SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO systems

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how Support Vector Machine (SVM), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones

Predicting Housing Value: A Comparison of Multiple Regression Analysis and Artificial Neural Networks

This paper compares the predictive performance of artificial neural networks (ANN) and multiple regression analysis (MRA) for single family housing sales. Multiple comparisons are made between the two data models in which the data sample size is varied, the funcional specifications is varied, and the temporal prediction is varied. We conclude that ANN performs better than MRA when a moderate to large data sample size is used. For our application, this "moderate to large data sample size" varied from 13% to 39% of the total data sample (506 to 1506 observations out of 3906 total observations). Our results give a plausible explanation why previous papers have obtained varied results when comparing MRA and ANN predictive performance for housing values.

A review of service quality and service delivery: Towards a customer co-production and customer-integration approach

© 2018, Emerald Publishing Limited. Purpose: The purpose of this paper is to provide researchers with an overview of the service quality and delivery domain, focussing on the inclusion of customer co-production and customer integration. Specifically, this paper concentrates on service quality (including quality measurement), the service environment, controls and their consequences. Design/methodology/approach: A comprehensive review of the literature is conducted, analysed and presented. Findings: The review shows that service delivery is both complex and challenging, particularly when considering the unique characteristics of services and the high level of customer involvement in their creation. The facilitation, transformation and usage framework identifies how failures can occur at each stage of service delivery, beginning with the characteristics of the service environment, while control theory offers insights into the formal and informal controls that may be applied in the facilitation and transformation stages, which may reduce the likelihood or extent of such failures. Originality/value: Despite the fact that it is widely accepted that service quality is an antecedent to customer satisfaction, it is surprising that this customer co-creation aspect has been largely neglected in the extant literature. As such, the role that customer co-production plays in service quality performance has been examined in this paper. It is hoped that this examination will enhance both theoretical and practical understanding of service quality. It would be useful to find modern tools that can help in improving service quality performance

Enhancing physical layer security of cognitive radio transceiver via chaotic OFDM

Due to the enormous potential of improving the spectral utilization by using Cognitive Radio (CR), designing adaptive access system and addressing its physical layer security are the most important and challenging issues in CR networks. Since CR transceivers need to transmit over multiple non-contiguous frequency holes, multi-carrier based system is one of the best candidates for CR's physical layer design. In this paper, we propose a combined chaotic scrambling (CS) and chaotic shift keying (CSK) scheme in Orthogonal Frequency Division Multiplexing (OFDM) based CR to enhance its physical layer security. By employing chaos based third order Chebyshev map which allows optimum bit error rate (BER) performance of CSK modulation, the proposed combined scheme outperforms the traditional OFDM system in overlay scenario with Rayleigh fading channel. Importantly, with two layers of encryption based on chaotic scrambling and CSK modulation, large key size can be generated to resist any brute-force attack, leading to a significantly improved level of security

Anisotropic Magneto-Thermopower: the Contribution of Interband Relaxation

Spin injection in metallic normal/ferromagnetic junctions is investigated taking into account the anisotropic magnetoresistance (AMR) occurring in the ferromagnetic layer. It is shown, on the basis of a generalized two channel model, that there is an interface resistance contribution due to anisotropic scattering, beyond spin accumulation and giant magnetoresistance (GMR). The corresponding expression of the thermopower is derived and compared with the expression for the thermopower produced by the GMR. First measurements of anisotropic magnetothermopower are presented in electrodeposited Ni nanowires contacted with Ni, Au and Cu. The results of this study show that while the giant magnetoresistance and corresponding thermopower demonstrates the role of spin-flip scattering, the observed anisotropic magnetothermopower indicates interband s-d relaxation mechanisms.Comment: 20 pages, 4 figure

NSU- LSMSA Makers Club: Advanced STEM Educational Modules and Enhanced Experiential Learning

This paper describes advanced educational activates of STEM makers club, which combines Northwestern State University (NSU) and The Louisiana School for Math, Science, and the Arts (LSMSA). The main objective of this Makers Club is to make students excited about STEM and to increase the number of students who are interested in STEM related careers. The newly founded club (NSU-LSMSA Makers Club) emphasizes innovation and hands-on experience for all participants. Furthermore, it promotes building a spirit of teamwork and sharping problem-solving skills. Different teaching modules have been developed and applied. The paper presents various structured activities reports experiential learning from its participants.Cockrell School of Engineerin

Enhancing secrecy rate in cognitive radio networks via stackelberg game

In this paper, a game theory based cooperation scheme is investigated to enhance the physical layer security in both primary and secondary transmissions of a cognitive radio network (CRN). In CRNs, the primary network may decide to lease its own spectrum for a fraction of time to the secondary nodes in exchange of appropriate remuneration. We consider the secondary transmitter node as a trusted relay for primary transmission to forward primary messages in a decode-and-forward (DF) fashion and, at the same time, allows part of its available power to be used to transmit artificial noise (i.e., jamming signal) to enhance primary and secondary secrecy rates. In order to allocate power between message and jamming signals, we formulate and solve the optimization problem for maximizing the secrecy rates under malicious attempts from EDs. We then analyse the cooperation between the primary and secondary nodes from a game-theoretic perspective where we model their interaction as a Stackelberg game with a theoretically proved and computed Stackelberg equilibrium. We show that the spectrum leasing based on trading secondary access for cooperation by means of relay and jammer is a promising framework for enhancing security in CRNs

Enhancing secrecy rate in cognitive radio networks via multilevel Stackelberg game

In this letter, physical layer (PHY) security is investigated for both primary and secondary transmissions of a cognitive radio network (CRN) that is in danger of malicious attempt by an eavesdropper (ED). In our proposed system, the secondary transmitter (ST) is acted as a trusted relay (TR) for primary transmission and the PHY security is facilitated by the cooperation between the primary transmitter (PT) and the ST using the multilevel Stackelberg game. In particular, we formulate and solve the optimization problem of maximizing secrecy rates in different phases of primary and secondary transmissions. Finally, numerical examples are provided to demonstrate that the spectrum leasing based on trading secondary access for cooperation is a promising framework for enhancing secrecy rate in CRNs

Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering

© 2001-2011 IEEE. Surface electromyography (sEMG)-based pattern recognition studies have been widely used to improve the classification accuracy of upper limb gestures. Information extracted from multiple sensors of the sEMG recording sites can be used as inputs to control powered upper limb prostheses. However, usage of multiple EMG sensors on the prosthetic hand is not practical and makes it difficult for amputees due to electrode shift/movement, and often amputees feel discomfort in wearing sEMG sensor array. Instead, using fewer numbers of sensors would greatly improve the controllability of prosthetic devices and it would add dexterity and flexibility in their operation. In this paper, we propose a novel myoelectric control technique for identification of various gestures using the minimum number of sensors based on independent component analysis (ICA) and Icasso clustering. The proposed method is a model-based approach where a combination of source separation and Icasso clustering was utilized to improve the classification performance of independent finger movements for transradial amputee subjects. Two sEMG sensor combinations were investigated based on the muscle morphology and Icasso clustering and compared to Sequential Forward Selection (SFS) and greedy search algorithm. The performance of the proposed method has been validated with five transradial amputees, which reports a higher classification accuracy (> 95%). The outcome of this study encourages possible extension of the proposed approach to real time prosthetic applications