Soumitra Debnath

Multidirectional Gradient Adjusted Predictor

In this paper we investigate the prediction scheme of Context Based Adaptive Lossless Image Coding (CALIC), the standard for lossless/near lossless image compression for continuous-tone finger-print images. We show that it is not sufficient to consider the prediction technique in a single direction for a fingerprint image as a whole for Gradient Adjusted Predictor (GAP). As a result, we propose an additional GAP scheme to achieve better speed and better prediction accuracy as and hence provide potential for further improvements in Lossless Image Compression. Experimental results indicate that the proposed scheme outperforms the existing GAP prediction for all the finger-print images tested, while the complexity of the prediction algorithm is improved by more than four times with the help of parallel implementation.

A simulation framework for capacity analysis in TV white space

The TV white space (TVWS) or equivalently the spatially available unused TV spectrum has drawn much attention recently due to its availability for opportunistic access by unlicensed users in fulfilling the spectrum demands. In this paper, we have evaluated the capacity of secondary (unlicensed) user network meeting the constraint on the aggregated secondary interference power at the TV receiver. Particular importance has been given to the interference modeling to define the protection region around the TV-receiver as per the Federal Communications Committee (FCC) rules. The impact of different system parameters such as power, transmission range, and density of secondary users contending for the same TV channel are also explored in the paper for different channel scenarios.

Improved p-norm energy detector in Generalized κ-μ fading channel for spectrum sensing in cognitive radio

The classical energy detection (CED) system is a well known technique for spectrum sensing in cognitive radio. Generalized p-norm detector for spectrum sensing in additive white Gaussian noise (AWGN) has been shown to provide improved performance over CED under certain conditions. Further, improved algorithm exists which works better than the classical energy detection algorithm. The present paper highlights the combined benefit of the p-norm energy detector and the improved algorithm for spectrum sensing in achieving a higher performance gain in AWGN and generalized κ-μ fading channels scenario over the CED scheme.

Cooperative Spectrum Sensing using Improved p-norm Detector in Generalized κ-μ Fading Channel

The classical energy detection (CED) system is a well-known technique for spectrum sensing in cognitive radio. Generalized p-norm detector for spectrum sensing in additive white Gaussian noise (AWGN) has been shown to provide improved performance over CED under certain conditions. Further, improved algorithm exists which works better than the classical energy detection algorithm. The present paper takes into account the combined benefit of the p-norm energy detector and the improved algorithm for spectrum sensing for individual cognitive user in a cooperative spectrum sensing system to achieve a significant performance gain in both AWGN and generalized κ-μ fading channels over the cooperative/ non-cooperative CED scheme.

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.

Amplify-and-forward relay based spectrum sensing with generalized selection combining

Diversity reception schemes are well-known effective techniques for mitigating the adverse effects of multipath fading in wireless mobile channels. This paper analyzes the performance of an amplify-and-forward (AF) relay-based cooperative spectrum sensing system with generalized selection combining (GSC) over a Rayleigh fading channel and compare the results with those of the conventional diversity combining schemes such as maximal-ratio-combining (MRC) and selection combining (SC). Novel closed-form expression has been derived for the average detection probability over the independently and identically distributed (i.i.d) diversity paths. Receiver operating characteristics (ROCs) and the average detection probability versus the average signal-to-noise ratio (SNR) curves have been presented for different scenarios of interest.

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.

Performance of an Energy Detector with Generalized Selection Combining for Spectrum Sensing

Diversity reception schemes are well-known to have the ability to mitigate the adverse effects of multipath wireless channels. This paper analyzes the performance of an energy detector with generalized selection combining (GSC) over a Rayleigh fading channel and compares the results with those of the conventional diversity combining schemes such as, maximal-ratio combining (MRC) and the selection combining (SC). Novel closed-form expressions have been derived for the average detection probability over the independently, identically distributed (i.i.d) diversity paths. Receiver operating characteristics (ROCs) and average detection probability versus SNR curves have been presented for different scenarios of interest.