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.

Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy counting, we consider the use of Wi-Fi probe requests that are continuously transmitted from Wi-Fi enabled smart devices for discovering nearby access points. To this end, Wi-Fi Pineapple equipment are used for passively capturing ambient probe requests from Wi-Fi devices, such as smart phones and tablets, where no connectivity to a Wi-Fi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multimodel (IMM) Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using Wi-Fi data collected at a university building show that the use of Wi-Fi probe requests in conjunction with IMM-based Kalman filters can be a viable solution for zone-level occupancy monitoring in smart buildings.

Millimeter-wave (mmWave) communications, along with massive multiple-input multiple-output (MIMO) systems, are recently being considered extensively as enabling technologies for emerging 5G cellular networks. The existing literature shows that the capacity in massive MIMO systems is limited by the pilot contamination effect, caused by the reuse of pilot sequences during the uplink channel estimation stage. It has also been proved that the pilot contamination effect increases with the angular spread (AS) of the multipath clusters. In this work, we study the relation between the AS and the capacity of a cellular network, where we show that there exists an optimal AS that maximizes the capacity. Furthermore, we observe that the optimal AS depends on the specific configuration of the network, such as the number of antennas installed at the base station, and the number of cells and users per cell. Since the AS depends on the operating frequency, we argue that the system capacity can be maximized by proper selection of the carrier frequency.

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. This paper proposes an efficient frequency-domain cyclic prefix 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.

Cognitive radio (CR) has received increasing attention and is considered an important solution to the spectral crowding problem. The main idea behind CR technology is to utilize the unused spectral resources which are determined to be available for secondary user by effective spectrum sensing techniques. However, CR technology significantly depends on the spectrum sensing techniques which are applied to detect the presence of primary user (PU) signals. This paper focuses on detecting OFDM primaries using novel frequency-domain cyclic prefix (CP) autocorrelation based compressive spectrum sensing algorithms. To counteract the practical wireless channel effects, frequency domain approaches for PU signal detection are developed. The proposed spectrum sensing method eliminates the effects of both noise uncertainty and frequency selective channels. Using the frequency domain autocorrelation approach results in highly increased flexibility, facilitating robust wideband multi-mode, multi-channel sensing with low complexity. It also allows to sense weak PU signals which are partly overlapped by other strong PU or CR transmissions.

The present work is devoted to the comprehensive analysis of the detrimental effects of noise uncertainty in wide-band multimode subband based spectrum sensing over frequency selective channels. Unlike existing analyses that are mostly limited to single-band detection, an analytical model is firstly formulated for the case of wide-band multimode subband based spectrum sensing, considering both noise uncertainty and frequency selectivity. This model is also extended for the case of detecting a reappearing primary user (PU), as well as for the sensing scenarios where the frequency range of a PU is unknown. Novel closed-form expressions are derived for the corresponding probabilities of false alarm and probabilities of detection. The derived expressions are subsequently employed in quantifying the effects of noise uncertainty and frequency selectivity. A subband based scheme for the sensing of multi-channel primary systems is presented and analyzed. The proposed method is found to exhibit significantly reduced sensing time and greatly improved robustness against noise uncertainty compared to the basic wideband energy detection method.

Spectrum scarcity has become a critical concern in wireless communication systems due to the limited availability of frequency spectrum. Hence, cognitive radio (CR) has been introduced as a solution for more effective use of the spectrum resources. Spectrum sensing (SS) is one of the key elements in the implementation of effective and reliable CR systems. Energy detection (ED) based SS is the most common sensing algorithm due to its low complexity. The main drawback of ED based SS is that it 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 noise uncertainty in the estimation of the noise variance. In this study, we apply a recently proposed enhanced ED based algorithm to the sensing of Wireless Microphone (WM) signals, demonstrating robustness to noise uncertainty in real-time testing with actual WM signals. This so-called Max-Min ED algorithm is based on subband division of a wideband signal using an analysis filter bank (AFB) and utilizing the difference of maximum and minimum subband energies as the test statistic. Following the introduction of analytical models and scenarios of ED based SS algorithms, the sensing algorithms are implemented and tested using National Instruments (NI) Universal Software Radio Peripheral (USRP) and the NI-LabVIEW software platform, together with the necessary toolboxes.

In this article, we focus on the RF and digital front-end design and implementation challenges associated with future 5G radio access systems with special emphasis on spectrally contained waveforms and small-cell system scenarios. In general, filter bank based multicarrier (FBMC) type of techniques have various potential benefits due to their excellent spectral containment compared to classical OFDM(A). However, these spectrally contained waveforms lose their intriguing spectral properties when power amplifier (PA) nonlinearities are considered. Flexible and efficient digital predistortion (DPD) algorithms are thus considered an interesting solution in order to restore the spectral containment of such advanced 5G waveforms. Noncontiguous spectrally-agile transmission is another key feature of future 5G systems for increasing data rates and spectral allocation flexibility. However, the PA nonlinearities impose even more severe challenges in such noncontiguous transmission scenarios due to the resulting spurious intermodulation emissions that can easily violate the emission limits or even desensitize the own receiver in frequency division duplexing based systems. Furthermore, at the network deployment level, different small-cell concepts are expected to play a major role in future 5G networks. Unlike the ordinary macro base-stations, the digital computing capabilities in small-cell base-stations are much more limited. Furthermore, they should also adopt lower-cost and small-size analog RF components, while still maintaining high energy-efficiency. The afore-mentioned constraints, along with advanced 5G waveforms, call for flexible and low-complexity DPD solutions, a challenge addressed in this article. We report novel DPD methods with built-in capability to direct the linearization performance to pre-defined frequencies or subbands in a flexible manner, and demonstrate their good performance and complexity benefits in the context of non-contiguous FBMC transmission.

Filter bank multicarrier (FBMC) is considered as one of the strong 5G waveform candidates due to better spectral containment compared to orthogonal frequency division multiplexing (OFDM). However, when considering the spectral leakage effects appearing in practical devices due to transmitter non-idealities, such as power amplifier (PA) nonlinearity and inphase-quadrature (I/Q) imbalance, the nice spectral properties of FBMC may be severely degraded, thus questioning the additional complexity needed to generate such waveforms in baseband. In this paper, digital pre-distortion (DPD) is investigated as a practical solution for both of these problems. We extend the study also to potential applications of spectrum sensing based cognitive radio in future wireless communications. We utilize the benefits of the analysis filter bank of FBMC receiver in wideband multichannel spectrum sensing. In this context, the power leakage due to transmitter non-idealities also makes it difficult to identify spectral gaps amongst relatively strong primary transmissions. It is demonstrated that effective DPD greatly facilitates both spectrum sensing and spectrum utilization functions and thus enhances the overall spectral efficiency in opportunistic dynamic spectrum use scenarios.

Cognitive radio is an emerging wireless technology that is capable of efficiently coordinating the use of the currently scarce spectrum resources, and spectrum sensing constitutes its most crucial operation. This paper proposes wideband multichannel spectrum sensing methods utilizing fast Fourier transform or filter-bank-based methods for spectrum analysis. Fine-grained spectrum analysis facilitates optimal energy detection in practical scenarios where the transmitted signal, channel frequency response, and/or receiver frequency response do not follow the commonly assumed boxcar model, which typically assumes, among other things, narrow-band communications with flat spectral characteristics. Such sensing schemes can be tuned to the spectral characteristics of the target primary user signals, allowing simultaneous sensing of multiple target primary signals with low additional complexity. This model is also extended to accounting for the specific scenario of detecting a reappearing primary user during secondary transmission, as well as in spectrum sensing scenarios where the frequency range of a primary user is unknown. Novel analytic expressions are derived for the corresponding probability of false alarm and probability of detection in each case, while the useful concept of the area under the receiver operating characteristics curve is additionally introduced as a single scalar metric for evaluating the overall performance of the proposed spectrum sensing algorithms and scenarios. The derived expressions have a rather simple algebraic representation, which renders them convenient to handle both analytically and numerically. The offered results are also extensively validated through comparisons with respective results from computer simulations and are subsequently employed in evaluating each technique analytically, which provides meaningful insights that are anticipated to be useful in future deployments of cognitive radio systems.

Direct-conversion radio receivers can offer highly integrated low-cost hardware solutions for cognitive radio (CR) devices. Such receivers are, however, also very sensitive to various radio frequency (RF) impairments such as IQ imbalance, which can considerably limit the spectrum sensing capabilities. Most of the existing spectrum sensing studies in literature assume an ideal RF receiver and hence neglect the impacts of such practical RF hardware limitations. In this article, we study energy detection (ED) based spectrum sensing in both single-channel and multi-channel direct-conversion receiver scenarios impaired by IQ imbalance. With complex Gaussian primary user (PU) signal models, we first derive the detection and false alarm probabilities in closed-form for both receiver scenarios. The analytical results, confirmed through extensive simulations, show that while the single-channel receiver scenario is fairly robust to IQ imbalance, the wideband multi-channel sensing receiver is very sensitive to the image channel crosstalk induced by IQ imbalance. More specifically, it is shown that the false alarm probability of multi-channel energy detection increases significantly, compared to ideal RF receiver case, and the exact performance depends on the image channel power level and IQ imbalance values. In order to prevent such degradation in the ability to identify free spectrum, a waveform level interference cancellation method is then proposed to mitigate the image channel crosstalk. The optimum cancellation coefficient yielding interference-free signal is first derived, being also complemented with a practical coefficient sample estimator. An explicit condition is also derived for which the proposed cancellation scheme deploying the practical coefficient sample estimator provides performance gain in the sensing decisions compared to uncompensated energy detection. Extensive computer simulations with various signal and imbalance conditions are provided which demonstrate that the proposed enhanced energy detection can suppress the image channel crosstalk efficiently, yielding detection and false alarm probabilities essentially identical to those of an ideal RF receiver.

Direct-conversion radio receivers can provide highly-integrated and low-cost hardware solutions for cognitive radio (CR) devices. However, such receiver structures are also known to suffer from various RF circuit imperfections, especially IQ imbalance. When studying the performance of various spectrum sensing methods, most existing work in literature neglect the effects of such RF imperfections. In this paper, we analyze the performance of a well-known eigenvalue based sensing method, namely maximum-to-minimum eigenvalue test, in the challenging multichannel spectrum sensing context under sensing receiver RF IQ imbalance. It is analytically shown that under RF IQ imbalance, the false alarm probability of the spectrum sensor is always higher compared to ideal RF front-end case. Then, in order to compensate for such degradation in the ability to identify free spectrum, a waveform level interference cancellation solution is deployed to enhance the performance, and optimum cancellation coefficient together with a practical sample estimator are formulated. Extensive computer simulations are provided to illustrate the degradation of false alarm probability in the presence of practical IQ imbalance values. Furthermore, the simulations also show that the proposed interference cancellation method can efficiently suppress the IQ imbalance effects, achieving identical false alarm probability with the ideal RF front-end case.

Wireless Local Area Networks (WLAN) and Wireless Personal Area Networks (WPAN) such as the Bluetooth are designed to operate in 2.4 GHz ISM band. Since both IEEE 802.15 based Bluetooth and IEEE 802.11 WLAN devices, among various others, use the same frequency band, interference may lead to significant performance degradation. Hence, Cognitive Radio (CR) technology has been considered for coordinating better the spectrum use in this band. Spectrum sensing is one of the most important functions in a CR. In this study, energy detector based spectrum sensing techniques are optimized for detecting Bluetooth signals, considering both the effect of non-flat power spectrum and frequency hopping characteristics. To reduce complexity and required number of samples for effective spectrum sensing, optimum weighting process is proposed for subband based spectrum sensing. Bluetooth sensing is analyzed also in the presence of WLANs at nearby frequencies.