Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> A. Hardware Requirements <s> Spectrum sensing technology is most vital to the implementation of a CR system using dynamic spectrum resource management. This paper suggested a CR system architecture with a wideband dual-stage spectrum sensing technique - a coarse and a fine spectrum sensing. Specifically, the coarse spectrum sensing technique adopted wavelet transforms in this architecture to provide a multi-resolution spectrum sensing (MRSS) feature. Analog implementation of the MRSS block offers wideband, low-power, and real-time operation. From the system simulation results, MRSS achieved 15-, 20-, and 30-dB detection margin for FM, VSB, and OFDM signals, having the corresponding signal power of -110, -120, and -120 dBm, respectively. <s> BIB001 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> A. Hardware Requirements <s> Noise variance and signal-to-noise ratio are important parameters for adaptive orthogonal frequency-division multiplexing (OFDM) systems since they serve as a standard measure of signal quality. Conventional algorithms assume that the noise statistics remain constant over the OFDM frequency band and, thereby, average the instantaneous noise samples to get a single estimate. In reality, noise is often made up of white Gaussian noise, along with correlated colored noise that unevenly affects the OFDM spectrum. This paper proposes a minimum mean square error (MMSE) filtering technique to estimate the noise power that takes into account the variation of the noise statistics across the OFDM subcarrier index, as well as across OFDM symbols. The proposed method provides many local estimates that allow tracking of the variation of noise statistics in frequency and time. The MMSE filter coefficients are obtained from the mean-squared-error expression, which can be calculated using the noise statistics. Evaluation of the performance with computer simulations shows that the proposed method tracks the local statistics of the noise more efficiently than conventional methods. <s> BIB002 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> A. Hardware Requirements <s> The spectrum sensing problem has gained new aspects with cognitive radio and opportunistic spectrum access concepts. It is one of the most challenging issues in cognitive radio systems. In this paper, a survey of spectrum sensing methodologies for cognitive radio is presented. Various aspects of spectrum sensing problem are studied from a cognitive radio perspective and multi-dimensional spectrum sensing concept is introduced. Challenges associated with spectrum sensing are given and enabling spectrum sensing methods are reviewed. The paper explains the cooperative sensing concept and its various forms. External sensing algorithms and other alternative sensing methods are discussed. Furthermore, statistical modeling of network traffic and utilization of these models for prediction of primary user behavior is studied. Finally, sensing features of some current wireless standards are given. <s> BIB003 </s> Spectrum Sensing Methodologies for Cognitive Radio Systems: A Review <s> A. Hardware Requirements <s> Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization in next generation cellular networks. A crucial requirement for future cognitive radio networks is wideband spectrum sensing: secondary users reliably detect spectral opportunities across a wide frequency range. In this article, various wideband spectrum sensing algorithms are presented, together with a discussion of the pros and cons of each algorithm and the challenging issues. Special attention is paid to the use of sub-Nyquist techniques, including compressive sensing and multichannel sub- Nyquist sampling techniques. <s> BIB004
Applications for spectrum sensing in cognitive radio needs high sampling rate, high resolution analog to digital converter (ADC) with large dynamic range and high speed signal processors. Noise variance estimation technique has been popularly used for optimal receiver BIB004 . Designs such as, channel estimation, soft information etc., as well as improved handoff, power control and channel allocation techniques BIB002 . The noise and estimation challenges are easier for these purposes as receivers are tuned to receive signals that are transmitted over a desired bandwidth. However, receivers have the ability to process the narrowband baseband signals with reasonably low complexity and low power processors. Cognitive radio terminals are then required to process transmission over a wideband for utilizing any opportunity BIB003 . Hence, a cognitive radio should be able to capture and analyze a relatively larger band and utilize any spectrum opportunities. These large operating bandwidths create additional requirements on the radio frequencies (RF) components such as antennas and power amplifiers. Hence high speed processing units are needed for performing computationally demanding signal processing task with 14 | P a g e www.ijacsa.thesai.org relatively low delay. Spectrum sensing can be performed through two different architectures, such as single radio and dual radio. In the case of the single radio, only one specific time slot is assigned for spectrum sensing. Due to the limitation in sensing time, only certain accuracy can be guaranteed for spectrum sensing result. However, the spectrum sensing efficiency is decreased as some part of the available time slot is used for sensing instead of data transmission. The merit of using the single radio architecture is because of its simplicity and low cost of implementation. However, in the dual radio architecture, one radio chain is allocated for data transmission and reception while the other chain is allocated for spectrum monitoring BIB001 . The limitations of such approach is that it increases power consumption and hardware cost. In practice, there are already available hardware and software platforms for cognitive radio such as GNU Radio, Universal Software Radio Peripheral and shared spectrum's XG Radio. Energy detector based sensing is mainly used in this platform because of its simplicity.