Rishi Prakash

Energy efficient radio resource management for heterogeneous wireless network using CoMP

The coordinated multi point (CoMP) transmission technique is considered a key feature in future wireless network to improve both cell edge users throughput by exploiting interference. However, to provide CoMP transmission several BSs need to be active, which eventually increases network energy consumption. The simultaneous active multiple BSs with different transmission characteristics in heterogeneous environment cause interferences on each other. In this paper, we study the energy efficient radio resource management (EE-RRM) scheme for heterogeneous wireless networks to reduce interference. In particular, our aim is to allocate subcarrier power by optimizing EE metric and minimize interference with knowledge of channel state information between BSs and user equipment. The EE-RRM problem is a fractional programming problem. In order to solve, we use Charnes–Cooper transformation technique and transform it into an equivalent concave optimization problem. The numerical results of our work present the effect of different interference, rate and power thresholds on the EE metric.

Energy efficient performance analysis of downlink CoMP in heterogeneous wireless network

In CoMP, simultaneous transmission from several neighbouring base stations (BS) in heterogeneous network can exploit the interference to enhance system performance. Simultaneously, participation of several BSs will also increase the energy consumption of network. Therefore, this technique needs to be analysed properly for energy saving, especially for heterogeneous scenario, where different BSs have different characteristics. In this paper, we have analysed the performance of wireless system in heterogeneous environment without and with CoMP in terms of outage probability and energy efficiency by changing coverage range.

Texture analysis and classification of polarimetric SAR images using histogram measures

In polarized synthetic aperture radar (SAR) image classification, texture produces very significant information about land cover. In order to perform the classification in a much better way, a detailed study of histogram measures has been performed. Good accuracies show that histogram measures are used which are best suited for several applications like land use monitoring, surface topology, crustal change, agricultural monitoring, map updating of urban areas, medical field and military system. Histogram measures improve the classification accuracy desirably. PALSAR data, that is unclassified radar images of different polarizations, of Solani River, Roorkee, India and its neighboring regions, is used for classifying the image based on texture features. The textures of images are found on the basis of histogram measures (mean, variance, standard deviation, correlation and skewness). As observed, the area can be characterized into a well-planned dispersed urban area with water bodies and vegetation. The accuracies vary for different classes. For efficient land cover classification, histogram measures have to be chosen suitably. Therefore, the role of various textural histogram measures is analyzed for their discriminative ability for SAR image classification into various land cover types like urban, vegetation and water bodies.

Energy consumption analysis of heterogeneous wireless network using coordinated multipoint transmission technique

In heterogeneous network several types of base station (BSs) are deployed to satisfying user traffic demands of future communication system and improve network performance. The coordinated multi point (CoMP) transmission technique is considered a key feature in future wireless network to improve both cell edge users throughput by exploiting interference. However, to provide CoMP transmission several BSs need to be active, which eventually increases network energy consumption. Therefore, there is need to investigate the effectiveness of CoMP technique for energy efficient communication, especially in heterogeneous network scenario, where different BSs have different characteristics. In this paper, we investigate the performance of wireless system in heterogeneous environment without and with CoMP. In particular, we analysed network outage with its energy consumption efficiency with varied coverage range. Mobiles are assumed to be connected to nearest BSs. Furthermore, we have also investigated energy efficiency vs. outage probability in terms of number of coordinating BSs for joint-transmission enabling sleep mode mechanism.

An adaptive technique based on separability index to classify PALSAR data

In this paper, we have developed an adaptive technique based on separability index for classification of PALSAR data. First of all this paper investigate the effect of sample size on accuracy assessment. Further, we have utilized minimum distance to mean classifier and studied the sample size varying from 20 to 500. A comprehensive analysis was made for different polarized images, i.e., HH, HV, VV and their combinations i.e., HH-HV, HV-VV, HH-VV, and HH-HV-VV. Further, to increase the classification accuracy, separability index, which tells about the idea of class separation in different polarization was analyzed. A decision tree based on separability index was developed and adaptive statistical approach based on mean and standard deviation was used to classify PALSAR data. Good overall accuracy and class wise accuracy was observed with this adaptive technique.

Study of Bayes Theorem for Classification of Synthetic Aperture Data

In this paper we describe a probabilistic approach of image classification based on Bayes classifier for SAR (Synthetic Aperture Radar) data. It classifies the images of different polarizations as well as the combination of these polarizations. The classification is carried out on the HH, HV and VV polarized image, their combination HH-HV, HH-VV and HV-VV and considering all the three polarized images, i.e., HH-HV-VV. It is an supervised classification approach which obtains the training data set from the region under observation. The results obtained are compared on the basis of better distinguishing ability of different classes by the classifier. The proposed classifier incorporates the use of Bayesian function for the evaluation of PDF (Probability Density Function) and with the consideration of PDF the decision function allocates a particular class to a pixel which better suits to that pixel.

Classification of polarimetric radar data with texture measure

In this paper we have classified polarimetric SAR image utilizing it capability to capture image in different polarization. Different land covers exhibit different sensitivity to differently polarized wave. Therefore, statistical measures (mean, variance and contrast) have been used to identify the best polarization to classify a particular land cover class (urban, water, vegetation, and canal). Statistical measure were applied on different polarized image choosing window size of 7 × 7. Based on texture measure, a decision tree was formed to classify the polarimetric SAR image. Good overall and class accuracies were observed for classified image.

Analysis and retrieval of soil parameters with specular scattering data at different incidence angle

In this paper, we have analyzed the angular response of specular scattering coefficient for different soil texture fields while varying soil moisture and surface roughness at C-band. An approach based on multi-incidence angle data has been developed to retrieve soil texture, soil moisture and surface roughness. An empirical relationship has been developed between normalized specular scattering coefficient and surface roughness parameters. This empirical relationship has been utilized along with the Kirchhoff Scalar Approximation to retrieve soil texture, soil moisture and surface roughness. Obtained results are in good agreement with ground truth data.