS is estimated using nonparametric, parametric, and eigen-analysis methods. The identification results are analyzed and compared for different estimation methods in order to select the best PDS estimation method for RTD signal identification. Neural networks are used for training and testing in the proposed approach. The proposed approach is tested using RTD signals obtained from the measurements carried out with radiotracer technique. The experimental results show that the proposed approach with features extracted from the PDS of the RTD signals calculated using eigen-analysis methods is the most robust and reliable in RTD signal identification.

Profit of Price with Supermodular Game for Spectrum Sharing in Cognitive Radio Using Genetic Algorithm

Cognitive radio is wireless communication system used for higher spectrum utilization for spectrum holes that are shared between primary (or licensed) users and secondary (or unlicensed) users. Primary service provider can sell unused holes for secondary service provider in which pricing is interested factor for market equilibrium. This process called spectrum trading. Pricing in spectrum sharing depends on spectrum demand from unlicensed users and requirements of primary users to gain payoff. In this paper, supermodular competitive game will be investigated considering Bertrand competition model among primary service providers, which is not clarified until now. In the competitive Bertrand game, the aim of spectrum trading process is to maximize the individual payoff or profit for each primary service provider. Moreover, genetic algorithm will be applied to demonstrate the effect of pricing in profit gained by primary service provider with optimizing profit function from abrupt changing in pricing and achieving market equilibrium.

EFFICIENT DETECTION OF LANDMINES FROM ACOUSTIC IMAGES

The Laser Doppler Vibrometer (LDV)-based Acoustic to Seismic (A/S) landmine detection system is one of the reliable and powerful landmine detection systems. The interpretation of the LDVbased A/S data is performed off-line, manually, depending heavily on the skills, experience, alertness and consistency of a trained operator. This takes a long time. The manually obtained results suffer from errors, particularly when dealing with large volumes of data. This paper proposes some techniques for the automatic detection of objects from the acoustic images which are obtained from the LDV-based A/S landmine detection system. These techniques are based on Corresponding author: F. E. Abd El-Samie (fathi sayed@yahoo.com).

Relay-based throughput maximization in multiple antennas cognitive radio networks

Cognitive radio is a promising technology that solves the problem of spectrum scarcity. A major challenge in cognitive radio is how to increase secondary throughput without interfering the primary user transmissions. In this paper, the effect of using multiple antennas at the relay is studied, and an optimal power allocation strategy is used to maximize the secondary throughput under power constraints by simply seeking an optimum value of the relay amplification factor. The analysis of the system is performed and simulation results show that there is an optimum value of the relay amplification factor at which the secondary throughput is maximized. Compared to using single antenna, the results show that using multiple antennas at the relay network increases the maximum achievable throughput and also improves the detection capability.

Synthetic aperture radar imaging with fractional Fourier transform and channel equalization

This paper investigates the Range-Doppler Algorithm based on the Fractional Fourier Transform (RDA-FrFT) to obtain High-Resolution (HR) images for targets in Synthetic Aperture Radar (SAR) imaging. A mathematical framework for the RDA-FrFT is developed in this paper with closed-form expressions for the range and azimuth compression. The channel effect is considered in this paper for the first time with three inverse techniques to reduce this effect; inverse filter deconvolution, Linear Minimum Mean Square Error (LMMSE) deconvolution, and regularized deconvolution. The performance of the RDA-FrFT is compared with the classical RDA, which is based on the Fourier Transform (FT). Simulation results reveal that the RDA-FrFT offers better focusing capabilities and greater side-lobe reduction ratios. The reflectivity profile obtained with the RDA-FrFT demonstrates a superior performance to the classical RDA. Results show also that the RDA-FrFT gives low Peak Side-Lobe (PSL) and Integrated Side-Lobe (ISL) levels after range and azimuth compression for the detected targets. Finally, the results reveal that the proposed regularized deconvolution technique enhances the performance of the RDA-FrFT significantly if the channel effect is considered.

Implementation of efficient portable low delay adder using FPGA

No doubt that in this technological era the arithmetic circuit is the core of most all the Digital Signal Processing applications (DSP), especially adder circuits. In this paper, various types of adder circuits have been implemented on Field Programmable Gate Array (FPGA). Furthermore, another architecture for adder called Carry Shifting Adder (CSHA) is proposed. This depends on shifting the carry to the next stage. Then its combined with a carry increment circuit to get a low delay. The goal of this paper is to efficiently carry out the proposed CSHA adder with carry increment circuit over FPGA kit. Simulations are done to find out the circuit area and delay. Also, this is compared with the related other adder circuits. The performance of this proposed adder circuit is better than other related ones in both area and delay.

PSO-adaptive power allocation for multiuser GFDM-based cognitive radio networks

Due to the scarcity of the available spectrum bands spectrum, increasing demand caused by emerging wireless applications for mobile users. Therefore, the need for a new paradigm to exploit the unutilized spectrum bands of already licensed bands is required. Cognitive Radio (CR) was proposed for enhancing the utilization of the radio frequency (RF) spectrum. Orthogonal Frequency Division Multiplexing (OFDM) was the promising technique for CR systems because of its functionality and flexibility for dynamic resource allocation. But, for 5th generation, OFDM has many problems such as its large PAPR, large amount of Out-Of-Band Radiation (OOB) and spectrum inefficiency due to the overhead of cyclic prefix (CP). Generalized Frequency Division Multiplexing (GFDM) in a non-orthogonal, filter bank based, digital multi-carrier transmission scheme was proposed for overcoming these problems due to its attractive features that addressed the requirements of increased communication systems applications such as Cognitive radio. In this paper, a proposed power allocation technique for downlink GFDM cognitive radio based on Particle Swarm Algorithm (PSO) will be suggested. Moreover, analysis for this will be presented. Further, Comparisons between the proposed Technique and other optimizations techniques will be investigated. Simulation results show that the proposed technique for GFDM systems gives better performance compared to dual optimal Lagrange method.

Enhancement of IR images using histogram processing and the Undecimated additive wavelet transform

This paper presents a fabulous enhancement approach for infrared (IR) images. This approach mixes the benefits of the undecimated Additive Wavelet Transform (AWT) with the homomorphic transform and Contrast Limited Adaptive Histogram Equalization (CLAHE). The basic idea of this approach depends on applying the CLAHE on the IR image. Then, the resultant image is decomposed into sub-bands using the AWT. The homomorphic enhancement is implemented on each sub-band, separately, up to the sixth sub-band. The homomorphic enhancement is applied on the IR image in the log domain by decomposing the image into illumination and reflectance components. The illumination is attenuated, while the reflectance is magnified. Applying this method on each sub-band gives more details in the IR image. The performance quality metrics for the suggested approach are entropy, average gradient, contrast, and Sobel edge magnitude. Simulation results reveal the success of the proposed approach in enhancing the quality of IR images.

Minimizing energy of cluster-based Cooperative Spectrum Sensing in CRN using Multi Objective Genetic Algorithm

Cooperative spectrum sensing assumes an essential part in cognitive radio network due to having the capacity to enhance spectrum sensing performance and reduce probability of error in fading and shadowing channels. In fact, clustering scheme and cooperative spectrum sensing are combined to reduce Jostle of reporting channel, improve performance of sensing and reduce the computational cost. Many methods of cooperative spectrum sensing have been proposed based on clustering technique. In this paper, proposed approach will be suggested based on clustering to minimize the total power consumed by CRN in order to perform spectrum sensing, transmit decision to cluster head, and transmit the final decision to the fusion center. This is done by using multi objective genetic algorithm. Simulation results show that our proposed algorithm can achieve better energy gain which is less than conventional cluster based cooperative spectrum sensing scheme. Moreover, it increases performance of CRN.