This article presents an efficient quasi-optimal sum rate (SR) maximization technique based on zero-forcing water-filling (ZFWF) algorithm directly applied to cognitive radio networks (CRNs). We have defined the non-convexity nature of the optimization problem in the context of CRNs while we have offered all necessary conditions to solve the related SR maximization problem, which considers power limit at cognitive transmitter and interference levels at primary users (PUs) and secondary users (SUs).A general expression capable to determine the optimal number of users as a function of the main system parameters, namely the signal-to-interference-plus-noise ratio (SINR) and the number of BS antennas is proposed. Our numerical results for the CRN performance are analyzed in terms of both BER and sum-capacity for the proposed ZF-WF precoding technique, and compared to the classical minimum mean square error (MMSE), corroborating the effectiveness of the proposed technique operating in multi user multiple input single output (MU-MISO) CRNs.

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Channel estimation techniques applied to cognitive radio networks (CRN) are analyzed for simultaneously primary and secondary channel estimations operating in underlay cognitive radio networks (uCRN). A complete base-band transmission including pilot sequence transmission, channel matrix estimation and optimal precoder matrix generation based on imperfect channel estimation are described. Also, the effect of imperfect channel estimation has been studied to provide means of developing techniques to overcome problems while enhancing the MIMO communication performance.

In cognitive radio (CR), the sensed aggregate bandwidth could be as large as several GHz. This is especially challenging if the bandwidths and central frequencies of the sensed signals are unknown and need to be detected. This work discusses a new method for multi-band spectrum-sensing based in edge detection. The proposed method uses a Welch power-spectrum-density (PSd) estimate and a multi-scale Wavelet approach to reveal the spectrum transition (edges), which is deployed to characterize the spectrum occupancy in CR scenarios where the operating frequency limits of the primary users are unknown. The focus in this work is to improve the performance of the multiband spectrum sensor by refining the edge location and error correcting misleading detections. In order to do so, a comprehensive analytical description and numerical analysis have been carried out by focusing on orthogonal-frequencydivision-multiplexing (OFDM) signals. Also, numerical results corroborate and give support to the effectiveness of the proposed multiband spectrum sensing method.

Cognitive radio networks (CRN) are constantly in need for new technologies able to improve their performance. Hence, study of new spectrum sensing (SS) techniques and devices is extremely important for a development of more accurate and sensible devices. The Hadamard ratio-based spectrum sensor (HrS) is a robust method able to accurately sense the presence of a wireless signal by applying a statistic test based on maximumlikelihood (ML) of collected signal data. A further performance analysis of HrS techniques under realistic MIMO (Multiple-Input and Multiple-Output) fading channels is the contribution of this work. As a result, simulations aim to demonstrate its efficiency and how applicable would be a HrS procedure when inserted in real non-line-of-sight (NLOS) MIMO channel scenarios.