In this paper, we present an efficient implementation of a non-contiguous orthogonal frequency division multiplexing (NC-OFDM) transceiver for cognitive radio systems. NC-OFDM is designed to transmit information in the presence of incumbent users, deactivating subcarriers located in the vicinity of these users to avoid interference. Given that the core component of an NC-OFDM transceiver is the fast Fourier transform (FFT), and that several of the subcarriers are deactivated, it is possible to reduce the execution time by "pruning" the FFT. We propose an algorithm that efficiently and quickly prunes the FFT for NC-OFDM transceivers. Results show that the proposed algorithm substantially outperforms other FFT pruning algorithms when a medium to large number of subcarriers have been deactivated.
The opportunistic usage of the spectrum must be done without causing any interference to the licensed spectrum users. Conventional non-adaptive wideband spectrum sensing approaches could potentially be inefficient since they generally employ the same scanning resolution, even though the spectrum might contain different types of signals, individually requiring scans with different resolutions. In this paper, we present a novel spectrum sensing framework that adapts its parameters across the spectrum of interest according to the characteristics of its occupancy. We also propose a dynamic scheduling algorithm for spectrum sensing which allocates different time resolutions to different portions of the spectrum. We demonstrate that the proposed algorithm improves the efficiency of spectrum sensing over a non-adaptive approach.
In this paper, we present a statistical analysis of the peak-to-average power ratio (PAPR) for non-contiguous orthogonal frequency division multiplexing (NC-OFDM) signals. When studying contiguous OFDM signals, most PAPR analysis techniques assume the symbols to be identically and independently distributed (i.i.d.). However, in an NC-OFDM transmission, where a large number of subcarriers could be deactivated, this assumption is no longer valid. The proposed PAPR analysis is derived specifically for the NC-OFDM transmission scenario. Results show that NC-OFDM signal exhibit higher PAPR values relative to contiguous OFDM transmission at the same information rate.
Abstract-In this paper, we present the details of a portable, powerful, and flexible software-defined radio development platform called the Kansas University Agile Radio (KUAR). The primary purpose of the KUAR is to enable advanced research in the areas of wireless radio networks, dynamic spectrum access, and cognitive radios. The KUAR hardware implementation and software architecture are discussed in detail. Radio configurations and applications are presented. Future research made possible by this flexible platform is also discussed.
In this paper, we present a quantitative comparison of two agile modulation techniques employed by cognitive radio transceivers operating in a dynamic spectrum access (DSA) network. One of the modulation technique is non-contiguous orthogonal frequency division multiplexing (NC-OFDM), which is designed to avoid interference with the transmissions of incumbent users by deactivating subcarriers within their vicinity. The other modulation technique under study is a variant of multicarrier code division multiple access (MC-CDMA). Although several studies comparing conventional OFDM and MC-CDMA has been conducted in literature to justify robust error performance of MC-CDMA, a quantitative performance evaluation of these schemes has not been performed when employed in a DSA network. Due to deactivated subcarriers in DSA networks, in this paper we showed their performance can be significantly different from the conventional setup. Analytical expressions for the error probability of an NC-OFDM transceiver have been derived and compared with computer simulation results. The results show that the error robustness of NC-OFDM is relatively constant regardless of the number of deactivated subcarriers, unlike MC-CDMA transmissions, whose error performance degrades with an increase in deactivated subcarriers.
Abstract-Genetic algorithms are best suited for optimization problems involving large search spaces. The problem space encountered when optimizing the transmission parameters of an agile or cognitive radio for a given wireless environment and set of performance objectives can become prohibitively large due to the high number of parameters and their many possible values. Recent research has demonstrated that genetic algorithms are a viable implementation technique for cognitive radio engines. However, the time required for the genetic algorithms to come to a solution substantionally increases as the system complexity grows.In this paper, we present a population adaptation technique for genetic algorithms that takes advantage of the information from previous cognition cycles in order to reduce the time required to reach an optimal decision. Our simulation results demonstrate that the amount of information from the previous cognition cycle can be determined from the environmental variation factor (EVF), which represents the amount of change in the environment parameters since the previous cognition cycle.
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