Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio

Abstract

A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional two-class hypothesis) is further divided into quantized regions, whereas the hypothesis that the primary user is present is conserved. The non-radio frequency energy harvesting-equiped secondary user transmits, when the primary user is absent, with transmission power based on the hypothesis result (the energy level of the sensed signal) and the residual energy in the battery: the lower the energy of the received signal, the higher the transmission power, and vice versa. Conversely, the lower is the residual energy in the node, the lower is the transmission power. This technique increases the throughput of a secondary link by providing a higher number of transmission events, compared to the conventional two-class hypothesis. Furthermore, transmission with low power for higher energy levels in the sensed signal reduces the probability of interference with primary users if, for instance, detection was missed. The familiar machine learning algorithm known as a support vector machine (SVM) is used in a one-versus-rest approach to classify the input signal into predefined classes. The input signal to the SVM is composed of three statistical features extracted from the sensed signal and a number ranging from 0 to 100 representing the percentage of residual energy in the node's battery. To increase the generalization of the classifier, k-fold cross-validation is utilized in the training phase. The experimental results show that an SVM with the given features performs satisfactorily for all kernels, but an SVM with a polynomial kernel outperforms linear and radial-basis function kernels in terms of accuracy. Furthermore, the proposed multi-class hypothesis achieves higher throughput compared to the conventional two-class hypothesis for spectrum sensing in cognitive radio networks.

Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

“…In [21], authors presented a framework based on the theory of multiclass to exploit the attainable throughput in CRN. Through the absence of the PU in the two-class hypothesis, the energy range of sensing signal is separated into quantized areas while during the presence of the PU, the sensing signal is conserved.…”

“…In this approach, a user can concurrently use various vacant channels for transmitting CR. In [21], the authors proposed an M/G/1 queuing model to resolve the spectrum handoff problem. The interarrivals to the M/G/1 queue are modeled and shared various vacant channels, for instance, the overall interarrivals from all CRUs.…”

“…Since the fuzzy-based scheme has no BCs, the handoff delay can be maximized when the channel underutilization is at its minimal level. DFHC [18], M/G/1 [21] and Zigbee [52] are reactive handoff schemes with no channel backup facility, therefore, the handoff delay is at maximum level and the channel underutilization is at its minimal level. DSA [53] is a hybrid handoff scheme which is considered as a middle course between proactive and reactive handoff schemes.…”

“…The stopover stay of the PU at the assigned channel depends upon its communication requirements and can be of any time period. Therefore the dwelling period is assumed and based upon the random distribution method [18,21]. The decision of either to wait and stay at the ongoing path or to execute the handoff event is based on the current result.…”

Using Encephalography to Classify Mind States Associated with Cycling Activities

“…(33) One successful example of a kernel-based learning algorithm is the SVM, which has been employed in many classification problems. (34)(35)(36)(37)(38) SVMs are machinelearning algorithms proposed by Cortes and Vapnik to categorize data into two groups. (39) The main basic concept of the SVM is to find a hyperplane that can separate two or more different classes of data.…”