Ahmed, Yahia Y. Ahmed Eldemerdash (2015) Spectrum awareness for cognitive radio systems. Doctoral (PhD) thesis, Memorial University of Newfoundland.
- Accepted Version
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Spectrum scarcity is an obstacle to deploy emerging high speed wireless services that require more frequency spectrum. Cognitive radio (CR) appears as a promising solution for the spectral congestion by allowing spectrum sharing between primary and secondary users in which optimum utilization of the available spectrum is achieved. Efficient coexistence between different users requires full knowledge of the activities in the spectrum of interest. Spectrum awareness is the terminology used to describe the techniques that detect the presence of signals in certain frequency bands, as well as identify the main parameters of such signals, e.g., modulation scheme. These two tasks are commonly referred by the terms spectrum sensing and signal identification, respectively. Blind signal identification was initially used by military applications, such as radio surveillance and electronic warfare, and has recently been extended to civilian applications. This problem becomes more challenging in multiple-input multiple-output (MIMO) scenarios due to the diverse transmission schemes that can be employed, e.g., spatial multiplexing (SM) and space-time block codes (STBCs). A large number of studies have been carried out for developing blind signal identification algorithms in single-input singleoutput (SISO) scenarios, including identification of the modulation format and recognition of single-carrier (SC) versus multicarrier transmissions. However, the problem of signal identification for MIMO systems remains at an incipient stage. In this dissertation, we develop novel algorithms to blindly identify the MIMO transmission scheme of the received signal. More specifically, in Chapters 2 and 3, we address the problem of identifying STBCs for the SC transmission. Unlike most of the work done to date, we show that STBC identification can be performed using a single receive antenna. Four algorithms are proposed in Chapter 2 to identify SM and Alamouti STBC. Then, the idea is extended to include additional STBCs in Chapter 3. The proposed algorithms show improved performance when compared with other algorithms in the literature. Moreover, neither modulation identification nor channel and noise power estimation are required by these algorithms. In Chapter 4 we investigate the identification of SM and Alamouti coded orthogonal frequency division multiplexing (OFDM) signals. A new discriminating feature and a novel decision criterion are developed. The proposed algorithm outperforms the algorithms in the literature with the advantages of requiring neither modulation identification nor channel and noise power estimation, and being more robust to the carrier frequency offset impairment. Furthermore, in Chapter 5, the problem of identifying SM and Alamouti SC frequency division multiple access (SC-FDMA) signals is studied when the receiver is equipped with a single antenna. To the best of our knowledge, this is the first work devoted to the identification of MIMO SC-FDMA signals. The theoretical performance analysis of the proposed algorithm is presented. Simulation results show the agreement between theoretical and simulation findings. The proposed algorithm requires neither modulation identification nor channel and noise power estimation. Finally, conclusions are drawn and possible extensions to signal identification in MIMO scenarios are discussed in Chapter 6.
|Item Type:||Thesis (Doctoral (PhD))|
|Additional Information:||Includes bibliographical references.|
|Keywords:||Cognitive Radio, Space-time block coding, Multiple-input multiple-output, Signal identification|
|Department(s):||Engineering and Applied Science, Faculty of|
|Library of Congress Subject Heading:||Cognitive radio networks; Radiofrequency spectroscopy; Blind source separation; MIMO systems; Orthogonal frequency division multiplexing; Frequency division multiple access|
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