TV white spaces exploitation for multimedia signal distribution

The purpose of this study is to determine a range of detection thresholds at which new dynamic spectrum access (DSA) devices can safely operate in unoccupied spectrum allocated to terrestrial television (TV) broadcasting and other services (i.e., the TV "white spaces"), without causing harmful interference to other authorized operations, which include digital and analog TV stations, wireless microphones and other services. Spectrum sensing data were collected at six different locations in Northern Virginia that were strategically chosen because they were within, outside and near the edges of the predicted service contours of digital TV stations serving the Washington, D.C. and Baltimore, MD metropolitan areas. We used a pair of DSA devices that jointly decide if a channel can be used or not. This emulates how SSC's and other DSA devices operate. The joint decision logic mitigates the propagation shadowing and hidden node problem. The separation between the DSA devices was less than approximately 10 km, which corresponds to the link range of a 10 W 802.16-based transmitter. The DSA detectors used a high sensitivity detector that is able to detect the ATSC pilot down to a -140 dBm level (ATSC signal power level of -128 dBm). The detector is not noise limited. "False alarms" are caused by detection of distant TV transmitters when the DSA system is located outside the Grade B contour. Based on analysis results from data recorded from six independent locations in Northern Virginia and 13 ATSC channels.

Spectrum sensing, a technique to recognize unused or idle spectrum, is a key function for cognitive radio (CR) and requires high precision and fast signal processing technique. To satisfy these requirements, two-stage sensing architecture that combines an energy detector and a feature detector was proposed by IEEE 802.22 working group (WG). For an energy detector, various techniques such as receive signal strength indicator (RSSI), multi resolution spectrum sensing (MRSS), fast Fourier transform (FFT) have been suggested and recently discrete wavelet packet transform (DWPT)-based method was proposed. In this paper, we propose a modified DWPT-based energy detector by using half-band elliptic IIR filters and double threshold method. Through simulation and complexity analysis, it is shown that the proposed scheme can reduce computational complexity and false detection probability compared to conventional DWPT-based energy detector for spectrum sensing in CR.

Previously developed techniques for modulation classification have assumed that both the signal and noise power are known. However, the signal or noise power estimations require that modulation type is known. To solve the problem, this paper introduces a method for joint power estimation and modulation classification (JPEMC), whose key point is employing the relationships between the second- and higher moments of received signal and signal and noise power. Simulation results demonstrate the efficiency of modulation classification of JPEMC and indicate that the power estimation can be used by later demodulators.

One of the many standards developed recently for television white space operation is the IEEE 802.11af wireless LAN in TV white space. This white space device as a secondary user (SU) must not cause damaging interference to an incumbent primary user(PU) such as the ISDB-T digital TV service and even to an existing Analog TV Broadcast. To ensure interference-free coexistence, a keep-out distance, d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> , between the white space device (WSD) transmitter and the PU within the licensed TV service contour should be strictly observed. However, it is important that this d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> must neither be overestimated nor underestimated. In this paper, co-channel d <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</inf> between IEEE S02.11af and the ISDB – T DTV and Analog service contours using primarily ITU-R P.1546-6 and ITU–R P.1411-9 recommendations was computed and compared for the eleven analog and digital channels operating in Cebu province, Philippines. Experimental tests conducted show that analog and digital TV broadcast channels are aptly protected from co-channel interference emanating from IEEE 802.11af secondary white space devices operating within its service contour. Interestingly, the study also shows that a pair of IEEE 802.11af devices can establish connectivity within the ITU-R P.1546-based keep-out distance without causing interference to ATV and DTV services.

Internet of things (IoT) will offer a new and revolutionary way to connect objects and/or devices to improve the day to day lives of citizens around the world. International Telecommunications Union (ITU) estimates that 25 billion devices will be connected to the internet by 2020. Nowadays, new research being carried out to define the technology and frequency bands for IoT. Narrow band LTE (NB-LTE) and the Ultra High Frequency (UHF) band could be an interesting solution for deployment of IoT. In this paper we propose the use of white spaces in the Digital Terrestrial Television (DTT) band (TV-White-Spaces) for spectrum sharing between DTT and IoT technologies. The scenarios consider a Digital Video Broadcasting-Terrestrial 2 (DVB-T2) network offering fixed rooftop reception as a primary service, and a NB-LTE network as a secondary service. The results have been obtained by laboratory measurements using professional equipment. We analyse the effect of the duty cycle on protection ratios, as well as peak rate and battery life for IoT devices. Our results provide the technical restrictions for the IoT NB-LTE node and devices, mainly on the maximum allowable Effective Isotropic Radiated Power (EIRP) that could transmit on the DTT band in terms of carrier separation, from co-channel and adjacent channel.

Analysis and prediction of presynaptic and postsynaptic neurotoxins by Chou's general pseudo amino acid composition and motif features

This paper proposes a blind modulation classification method for adaptive coding and modulation signals base on the difference in high-order cumulants of different modulation types. Use high-order cumulants to establish features to design modulation classification algorithm and decision thresholds. Because of the time-varying characteristics of adaptive coding and modulation signals, the segmentation classification method is used to realize the modulation classification of each frame in the time dimension. Finally, the designed algorithm is simulated, and the classification performance of the algorithm under different modulation types, segment lengths and signal-to-noise ratios is analyzed.

The lack of sufficient fixed-line communication infrastructure in African rural areas has resulted in wireless communication being the only cost effective alternative solution for broadband connectivity. However, access to valuable spectrum—specifically sub-1 GHz spectrum—is mostly allocated to broadcasting or mobile telephony. The global digital switch over (DSO) of television (TV) broadcasting systems will see more sub-1 GHz TV band spectrum being made available for the digital dividend and also result in more TV white space (TVWS) spectrum. In order to ensure dynamic and efficient utilization of the TV white space spectrum, there is an increasing trend to use cognitive radiosystems that use geo-location spectrum databases and spectrum sensing as an enabling technology. In this paper, we overview the relevant signals and standards and present field measurement results showing the actual usage of TV bands before the DSO in selected urban and rural areas of Southern Africa. Measurements were conducted using low-cost and high-grade radio instruments. The low-cost spectrum analyser was built in-house using the Universal Software Radio Peripheral (USRP-2) and GNU Radio software. A metric to quantify available TV white space, based on the minimum acceptable field strength, is introduced and applied to quantify the availability of TV white space. Our results show medium spectrum usage in urban areas and very low spectrum usage in rural areas, making TVWS an attractive solution for rural broadband connectivity.

The aim of this thesis is the study of techniques for efficient management and use of the spectrum based on cognitive radio technology. The ability of cognitive radio technologies to adapt to the real-time conditions of its operating environment, offers the potential for more flexible use of the available spectrum. In this context, the international interest is particularly focused on the “white spaces” in the UHF band of digital terrestrial television. Spectrum sensing and geo-location database have been considered in order to obtain information on the electromagnetic environment. Different methodologies have been considered in order to investigate spectral resources potentially available for the white space devices in the TV band. The adopted methodologies are based on the geo-location database approach used either in autonomous operation or in combination with sensing techniques. A novel and computationally efficient methodology for the calculation of the maximum permitted white space device EIRP is then proposed. The methodology is suitable for implementation in TV white space databases. Different Italian scenarios are analyzed in order to identify both the available spectrum and the white space device emission limits. Finally two different applications of cognitive radio technology are considered. The first considered application is the emergency management. The attention is focused on the consideration of both cognitive and autonomic networking approaches when deploying an emergency management system. The cognitive technology is then considered in applications related to satellite systems. In particular a hybrid cognitive satellite-terrestrial is introduced and an analysis of coexistence between terrestrial and satellite networks by considering a cognitive approach is performed.

SummaryAutomatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency‐selective multipath fading, and time‐varying channels in real‐world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood‐based (LB) and feature‐based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal‐to‐noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.

Dominant frequency (DF) of electrophysiological data is an effective approach to estimate the activation rate during Atrial Fibrillation (AF) and it is important to understand the pathophysiology of AF and to help select candidate sites for ablation. Frequency analysis is used to find and track DF. It is important to minimize the catheter insertion time in the atria as it contributes to the risk for the patients during this procedure, so DF estimation needs to be obtained as quickly as possible. A comparison of computation tim- es taken for spectrum estimation analysis is presented in this paper. Fast Fourier Transform (FFT), Blackman-Tukey (BT), Autoregressive (AR) and Multiple Signal Classification (MUSIC) methods are used to obtain the frequency spectrum of the signals. The time to produce DF was measured for each method. The method which takes the shortest time for analysis is selected for real time application purpose.

Packet classification is the core technology of network layer and an important means to ensure the security of network system. With the rapid development of network technology, higher requirements are put forward for the speed of network packet classification. This paper improves the traditional single thread package classification framework, A new parallelization method for fast packet classification (MpFPC) based on distributed computing is proposed, the method adopts the packet classification idea based on decision tree, but compared with the traditional algorithm, a rule mapping preprocessing process is added before constructing the classification decision tree, which effectively removes the rule redundancy and conflict, so as to avoid the rule replication problem of the traditional decision-tree-based method. In addition, the method can group the rules and data packets at the same time, which improves the packet classification efficiency. Experimental results show that MpFPC method has high classification efficiency and has obvious speed advantage compared with Uscuts method with time complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(k$ </tex-math></inline-formula> log <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> ). In addition, the test results also show that the classification speed of MpFPC will increase with the increasing number of computing nodes, which provides a new possible way to meet the classification wire-speed requirement.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Automatic modulation classification (AMC) is widely used in both civilian and military applications. This paper presents a new method for the automatic classification of higher order QAM signals via Rayleigh fading channel, using a modified Joint Power Estimation and Modulation Classification (JPEMC) algorithm and Fisher criterion. The proposed algorithm has low computational complexity. The advantages of our new algorithm are we don't need the channel information as a prior and the estimation value of SNR. The proposed approach was evaluated in Monte Carlo simulations. The experimental results show that the percentage of identification can reach 98% when SNR is more than 5dB.

In this paper, we present a practical identification approach of video fragment for digital video files. Before analyzing the video content, we must decode it based on its encoding format first. In order to effectively identify the format of a fragment, a format classification is performed before the format identification. The methods of format classification and identification are discriminative subspace clustering (DiSC) and the Knearest neighbor (KNN).Because of losing the meta-information, we add a maximum similar header (MSH) to the front of the fragment to recover the video content. We adopt a simple key frame detection method using standard deviation and mean value. Motion vectors of macro blocks are utilized to classify the video features for effectively identifying the video. Several edges of frames are accumulated and compose a video feature. The experimental results show the evaluations of the video format classification and identification, fragment recovery, and content identification.