Publication: Spectrum Sensing and Interference Mitigation in Cognitive Radio Networks 

Authors:
Gong, X.
Ph. D. Dissertation
 
School:
RWTH Aachen University
Adress:
Institute for Integrated Signal Processing Systems
Date:
Feb. 2014
Language:
English

Abstract

One concept to increase spectral efficiency is dynamic spectrum access (DSA). The secondary users coexist with the primary users in the same radio-frequency spectrum. Such shared usage demands for an interference avoidance or interference mitigation at the secondary users. This is especially challenging due to the limited cooperation between the primary users (PUs) and the secondary users (SUs). Motivated by this fact, the focus of this thesis is a comprehensive study on interference management strategies and the characterization of the achievable performance of secondary systems. Spectrum sensing aims at detecting the presence or absence of the PUs. The main challenge encountered is the high requirement on sensitivity, reliability, and agility, especially in case of incomplete knowledge of the transmission channels. Therefore, the SUs need to efficiently utilize the limited a priori knowledge related to the primary transmission to improve the sensing performance. In this thesis, the generalized likelihood ratio test framework is applied to cooperative sensing problems with an unknown structure of the primary signal space and unknown noise variances at the SUs. The efficiency of the resulting spectrum sensing algorithms is demonstrated as well as the effectiveness in countering the “hidden primary user” problem. Based on limited knowledge related to the primary transmission, the SUs’ transceiver strategies are optimized in order to achieve the tradeoff between improved secondary network throughput and, most critically, constrain the performance loss of the primary transmission. For a single-antenna spectrum sharing system, the power allocation strategies are investigated for the SUs subject to different quality of service constraints on the primary link. Not only optimal and low-complexity near-optimal power allocation strategies are developed, but also the achievable performance of the system is approximately evaluated in closed form. Additionally, for multi-antenna spectrum sharing networks, efficient transceiver optimization strategies are developed under the consideration of imperfect channel state information. The robustness, optimality, and convergence behavior of the different proposed algorithms are quantitatively verified and compared. The essential “cognitive” property of DSA in cognitive radio networks consists of two aspects: the acquirement of the useful information from the environment and the utilization of such information to improve the spectrum efficiency. This is demonstrated with the study of a hybrid paradigm, in which the SU exploits the spectrum sensing and location information to adapt the transmit power level. Compared to the standard paradigms, e.g., opportunistic transmission and spectrum sharing without sensing, the proposed strategies in the hybrid paradigm achieve better performance.

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