Quality and Availability of spectrum based routing for Cognitive radio enabled IoT networks

With the recent emergence and its wide spread applicability Internet of Things (IoT) is putting pressure on network resources and most importantly on availability of spectrum. Spectrum scarcity is the issue to be addressed in networking within IoT. Cognitive radio is the technology which addresses the problem of spectrum scarcity in an efficient way. Equipping the IoT devices with cognitive radio capability will lead to a new dimension called cognitive radio enabled IoT devices. To achieve ON-demand IoT solutions and interference free communications cognitive radio enabled IoT devices will become an effective platform for many applications. As there is high dynamicity in availability of spectrum it is challenging for designing an efficient routing protocol for secondary users in cognitive device networks. In this work we are going to estimate spectrum quality and spectrum availability based on two parameters called global information about spectrum usage and instant spectrum status information. Enhanced energy detector is used at each and every node for better probability of detection. For estimating spectrum quality and availability we are introducing novel routing metrics. To have restriction on the number of reroutings and to increase the performance of routing in our proposed routing metric only one retransmission is allowed. Then, two algorithms for routing are designed for evaluating the performance of routing and we find that the bit error rates of proposed algorithms (nodes are dynamic) have decreased a lot when compared to conventional methods (Nodes are static) and throughput of proposed algorithm also improved a lot

Sensing and Sharing Schemes for Spectral Efficiency of Cognitive Radios

Increase in data traffic, number of users and their requirements laid to a necessity of more bandwidth. Cognitive radio is one of the emerging technology which addresses the spectrum scarcity issue. In this work we study the advantage of having collaboration between cognitive enabled small cell network and primary macrocell. Different from the existing works at spectrum sensing stage we are applying enhanced spectrum sensing to avoid probability of false alarms and missed detections which has impact on spectral efficiency. Later power control optimization for secondary users known as Hybrid spectrum sharing is used for further improvement of spectral efficiency. Furthermore, the failed packets of Primary users are taken care by high ranked relays which in turn decreases the average Primary user packet delay by 20% when compared between assisted Secondary user method and non-assisted Secondary user method.

Quality and Availability of spectrum based routing for Cognitive radio enabled IoT networks

With the recent emergence and its wide spread applicability Internet of Things (IoT) is putting pressure on network resources and most importantly on availability of spectrum. Spectrum scarcity is the issue to be addressed in networking within IoT. Cognitive radio is the technology which addresses the problem of spectrum scarcity in an efficient way. Equipping the IoT devices with cognitive radio capability will lead to a new dimension called cognitive radio enabled IoT devices. To achieve ON-demand IoT solutions and interference free communications cognitive radio enabled IoT devices will become an effective platform for many applications. As there is high dynamicity in availability of spectrum it is challenging for designing an efficient routing protocol for secondary users in cognitive device networks. In this work we are going to estimate spectrum quality and spectrum availability based on two parameters called global information about spectrum usage and instant spectrum status information. Enhanced energy detector is used at each and every node for better probability of detection. For estimating spectrum quality and availability we are introducing novel routing metrics. To have restriction on the number of reroutings and to increase the performance of routing in our proposed routing metric only one retransmission is allowed. Then, two algorithms for routing are designed for evaluating the performance of routing and we find that the bit error rates of proposed algorithms (nodes are dynamic) have decreased a lot when compared to conventional methods (Nodes are static) and throughput of proposed algorithm also improved a lot

Modified linear prediction method for directions of arrival estimation of narrow-band plane waves

A modified linear prediction (MLP) method is proposed in which the reference sensor is optimally located on the extended line of the array. The criterion of optimality is the minimization of the prediction error power, where the prediction error is defined as the difference between the reference sensor and the weighted array outputs. It is shown that the L<SUB>2</SUB>-norm of the least-squares array weights attains a minimum value for the optimum spacing of the reference sensor, subject to some soft constraint on signal-to-noise ratio (SNR). How this minimum norm property can be used for finding the optimum spacing of the reference sensor is described. The performance of the MLP method is studied and compared with that of the linear prediction (LP) method using resolution, detection bias, and variance as the performance measures. The study reveals that the MLP method performs much better than the LP technique

Adaptive estimation of eigensubspace and tracking the directions of arrival

In this paper, we present an adaptive method to estimate the eigensubspace and directions-of-arrival (DOAs) of multiple narrowband plane waves. We first develop, for the arbitrary array and asymptotic case, an approximate complex Newton-update formula for recursively seeking the eigenvector corresponding to the minimum eigenvalue of the data covariance matrix of the underlying complex, stationary signal scenario. The development of the algorithm involves complex differentiation and use of exact gradient and a refined approximation to the Hessian of the cost function in the Newton-update formula derived by Abatzaglou et al. (1991). For seeking the complete noise subspace, we combine this algorithm with the matrix level inflation technique suggested by Mathew et al. (1995). Next, we consider nonstationary signal sources and present the adaptive procedure for tracking the noise subspace and directions of arrival of the moving sources. Tracking of angles of arrival is accomplished by computing the minimum-norm polynomial coefficients and deriving an elegant relationship between the changes in the values of the coefficients and the values of the roots of the polynomial on a snapshot basis. Computer simulations are included to demonstrate the quality of estimated noise subspace and accuracy in the estimates of DOAs. Results are compared with those obtained using some of the existing methods for adaptive subspace estimation (Yang and Kaveh, 1988 ; Yu, 1991) and tracking of angles (Yu, 1991)

Modified linear prediction method for directions of arrival estimation of narrow-band plane waves

A modified linear prediction (MLP) method is proposed in which the reference sensor is optimally located on the extended line of the array. The criterion of optimality is the minimization of the prediction error power, where the prediction error is defined as the difference between the reference sensor and the weighted array outputs. It is shown that the L2-norm of the least-squares array weights attains a minimum value for the optimum spacing of the reference sensor, subject to some soft constraint on signal-to-noise ratio (SNR). How this minimum norm property can be used for finding the optimum spacing of the reference sensor is described. The performance of the MLP method is studied and compared with that of the linear prediction (LP) method using resolution, detection bias, and variance as the performance measures. The study reveals that the MLP method performs much better than the LP technique