Satellites play a crucial role in Internet of Things (IoT) applications that require precise positioning. Satellite orbit prediction serves as the foundation for providing accurate terminal location services. However, traditional satellite orbit prediction faces challenges like measurement errors, estimation errors, and unmodeled orbit disturbances, leading to low prediction accuracy. To address this issue, this article introduces a groundbreaking satellite digital twin (DT) system based on container technology. This system facilitates real-time mirroring, monitoring, optimization, and control of satellite orbit prediction with low power consumption. Leveraging the advantages of container technology allows for convenient and efficient model updating. Furthermore, a new satellite orbit error prediction model is explored within this system. This model utilizes the seasonal-trend decomposition using locally weighted regression (STL) method and the temporal convolutional network (TCN) algorithm. By decomposing satellite orbit data into multiple components, the proposed model achieves enhanced future orbit Prediction by combining predicted values from each component. Different from existing machine learning (ML) orbit prediction models, our proposed model explores the variation patterns of satellite orbit data from a trend and cycle perspective, rather than relying solely on collecting more data and training larger models to improve prediction accuracy, which makes the novel prediction scheme get good performance while keeping low prediction complexity. Extensive experiments validate the effectiveness of the proposed method using two publicly available satellite orbit datasets (ILRS catalogue and TLE catalogue). The experimental results show that compared with traditional orbit prediction models, the novel DT system has less model update time and occupies less memory. The mean absolute error (MAE) value of the new model is lower than the five ML models in existing researches, which proves that the proposed STL-TCN model has higher prediction accuracy than existing ML orbit prediction models. In addition, we discussed the impact of atmospheric pressure density on the STL-TCN model, and experiments have shown that the correction of different atmospheric pressure density models has a very small impact on the prediction accuracy of the STL-TCN model. Finally, we further investigate the generalization ability of the STL-TCN model for other satellite orbits and future time orbits, and the results show that the novel model has satisfactory generalization ability.

The Internet of Things (IoT) network is a complex system interconnected by different types of devices, e.g., sensors, smartphones, computers, etc.. Community detection is a critical component to understand and manage complex IoT networks. Although several community detection algorithms were proposed, they in general suffer several issues, such as lack of optimal solutions and scalability, and difficulty to be applied to a dynamic IoT environment. In this work, we propose a framework that uses Distributed Community Detection (DCD) algorithms based on Gromov-Wasserstein (GW) metric, namely GW-DCD, to support scalable community detection and address the issues with the existing community detection algorithms. The proposed GW-DCD applies Gromov-Wasserstein metric to detect communities of IoT devices embedded in a Euclidean space or in a graph space. GW-DCD is able to handle community detection problems in a dynamic IoT environment, utilizing translation/rotation invariance properties of the GW metric. In addition, distributed community detection approach and parallel matrix computations can be integrated into GW-DCD to shorten the execution time of GW-DCD. Finally, a new metric, i.e., Gromov-Wasserstein driven mutual information (GWMI), is derived to measure the performance of community detection by considering internal structure within each community. Numerical experiments for the proposed GW-DCD were conducted with simulated and real-world datasets. Compared to the existing community detection algorithms, the proposed GW-DCD can achieve a much better performance in terms of GWMI and the runtime.

High-dimensional signal processing and data analysis have been appealing to researchers in recent decades. Outlier detection and sample-size determination are two essential pre-processing tasks for many signal processing applications. However, fast outlier detection for tensor data with arbitrary orders is still in high demand. Furthermore, sample-size determination for random tensor data has not been addressed in the literature. To fill this knowledge gap, we first derive new tensor Chernoff tail-bounds for random Hermitian tensors. According to our derived tail-bounds, we propose a novel approach for joint outlier detection and sample-size determination. The mathematical relationship among outlier-threshold (sample-size-threshold) probability, outlier-threshold spectrum, and critical sample-size along with the computational-complexity reduction brought by our proposed new analytic approach over the existing methods is also investigated through numerical evaluation over a variety of real tensor data.

In this letter, a novel light-weight audio mosaic scheme using permutations of linear predictive coding (LPC) coefficients as LPC has been widely adopted in many audio codecs (coder-encoders) nowadays. We establish the theoretical secrecy analysis with respect to the degree of the original LPC polynomial and the minimum-phase population of permuted LPC polynomials. The mosaic (destructuring) performance in terms of DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) is also evaluated through simulations. In comparison with the existing audio-mosaic method using waveform permutations, our proposed new audio-mosaic scheme using LPC-coefficient permutations can achieve a much better mosaic performance (a much higher DCT-KLD) subject to the same key size.

Nowadays, wireless local-area networks (WLANs) are widely deployed in residential and commercial areas. The coverage quality is essential to users. The full coverage appears to be one of the most crucial problems to be considered during the network and access-point/router deployment (placement). We formulate the light-of-sight (LoS) coverage problem using the visibility-graph framework. In this work, for arbitrary multiply-connected or simply-connected polygonal/prismatic fields-of-interest subject to an arbitrary link-range restriction, we investigate how the full LoS coverage can be achieved by a minimum number of access-points/routers. Based on the new mathematical lemmas we derive, we design a novel graph-theoretical approach accordingly. Our proposed new scheme can be deemed the first-ever systematic approach to the best of our knowledge. Our proposed new approach is also evaluated in terms of the coverage efficiency, the number of access-points/routers, and the peak link-distance ratio for full LoS coverage in comparison with the existing solution to the art gallery problem.

The approach of least squares (LSs) has been quite popular and widely adopted for the common linear regression analysis, which can give rise to the solution to an arbitrary critically-, over-, or under-determined system. Such a linear regression analysis can be easily applied for linear estimation and equalization in signal processing for cybernetics. Nonetheless, the current LS approach for linear regression is unfortunately limited to the dimensionality of data, that is, the exact LS solution can involve only a data matrix. As the dimension of data increases and such data need to be represented by a tensor, the corresponding exact tensor-based LS (TLS) solution does not exist due to the lack of a pertinent mathematical framework. Lately, some alternatives such as tensor decomposition and tensor unfolding were proposed to approximate the TLS solutions to the linear regression problems involving tensor data, but these techniques cannot provide the exact or true TLS solution. In this work, we would like to make the first-ever attempt to present a new mathematical framework for facilitating the exact TLS solutions involving tensor data. To demonstrate the applicability of our proposed new scheme, numerical experiments regarding machine learning and robust speech recognition are illustrated and the associated memory and computational complexities are also studied.

Nowadays, systems need to be built and/or characterized to handle exceptional circumstances or adapt to a world itself more complex. A typical phenomenon can often be found that systems are required to accommodate high-dimensional inputs and outputs. Although the theories and methods for inverting a single-input single-output (SISO) linear-time-invariant (LTI) system have been well established, the generalized framework (consisting of theories and algorithms) for extending to arbitrary high-dimensional multi-input multi-output (MIMO) scenarios is still unsubstantial in the existing literature as this extension is far from trivial. In this work, we would like to develop such a new framework for governing the inversion of arbitrary high-dimensional discrete-time MIMO LTI systems, where any individual transfer function from a certain input to a certain output may have the infinite-impulse-response (IIR) characteristics. We propose two new inversion algorithms to invert the transfer-function tensors (TFTs) of arbitrary MIMO LTI systems. The pertinent computational complexities are also investigated for our proposed two TFT-inversion algorithms. The approximation of the inverse of an arbitrary TFT by a finite-impulse-response (FIR) TFT is studied and the corresponding approximation-error analysis is derived as well. Finally, numerical evaluations are presented to study the computational complexities with respect to different TFT dimensions and ranks.

Traffic prediction is a very important mechanism in intelligent transportation systems for applications including routing planning and traffic control. In order to infer multifarious traffic information, one/two-relational traffic data in the vector/matrix form need to be expanded to multi-relational traffic data in an arbitrary tensor form. However, none of the existing approaches is capable of performing traffic prediction by characterizing and tracking the inherent nonlinear dynamics which are often encountered in realistic time-series analysis. Although the extended Kalman filter (EKF) has been proven to be quite promising in inferring nonlinear dynamics from time series, but the current EKF approach still suffers, unfortunately, from a serious drawback that state variables have to be represented in vector form. In fact, the characteristics of multi-relational states in practice can never been manifested accurately in practice and the performance of an EKF would be greatly restricted thereby. In this work, we introduce a new tensor extended Kalman filter (TEKF) approach to accommodate arbitrary input, output, and state variables all in arbitrary tensor forms. We also propose a new tensor-based expectation-maximization (EM) algorithm to estimate the nonlinear state-transition and observation-model mappings. The computational and memory complexities of the proposed TEKF approach are also studied in this paper. Finally, numerical experiments are conducted to evaluate the traffic prediction performance of the proposed new TEKF approach over the simulated and realworld traffic datasets in comparison with three other existing deep-learning prediction methods.

Data perturbation is deemed a common problem in data processing. It is often inevitable to avoid noisy or misleading data which may arise from real-world collection or model imprecision. Besides, when data privacy is concerned, data perturbation is used as a prevalent data-protection approach, which alters individual data in a way such that the summary statistics still remain more or less the same. Since many data-mining problems can be formulated as tensor equations for characterizing multi-relational data, the main focus of this work is to perform a new perturbation analysis of tensor equations. From our recent study on tensor inversion, we propose a new mathematical framework to invert an arbitrary tensor but the existing iterative algorithms cannot always do so. In this work, we will establish the theoretical tensor-perturbation analysis to quantify the crucial query performance in terms of normalized error-norm with respect to perturbation degree and condition number. The condition number can be taken as a new measure to determine how the solution of a tensor equation varies as the entries are perturbed. Information-retrieval experiments for conducting the perturbation analysis of the solutions to tensor equations over both artificial and real data are undertaken and studied finally.

Kalman filter is one of the most important estimation algorithms, which estimates certain unknown variables given the measurements observed over time subject to a dynamic system, for many applications in science and engineering including environmental science, ecometrics, robotics, financial analysis, data mining, etc. It is often necessary to characterize multiple relationships among various kinds of signals/data in tensor form. The conventional Kalman filter paradigm is based on the low-dimensional state-space representation, which is restricted by the state-transition, observation-model, process-noise covariance, and observation-noise covariance matrices. However, we often need to express some or all of them in terms of tensors in practice. Very lately, the aforementioned Kalman filter in tensor form was tackled using tensor decomposition but the exact estimator has never been established so far. In this work, we propose a new generalized Kalman filter framework consisting of state, state-transition model, observation-model, process-noise covariance, and observation-noise covariance tensors of arbitrary orders by applying the ShermanMorrisonWoodbury identity and block tensor inverse, which we call "Tensor Kalman Filter" (TKF). Our proposed new approach can produce the exact Kalman filter estimator without any need of tensor decomposition (approximation). The pertinent computational- and memory-complexity studies are also provided in this paper.

High-accuracy satellite-orbit prediction is perceived to be very important for future sixth-generation (6G)) communication networks. It is crucial to acquire the precise satellites' instantaneous location information in order to facilitate the future satellite-aided communication networks. Because of the nonlinear characteristics of satellite orbits, we propose a new advanced artificial neural network (ANN) which is built upon the Levenberg-Marquardt algorithm for robust satellite-orbit prediction. Since the Levenberg-Marquardt algorithm (LMA) involves a second-order derivative of the cost function, our proposed novel LMA-based ANN approach can achieve a better performance compared to the conventional first-order derivative methods, including stochastic gradient and conjugate gradient methods. A standard satellite-orbit dataset, namely Two-Line Element (TLE) Catalog, is employed to validate our proposed new LMA-based ANN approach. Numerical results are presented to demonstrate the effectiveness of our proposed novel LMA-based ANN approach for satellite-orbit prediction.

This article introduces a novel physical-layer (PHY-layer) authentication scheme, called transfer learning-based PHY-layer authentication (TL-PHA), aiming to achieve fast online user authentication that is highly desired for latency-sensitive applications such as edge computing. The proposed TL-PHA scheme is characterized by incorporating with a novel convolutional neural network architecture, namely, the triple-pool network (TP-Net), for achieving lightweight and online classification, as well as effective data augmentation methods for generation of data set samples for the network model training. To assess the performance of the proposed scheme, we conducted two sets of experiments, including the one using computer-simulated channel data and the other utilizing real experiment data generated by our wireless testbed. The results demonstrate the superiority of the proposed scheme in terms of authentication accuracy, detection rate, and training complexity compared to all the considered counterparts.

Recent research attention has been paid to solve tensor equations. Existing solutions to tensor equations are mostly based on the iterative approach due to lack of sufficient theoretical framework governing how to find the inverse of an arbitrary tensor. In this work, we aim to establish a new theoretical framework missing from the literature so that a new algorithm is devised to determine the exact inverse of an arbitrary tensor, which is beyond the capability of the current iterative algorithms. We present theorems to derive the general formula of both inverse and pseudo inverse of an arbitrary tensor so that the inverse of an arbitrary tensor can be constructed from the tensor itself and its partial inverse. A new tensor inversion algorithm is introduced to carry out the exact inverse or the Moore-Penrose inverse should it not be invertible. The main contribution of our proposed approach is that we can always solve any tensor equation while additional restrictions have to be imposed for the existing iterative algorithms to converge on the other hand. The memory- and computational-complexities of our proposed new approach and existing iterative algorithms are also analyzed and compared.

Quantization is an important technique to transform the input sample values from a large set (or a continuous range) into the output sample values in a small set (or a finite set). It has been applied broadly for lossy-data compression, pattern recognition, probability density estimation, and clustering. Vector quantization (VQ) is a prevalent image-compression technique, which treats image matrices as stretched vectors and then finds the representative stretched vectors accordingly for a given image data set. One can use tensor data representation to directly characterize the original two-dimensional image data rather than stretch the image matrix into a long vector so as to destroy the original two-dimensional data structure. In this work, we propose a new tensor quantization (TQ) framework which does not need to reduce the dimensionality of the original image data and destroy the original two-dimensional spatial relationship among data; these two serious drawbacks of vector quantization are well known. We first present tensor calculus and then propose a new parallel tensor-inversion algorithm for TQ thereupon. We also establish the pertinent theoretical proof to justify that our proposed new TQ approach is superior to the existing VQ approach especially as the image dimension becomes large. Finally, numerical experiments to evaluate the image-compression performances of VQ and TQ are demonstrated and their corresponding computational-complexities are also compared.

Matrix calculations are often required for the analysis of any big-data cloud computing system. It is quite common to process big-data associated matrices possessing the sparsity and low-rank properties. In order to efficiently deal with big-data matrices, we propose a new divide-and-iterate framework, which can be invoked to solve an enormously large linear system of equations by taking advantage of factored matrices. The Kaczmarz algorithm (KA) is utilized here to design the parallel iterative algorithms which are capable of solving a large system of equations by iteratively updating the solution through the reduction into the factorized subsystems in parallel. The convergences of our proposed new iterative algorithms are justified by the rigorous proofs. Besides, the time- and memory-complexities are studied to demonstrate the resource efficiency of the proposed algorithms. Numerical experiments are also presented to illustrate the effectiveness of this proposed new framework.

Mosaic is a popular approach to provide privacy of data and image. However, the existing demosaicing techniques cannot accomplish efficient perfect-reconstruction. If the receiver wants to recover the original image, the extra transmission of the original subimage to be mosaicked is necessary, which consumes much channel resource and is therefore inefficient. In this paper, we propose a novel efficient recoverable cryptographic mosaic technique by permutations. A mosaic, or a privacy-protected subimage, can be constructed through either of the three permutations (Busch's, Wu's, and Sun's/Minmax). These three permutations are designed to maximize the objective function as the sum of the absolute row/column index-differences. This objective is related to the sum of the pixel-to-pixel cross-correlation by our pertinent theoretical study. To measure the effectiveness of the image-mosaicing methods, we propose two image-discrepancy measures, namely summed cross-correlation (SCC) and Kullback-Leibler divergence of discrete cosine transform (DCT-KLD). Compared to the big majority of random permutations for image-mosaicing, our proposed three permutation methods can achieve much better performances in terms of SCC. Nevertheless, the advantage of the three proposed permutation methods over random permutations is not obvious according to DCT-KLD.

As the powerful cloud-computing infrastructures become more and more popular, the potential of their applications for dealing with the challenges emerging in cognitive radio networks (CRNs) is under scientific investigation. By making use of the parallel computing capacity of the cloud, we propose innovative parallel QR-factorization algorithms to establish an adaptive transmitter system by dynamically selecting the antennae. Our proposed parallel algorithms can efficiently calculate a tight (achievable) lower-bound of the free distance, which determines the error probability of the symbol detection at the receiver. In this paper, we devise a new parallel QR-based antenna selection scheme in the transmitter to maximize the above-stated lower-bound for achieving the nearly optimal symbol detection at the receiver. Monte Carlo simulation results demonstrate that our proposed parallel method leads to a better bit-error-rate (BER) performance than the conventional singular-value-decomposition (SVD) based scheme. The time complexity analysis is also presented for our proposed parallel algorithms.

In this paper, we propose a blind binary low-density parity-check (LDPC) encoder identification scheme for M-quadrature amplitude modulation (M-QAM) signals. The expectation-maximization (EM) algorithm is developed to estimate the unknown signal amplitude, noise variance, and phase offset for M-QAM signals. The a posteriori probabilities (APPs) of the coded bits are obtained from the APPs of the transmitted symbols according to the M-QAM mapper. Monte Carlo simulation results demonstrate the effectiveness of our proposed new blind binary LDPC encoder identification scheme for different modulation orders. The average iteration number needed for the EM algorithm to converge is also investigated for different modulation orders.

In this paper, we propose a new parallel structure of the linear frequency-domain equalization approach for continuous phase modulated (CPM) signals. Since CPM is a nonlinear modulation technique, the corresponding equalizer design is mathematically intractable. However, it is possible to decompose any CPM signal into a sum of linearly modulated signals through Laurent decomposition. By utilizing Laurent decomposition, the nonlinear nature of CPM is manifested by the mapping of the input symbols onto the “pseudo-coefficients”. This enables us to establish a time-domain polyphase matrix signal model, which can characterize various block-based CPM systems. Such a polyphase matrix model can yield a linear equalizer as its matrix inverse. Moreover, we propose a matrix inverse approximation algorithm to design the equalizers for CPM systems in a parallel paradigm. The algorithmic complexity for the optimal equalizer design is thus significantly reduced. Monte Carlo simulations are taken in compliance with the wireless personal-area network (WPAN) standard. Two primary equalizers, namely minimum-mean-square-error (MMSE) and zero-forcing (ZF) equalizers, are adopted therein. Simulation results demonstrate that our proposed new parallel MMSE/ZF equalizer would lead to a slightly worse bit-error-rate performance than the conventional MMSE/ZF equalizer. Nevertheless, the former scheme would reduce a lot of computational complexity compared to the latter method.

In this paper, we would like to tackle joint blind frame synchronization and encoder identification of binary low-density parity-check (LDPC) codes for binary phase-shift keying (BPSK) signals. The unknown encoder and the unknown time-delay can be blindly estimated at the same time using the average log-likelihood ratios (LLRs) of syndrome a posteriori probability (APP). To reduce the complexity of the blind frame synchronization, we propose a two-stage search method with a search step-size q by exploiting the quasi-cyclic property of the parity-check matrix. Our proposed new joint scheme is evaluated by the probability of detection resulting from numerous Monte Carlo simulations. The simulation results demonstrate the effectiveness of our proposed joint blind frame-synchronization and encoder-identification scheme for multi-path situations.

In this paper, we propose a new low-complexity optimal transmitting antenna selection algorithm for multiple-input multiple-output (MIMO) systems. Different from the conventional optimal antenna selection methods, our proposed algorithm can approximate the inverse matrix subject to an error tolerance. However, the antenna selection optimality can often be obtained as the same outcome from the exact matrix inverse. Furthermore, our proposed matrix inverse approximation algorithm can be greatly expedited by using parallel computing. With significantly reduced complexity using many microprocessors, our proposed method is very appealing to the future MIMO technologies.

The optimal data-fusion rule was first established for multiple-sensor detection systems in 1986. The probability of false alarm and the probability of miss detection required in this data-fusion rule are quite difficult to precisely enumerate in practice. Although the improved data-fusion implementation techniques are available, most existing cooperative spectrum-sensing techniques are still based on the simple energy-detection algorithm, which is prone to failure in many scenarios. In our previous paper, we proposed a novel adaptive cooperative spectrum-sensing scheme based on Jarque-Bera (JB) statistics. However, the commonly-used sample-average estimator for the cumulative weights becomes unreliable in time-varying environments. To overcome this drawback, in this paper, we adopt a temporal discount factor, which is crucial to the probability estimators. New theoretical analysis to justify the advantage of our proposed new estimators over the conventional sample-average estimators and to determine the optimal numerical value of the proposed discount factor is presented. The Monte Carlo simulation results are also provided to demonstrate the superiority of our proposed adaptive cooperative spectrum sensing method in time-varying environments.

The finite impulse response (FIR) filter design has been a hot topic over many decades due to its guaranteed stability and wide variety of applications. The simplest way to design an FIR low-pass filter is to truncate the infinite-long sine sequence. This direct approach is a special case of window-FIR filter design with the rectangular window. Other windows were also proposed to deal with this filter design problem. Alternatively, given a fixed filter (window) length, optimization techniques can be employed to design the best window sequences. In this paper, we would like to present a novel computationally-efficient optimal window design algorithm for the existing window-FIR approach. Since the signal processing storage devices become less costly and more powerful, designing long FIR filters becomes prevalent in the modern telecommunication and signal processing applications. Therefore, the computationally-efficient filter design schemes are in urgent demand. Our proposed technique is based on a fast eigen-decomposition algorithm. The computational complexity of our method is O(N2log(N)) compared to O(N3) of the conventional method.

In this paper, an important statistical signal processing characteristic, namely Gaussianity or normality, is studied. In contrast to the existing Gaussianity measures, we propose a novel measure, which is based on Kullback-Leibler divergence (KLD) between the Gaussian probability density function (PDF) and the generalized Gaussian PDF incorporated with the skewness for the normality test. In our studies, conventional normality tests may often not be robust when they are employed for the non-Gaussian processes with symmetric PDFs. We call this new test as the KGGS test. Our proposed KGGS test is heuristically justified to be more robust than conventional tests for different PDFs, especially symmetric PDFs. A popular application of the normality test for QPSK signal detections is also presented to verify the effectiveness of our proposed technique and the simulation results demonstrate that our new KGGS test would outperform all others even for sparse data samples.

ABSTRACT The mobile WiMAX standard promises to enable low‐cost mobile Internet applications over extensive areas and to meet the capacity requirements by combining advanced multiple input multiple output (MIMO) and relay transmission techniques. In this paper, we propose a solution to increase the channel capacity between wireless links and to conserve the average required uplink transmission power consumption simultaneously through deploying relay stations' (RSs) locations judiciously. Two relaying schemes, analogue (amplify and forward) relaying and digital (decode and forward) relaying from a mobile device to the base station (BS) through a relay node, are adopted with weighting filters to increase the channel capacity. Based on the enhanced channel capacity, a new manipulation way to save power is introduced by deploying RSs strategically where the branch‐and‐bound (BB) algorithm is applied to determine the placements of RSs. Our simulation results demonstrate the significant improvement of network capacity by applying the weighting filter techniques and the great power saving of the average total network power by utilizing the BB algorithm to arrange RSs locations. Copyright © 2011 John Wiley & Sons, Ltd.

A novel bind estimation method for encoder parameters operating over the noisy received signal is proposed in this paper. This scheme can blindly identify the turbo encoder adopted at the transmitter so as to correctly decode the received signal sequence. An iterative expectation-maximization algorithm is designed to estimate the coding parameters, which are the weighting coefficients in a recursive convolutional encoder. These coefficients are associated with the feedback and forward connections in the encoder. To tackle this blind encoder-parameter estimation, we separate the feedback portion from the forward structure and then convert the recursive systematic convolutional encoder into a non-systematic convolutional encoder preceded by a feedback encoder. Our new encoder structure will be investigated. The effect of the separate feedback encoder on the state sequence resulting from the forward convolutional encoder will be studied. Monte Carlo simulation results will be demonstrated to evaluate the effectiveness of our proposed new scheme.

We consider the problems of 1) estimating the physical locations of nodes in an indoor wireless network, and 2) estimating the channel noise in a MIMO wireless network, since knowing these parameters are important to many tasks of a wireless network such as network management, event detection, location-based service, and routing. A hierarchical support vector machines (H-SVM) scheme is proposed with the following advantages. First, H-SVM offers an efficient evaluation procedure in a distributed manner due to hierarchical structure. Second, H-SVM could determine these parameters based only on simpler network information, e.g., the hop counts, without requiring particular ranging hardware. Third, the exact mean and the variance of the estimation error introduced by H-SVM are derived which are seldom addressed in previous works. Furthermore, we present a parallel learning algorithm to reduce the computation time required for the proposed H-SVM. Thanks for the quicker matrix diagonization technique, our algorithm can reduce the traditional SVM learning complexity from O(n3) to O(n2) where n is the training sample size. Finally, the simulation results verify the validity and effectiveness for the proposed H-SVM with parallel learning algorithm.

Adaptive-modulation transceivers have been widely used in wireless communications nowadays. The tradeoff between symbol error rate and data rate can be tuned by adjusting the constellation size. In this paper, we propose a constellation subset selection (CSS) approach and design the novel efficient approximation algorithms to tackle the CSS problems. The approximation ratios for these algorithms are derived. The theoretical studies on how to control the target symbol error rate by selecting an appropriate parameter K are also presented. Monte Carlo simulation results show that our CSS scheme really can reach below the target error probability.

Much progress in the Vehicular Ad Hoc Network(VANET) domain are becoming increasing important in multiple application fields of car-to-car communication. Most research in VANET relies on simulations for evaluation. The mobility model plays an important role to model exact road traffic. However, most mobility models only concern about the mobility without drivers' reactions. The simulation results without an mathematical model are hard to explain the relationship between the parameters and the performance of the proposed protocols. We provide an analytical model for Interactive VANET services which is easy to modify as the proposed environment. In addition, the performance of proposed services can be tuned up with the analytical model supporting. We show that the simulation results obtained when nodes moving with drivers' reactions is significantly different from the commonly used mobility model.

In wireless communications, the channel gain usually varies according to the Rayleigh distribution. Since the channel condition is time-varying, the fixed-rate convolutional encoder cannot facilitate the best error protection that can be achieved in practice. To mitigate this drawback, we propose a new variable-rate convolutional encoder which can adapt to the dynamical channel conditions subject to the channel state information (CSI) available at the transmitter. Appropriate coding rates are selected based on the instantaneous channel state information and thus robust error protection can be undertaken. Through numerous Monte Carlo simulations, we compare the performances in terms of the average error rate between the conventional fixed-rate convolutional encoder and the variable-rate convolutional encoder for different channels. At the same average transmission rate, our proposed new method outperforms the conventional fixed-rate convolutional encoder by a margin of 3-dB signal-to-noise ratio.

For the prevalent research in wireless sensor networks, the main objective is to maximize the lifetime of a sensor network subject to the battery-energy at the sensor nodes. Various heuristic approaches have been proposed to achieve this objective and many simulation results have been presented in the existing literature. However, hardly exists any analytic framework to govern this network lifetime issue. In this paper, we analytically determine the lifetime of a sensor network under different data reporting schemes. Those schemes include direct data reporting by each node to the sink or the base station, cluster-data reporting based on the hexagonal clustering with arbitrary cluster-heads, and cluster-data reporting based on the hexagonal clustering with centroid cluster-heads.We will evaluate the lifetimes of the sensor network under these different reporting methods analytically or via simulations. Moreover, the effects of both path-loss exponent and compression-ratio on the network performances subject to these aforementioned schemes will also be investigated.

Handoff of mobile users is a challenging task for heterogeneous networks. Hard handoff (HHO) and soft handoff (SHO) are two popular schemes. There hardly exists any in-depth research dedicated to a new hybrid handoff method incorporating these two basic schemes so as to retain the advantages from both of them. In this paper, we propose a novel hybrid handoff method by allocating the available frequency bands to HHO and SHO in a sophisticated approach, which is based on mathematical combinatorial or symmetric design (SD). Due to the neat mathematical property regarding the intersection numbers (indicating the particular commonly-shared bands between each pair of cells) from block design theory, the ratio of common (or commonly-shared) frequency bands (CFBs) reserved for SHO between each pair of cells can be guaranteed regardless of the users' mobility. Hence, our proposed new hybrid scheme can be widely adopted, even in high-mobility network environments. Finally, numerical and simulation results are also studied extensively to verify the proposed hybrid handoff scheme and they demonstrate that our method can reduce new call blocking probability significantly as one of the major advantages.

Source localization for wideband signals using near-field acoustic sensor networks has been drawing a lot of research interest recently. In this paper, we would like to tackle the source localization problem based on the realistic assumption where the sources are corrupted by spatially-non-white noise. We explore the respective limitations of our recently proposed algorithm, namely EM source localization algorithm, and design a new direction-of-arrival (DOA) estimation based (DEB) source localization algorithm. The simulation results demonstrate that our proposed DEB algorithm significantly outperforms the previous EM method.

The transmitter identification of digital television (DTV) systems becomes crucial nowadays. Transmitter identification (TxID, or transmitter fingerprinting) technique is used to detect, diagnose and classify the operating status of any radio transmitter of interest. The TxID system is specified in Advanced Television System Committee (ATSC) A/110 standard where pseudo random sequences are proposed to be embedded into the DTV signals before transmission. Thus, the transmitter identification can be realized by invoking the cross-correlation functions between the received signal and the possible candidates of the pseudo random sequences. The buried ratio or injection level of injected Kasami sequences in DTV-TxID systems will both affect the identification correctness and the DTV reception quality. In this paper, we investigate the important unsolved optimization problem for injection level. We present the new analysis here for the realistic scenario consisting of multiple transmitters and receivers over the additive white Gaussian noise channel. The signal-to-interference-plus-noise ratios for the TxID signal detection and the subject TV signal reception are both considered as two essential measures for single-frequency networks. Besides, we design a novel efficient injection-level optimization scheme for TxID simply based on the given information including the signal-to-noise ratio at the receiver and the locations of the transmitters and the receiver(s). Finally, several examples are also demonstrated for the TxID injection-level optimization in this paper.

Among various network topologies, tree-like networks, also known as hierarchical networks are proposed to decrease the overhead of the routing table especially for the situation involving many network nodes. Usually, the routing table size and the routing complexity are the two crucial concerns in designing a large network. Although there have been various algorithms to optimise the routing strategies for the hierarchical networks, hardly exists any work in studying and evaluating the routing table size and the routing complexity rigorously in the statistical sense. In this study, the authors generalise a new mathematical framework by applying the point process in random geometry. The new framework proposed by the authors leads to the explicit statistical measures of the routing table size and the routing complexity, which can be specified as the functions of the hierarchical network parameters including the number of the hierarchical levels and the cluster population for each hierarchical level. After the relationship between the network topology and these two network performance measures (routing complexity and routing table size) is established, a cluster-population optimisation method for hierarchical networks is presented. The simulation results are also provided to demonstrate the advantage of a hierarchical network over the associated conventional network without hierarchy.

The hierarchical network structure was proposed in the early 80s and becomes popular nowadays. The routing complexity and the routing table size are the two primary performance measures in a dynamic route guidance system. Although various algorithms exist for finding the best routing policy in a hierarchical network, hardly exists any work in studying and evaluating the aforementioned measures for a hierarchical network. In this paper, a new mathematical framework to carry out the averages of the routing complexity and the routing table size is proposed to express the routing complexity and the routing table size as the functions of the hierarchical network parameters such as the number of the hierarchical levels and the subscriber density (cluster-population) for each hierarchical level.

In this paper, a new design approach is presented for the transmitter filters which can match the desired or available spectra in a cognitive communication system. Since the corresponding receiver (recovery) filters are necessary to be considered, our proposed design scheme also imposes the constraints on the approximation errors associated with the receiver filters. A new measure is proposed here to quantify the difference between the desired spectrum and the actual spectrum resulting from the transmitter filter based on the Possion-Jesen formula. Moreover, different from many existing works for designing spectrum-shaping filters, our scheme also considers the inverse system at the receiver (receiver filter) jointly to recover the distorted signals caused by the transmitter filter. The approximation error of the inverse filter is measured by our recently derived L2error function. Our proposed filter design scheme for telecommunication transceivers should be able to provide novel spectrum-shaping solutions to the future cognitive radio technology.

In order to mitigate the overhead of the routing protocols in any large-scale network, the hierarchical protocol has been proposed in the early 1980's. However, the message relay would entail the huge throughput reduction in such a hierarchical network. Thanks to the recently developed network coding techniques, a high-throughput low-complexity hierarchical protocol can be facilitated due to the multiple-source relay-based data transmissions built on the network coding schemes, especially for the data communications between the adjacent network levels. In this paper, we will apply the random geometry theory to evaluate the network performance of the proposed hierarchical routing method in the statistical average sense. Three essential measures, namely routing complexity, end-to-end throughput and average network throughput, are formulated and derived thereby. The numerical results demonstrate that the significant improvements in terms of these three network performance measures can be achieved by the proposed hierarchical routing protocol using network coding according to our new statistical analysis.

Adaptive modulation communication systems have been popular nowadays. The tradeoff between the symbol error rate and the data rate resulting from the constellation option is crucial in adaptive modulation. In this paper, we propose a constellation subset selection (CSS) approach to seek this tradeoff and design novel efficient approximation algorithms to tackle the CSS problems. The new theorems and studies on the algorithmic and systematic aspects for the constellation subset selection are facilitated. Our attempt to cope with the CSS problems would be valuable for the future communication systems with adjustable constellation sets.

In this paper, we propose a new family of 64-QAM based sequences for achieving the lowest PMEPR and the highest code rate compared to all other 64-QAM based schemes, which can be applied for OFDM systems. The construction of the proposed sequences is simple and the theoretical analysis is presented.

In this paper, we study the minimum-latency broadcast scheduling problem in the probabilistic model. We establish an explicit relationship between the tolerated transmission-failure probability and the latency of the corresponding broadcast schedule. Such a tolerated transmission-failure probability is calculated in the strict sense that the failure to receive the message at any single node will lead to the entire broadcast failure and only if all nodes have successfully received the message do we consider it a success. We design a novel broadcast scheduling algorithm such that the broadcast latency is evaluated under such a strict definition of failure. The latency bound we derive is a strong result in the sense that our algorithm achieves a low broadcast latency under this rather strict broadcast-failure definition. Simulation results are also provided to justify our derived theoretical latency bound.

Constellation subset selection (CSS) is a new topic emerging in the adaptive modulation communications. How to choose the subset of the original constellation phasers appears to be challenging and interesting to the researchers. There hardly exists any literature which studies the feasibility and the solution of constellation subset selection in details. Here we dedicate to this problem in both theoretical exploration and algorithm design. In this paper, we introduce the detailed theoretical analysis regarding the mathematical properties of the commonly-used constellations and then facilitate the constellation subset selection problem. The CSS problem can be formulated as the maximization of the minimum inter-phaser distance within a constellation subset subject to the symmetry and rate constraints. Based on our problem formulation, we design two algorithms to solve this problem thereby. Our complexity analysis evinces the effectiveness of the proposed algorithms.

The inverse system approximation using the finite impulse responses (FIR) and the corresponding total-model-order determination are essential to a broad area of signal processing, telecommunication, control applications such as acoustic echo cancellation, communication equalization, plant control, etc. To the best of our knowledge, there exists no explicit formulation of the exact Li convolutive error for any arbitrary filter (system) and the corresponding truncated inverse filter. In addition, the approach to determine the minimum total-model-order of the inverse filter subject to the maximum allowable Li convolutive error is also in demand. In this paper, we first derive the formula of the Li convolutive error measure with respect to the filter coefficients for any arbitrary system. According to this new error measure, we design an optimal inverse FIR filter given the exact model orders to achieve the minimum convolutive error. Then, we propose a new algorithm to determine the minimum total-model-order of the appropriate truncated inverse filter to achieve a specified convolutive error based on the discrete filled function approach. A new tradeoff objective function can therefore be facilitated. The numerical evaluation is demonstrated for such a tradeoff between the total-model-order and the system performance, e.g., the bit error rate (BER) for a communication receiver compensated by an FIR equalizer.

In order to decrease the overhead of the dynamic routing mechanisms in large networks, the hierarchical routing protocols have been proposed in the early 80's. The routing complexity and the routing table size are the two most important functional blocks in a dynamic route guidance system. Although various algorithms exist for finding the best routing policy on a hierarchical network, hardly exists any work in studying and evaluating the aforementioned measures of routing complexity and routing table size for a hierarchical network. In this paper, by applying the random geometry theory, we can generalize the mathematical framework from the previous work which discussed the worst-case deterministic models. Our proposed new framework can carry out the averages of the routing complexity and the routing table size, which can be specified as the functions of the hierarchical network parameters such as the number of the hierarchical levels and the subscriber densities (cluster-population) for each hierarchical level. After establishing the relationship between the structure of a hierarchical network and these two crucial network performance measures (routing complexity and routing table size), we present a novel cluster-population optimization method for hierarchical networks and the associated statistical analysis.

Constellation subset selection is a new topic emerging in the adaptive modulation communications. How to choose the subset of the original constellation phasers appears to be challenging and interesting to the researchers. There hardly exists any literature which studies the feasibility and the solution of constellation subset selection in details. Here we dedicate to this problem in both theoretical exploration and algorithm design. In this paper, we introduce the detailed theoretical analysis regarding the mathematical properties of the commonly-used constellations and then facilitate the constellation subset selection problem. Based on our problem formulation, we design two algorithms to solve this problem thereby. Our complexity analysis evinces the effectiveness of the proposed algorithms.

This paper introduces a novel adaptive guard-interval scheme for multiple-input multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems. Conventional OFDM systems set the guard-intervals large enough to combat the inter-symbol interference (ISI). However, such long guard-intervals would often lead to the severe throughput reduction. We design a non-pilot-aided channel-length estimation scheme, which does not require the additional pilot overhead, and propose a new MIMO-OFDM system built on such an adaptive prefix mechanism triggered by the feedback channel length information. Our simulations show that the proposed scheme greatly outperforms the conventional MIMO-OFDM systems.

In the IEEE 802.11 Distributed Coordination Function (DCF) protocol, there are two access modes: the basic access mode and request-to-send/clear-to-send (RTS/CTS) access mode. However, the effect of wireless channel, e.g., the channel signal-to-noise ratio (SNR), and the associated appropriate choice between these two access modes are hardly addressed in the existing literature. In this paper, we design a linear programming algorithm to reduce the complexity of the throughput optimization with respect to the minimum contention window size, which arises from a complicated and difficult nonlinear integer programming problem. Since the network performance of the DCF protocol has been shown to tremendously depend on the channel SNR and the number of competing stations, we propose a new algorithm which selects the access mode and the size of minimum contention window jointly by utilizing the estimated parameters from the feedback network information. We numerically evaluate our proposed new DCF protocol and the results show that our new MAC scheme significantly outperforms the original IEEE 802.11 protocol. Copyright © 2009 ACM.

The maximum-length shift-register sequences (m-sequences) are the often-used pseudo-random sequences for multi-access communications. It is well known that the generation of the m-sequences relies on the initial seed and the cyclic shift. Although we can identify a particular m-sequence using a unique initial seed and the step of the cyclic shift, the categorization or the classification of the m-sequence structures has never been studied to the best of our knowledge. In this paper, we study the m-sequence structures by means of the attributes (common subsequences or patterns). Such patterns are a set of binary sequences with finite length occurring in the full-length m-sequences and they can be used to denote the special attributes (common features) for transceivers. In addition, we design a parallel method to compute all the possible positions of bits "0" and "1" for each underlying pattern using the Berlekamp's algorithm and then we employ the solution to a generalized traveling salesman problem for constructing the shortest binary sequences, each of which contains all underlying patterns. From these shortest binary sequences, we can thus evaluate the number of m-sequences that include the underlying patterns. We also define the attributability, and discriminability for the population analysis of the jointly- or exclusively-attributed m-sequences. Copyright © 2009 ACM.

In modern communication systems and protocols, the sophisticated packet design, which involves multiple-layer requirements, is in demand. Hence any need for adding more features, system control functions, and reserved prefix headers will cause the dramatic change in the packet format and the corresponding communication standard revision. To combat this problem, we introduce a new concept of cursor patterns and propose to employ these cursor pattern sequences, together with a new pattern exclusive coding scheme, to explore the possibility for removing the restriction of the current packet boundaries. In this paper, we derive the crucial mathematical properties for the pattern exclusive codes with respect to an arbitrary set of cursor patterns, such as the coding efficiency of the PECs and the peak-to-average-power ratio measure of transmitting the PECs with respect to a pattern set. Our proposed method to use the pattern sets with the associated PECs can have potentials towards a number of communication applications, such as data payload limiters, transmitter identification, and even short training sequence for estimation and synchronization, etc.

In this paper, we study the channel-aware minimum-latency broadcast scheduling problem using the probabilistic model. We establish an explicit relationship between the tolerated transmission-failure probability and the latency of the corresponding broadcast schedule. Such a tolerated transmission-failure probability is calculated in the strict sense that the failure to receive the message at any single node will lead to the entire broadcast failure and only if all nodes have successfully received the message, do we consider it a successful broadcast. We design a novel reconfigurable broadcast scheduling algorithm such that the latency is evaluated under such a strict definition of failure. Our derived latency bound associated with this new randomized algorithm is substantial to guarantee the low broadcast latency for the complete broadcasting success thereby.

Recently, there has been a steady trend toward the development of subscriber stations (SSs) to enable the ubiquitous communications. In the mobile environment, the power consumption of an SS is an important performance indicator because its battery life is limited. Many existing power-saving schemes for the IEEE 802.16e Mobile WiMax system have been proposed, such as scheduling algorithms for the sleep intervals. However, this type of approaches may be unrealistic for heavy network traffic since the SSs almost always have data to transmit. In this paper, we propose a new power-saving scheme by introducing the pair-wise matching procedure between the SSs prior to uplink data transmission in the WiMax mesh mode. According to the quality-of-service (QoS) requirement, we can preset the desired packet error rate (PER) or signal-to-interference-plus-noise-ratio (SINR) at the receiver end. Given the network topology and the channel state information, the required transmitting power per coded bit at each SS can be calculated. Then we may establish a cost function associated with the required transmitting power per coded bit and thus the optimal matching can be achieved by our proposed minimum weight matching algorithm. The numerical results show the significant improvement of the transmitting power consumption using our proposed method over the conventional scheme when we consider three aspects such as channel effects, coding/modulation options and network topology.

In this paper, we design a novel computationally-efficient linear programming (LP) algorithm to maximize the throughput with respect to the minimum contention window size for the IEEE 802.11 Distributed Coordination Function (DCF) protocol. Based on our LP scheme, a new DCF protocol which can select the best access mode and the optimal size of the minimum contention window is proposed by considering the channel condition and the number of competing stations jointly. The numerical results demonstrate that our proposed DCF protocol significantly outperforms the conventional method.

Channel estimation and equalization techniques are crucial for the ubiquitous wireless communication systems. Conventional receivers for most wireless standards preset the channel length to the maximal expected duration of the channel impulse response for the adopted channel estimation and equalization algorithms. The excessive channel length often significantly increases the implementational complexity of the wireless receivers and leads to the redundant information which would induce the additional estimation errors. Moreover, such a scheme does not allow the dynamic memory allocation for variable channel lengths. This could further increase the power consumption and reduce the battery life of a mobile device. The knowledge of the actual channel length would, in principle, help the system designers decrease the complexity of the channel estimators using maximum likelihood (ML) and minimum-mean-square-error (MMSE) algorithms. In this paper, we address this important channel length estimation problem and propose a novel algorithm to estimate the channel length without the need of pilots or training sequence. In addition, we provide the analysis on the effectiveness of the proposed non-pilot-aided channel length estimator through Monte Carlo simulations.

The inverse system approximation using the finite impulse responses (FIR) and the corresponding model-order determination are important to a broad area of communications and signal processing applications. However, there exists no algorithm to determine the minimum total model-order of the appropriate truncated inverse filter to achieve a specified Li approximation error. In this paper, we design a novel model-order determination algorithm, which can be utilized for efficient dynamic memory allocation on cost-effective transceiver platforms since such a minimum total-model-order is proportional to the memory usage for implementing any inverse filter (equalizer).

The inverse system approximation using the finite impulse responses (FIR) and the corresponding model-order determination are essential to a broad area of signal processing applications such as seismic data processing, communication equalization, acoustic echo cancelation, plant control, etc. To the best of our knowledge, there exists no explicit formulation of the exact mean-square approximation error for the truncated inverse filters. Therefore, in this paper, we derive the exact L2approximation error function with respect to the model orders for the truncated inverse filter using an FIR. Our newly derived L2approximation error evaluation can be employed for the communication or signal processing system design involving the inverse filtering in the future.

In this paper the tradeoff between energy and delay for wireless networks is studied. A network using a request-to-send (RTS) and clear-to-send (CTS) type medium access control (MAC) protocol is considered. A generic framework is developed that allows us to obtain the joint statistics of energy and delay through their joint generating function, when the effects of an imperfect channel are incorporated in the model. Several energy and delay tradeoffs are studied using the joint generating function. These include the average energy vs. average delay, average delay with energy constraint, etc. The proposed analytical method is verified through simulations.

In this work, we investigate the tradeoff between energy and delay for wireless networks utilizing the IEEE 802.11 standard for medium access control. The proposed analysis provides the joint distribution of the energy and delay of a transmitted data packet, which is then used to evaluate the corresponding average values. Our analysis takes into account the effect of the channel noise on the transmission of the RTS, CTS, data, and ACK packets. Furthermore, channel coding is incorporated in the analysis by estimating the packet error probability using error-exponent-based bounds under a memoryless channel. Using these analysis tools, the code rate and the signal-to-noise ratio per dimension are optimized to achieve minimum average delay per information bit.