Joint Spectrum Sensing and Resource Allocation for OFDM-based Transmission with a Cognitive Relay

In this paper, we investigate the joint spectrum sensing and resource allocation problem to maximize throughput capacity of an OFDM-based cognitive radio link with a cognitive relay. By applying a cognitive relay that uses decode and forward (D&F), we achieve more reliable communications, generating less interference (by needing less transmit power) and more diversity gain. In order to account for imperfections in spectrum sensing, the proposed schemes jointly modify energy detector thresholds and allocates transmit powers to all cognitive radio (CR) subcarriers, while simultaneously assigning subcarrier pairs for secondary users (SU) and the cognitive relay. This problem is cast as a constrained optimization problem with constraints on (1) interference introduced by the SU and the cognitive relay to the PUs; (2) miss-detection and false alarm probabilities and (3) subcarrier pairing for transmission on the SU transmitter and the cognitive relay and (4) minimum Quality of Service (QoS) for each CR subcarrier. We propose one optimal and two sub-optimal schemes all of which are compared to other schemes in the literature. Simulation results show that the proposed schemes achieve significantly higher throughput than other schemes in the literature for different relay situations.Comment: EAI Endorsed Transactions on Wireless Spectrum 14(1): e4 Published 13th Apr 201

Awareness on Entrepreneurial Orientation Among Management Students in Vellore

Purpose: Thus, the objective of this study was to analyze, by means of multivariate techniques, an instrument whose function is to measure the learning of teaching Entrepreneurship, verifying the change in the entrepreneurial profile between participants and non-participants of the entrepreneurial formation process. The research was carried out among university students of the Business Administration course, Vellore.   Theoretical Framework: Entrepreneurship is a socio-economic phenomenon that has been valued for its influence on the growth and development of regional and national economies. The main agent promoting this phenomenon is the entrepreneur; a subject endowed with multiple characteristics who act in a dynamic way and is focused on reaping results, the fruits of their personal efforts. The insertion and search for enterprising subjects in different societies have been noticeable in public, economic and educational programs and policies. Entrepreneurial education is highlighted as one of the most efficient ways to disseminate culture and train new entrepreneurs. From this perspective, the teaching of Entrepreneurship stands out as a resource used for the formation of new entrepreneurs.   Design/Methodology/Approach: The present study aims to analyze the entrepreneurial intention of students from four Institutions in Vellore, relating it to entrepreneurial self-efficacy and academic self-efficacy, and to try to understand which factors influence it. The sample of this study consists of 290 students from four Institutions in Vellore, of which 114 (39.3%) are male and 176 (60.7%) are female.   Findings: Regarding the degree of importance that students attribute to the Institution support structures for the creation of a company, there is a statistically significant difference, in which the GTEC students stand out, who are the ones who attach the most importance to ”Spaces and equipment for starting the business". With regard to the remaining questions, there are no statistically significant differences between the mean values of the answers given depending on the Institution.   Research, Practical/ & Social Implications: In the analysis of the differences between Institutions, it was verified the existence of statistically significant differences in the questions related to the desire to create their own company and to have a concrete business idea to create, in which SPIM showed a higher average in relation to the other three Institutions. Regarding the other two research questions, “I consider myself capable of creating a company”, and “I am able to work on my own”, there were no statistically significant differences between the four Institutions.   Originality/Value: The present study adds value among the management students with regard to the awareness on entrepreneurship which leads to the country’ economic development

Worst Case Attack on Quantization Based Data Hiding

Currently, most quantization based data hiding al-gorithms are built assuming specific distributions of at-tacks, such as additive white Gaussian noise (AWGN), uniform noise, and so on. In this paper, we prove that the worst case additive attack for quantization based data hiding is a 3-δ function. We derive the expression for the probability of error (Pe) in terms of distortion compensation factor, α, and the attack distribution. By maximizing Pe with respect to the attack distribution, we get the optimal placement of the 3-δ function. We then experimentally verify that the 3-δ function is in-deed the worst case attack for quantization based data hiding.

Competitive Spectrum Trading in Dynamic Spectrum Access Markets: A Price War

Abstract—The concept of dynamic spectrum access (DSA) enables the licensed spectrum to be traded in an open market where the unlicensed users can freely buy and use the available licensed spectrum bands. However, like in the other traditional commodity markets, spectrum trading is inevitably accompanied by various competitions and challenges. In this paper, we study an important business competition activity – price war in the DSA market. A non-cooperative pricing game is formulated to model the contention among multiple wireless spectrum providers for higher market share and revenues. We calculate the Pareto optimal pricing strategies for all providers and analyze the motivations behind the price war. The potential responses to the price war are in-depth discussed. Numerical results demonstrate the efficiency of the Pareto optimal strategy for the game and the impact of the price war to all participants. I

X-GRL: An Empirical Assessment of Explainable GNN-DRL in B5G/6G Networks

The rapid development of artificial intelligence (AI) techniques has triggered a revolution in beyond fifth-generation (B5G) and upcoming sixth-generation (6G) mobile networks. Despite these advances, efficient resource allocation in dynamic and complex networks remains a major challenge. This paper presents an experimental implementation of deep reinforcement learning (DRL) enhanced with graph neural networks (GNNs) on a real 5G testbed. The method addresses the explainability of GNNs by evaluating the importance of each edge in determining the model's output. The custom sampling functions feed the data into the proposed GNN-driven Monte Carlo policy gradient (REINFORCE) agent to optimize the gNodeB (gNB) radio resources according to the specific traffic demands. The demo demonstrates real-time visualization of network parameters and superior performance compared to benchmarks.Comment: 3 pages, 8 figure

Performance Issues on K-Mean Partitioning Clustering Algorithm

In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called data segmentation. Clustering is process of grouping the data objects such that all objects in same group are similar and object of other group are dissimilar. In literature, many categories of cluster analysis algorithms present. Partitioning methods are one of efficient clustering methods, where data base is partition into groups in iterative relocation procedure. K-means is widely used partition method. In this paper, we presented the k-means algorithm and its mathematical calculations for each step in detailed by taking simple data sets. This will be useful for understanding performance of algorithm. We also executed k-means algorithm with same data set using data mining tool Weka Explorer. The tool displays the final cluster points, but won’t give internal steps. In our paper, we present each step calculations and results. This paper helpful to user, who wants know step by step process. We also discuss performance issues of k-means algorithm for further extension.

Performance Issues on K-Mean Partitioning Clustering Algorithm

In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called data segmentation. Clustering is process of grouping the data objects such that all objects in same group are similar and object of other group are dissimilar. In literature, many categories of cluster analysis algorithms present. Partitioning methods are one of efficient clustering methods, where data base is partition into groups in iterative relocation procedure. K-means is widely used partition method. In this paper, we presented the k-means algorithm and its mathematical calculations for each step in detailed by taking simple data sets. This will be useful for understanding performance of algorithm. We also executed k-means algorithm with same data set using data mining tool Weka Explorer. The tool displays the final cluster points, but won’t give internal steps. In our paper, we present each step calculations and results. This paper helpful to user, who wants know step by step process. We also discuss performance issues of k-means algorithm for further extension.