Anirudh Agarwal

Cystic Fibrosis: Need for Mass Deployable Screening Methods

Cystic fibrosis (CF) is an autosomal recessive disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. CFTR is a member of the adenosine triphosphate (ATP)-binding cassette superfamily of proteins and it functions as a chloride channel. CFTR largely controls the working of epithelial cells of the airways, the gastrointestinal tract, exocrine glands, and genitourinary system. Cystic fibrosis is responsible for severe chronic pulmonary disorders in children. Other maladies in the spectrum of this life-limiting disorder include nasal polyposis, pansinusitis, rectal prolapse, pancreatitis, cholelithiasis, insulin-dependent hyperglycemia, and cirrhosis. This review summarizes the recent state of art in the field of cystic fibrosis diagnostic methods with the help of CF literature published so far and proposes new research domains in the field of cystic fibrosis diagnosis.

Spectrum Occupancy Prediction for Realistic Traffic Scenarios:Time Series versus Learning-Based Models

Spectrum occupancy information is necessary in a cognitive radio network (CRN) as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access (DSA). However, in a CRN, it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior. In this context, the spectrum occupancy prediction proves to be very useful in enhancing the quality of experience of the secondary user. This paper investigates the practical prowess of various time-series modeling approaches and the machine learning (ML) techniques for predicting spectrum occupancy, based on a spectrum measurement campaign conducted in Jaipur, Rajasthan, India. Moreover, the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data. Nevertheless, prediction through ML-based recurrent neural network proves to perform reasonably well, thereby providing an accurate future spectrum occupancy information for DSA.

Learning-based predictive dynamic spectrum access framework: a practical perspective for enhanced QoE of secondary users

In a cognitive radio environment, the optimality in channel selection by a secondary user (SU) is directly dependent on its spectrum-sensing efficiency, and quality of experience (QoE) in terms of the channel-switching frequency (CSF) and the interference caused to the primary users (PUs). Modelling the spectrum through statistical methods becomes, sometimes, difficult due to the lack of a-priori information of the PU activity. This work proposes a framework for learning-based prediction of the future idle times of the PUs thereby opportunistically allocating the channel with enhanced QoE of SUs. The idea is to minimise the spectrum-sensing energy requirement by sensing only if the channel is predicted to be idle, thereby reducing the CSF and mitigating the SU–PU interference. Initially, the authors have tested the accuracy of the prediction approach in various traffic scenarios for a single PU channel case. Later, it is extended to the multiple channel case for a particular data traffic. Furthermore, a practical scenario has been considered where the efficacy of the proposed framework is validated for PU data traffic in GSM and ISM bands. The results highlight the practicability of prediction-based opportunistic dynamic spectrum access with improvement in the SU QoE over random channel selection.

A novel noise floor estimation technique for optimized thresholding in spectrum sensing

An accurate detection of unused part of the spectrum is a key requirement in cognitive radio system. In this context, it is necessary to set an appropriate threshold between signal and noise. As noise power is instantaneous in nature, a periodic estimation of noise power is required. In this paper, we have proposed a combination methodology, that uses Rank order filtering in tandem with a gradient based approach. The performance of the same has been compared with two other well-known existing techniques for noise floor estimation in nonfading as well as fading scenarios, where it is found that the proposed technique outperforms the other two in terms of minimum mean square error in the estimation of spectrum occupancy.

Hardware implementation of k-means clustering based spectrum sensing using usrp in a cognitive radio system

An accurate detection of spectrum opportunities is a key factor in governing the efficient spectrum usage in a cognitive radio (CR) system. Energy detection based spectrum sensing has been widely used due to its ease of implementation with lower computational complexity; however, its robustness and performance are highly affected by the noise uncertainty. In the present work, a real time hardware implementable spectrum sensor has been realized and tested for an unsupervised learning based K-means clustering approach, to detect the white spaces in the spectrum. A CR network with one primary transmitter and two secondary nodes has been considered for which the data is collected over an FM band using a software defined radio peripheral, i.e. USRP B210. The whole system has been implemented with the help of MATLAB Simulink & Xilinx System Generator. The decision accuracy of the proposed algorithm is verified at different values of the signal-to-noise ratios (SNRs) and found that the classification based sensing is quite accurate even at low SNR region.

Secondary User QoE Enhancement Through Learning Based Predictive Spectrum Access in Cognitive Radio Networks

Quality of experience (QoE) of a secondary spectrum user is mainly governed by its spectrum utilization, the energy consumption in spectrum sensing and the impact of channel switching in a cognitive radio network. It can be enhanced by prediction of spectrum availability of different channels in the form of their idle times through historical information of primary users’ activity. Based on a reliable prediction scheme, the secondary user chooses the channel with the longest idle time for transmission of its data. In contrast to the existing method of statistical prediction, the use and applicability of supervised learning based prediction in various traffic scenarios have been studied in this paper. Prediction accuracy is investigated for three machine learning techniques, artificial neural network based Multilayer Perceptron (MLP), Support Vector Machines (SVM) with Linear Kernel and SVM with Gaussian Kernel, among which, the best one is chosen for prediction based opportunistic spectrum access. The results highlight the analysis of the learning techniques with respect to the traffic intensity. Moreover, a significant improvement in spectrum utilization of the secondary user with reduction in sensing energy and channel switching has been found in case of predictive dynamic channel allocation as compared to random channel selection.

Impact of primary user duty cycle on cognitive secondary user utilization efficiency in a generalized κ-µ fading channel

Efficient spectrum utilization by secondary users (SU) during the inactivity phase of primary users (PU) is of utmost interest for dynamic spectrum access in a cognitive radio environment. In this paper, we investigate the utilization efficiency of cognitive radio users with respect to the realistic estimate of PU duty cycle (DC) in AWGN and generalized κ-μ fading channels for a fixed interference ratio. We have adopted a two-state hidden Markov model (HMM) for capturing PU activity. PU duty cycle is calculated by considering exponentially distributed ON-OFF channel model. Performance evaluation has been done by two approaches: one is the conventional approach which is widely used and the other is based on a-posterior log-likelihood ratio (LLR). As per prior investigations, the LLR approach is found to be very useful in evaluating SU utilization in AWGN channels. In the present work, we have extended similar analysis to generalized fading channel model. Our analytical studies demonstrate a significant decrease in SU utilization efficiency in a fading environment as compare to AWGN while LLR approach still outperforming the results of the conventional scheme.