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

Spectrum Sensing and Resource Allocation for Multicarrier Cognitive Radio Systems Under Interference and Power Constraints

http://asp.eurasipjournals.com/content/2014/1/68International audienceMulticarrier waveforms have been commonly recognized as strong candidates for cognitive radio. In this paper, we study the dynamics of spectrum sensing and spectrum allocation functions in cognitive radio context using very practical signal models for the primary users (PUs), including the effects of power amplifier nonlinearities. We start by sensing the spectrum with energy detection-based wideband multichannel spectrum sensing algorithm and continue by investigating optimal resource allocation methods. Along the way, we examine the effects of spectral regrowth due to the inevitable power amplifier nonlinearities of the PU transmitters. The signal model includes frequency selective block-fading channel models for both secondary and primary transmissions. Filter bank-based wideband spectrum sensing techniques are applied for detecting spectral holes and filter bank-based multicarrier (FBMC) modulation is selected for transmission as an alternative multicarrier waveform to avoid the disadvantage of limited spectral containment of orthogonal frequency-division multiplexing (OFDM)-based multicarrier systems. The optimization technique used for the resource allocation approach considered in this study utilizes the information obtained through spectrum sensing and knowledge of spectrum leakage effects of the underlying waveforms, including a practical power amplifier model for the PU transmitter. This study utilizes a computationally efficient algorithm to maximize the SU link capacity with power and interference constraints. It is seen that the SU transmission capacity depends critically on the spectral containment of the PU waveform, and these effects are quantified in a case study using an 802.11-g WLAN scenario

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

Effects of RF Imperfections on Interference Rejection Combining Based Black-Space Cognitive Radio

In this paper, we investigate the effects of RF transceiver's imperfections on the multi-antenna interference rejection combing (IRC) based black-space cognitive radio (BS-CR) operation. In particular, we explore the effects of power amplifier (PA) nonlinearities and carrier frequency offset (CFO) on the blind IRC technique. The BS-CR operation mode supports effective reuse of the primary user (PU) spectrum, especially for relatively short-distance CR communication. We assume that both the PU system and the BS-CR use orthogonal frequency division multiplexing (OFDM) waveforms with common numerology. In this case the PU interference on the BS-CR signal is strictly flat-fading at subcarrier level, and it can be suppressed using subcarrier-wise IRC processing. Spatial sample covariance matrix-based IRC adaptation is applied during silent gaps in CR operation. We propose an analytical framework for modeling CFO effects, together with experimental study of CFO and PA nonlinearity effects. The performance of the IRC scheme is tested considering terrestrial digital TV broadcasting (DVB-T) as the primary service. The validity of the offered expressions for CFO effects are justified through comparisons with respective results from computer simulations. The effect of CFO between the primary and secondary systems is found to be critical for BS-CR operation, while the effect of CR transmitter's nonlinearity is no worse than in basic OFDM schemes, and the PU transmitter's nonlinearity has minor effect on BS-CR operation.acceptedVersionPeer reviewe

Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

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. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.publishedVersionPeer reviewe

Novel extended modified twin test based sensing for cooperative communication under noise uncertainty

With the evolution of 5G wireless communication systems, we have witnessed an increase in the demand for wireless broadband applications and services. However, fixed allocation of the frequency spectrum has led to an under-utilization of the spectral resources, making it hard to find unoccupied bands to deploy new services. To address the spectrum scarcity problem, a new and promising technology has emerged, namely cognitive radio. In particular, centralized cooperative spectrum sensing (CSS) is becoming an effective strategy to discover unused frequency bands, since it allows to overcome issues related to shadowing and noise uncertainty. Here, we propose an extension of the modified twin test based on a double test that takes into account correlated observations in a real communication scenario, considering different noise uncertainty values. In particular, our test employs two fusion rules together (i.e. OR and Majority), salvaging those detection cases that would otherwise go undetected due to the noise uncertainty. The obtained results show that the proposed method outperforms the conventional CSS and modified twin test, highlighting its robustness in the presence of noise uncertainty

Sparse Frequency Domain Spectrum Sensing and Sharing Based on Cyclic Prefix Autocorrelation

Cognitive radio (CR) is considered an important solution to the current spectral scarcity, which is expected to be a significant issue in the next generation of wireless communication systems, namely 5G. Wideband spectrum sharing and sensing constitute highly desirable features of CR systems as they aim to increase the probability of identifying available spectral bands, which ensures a more efficient resource utilization. The present work proposes an efficient frequency-domain cyclic prefix (CP) autocorrelation based wideband spectrum sensing and sharing method that can provide accurate detection of orthogonal frequency-division multiplexing (OFDM) based primaries in wideband CR systems. Novel analytic expressions are derived for the corresponding threshold, probability of false alarm and probability of detection in the presence of noise uncertainty (NU) and frequency selectivity. The derived models are validated by extensive comparisons with respective results from computer simulations. It is demonstrated that the introduced autocorrelation based sensing method is able to counteract NU and the frequency-selective multipath channel effects in realistic wideband communication scenarios. Furthermore, the method facilitates partial band sensing, allowing the sensing of weak OFDM-type primary user (PU) signals in channels which are partly overlapped by other strong PU or CR transmissions. This is considered a crucial element in practical spectrum sharing scenarios. Since, the proposed sensing method makes use of sparsity in the spectral domain, it can be technically considered as compressed sensing method. The flexibility of this approach supports robust wideband multi-mode, multi-channel sensing with low complexity. Finally, it is shown that the offered results are particularly useful in the context of spectrum sharing as their high performance and reduced complexity can enable the co-existence of non-exhaustive yet highly efficient algorithms.acceptedVersionPeer reviewe

Behavioral Modeling of Power Amplifiers with Modern Machine Learning Techniques

In this study, modern machine learning (ML) methods are proposed to predict the dynamic non-linear behavior of wideband RF power amplifiers (PAs). Neural networks, k-nearest neighbor, and several tree-based ML algorithms are first adapted to handle complex-valued signals and then applied to the PA modeling problem. Their modeling performance is evaluated with measured data from two base station PAs. Gradient boosting is seen to outperform the other ML approaches and to give comparable performance to the generalized memory polynomial (GMP) reference model in terms of both the normalized mean squared error (NMSE) and adjacent channel error power ratio (ACEPR). This is the first study in the open literature to consider modern ML approaches, besides neural networks, for PA behavioral modeling.acceptedVersionPeer reviewe