This paper presents a weighted diversity combining technique for the cyclostationarity detection based spectrum sensing of orthogonal frequency division multiplexing signals in cognitive radio. In cognitive radio systems, secondary users must detect the desired signal in an extremely low signal-to-noise ratio (SNR) environment. In such an environment, multiple antenna techniques (space diversity) such as maximum ratio combining are not effective because the energy of the target signal is also extremely weak, and it is difficult to synchronize some received signals. The cyclic autocorrelation function (CAF) is used for traditional cyclostationarity detection based spectrum sensing. In the presented technique, the CAFs of the received signals are combined, while the received signals themselves are combined with general space diversity techniques. In this paper, the value of the CAF at peak and non-peak cyclic frequencies are computed, and we attempt to improve the sensing performance by using different weights for each CAF value. The results were compared with those from conventional methods and showed that the presented technique can improve the spectrum sensing performance.

Technological developments in wireless communication have led to an increasing demand for radio frequencies. This has necessitated the practice of spectrum sharing to ensure optimal usage of the limited frequencies, provided this does not cause interference. This paper presents a framework for managing an unexpected situation in which a primary user experiences harmful interference with regard to database-driven secondary use of spectrum allocated to the primary user towards 5G mobile networks, where the primary user is assumed to be a radar system. In our proposed framework, the primary user informs a database that they are experiencing harmful interference. Receiving the information, the database updates a primary exclusive region in which secondary users are unable to operate in the licensed spectrum. Subsequent to the update, this primary exclusive region depends on the knowledge about the secondary users when the primary user experiences harmful interference, knowledge of which is stored in the database. We assume a circular primary exclusive region centered at a primary receiver and derive an optimal radius of the primary exclusive region by applying stochastic geometry. Then, for each type of knowledge stored in the database for the secondary user, we evaluate the optimal radius for a target probability that the primary user experiences harmful interference. The results show that the more detailed the knowledge of the secondary user's density and transmission power stored in the database, the smaller the radius that has to be determined for the primary exclusive region after the update and the more efficient the spatial reuse of the licensed spectrum that can be achieved.

Deterioration of sewer pipes is one of very important problems in Japan. Sewer inspections have been carried out mainly by visual check or wired remote robots with a camera. However, such inspection schemes involve high labor and/or monetary cost. Sewer inspection with boat-type video cameras or unwired robots takes a long time to check the result of the inspection because video data are obtained after the equipment is retrieved from the pipe. To realize low cost, safe and quick inspection of sewer pipes, we have proposed a sewer inspection system using drifting wireless sensor nodes. Water, soil, and the narrow space in the pipe make the long-range and high throughput wireless radio communication difficult. Therefore, we have to identify suitable radio frequency and antenna configuration based on wireless communication characteristics in sewer pipes. If the frequency is higher, the Fresnel zone, the needed space for the line of sight is small, but the path loss in free space is large. On the other hand, if the frequency is lower, the size of the Fresnel zone is large, but the path loss in free space is small. We conducted wireless communication experiments using 920MHz, 2.4GHz, and 5GHz band off-the-shelf devices in an experimental underground pipe. The measurement results show that the wireless communication range of 5GHz (IEEE 802.11a) is over 8m in a 200mm-diameter pipe and is longer than 920MHz (ARIB STD-T108), 2.4GHz (IEEE 802.11g, IEEE 802.15.4) band at their maximum transmission power. In addition, we confirmed that devices that use IEEE 802.11a and 54Mbps bit rate can transmit about 43MB data while they are in the communication range of an AP and drift at 1m/s in a 200mm-diameter pipe, and it is bigger than one of devices that use other bit rate.

In this letter, we present a spectrally efficient multicast method which enables a transmitter to simultaneously transmit multiple multicast streams without any interference among multicast groups. By using unique combiners at receivers with multiple antennas within each multicast group, the proposed method simplifies multiple channels between the transmitter and the receivers to an equivalent channel. In addition, we establish the sufficient condition for the system configuration which should be satisfied for the channel simplification and provide a combiner design technique for the receivers. To remove interference among multicast groups, the precoder for the transmitter is designed by utilizing the equivalent channels. By exploiting time resources efficiently, the channel simplification (CS) based method achieves a higher sum rate than the time division multiplexing (TDM) based method, which the existing multicast techniques fundamentally employ, at high signal-to-noise ratio (SNR) regime. Furthermore, we present a multicast method combining the CS based method with the TDM based method to utilize the benefits of both methods. Simulation results successfully demonstrate that the combined multicast method obtains a better sum rate performance at overall SNR regime.

This paper considers a massive multiple-input-multiple-output (MIMO) relaying system with multi-pair single-antenna users. The relay node adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) stratagem for reception/transmission. We analyze the spectral efficiency (SE) and power scaling laws with respect to the number of relay antennas and other system parameters. First, by using the law of large numbers, we derive the closed-form expression of the SE, based on which, it is shown that the SE per user increases with the number of relay antennas but decreases with the number of user pairs, both logarithmically. It is further discovered that the transmit power at the source users and the relay can be continuously reduced as the number of relay antennas becomes large while the SE can maintains a constant value, which also means that the energy efficiency gain can be obtained simultaneously. Moreover, it is proved that the number of served user pairs can grow proportionally over the number of relay antennas with arbitrary SE requirement and no extra power cost. All the analytical results are verified through the numerical simulations.

Energy-efficient resource allocation is considered in sensing-based spectrum sharing for cooperative cognitive radio networks (CCRNs). The secondary user first listens to the spectrum allocated to the primary user (PU) to detect the PU state and then initiates data transmission with two power levels based on the sensing decision (e.g., idle or busy). Under this model, the optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the PU. Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation strategy. Finally, numerical results show that the energy-efficient design greatly improves EE compared with the conventional spectrum-efficient design.

To improve the recognition rate of the speech emotion, new spectral features based on local Hu moments of Gabor spectrograms are proposed, denoted by GSLHu-PCA. Firstly, the logarithmic energy spectrum of the emotional speech is computed. Secondly, the Gabor spectrograms are obtained by convoluting logarithmic energy spectrum with Gabor wavelet. Thirdly, Gabor local Hu moments(GLHu) spectrograms are obtained through block Hu strategy, then discrete cosine transform (DCT) is used to eliminate correlation among components of GLHu spectrograms. Fourthly, statistical features are extracted from cepstral coefficients of GLHu spectrograms, then all the statistical features form a feature vector. Finally, principal component analysis (PCA) is used to reduce redundancy of features. The experimental results on EmoDB and ABC databases validate the effectiveness of GSLHu-PCA.

We create a practical method to set the segment size of the Welch FFT for wideband and long-term spectrum usage measurements in the context of hierarchical dynamic spectrum access (DSA). An energy detector (ED) based on the Welch FFT can be used to detect the presence or absence of primary user (PU) signal and to estimate the duty cycle (DC). In signal detection with the Welch FFT, segment size is an important design parameter since it determines both the detection performance and the frequency resolution. Between these two metrics, there is a trade-off relationship which can be controlled by adjusting the segment size. To cope with this trade-off relationship, we define an optimum and, more easy to analyze sub-optimum segment size design criterion. An analysis of the sub-optimum segment size criterion reveals that the resulting segment size depends on the signal-to-noise ratio (SNR) and the DC. Since in practice both SNR and DC are unknown, proper segment setting is difficult. To overcome this problem, we propose an adaptive segment size selection (ASSS) method that uses noise floor estimation outputs. The proposed method does not require any prior knowledge on the SNR or the DC. Simulation results confirm that the proposed ASSS method matches the performance achieved with the optimum design criterion.

This paper proposes a blind, inaudible, robust digital-audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.

In this paper, we propose a single-channel speech enhancement method for a push-to-talk enabled wireless communication device. The proposed method is based on adaptive weighted β-order spectral amplitude estimation under speech presence uncertainty and enhanced instantaneous phase estimation in order to achieve flexible and effective noise reduction while limiting the speech distortion due to different noise conditions. Experimental results confirm that the proposed method delivers higher voice quality and intelligibility than the reference methods in various noise environments.

A parallel Aho-Corasick (AC) approach, named PAC-k, is proposed for string matching in deep packet inspection (DPI). The proposed approach adopts graphic processing units (GPUs) to perform the string matching in parallel for high throughput. In parallel string matching, the boundary detection problem happens when a pattern is matched across chunks. The PAC-k approach solves the boundary detection problem because the number of characters to be scanned by a thread can reach the longest pattern length. An input string is divided into multiple sub-chunks with k characters. By adopting the new starting position in each sub-chunk for the failure transition, the required number of threads is reduced by a factor of k. Therefore, the overhead of terminating and reassigning threads is also decreased. In order to avoid the unnecessary overlapped scanning with multiple threads, a checking procedure is proposed that decides whether a new starting position is in the sub-chunk. In the experiments with target patterns from Snort and realistic input strings from DEFCON, throughputs are enhanced greatly compared to those of previous AC-based string matching approaches.

Effects of electron beam irradiation at 15 keV on graphene are investigated by optical and electron characterization using Raman and two-terminal resistance measurement and photoconductivity measurement. In Raman spectra, increase of defects in D-peak to G-peak ratio by increase of electron irradiation by 70 mC/cm2 was found. Resistance of graphene showed an increase after the irradiation. Rather sensitive change was found in photoconductivity of irradiated graphene under ultra-violet (UV) illumination, suggesting irradiation induced defects affect a photoconductivity properties of the graphene.

Cognitive radio sensor networks (CRSNs) with their dynamic spectrum access capability appear to be a promising solution to address the increasing challenge of spectrum crowding faced by the traditional WSN. In this paper, through maximizing the utility index of the CRSN, a node density-adaptive spectrum access strategy for sensor nodes is proposed that takes account of the node density in a certain event-driven region. For this purpose, considering the burst real-time data traffic, we analyze the energy efficiency (EE) and the packet failure rate (PFR) combining network disconnected rate (NDR) and packet loss rate (PLR) during the channel switching interval (CSI) for both underlay and interweave spectrum access schemes. Numerical results confirm the validity of our theoretical analyses and indicate that the adaptive node density threshold (ANDT) exists for underlay and interweave spectrum access scheme switching.

We propose a cooperative spectrum sensing scheme based on sub-Nyquist sampling in cognitive radios. Our main purpose is to understand the uncertainty caused by sub-Nyquist sampling and to present a sensing scheme that operates at low sampling rates. In order to alleviate the aliasing effect of sub-Nyquist sampling, we utilize cooperation among secondary users and the sparsity order of channel occupancy. The simulation results show that the proposed scheme can achieve reasonable sensing performance even at low sampling rates.

In our previous paper, we presented a concept of “Baseband Radio” as an ideal of future wireless communication scheme. Furthermore, for enhancing the adaptability of baseband radio, the adaptive baseband radio was discussed as the ultimate communication system; it integrates the functions of cognitive radio and software-defined radio. In this paper, two transmission schemes that take advantage of adaptive baseband radio are introduced and the results of a performance evaluation are presented. The first one is a scheme based on DSFBC for realizing higher reliability; it allows the flexible use of frequency bands over a wide range of white space. The second one is a low-power-density communication scheme with spectrum-spreading by means of frequency-domain differential coding so that the secondary system does not seriously interfere with primary-user systems that have been assigned the same frequency band.

This paper describes recent progress of photonically-enabled systems for millimeter-wave and terahertz measurement applications. After briefly explaining signal generation schemes as a foundation of photonics-based approach, system configurations for specific applications are discussed. Then, practical demonstrations are presented, which include frequency-domain spectroscopy, phase-sensitive measurement, electric-field measurement, and 2D/3D imaging.

In this paper, we propose a novel censor-based cooperative spectrum sensing strategy, called adaptive energy-efficient sensing (AES), in which both sequential sensing and censoring report mechanism are employed, aiming to reduce the sensing energy consumption of secondary user relays (SRs). In AES, an anchor secondary user (SU) requires cooperative sensing only when it does not detect the presence of PU by itself, and the cooperative SR adopts decision censoring report only if the sensing result differs from its previous one. We derive the generalized-form expressions false alarm and detection probabilities over Rayleigh fading channels for AES. The sensing energy consumption is also analyzed. Then, we study sensing energy overhead minimization problem and show that the sensing time allocation can be optimized to minimize the miss detection probability and sensing energy overhead. Finally, numerical results show that the proposed strategy can remarkably reduce the sensing energy consumption while only slightly degrading the detection performance compared with traditional scheme.

Cognitive radio (CR) is an important technology to provide high-efficiency data communication for the IoT (Internet of Things) era. Signal detection is a key technology of CR to detect communication opportunities. Energy detection (ED) is a signal detection method that does not have high computational complexity. It, however, can only estimate the presence or absence of signal(s) in the observed band. Cyclostationarity detection (CS) is an alternative signal detection method. This method detects some signal features like periodicity. It can estimate the symbol rate of a signal if present. It, however, incurs high computational complexity. In addition, it cannot estimate the symbol rate precisely in the case of single carrier signal with a low Roll-Off factor (ROF). This paper proposes a method to estimate coarsely a signal's bandwidth and carrier frequency from its power spectrum with lower computational complexity than the CS. The proposed method can estimate the bandwidth and carrier frequency of even a low ROF signal. This paper evaluates the proposed method's performance by numerical simulations. The numerical results show that in all cases the proposed coarse bandwidth and carrier frequency estimation is almost comparable to the performance of CS with lower computational complexity and even outperforms in the case of single carrier signal with a low ROF. The proposed method is generally effective for unidentified classification of the signal i.e. single carrier, OFDM etc.

This paper investigates a signal area (SA) estimation method for wideband and long time duration spectrum measurements for dynamic spectrum access. SA denotes the area (in time/frequency domain) occupied by the primary user's signal. The traditional approach, which utilizes only Fourier transform (FT) and energy detector (ED) for SA estimation, can achieve low complexity, but its estimation performance is not very high. Against this issue, we apply post-processing to improve the performance of the FT-based ED. Our proposed method, simple SA (S-SA) estimation, exploits the correlation of the spectrum states among the neighboring tiles and the fact that SA typically has a rectangular shape to estimate SA with high accuracy and relatively low complexity compared to a conventional method, contour tracing SA (CT-SA) estimation. Numerical results will show that the S-SA estimation method can achieve better detection performance. The SA estimation and processing can reduce the number of bits needed to store/transmit the observed information compared to the FT-based ED. Thus, in addition to improved detection performance it also compresses the data.

A major challenge in wideband spectrum sensing, in cognitive radio system for example, is the requirement of a high sampling rate which may exceed today's best analog-to-digital converters (ADCs) front-end bandwidths. Compressive sampling is an attractive way to reduce the sampling rate. The modulated wideband converter (MWC) proposed recently is one of the most successful compressive sampling hardware architectures, but it has high hardware complexity owing to its parallel channels structure. In this paper, we design a single channel sub-Nyquist sampling scheme to bring substantial savings in terms of not only sampling rate but also hardware complexity, and we also present a wideband power spectrum sensing and reconstruction method for bandlimited wide-sense stationary (WSS) signals. The total sampling rate is only one channel rate of the MWC's. We evaluate the performance of the sensing model by computing the probability of detecting signal occupancy in terms of the signal-to-noise ratio (SNR) and other practical parameters. Simulation results underline the promising performance of proposed approach.