Experimental Evaluation of Spectrum Sensing Algorithms for Wireless Microphone Signal

Spectrum congestion has become a critical concern in wireless communication systems due to the limited availability of frequency spectrum. Hence, efficient utilization of spectrum is one of the most important challenges in the evolution of wireless communi-cation systems and radio devices. Cognitive radio (CR) has been introduced as an effec-tive solution for spectrum utilization. Spectrum sensing (SS) is one of the key elements in the implementation of effective and reliable CR systems. SS algorithms are used to obtain awareness about the spectrum usage and existence of primary users in a certain spectrum band. Energy detection (ED) based SS is the most common sensing algorithm due to its low computation and implementation complexity. On the other hand, ED based SS is highly dependent on the precise knowledge of the receiver noise variance. Hence, the performance of the ED algorithm is degraded significantly, when there is uncertainty in the estimation of the noise variance. In this thesis, the wireless microphone (WM) system using the CR concept is intro-duced and the sensing performance of WM signals using three different algorithms are studied. The considered algorithms are based on the ED, namely fast Fourier transform (FFT) based ED, analysis filter bank (AFB) based ED and maximum-minimum ED (Max-Min ED) are studied. Following the analytical models and scenarios of energy detector based SS algorithms, the sensing algorithms are implemented using National Instruments’ (NI) Universal Software Radio Peripheral (USRP) and the NI-LabVIEW software platform, together with the necessary toolboxes. This prototype implementa-tion provides reliable performance evaluation of these spectrum sensing approaches us-ing real world receiver implementation and communication signals from a signal genera-tor, as well as actual WM signals. The results of this study suggest that the performance of Max-Min ED is more robust than FFT & AFB based ED under realistic noise vari-ance uncertainty

Enhanced Spectrum Sensing Techniques for Cognitive Radio Systems

Due to the rapid growth of new wireless communication services and applications, much attention has been directed to frequency spectrum resources. Considering the limited radio spectrum, supporting the demand for higher capacity and higher data rates is a challenging task that requires innovative technologies capable of providing new ways of exploiting the available radio spectrum. Cognitive radio (CR), which is among the core prominent technologies for the next generation of wireless communication systems, has received increasing attention and is considered a promising solution to the spectral crowding problem by introducing the notion of opportunistic spectrum usage. Spectrum sensing, which enables CRs to identify spectral holes, is a critical component in CR technology. Furthermore, improving the efficiency of the radio spectrum use through spectrum sensing and dynamic spectrum access (DSA) is one of the emerging trends. In this thesis, we focus on enhanced spectrum sensing techniques that provide performance gains with reduced computational complexity for realistic waveforms considering radio frequency (RF) impairments, such as noise uncertainty and power amplifier (PA) non-linearities. The first area of study is efficient energy detection (ED) methods for spectrum sensing under non-flat spectral characteristics, which deals with relatively simple methods for improving the detection performance. In realistic communication scenarios, the spectrum of the primary user (PU) is non-flat due to non-ideal frequency responses of the devices and frequency selective channel conditions. Weighting process with fast Fourier transform (FFT) and analysis filter bank (AFB) based multi-band sensing techniques are proposed for overcoming the challenge of non-flat characteristics. Furthermore, a sliding window based spectrum sensing approach is addressed to detect a re-appearing PU that is absent in one time and present in other time. Finally, the area under the receiver operating characteristics curve (AUC) is considered as a single-parameter performance metric and is derived for all the considered scenarios. The second area of study is reduced complexity energy and eigenvalue based spectrum sensing techniques utilizing frequency selectivity. More specifically, novel spectrum sensing techniques, which have relatively low computational complexity and are capable of providing accurate and robust performance in low signal-to-noise ratio (SNR) with noise uncertainty, as well as in the presence of frequency selectivity, are proposed. Closed-form expressions are derived for the corresponding probability of false alarm and probability of detection under frequency selectivity due the primary signal spectrum and/or the transmission channel. The offered results indicate that the proposed methods provide quite significant saving in complexity, e.g., 78% reduction in the studied example case, whereas their detection performance is improved both in the low SNR and under noise uncertainty. Finally, a new combined spectrum sensing and resource allocation approach for multicarrier radio systems is proposed. The main contribution of this study is the evaluation of the CR performance when using wideband spectrum sensing methods in combination with water-filling and power interference (PI) based resource allocation algorithms in realistic CR scenarios. Different waveforms, such as cyclic prefix based orthogonal frequency division multiplexing (CP-OFDM), enhanced orthogonal frequency division multiplexing (E-OFDM) and filter bank based multicarrier (FBMC), are considered with PA nonlinearity type RF impairments to see the effects of spectral leakage on the spectrum sensing and resource allocation performance. It is shown that AFB based spectrum sensing techniques and FBMC waveforms with excellent spectral containment properties have clearly better performance compared to the traditional FFT based spectrum sensing techniques with the CP-OFDM. Overall, the investigations in this thesis provide novel spectrum sensing techniques for overcoming the challenge of noise uncertainty with reduced computational complexity. The proposed methods are evaluated under realistic signal models

Subband based Cooperative Spectrum Sensing

Wireless communication technology with traditional rigid spectrum allocations and low scalability is wasting lots of spectral resources. Spectral congestion is becoming critical with heavily increasing utilization of wireless communications technology. Cognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. The studies of this thesis focus on energy detection (ED) based semi-blind sensing schemes. ED based sensing only requires the knowledge of noise variance, which can be obtained according to the previous noise measurements. To counteract the practical wireless channel effects, collaborative approach of PU signal estimation i.e., cooperative spectrum sensing (CSS) techniques are investigated. CSS eliminates the problems of both hidden nodes and fading multipath channels. Additionally, subband based CSS scheme will be developed. Fast Fourier transform (FFT) and analysis filter bank (AFB) based receiver side processing methods are used. Subband energies are then processed for ED based CSS methods. The studies show that filter bank based multicarrier (FBMC) waveform with better spectral containment improves the performance significantly. Additionally, cooperative maximum-minimum energy detection (Max-Min ED) method is proposed. The proposed method is immune to the noise uncertainty effects, which is a critical issue in traditional ED based spectrum sensing. Cooperative maximum-minumum energy detection (Max-Min ED) shows better spectrum sensing performance compared with traditional CSS schemes under noise uncertainty conditions. Overall, the thesis contributes to better understanding and handling of subband based CSS in CR system. The proposed novel cooperative Max-Min ED greatly reduces the complexity compared to existing techniques which are robust to the noise uncertainty effects. These contributions are expected to provide a useful tool for the design and implementation of flexible, efficient, and simple spectrum sensing mechanism for CR technology

Frequency Domain Autocorrelation Based Compressed Spectrum Sensing for Cognitive Radio

As wireless applications are growing rapidly in the modern world, this results in the shortage of radio spectrum due to the fixed allocation of spectrum by governmental agencies for different wireless technologies. This problem raises interest to utilize spectrum in a more efficient way, in order to provide spectrum access to other users when they need it. In wireless communications systems, cognitive radio (CR) is getting much attention due to its capability to combat with this scarcity problem. A CR senses the available spectrum band to check the activity of primary users (PU). It utilizes the unused spectral resources by providing access to secondary users (SU). Spectrum sensing (SS) is one of the most critical issues in cognitive radio, and there are various SS methods for the detection of PU signals. An energy detector (ED) based SS is the most common sensing method due to its simple implementation and low computational complexity. This method works well in ideal scenarios but its detection performance for PU signal degrades drastically under low SNR values in the presence of noise uncertainty. Eigenvalue-based SS method performs well with such real-life issues, but it has very high computational complexity. This raises a demand for such a detector which has less computational complexity and can perform well in practical wireless multipath channels as well as under noise uncertainty. This study focuses on a novel variant of autocorrelation detector operating in the frequency domain (FD-AC). The method is applicable to PUs using the OFDM waveform with the cyclic prefix (CP). The FD-AC method utilizes fast Fourier transform (FFT) and detects an active PU through the CP-induced correlation peak estimated from the FFT-domain samples. It detects the spectral holes in the available electromagnetic spectrum resources in an efficient way, in order to provide opportunistic access to SUs. The proposed method is also insensitive to the practical wireless channel effects. Hence, it works well in frequency selective channels. It also has the capability to mitigate the effects of noise uncertainty and therefore, it is robust to noise uncertainty. FD-AC facilitates partial band sensing which can be considered as a compressed spectrum sensing method. This allows sensing weak PU signals which are partly overlapped by other strong PU or CR transmissions. On the other hand, it helps in the reduction of computational complexity while sensing PU signal in the available spectrum band, depending on the targeted sensitivity. Moreover, it has highly increased flexibility and it is capable of facilitating robust wideband multi-mode sensing with low complexity. Its performance for the detection of PU signal does not depend on the known time lag, therefore, it can perform well in such conditions where the detailed OFDM signal characteristics are not known