S.R. Park

A computationally efficient scheme for feature extraction with kernel discriminant analysis

The kernel discriminant analysis (KDA), an extension of the linear discriminant analysis (LDA) and null space-based LDA into the kernel space, generally provides good pattern recognition (PR) performance for both small sample size (SSS) and non-SSS PR problems. Due to the eigen-decomposition technique adopted, however, the original scheme for the feature extraction with the KDA suffers from a high complexity burden. In this paper, we derive a transformation of the KDA into a linear equation problem, and propose a novel scheme for the feature extraction with the KDA. The proposed scheme is shown to provide us with a reduction of complexity without degradation of PR performance. In addition, to enhance the PR performance further, we address the incorporation of regularization into the proposed scheme. HighlightsWe propose a complexity-reduced scheme for the kernel discriminant analysis.The proposed scheme does not cause degradation of recognition accuracy performance.The core is the transformation of the original problem into a linear equation problem.We further address the incorporation of regularization into the proposed scheme.

Effect of simultaneous transmit and receive antennas on cooperative cognitive radio networks

In this paper, we address the performance improvement of cooperative cognitive radio networks (CCRNs) with secondary users exploiting multiple antennas in the multiple input multiple output (MIMO) scenario. In order to expand the region of achievable rate by enabling full-duplex communication between secondary users, we adopt simultaneous transmitting and receiving antennas for the secondary users. The link capacities of the proposed framework are analyzed under the assumption of full channel side information. It is shown through numerical evaluation that the proposed MIMO CCRN framework can provide a considerable performance gain over the conventional CCRN and conventional MIMO CCRN frameworks.

On Full-Duplex Communication in Cooperative Cognitive Radio Networks

Taking a full-duplex (FD) technique into account, a design of cooperative cognitive radio networks (CCRN) is addressed. The FD communication in the CCRN is enabled by employing simultaneous transmitting and receiving antennas for the secondary users. From the preliminary results via numerical evaluation, it is expected that the proposed FD multiple-input-multiple-output CCRN framework can provide a performance gain over the conventional CCRN frameworks.

Full-duplex communication in cooperative cognitive radio networks: Preliminary results

We address the design of cooperative cognitive radio networks (CCRN) adopting a full-duplex (FD) technique. In order to enable FD communication in the CCRN, simultaneous transmitting and receiving antennas are employed for the secondary users. Some preliminary results from numerical evaluation indicate that the proposed FD multiple-input-multiple-output CCRN framework can provide a considerable performance gain over the conventional CCRN frameworks.