Joint Detection and Classification of the OFDM-based Mobile WiMAX and LTE Signals for Cognitive Radio.

摘要

Spectrum awareness is one of the most challenging requirements in cognitive radio (CR). To adequately adapt to the changing radio environment, it is necessary for the CR to be able to perform joint detection and classification of low signal-to-noise ratio (SNR) signals. The wireless industry has recently shown great interest in orthogonal frequency division multiplexing (OFDM) technology, due to advantages, such as efficient use of the spectrum, resistance to frequency selective fading, and elimination of intersymbol interference. As such, joint detection and classification of OFDM signals has been intensively researched recently.;In this thesis, the second-order cyclostationarity of the OFDM-based mobile WiMAX and LTE signals is studied, and closed-from expressions for the cyclic autocorrelation function (CAF) and cyclic frequencies (CFs) of both signals are derived. Furthermore, two cyclostationarity-based algorithms for joint detection and classification of these signals are developed, and the joint detection and classification performance, as well as the complexity of the proposed algorithms are investigated. Simulation results show the efficiency of the proposed algorithms under low SNRs, short sensing times, and diverse channel conditions.;The existing techniques for joint detection and classification of OFDM signals either involve complex feature recognition procedures or introduce new overheads by creating features in the signals for detection and classification purposes. As such, the OFDM standard signals should be investigated and existing features should be exploited for their joint detection and classification. The cyclostationarity of OFDM signals in two of the most popular wireless communications standards, namely, mobile Worldwide Interoperability for Microwave Access (WiMAX) and third Generation Partnership Project Long Term Evolution (3GPP LTE), is studied here for the purpose of their joint detection and classification.