An Integrated Approach for Jammer Detection using Software Defined Radio

AbstractDue to shared nature of wireless communication any malicious user can easily monitored communication between two devices and emits false message to block communication. Nowadays increased use of software defined radio (SDR) technology makes any types of jammer device using same hardware with little modification in software. A jammer transmits radio signal to block legitimate communication either overlapping signal with more power or reducing signal to noise ratio. In this paper we have survey different jammer detection methods for efficient detection of jammers presence in system. Existing jammer detection methods like packet delivery ratio (PDR), packet send ratio (PSR), bad packet ratio (BPR) and signal to noise ratio (SNR) can effectively detects jammer, here we have proposed novel method for jammer detection using communication parameter used in SDR like synchronization indicator, iteration and adaptive signal to jammer plus noise ratio (ASNJR). This system uses that parameter which is readily available in system so computation has been reduced and ASNJR also has been adaptively updated with and without presence of jammer. Experimental result show that this system based on SDR effectively detects presence of jammer

Comparative Analysis of Procedures and Solutions to Improve Energy Efficiency of Massive MIMO

The blustery growth of high data rate applications leadsto more energy consumption in wireless networks to satisfy servicequality.Therefore, energy-efficient communications have been paidmore attention to limited energy resources and environmentallyfriendly transmission functioning. Countless publications arepresent in this domain which focuses on intensifying networkenergy efficiency for uplink-downlink transmission.It is done eitherby using linear precoding schemes, by amending the number ofantennas per BS, by power control problem formulation, antennaselection schemes, level of hardware impairments, and byconsidering cell-free (CF) Massive-MIMO.After reviewing thesetechniques, still there are many barriers to implement thempractically. The strategies mentioned in this review show theperformance of EE under the schemes as raised above. The chiefcontribution of this work is the comparative study of how MassiveMIMO EE performs under the background of different methodsand architectures and the solutions to few problem formulationsthat affect the EE of network systems. This study will help choosethe best criteria to improve EE of Massive MIMO whileformulating a newer edition of testing stand-ards.This surveyprovides the base for interested readers in energy efficient MassiveMIMO

Performance Analysis of LEACH with Deep Learning in Wireless Sensor Networks

Thousands of low-power micro sensors make up Wireless Sensor Networks, and its principal role is to detect and report specified events to a base station. Due to bounded battery power these nodes are having very limited memory and processing capacity. Since battery replacement or recharge in sensor nodes is nearly impossible, power consumption becomes one of the most important design considerations in WSN. So one of the most important requirements in WSN is to increase battery life and network life time. Seeing as data transmission and reception consume the most energy, it’s critical to develop a routing protocol that addresses the WSN’s major problem. When it comes to sending aggregated data to the sink, hierarchical routing is critical. This research concentrates on a cluster head election system that rotates the cluster head role among nodes with greater energy levels than the others.  We used a combination of LEACH and deep learning to extend the network life of the WSN in this study.  In this proposed method, cluster head selection has been performed by Convolutional Neural Network (CNN). The comparison has been done between the proposed solution and LEACH, which shows the proposed solution increases the network lifetime and throughput

Single Image Super-Resolution through Sparse Representation via Coupled Dictionary learning

Abstract-Single Image Super-Resolution (SISR) through sparse representation has received much attention in the past decade due to significant development in sparse coding algorithms. However, recovering high-frequency textures is a major bottleneck of existing SISR algorithms.  Considering this, dictionary learning approaches are to be utilized to extract high-frequency textures which improve SISR performance significantly. In this paper, we have proposed the SISR algorithm through sparse representation which involves learning of Low Resolution (LR) and High Resolution (HR) dictionaries simultaneously from the training set. The idea of training coupled dictionaries preserves correlation between HR and LR patches to enhance the Super-resolved image. To demonstrate the effectiveness of the proposed algorithm, a visual comparison is made with popular SISR algorithms and also quantified through quality metrics. The proposed algorithm outperforms compared to existing SISR algorithms qualitatively and quantitatively as shown in experimental results. Furthermore, the performance of our algorithm is remarkable for a smaller training set which involves lesser computational complexity. Therefore, the proposed approach is proven to be superior based upon visual comparisons and quality metrics and have noticeable results at reduced computational complexity

Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Performance Analysis and Simulation of Rain Attenuation Models at 12–40 GHz Band for an Earth Space Path over Indian Cities

AbstractAs the shortage of the radio spectrum infers challenges in the Earth–Space Communication Link (ESCL) particularly in satellite communication, it is important to gauge the measure of attenuation to guarantee proficient usage of the spectrum. By the shortage in the frequency spectrum, the coming period will request more information to be exchanged at unquestionably more speed. Furthermore, this will interest for the higher frequencies. Keeping in mind the end goal to exploit present day space communications innovation, acknowledgment of a household correspondence satellite framework with impressive communication limit at Ku–band is attainable. Climate is exceptionally unverifiable and labile hydrometeor activities make issue in a satellite communication. The real issue connected with the communication links at these high frequency radio waves is the rain attenuation estimation. In this paper, a performance analysis and simulation of rain attenuation models is shown for frequency range of 12–40 GHz over ESCL at real urban communities of India by considering information of NSS–6 satellite