Energy Detector Based Spectrum Sensing Performance Analysis over Fading Environment

239-244Energy detection approach for sensing of spectrum is an extremely effective method of detection in comparison of other spectrum sensing methods when secondary user lacks adequate knowledge of primary user's channel conditions. Because of multipath propagation and shadowing effects, performance of energy detector employed in a cognitive radio system is severely influenced. In this paper, we have evaluated performance of energy detector over fading environment. Hypothesis testing was utilized for spectrum sensing to find out whether the primary user's signal was available or missing. Performance assessment for spectrum sensing using the energy detector was carried out primarily on the basis of probability of false alarm and probability of detection. We have examined the impact of SNR on probability of detection in order to assess the performance of spectrum sensing using energy detector. Also the receiver operating characteristic curve was plotted for performance analysis of spectrum sensing employing energy detector. In addition, we also examined the impact of threshold value on the probability of the false alarm. We have found that probability of detection improves when we increase the value of signal to noise ratio and use more number of samples. We have also observed that false alarm probability decreases when we increase the threshold value

Energy Detector Based Spectrum Sensing Performance Analysis over Fading Environment

Cognitive radio is a new concept of wireless communication that offers increased usage of the limited spectral resource and is considered to be a revolutionary technology that will influence how radio spectrum is accessed, accessed and controlled in the future. Spectrum sensing is needed to allow optimal use of spectral resource. Secondary user performs spectrum sensing to recognize the transmission possibilities. Secondary users have lower priority when using spectrum, so a basic principle is that secondary users should avoid / minimize interference with primary users. We seek to identify the transmission from primary users for the spectrum sensing. Detection of the primary transmitter assists in the recognition of the spectrum it uses. Utilizing spectrum sensing approach, secondary user starts communication if it detects a weak signal or white space. Because of multipath propagation and shadowing effects, primary transmitter's detection is severely influenced. There are numerous spectrum sensing mechanisms and one of them is energy detection approach.  In this paper, we have examined the impact of SNR on probability of detection in order to assess the performance of spectrum sensing using energy detector. Also the receiver operating characteristic curve was plotted for performance analysis of spectrum sensing employing energy detector. In addition, we also examined the impact of threshold value on the probability of the false alarm

Amateur radio sensing technique using a combination of energy detection and waveform classification

A critical problem in spectrum sensing is to create a detection algorithm and test statistics. The existing approaches employ the energy level of each channel of interest. However, this feature cannot accurately characterize the actual application of public amateur radio. The transmitted signal is not continuous and may consist only of a carrier frequency without information. This paper proposes a novel energy detection and waveform feature classification (EDWC) algorithm to detect speech signals in public frequency bands based on energy detection and supervised machine learning. The energy level, descriptive statistics, and spectral measurements of radio channels are treated as feature vectors and classifiers to determine whether the signal is speech or noise. The algorithm is validated using actual frequency modulation (FM) broadcasting and public amateur signals. The proposed EDWC algorithm's performance is evaluated in terms of training duration, classification time, and receiver operating characteristic. The simulation and experimental outcomes show that the EDWC can distinguish and classify waveform characteristics for spectrum sensing purposes, particularly for the public amateur use case. The novel technical results can detect and classify public radio frequency signals as voice signals for speech communication or just noise, which is essential and can be applied in security aspects

Entropy and Energy Detection-based Spectrum Sensing over F Composite Fading Channels

In this paper, we investigate the performance of energy detection-based spectrum sensing over F composite fading channels. To this end, an analytical expression for the average detection probability is firstly derived. This expression is then extended to account for collaborative spectrum sensing, square-law selection diversity reception and noise power uncertainty. The corresponding receiver operating characteristics (ROC) are analyzed for different conditions of the average signal-to-noise ratio (SNR), noise power uncertainty, time-bandwidth product, multipath fading, shadowing, number of diversity branches and number of collaborating users. It is shown that the energy detection performance is sensitive to the severity of the multipath fading and amount of shadowing, whereby even small variations in either of these physical phenomena can significantly impact the detection probability. As a figure of merit to evaluate the detection performance, the area under the ROC curve (AUC) is derived and evaluated for different multipath fading and shadowing conditions. Closed-form expressions for the Shannon entropy and cross entropy are also formulated and assessed for different average SNR, multipath fading and shadowing conditions. Then the relationship between the Shannon entropy and ROC/AUC is examined where it is found that the average number of bits required for encoding a signal becomes small (i.e., low Shannon entropy) when the detection probability is high or when the AUC is large. The difference between composite and traditional small-scale fading is emphasized by comparing the cross entropy for Rayleigh and Nakagami-m fading. A validation of the analytical results is provided through a careful comparison with the results of some simulations.Comment: 30 pages, 11 figures, 1 table, Submitted to IEEE TCO

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

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

Multiantenna Interference Mitigation Schemes and Resource Allocation for Cognitive Radio

Maximum and efficient utilization of available resources has been a central theme of research on various areas of science and engineering. Wireless communication is not an exception to this. With the rapid growth of wireless communication applications, radio frequency spectrum has become a valuable commodity. Supporting very high demands for data rate and throughput has become a challenging problem which requires innovative solutions. Dynamic spectrum sharing (DSS) based cognitive radio (CR) is envisioned as a promising technology for future wireless communication systems, such as fifth generation (5G) further development and sixth generation (6G). Extensive research has been done in the areas of CRs and it is considered to mitigate 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 the first part of this thesis, we focus on enhancing the spectrum usage of CR’s using interference cancellation methods that provides considerable performance gains with realistic computational complexity, especially, in the context of the widely used multicarrier waveforms. The primary focus is on interference rejection combining (IRC) methods, applied to the black-space cognitive radio (BS-CR). Earlier studies on the BS-CR in the literature were focused on using CRs as repeaters for the primary transmitter to guarantee that the CR is not causing significant interference to nearby primary users’ receivers. This kind of approaches are transmitter-centric in nature. In this thesis, receiver-centric approaches such as multi-antenna diversity combining, especially enhanced IRC methods, are considered and evaluated. IRC methods have been widely studied and adopted in several practical wireless communication systems. We focus on developing such BS-CR schemes under strong interference conditions, which has not been studied in the CR literature so far. Spatial covariance matrix estimation under mobility and high carrier frequencies is found to be the most critical part of such scheme. Algorithms and methods to mitigate these effects are developed in this thesis and they are evaluated under realistic BS-CR receiver operating conditions. We use sample covariance estimation approach with silent gaps in the CR transmisison. Covariance interpolation between silent gaps improves greatly the robustness with time-varying channels. Good link performance can be reached with low mobility at carrier frequency considered for the TV white-spaced case. The proposed BS-CR scheme could be feasible at below 6 GHz frequencies with pedestrian mobilities. The second part of this thesis investigates the effect of radio frequency (RF) impairments on the performance of the cognitive wireless communication. There are various unavoidable imperfections, mainly due to the limitations of analog high-frequency transmitter and receiver circuits. These imperfections include power amplifier (PA) non-linearities, receiver nonlinearities, and carrier frequency offset (CFO), which are considered in this study. These effects lead to significant signal distortion and, as a result of this, the wireless link quality may deteriorate. In multicarrier communications such signal distortions may lead to additional interference, and it is important to evaluate their effects on spectrum sensing quality and on the performance of the proposed BS-CR scheme. This part of the thesis provides critical analysis and insights into such issues caused by RF imperfections and demonstrates the need for designing proper compensation techniques required to avoid/reduce such degradations. It is found that the transmitter’s PA nonlinearities affect in the same way as in basic OFDM systems and BS-CR receiver’s linearity requirements are similar to those for advanced DSP-intensive software defined radios. The CR receiver’s CFO with respect to the PU has the most critical effect. However, synchronizing the CR with the needed high accuracy is considered achievable due to the PU signal’s high-power level. The final part of the thesis briefly looks at alternate waveforms and techniques that can be used in CRs. The filter bank multicarrier (FBMC) waveforms are considered as an alternative to the widely used OFDM schemes. Here the core idea is interference avoidance, targeting to reduce the interference leakage between CRs and the primary systems, by means of using a waveform with good spectrum localization properties. FBMC system’s performance is compared with OFDM based system in the context of CRs. The performance is compared from a combined spectrum sensing and resource allocation point of view through simulations. It is found that well-localized CR waveforms improve the CR link capacity, but with poorly localized primary signals, these possibilities are rather limited

Advanced RFI detection, RFI excision, and spectrum sensing : algorithms and performance analyses

Because of intentional and unintentional man-made interference, radio frequency interference (RFI) is causing performance loss in various radio frequency operating systems such as microwave radiometry, radio astronomy, satellite communications, ultra-wideband communications, radar, and cognitive radio. To overcome the impact of RFI, a robust RFI detection coupled with an efficient RFI excision are, thus, needed. Amongst their limitations, the existing techniques tend to be computationally complex and render inefficient RFI excision. On the other hand, the state-of-the-art on cognitive radio (CR) encompasses numerous spectrum sensing techniques. However, most of the existing techniques either rely on the availability of the channel state information (CSI) or the primary signal characteristics. Motivated by the highlighted limitations, this Ph.D. dissertation presents research investigations and results grouped into three themes: advanced RFI detection, advanced RFI excision, and advanced spectrum sensing. Regarding advanced RFI detection, this dissertation presents five RFI detectors: a power detector (PD), an energy detector (ED), an eigenvalue detector (EvD), a matrix-based detector, and a tensor-based detector. First, a computationally simple PD is investigated to detect a brodband RFI. By assuming Nakagami-m fading channels, exact closed-form expressions for the probabilities of RFI detection and of false alarm are derived and validated via simulations. Simulations also demonstrate that PD outperforms kurtosis detector (KD). Second, an ED is investigated for RFI detection in wireless communication systems. Its average probability of RFI detection is studied and approximated, and asymptotic closed-form expressions are derived. Besides, an exact closed-form expression for its average probability of false alarm is derived. Monte-Carlo simulations validate the derived analytical expressions and corroborate that the investigated ED outperforms KD and a generalized likelihood ratio test (GLRT) detector. The performance of ED is also assessed using real-world RFI contaminated data. Third, a blind EvD is proposed for single-input multiple-output (SIMO) systems that may suffer from RFI. To characterize the performance of EvD, performance closed-form expressions valid for infinitely huge samples are derived and validated through simulations. Simulations also corroborate that EvD manifests, even under sample starved settings, a comparable detection performance with a GLRT detector fed with the knowledge of the signal of interest (SOI) channel and a matched subspace detector fed with the SOI and RFI channels. At last, for a robust detection of RFI received through a multi-path fading channel, this dissertation presents matrix-based and tensor-based multi-antenna RFI detectors while introducing a tensor-based hypothesis testing framework. To characterize the performance of these detectors, performance analyses have been pursued. Simulations assess the performance of the proposed detectors and validate the derived asymptotic characterizations. Concerning advanced RFI excision, this dissertation introduces a multi-linear algebra framework to the multi-interferer RFI (MI-RFI) excision research by proposing a multi-linear subspace estimation and projection (MLSEP) algorithm for SIMO systems. Having employed smoothed observation windows, a smoothed MLSEP (s-MLSEP) algorithm is also proposed. MLSEP and s-MLSEP require the knowledge of the number of interferers and their respective channel order. Accordingly, a novel smoothed matrix-based joint number of interferers and channel order enumerator is proposed. Performance analyses corroborate that both MLSEP and s-MLSEP can excise all interferers when the perturbations get infinitesimally small. For such perturbations, the analyses also attest that s-MLSEP exhibits a faster convergence to a zero excision error than MLSEP which, in turn, converges faster than a subspace projection algorithm. Despite its slight complexity, simulations and performance assessment on real-world data demonstrate that MLSEP outperforms projection-based RFI excision algorithms. Simulations also corroborate that s-MLSEP outperforms MLSEP as the smoothing factor gets smaller. With regard to advanced spectrum sensing, having been inspired by an F–test detector with a simple analytical false alarm threshold expression considered an alternative to the existing blind detectors, this dissertation presents and evaluates simple F–test based spectrum sensing techniques that do not require the knowledge of CSI for multi-antenna CRs. Exact and asymptotic analytical performance closed-form expressions are derived for the presented detectors. Simulations assess the performance of the presented detectors and validate the derived expressions. For an additive noise exhibiting the same variance across multiple-antenna frontends, simulations also corroborate that the presented detectors are constant false alarm rate detectors which are also robust against noise uncertainty