A fast parameter estimation method with a coarse estimation and a fine estimation for polyphase P coded signals is proposed. For a received signal with N sampling points, the proposed method has an improved performance when the signal-to-noise ratio (SNR) is larger than 2dB and a lower computational complexity O(N logs N) compared with the latest time-frequency rate estimation method whose computational complexity is O(N2).

Multichannel speech enhancement systems (MSES') have been widely utilized for diverse types of speech interface applications. A state-of-the-art MSES primarily utilizes multichannel minima-controlled recursive averaging for noise estimations and a parameterized multichannel Wiener filter for noise reduction. Many MSES' are implemented in the frequency domain, but they are computationally burdensome due to the numerous complex matrix operations involved. In this paper, a novel MSES intended to reduce the computational complexity with improved performance is proposed. The proposed system is implemented in the mel-filterbank domain using a frequency-averaging technique. Through a performance evaluation, it is verified that the proposed mel-filterbank MSES achieves improvements in the perceptual speech quality with a reduced level of computation compared to a conventional MSES.

In this paper, invulnerability and attack strategies are discussed for the undirected unweighted urban road networks and the directed weighted taxi networks of Beijing. Firstly, five new attack strategies, i.e., Initial All Degree (IAD), Initial All Strength (IAS), Recalculated Closeness (RC), Recalculated All Degree (RAD) and Recalculated All Strength (RAS) and five traditional attack strategies, i.e., Initial Degree (ID), Initial Betweenness (IB), Initial Closeness (IC), Recalculated Degree (RD) and Recalculated Betweenness (RB) are adopted to provoke the nodes failure. Secondly, we assess the impacts of these attack strategies using two invulnerability metrics, i.e., S (the relative size of the giant component) and E (the average network efficiency) through simulation experiments by MATLAB. Furthermore, we obtain some conclusions on the basis of the simulation results. Firstly, we discover that IB is more efficient than others for the undirected unweighted 5th ring Beijing road network based on S, and IB is more efficient than others at the beginning while ID is more efficient than IB at last based on E, while IAD causes a greater damage than IAS for the directed weighted 5th ring Beijing taxi network no matter with metrics S or E. Secondly, we find that dynamic attacks are more efficient than their corresponding static attacks, and RB is more destructive than others in all attack graphs while RAD is more destructive than RAS in all attack graphs. Moreover, we propose some suggestions to advance the reliability of the networks according to the simulation results. Additionally, we notice that the damage between ID (RD) and IAD (RAD) is similar due to the large proportion of two-way roads, and we realize that global measures should be employed to estimate the best attack strategy on the basis of that we find the best attack strategy changes with the nodes failure.

Pseudorandom number generators have been widely used in Monte Carlo methods, communication systems, cryptography and so on. For cryptographic applications, pseudorandom number generators are required to generate sequences which have good statistical properties, long period and unpredictability. A Dickson generator is a nonlinear congruential generator whose recurrence function is the Dickson polynomial. Aly and Winterhof obtained a lower bound on the linear complexity profile of a Dickson generator. Moreover Vasiga and Shallit studied the state diagram given by the Dickson polynomial of degree two. However, they do not specify sets of initial values which generate a long period sequence. In this paper, we show conditions for parameters and initial values to generate long period sequences, and asymptotic properties for periods by numerical experiments. We specify sets of initial values which generate a long period sequence. For suitable parameters, every element of this set occurs exactly once as a component of generating sequence in one period. In order to obtain sets of initial values, we consider a logistic generator proposed by Miyazaki, Araki, Uehara and Nogami, which is obtained from a Dickson generator of degree two with a linear transformation. Moreover, we remark on the linear complexity profile of the logistic generator. The sets of initial values are described by values of the Legendre symbol. The main idea is to introduce a structure of a hyperbola to the sets of initial values. Our results ensure that generating sequences of Dickson generator of degree two have long period. As a consequence, the Dickson generator of degree two has some good properties for cryptographic applications.

Inter-switch networks for HPC systems and data-centers can be improved by applying random shortcut topologies with a reduced number of hops. With minimal routing in such networks; however, deadlock-freedom is not guaranteed. Multiple Virtual Channels (VCs) are efficiently used to avoid this problem. However, previous works do not provide good trade-offs between the number of required VCs and the time and memory complexities of an algorithm. In this work, a novel and fast algorithm, named ACRO, is proposed to endorse the arbitrary routing functions with deadlock-freedom, as well as consuming a small number of VCs. A heuristic approach to reduce VCs is achieved with a hash table, which improves the scalability of the algorithm compared with our previous work. Moreover, experimental results show that ACRO can reduce the average number of VCs by up to 63% when compared with a conventional algorithm that has the same time complexity. Furthermore, ACRO reduces the time complexity by a factor of O(|N|⋅log|N|), when compared with another conventional algorithm that requires almost the same number of VCs.

Hybrid analog/digital precoding has attracted growing attention for millimeter wave (mmWave) communications, since it can support multi-stream data transmission with limited hardware cost. A main challenge in implementing hybrid precoding is that the channels will exhibit frequency-selective fading due to the large bandwidth. To this end, we propose a practical hybrid precoding scheme with finite-resolution phase shifters by leveraging the correlation among the subchannels. Furthermore, we utilize the sparse feature of the mmWave channels to design a low-complexity algorithm to realize the proposed hybrid precoding, which can avoid the complication of the high-dimensionality eigenvalue decomposition. Simulation results show that the proposed hybrid precoding can approach the performance of unconstrained fully-digital precoding but with low hardware cost and computational complexity.

Non-orthogonal multiple access (NOMA) is a promising multiple access scheme for further improving the spectrum efficiency compared to orthogonal multiple access (OMA) in the 5th Generation (5G) mobile communication systems. As inter-user interference cancellers for NOMA, two kinds of receiver structures are considered. One is the reduced complexity-maximum likelihood receiver (R-ML) and the other is the codeword level interference canceller (CWIC). In this paper, we show that the R-ML is superior to the CWIC in terms of scheduling flexibility. In addition, we propose a link to system (L2S) mapping scheme for the R-ML to conduct a system level evaluation, and show that the proposed scheme accurately predicts the block error rate (BLER) performance of the R-ML. The proposed L2S mapping scheme also demonstrates that the system level throughput performance of the R-ML is higher than that for the CWIC thanks to the scheduling flexibility.

The application of complex network theory to power grid analysis has been a hot topic in recent years, which mainly manifests itself in four aspects. The first aspect is to model power system networks. The second aspect is to reveal the topology of the grid itself. The third aspect is to reveal the inherent vulnerability and weakness of the power network itself and put forward the pertinent improvement measures to provide guidance for the construction of power grid. The last aspect is to analyze the mechanism of cascading failure and establish the cascading fault model of large power failure. In the past ten years, by using the complex network theory, many researchers have investigated the structural vulnerability of power grids from the point of view of topology. This letter studies the structural vulnerability of power grids according to the effect of selective node removal. We apply several kinds of node centralities including recently-presented second-order centrality (SOC) to guide the node removal attack. We test the effectiveness of all these centralities in guiding the node removal based on several IEEE power grids. Simulation results show that, compared with other node centralities, the SOC is relatively effective in guiding the node removal and can destroy the power grid with negative degree-degree correlation in less steps.

An online nonnegative matrix factorization (NMF) algorithm based on recursive least squares (RLS) is described in a matrix form, and a simplified algorithm for a low-complexity calculation is developed for frame-by-frame online audio source separation system. First, the online NMF algorithm based on the RLS method is described as solving the NMF problem recursively. Next, a simplified algorithm is developed to approximate the RLS-based online NMF algorithm with low complexity. The proposed algorithm is evaluated in terms of audio source separation, and the results show that the performance of the proposed algorithms are superior to that of the conventional online NMF algorithm with significantly reduced complexity.

In this paper, the differences in navigation information design, which is important for kiosk-type pedestrian navigation systems, were experimentally examined depending on presence or absence of carriable navigation information in order to acquire the knowledge to contribute design guidelines of kiosk-type pedestrian navigation systems. In particular, we used route complexity information calculated using a regression equation that contained multiple factors. In the absence of carriable navigation information, both the destination arrival rate and route deviation rate improved. Easy routes were designed as M (17 to 39 characters in Japanese), while complicated routes were denoted as L (40 or more characters in Japanese). On the contrary, in the presence of carriable navigation information, the user's memory load was found to be reduced by carrying the same navigation information as kiosk-type terminals. Thus, the reconsideration of kiosk-type pedestrian navigation systems design, e.g., the means of presenting navigation information, is required. For example, if the system attaches importance to a high destination arrival rate, L_Carrying without regard to route complexity is better. If the system attaching importance to the low route deviation rate, M_Carrying in the case of easy routes and L_Carrying in the case of complicated routes have been better. Consequently, this paper presents the differences in the designs of pedestrian navigation systems depending on whether carriable navigation information is absent or present.

Variance analysis is an important research topic to assess the quality of estimators. In this paper, we analyze the performance of the least ℓp-norm estimator in the presence of mixture of generalized Gaussian (MGG) noise. In the case of known density parameters, the variance expression of the ℓp-norm minimizer is first derived, for the general complex-valued signal model. Since the formula is a function of p, the optimal value of p corresponding to the minimum variance is then investigated. Simulation results show the correctness of our study and the near-optimality of the ℓp-norm minimizer compared with Cramér-Rao lower bound.

In this paper, we investigate a user and antenna joint selection problem in multi-user large-scale MIMO downlink networks, where a BS with N transmit antennas serves K users, and N is much larger than K. The BS activates only S(S≤N) antennas for data transmission to reduce hardware cost and computation complexity, and selects the set of users to which data is to be transmitted by maximizing the sum-rate. The optimal user and antenna joint selection scheme based on exhaustive search causes considerable computation complexity. Thus, we propose a new joint selection algorithm with low complexity and analyze the performance of the proposed scheme in terms of sum-rate and complexity. When S=7, N=10, K=5, and SNR=10dB, the sum-rate of the proposed scheme is 5.1% lower than that of the optimal scheme, while the computation complexity of the proposed scheme is reduced by 99.0% compared to that of the optimal scheme.

Identifying IDC (Internet Data Center) IP addresses and analyzing the connection relationship of IDC could reflect the IDC network resource allocation and network layout which is helpful for IDC resource allocation optimization. Recent research mainly focuses on minimizing electricity consumption and optimizing network resource allocation based on IDC traffic behavior analysis. However, the lack of network-wide IP information from network operators has led to problems like management difficulties and unbalanced resource allocation of IDC, which are still unsolved today. In this paper, we propose a method for the IP identification and connection relationship analysis of IDC based on the flow connection behavior analysis. In our method, the frequent IP are extracted and aggregated in backbone communication network based on the traffic characteristics of IDC. After that, the connection graph of frequent IP (CGFIP) are built by analyzing the behavior of the users who visit the IDC servers, and IDC IP blocks are thus identified using CGFIP. Furthermore, the connection behavior characteristics of IDC are analyzed based on the connection graphs of IDC (CGIDC). Our findings show that the method can accurately identify the IDC IP addresses and is also capable of reflecting the relationships among IDCs effectively.

As the development of sensor and machine learning technologies has progressed, it has become increasingly important to detect patterns from probabilistic data streams. In this paper, we focus on complex event processing based on pattern matching. When we apply pattern matching to probabilistic data streams, numerous matches may be detected at the same time interval because of the uncertainty of data. Although existing methods distinguish between such matches, they may derive inappropriate results when some of the matches correspond to the real-world event that has occurred during the time interval. Thus, we propose two grouping methods for matches. Our methods output groups that indicate the occurrence of complex events during the given time intervals. In this paper, first we describe the definition of groups based on temporal overlap, and propose two grouping algorithms, introducing the notions of complete overlap and single overlap. Then, we propose an efficient approach for calculating the occurrence probabilities of groups by using deterministic finite automata that are generated from the query patterns. Finally, we empirically evaluate the effectiveness of our methods by applying them to real and synthetic datasets.

A high-image-rejection wireless receiver with an N-phase active RC complex filter is proposed and analyzed. Signal analysis shows that the double-conversion receiver with (N+N2) mixers corrects the gain and phase mismatches of the adjacent image. Monte Carlo simulations evaluate the relation between image-rejection performances and the dispersions of device parameters for the double-conversion wireless receiver. The Monte Carlo simulations show that the image rejection ratio of the adjacent image depends almost only on R and C mismatches in the complex filter.

In this paper, we present a low and variable computation complexity decoder based on K-Best for uncoded detection in spatially multiplexed MIMO systems. In the variable complexity K-Best (VKB), the detection of each symbol is carried out using only a symbol constellation of variable size. This symbol constellation is obtained by considering the channel properties and a given target SNR. Simulations show that the proposed technique almost matches the performance of the original K-Best decoder. Moreover, it is able to reduce the average computation complexity by at least 75% in terms of the number of visited nodes.

This paper proposes novel simplified maximum likelihood detection for XOR physical layer network coding (XOR-PNC) in bi-directional wireless relay systems with Quaternary phase shift keying (QPSK). The proposed detection applies unitary precoding to achieve superior performance without computationally prohibitive exhaustive search. The performance of the XOR employing the proposed simplified MLD with the precoding is analyzed in relay systems with orthogonal frequency division multiplexing (OFDM). The performance of the XOR-PNC with the proposed techniques is also evaluated by computer simulation. The XOR-PNC with the proposed techniques achieves about 7dB better performance than the amplify-and-forward physical layer network coding in the 5-path fading channel at BER=10-4. It is also shown that the XOR-PNC with the proposed techniques achieves better performance than that without precoding.

Compressed sensing (CS)-based wideband spectrum sensing approaches have attracted much attention because they release the burden of high signal acquisition costs. However, in CS-based sensing approaches, highly non-linear reconstruction methods are used for spectrum recovery, which require high computational complexity. This letter proposes a two-step compressive wideband sensing algorithm. This algorithm introduces a coarse sensing step to further compress the sub-Nyquist measurements before spectrum recovery in the following compressive fine sensing step, as a result of the significant reduction in computational complexity. Its enabled sufficient condition and computational complexity are analyzed. Even when the sufficient condition is just satisfied, the average reduced ratio of computational complexity can reach 50% compared with directly performing compressive sensing with the excellent algorithm that is used in our fine sensing step.

An information flow problem is a graph-theoretical formalization of the transportation of information over a complicated network. It is known that a linear network code plays an essential role in a certain type of information flow problems, but it is not understood clearly how contributing linear network codes are for other types of information flow problems. One basic problem concerning this aspect is the linear solvability of information flow problems, which is to decide if there is a linear network code that is a solution to the given information flow problem. Lehman et al. characterize the linear solvability of information flow problems in terms of constraints on the sets of source and sink nodes. As an extension of Lehman's investigation, this study introduces a hierarchy constraint of messages, and discusses the computational complexity of the linear solvability of information flow problems with the hierarchy constraints. Nine classes of problems are newly defined, and classified to one of three categories that were discovered by Lehman et al.

The key escrow problem and high computational cost are the two major problems that hinder the wider adoption of hierarchical identity-based signature (HIBS) scheme. HIBS schemes with either escrow-free (EF) or online/offline (OO) model have been proved secure in our previous work. However, there is no much EF or OO scheme that has been evaluated experimentally. In this letter, several EF/OO HIBS schemes are considered. We study the algorithmic complexity of the schemes both theoretically and experimentally. Scheme performance and practicability of EF and OO models are discussed.