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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.

This study explores and analyzes students’ difficulties in learning an ERP system to help design more appropriate teaching methods and materials. Global enterprises have widely used ERP systems to manage their operations effectively and efficiently. Hence, many business schools have offered courses on ERP systems to sharpen ERP skills for their students. To help design more appropriate teaching methods and materials for ERP learning, one must know students’ difficulties in understanding. This study analyzes students’ difficulties in learning the Oracle E-Business Suite ERP system through interviews and qualitative analysis. As a result, this study identifies five categories of problems in the various areas of the Revised Bloom’s Taxonomy. Their relevant educational objectives can guide the redesign of ERP teaching methods and materials. One of the difficulties belongs to the area of Remember Factual Knowledge. The rest of them are in Understand, Remember, Apply, and Analysis of Procedural Knowledge. Lastly, this study provides some implications for teaching ERP.

Specific emitter identification (SEI) is a highly potential technology for physical layer authentication that is one of the most critical supplement for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristic of hardware circuits, which difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intra-category distance and separable inter-category distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98\%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The codes of this paper can be downloaded from GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)

This letter introduces a novel joint power-allocation (PA) and relay beamforming optimization (JPBO) algorithm for non-orthogonal multiple-access (NOMA) amplify-and-forward (AF) relay systems to achieve the maximum weighted sum-rate (WSR). In our proposed new framework, the WSR maximization problem is transformed into a tractable weighted-sum minimum mean-square-error (WSMMSE) problem. Then, the PA and the relay beamforming-matrix (BM) are alternately optimized by use of successive convex approximation (SCA) and Lagrange-multiplier (LM) algorithm. Simulation results demonstrate that our proposed new algorithm can significantly improve the WSR with a faster convergence than the existing method.

Femtocells can extend the last-mile accessibility for the current cellular networks so as to serve more users indoors and improve the spectrum utility. However, since the indoor femtocell establishment can be ad hoc, HeNBs may also be inevitably vulnerable to the rogue-HeNB (uncoordinated or illegitimate HeNB) attack in practice. For detecting rogue-HeNBs, a two-dimensional received signal-to-interference-plus-noise ratio (SINR) map is exploited to construct a spatial radio-activity feature map (RAFM). Mathematical proof is derived to justify that the existence of any femtocell interference corresponds to a flat-bottom convex region in the RAFM. Advanced image processing techniques are designed here to localize the radio interference (rogue-HeNB) over the RAFM. Thus, a novel positioning scheme is proposed in this paper to undertake the accurate rogue-HeNB localization for the femtocell networks. Different indoor scenarios are evaluated through the real experiments for our proposed method. The average accuracy can be within one meter for a 6.75 meter by 8.1 meter laboratory.

Cyber security has become an important problem nowadays as almost everyone is often linked to the Internet for business and entertainment. Conventional cryptographers fail to address timely issues regarding cyber-attacks, such as cyber identity theft. In this work, we propose a novel idea, namely, a bandage-cover cryptographer (BCC), which is completely software-defined and protocol-free. Besides, this new cryptographic approach can enable camouflages to confuse data-mining robots, which are often encountered in the cyber world nowadays. Because all of the existing cryptographers aim to protect the entire data (document and file) altogether, they cannot have camouflagibility to mislead data-mining robots. Conversely, by our proposed novel BCC, one can select arbitrary contexts or parts of the data (related to individual identify and/or private confidential information) under protection. To evaluate such a first-ever cryptographer capable of misleading data-mining robots, we define two new metrics, namely: 1) vulnerability and 2) camouflage rates. The theoretical analyses of vulnerability rate and camouflage rate for our proposed new BCC are also presented in this article to demonstrate the corresponding effectiveness.

In this paper, we propose a novel hierarchical radio-frequency identification (RFID) tag-recognition method based on blind source separation (BSS), graph-based automatic modulation classification (AMC), and direct-sequence spread-spectrum (DSSS). In our proposed method, RFID tags can be modulated using different modulation schemes according to different scenarios (e.g., different users or different tag devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple commands. In the signal separation phase, BSS is employed to separate different transmitted signals. Then in the first hierarchy of the recognition phase, different modulation types are adopted to distinguish different users, the graph-based AMC is built upon the periodicity of the modulated signals: the cyclic spectrum of the received signal is established; the graph representation is then constructed according to the cyclic spectrum. Ultimately, robust features are extracted from the graph representation. In the second hierarchy of the recognition phase, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags; the signature sequence set with low cross-correlations can be generated from Kasami sequences. In the third hierarchy of the recognition phase, the information data are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit signals simultaneously in the same frequency band where each tag signal can still be separated and identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new RFID scheme.

A novel pulse-shaping scheme using multiwavelets (MWs) for the non-orthogonal multiple-access (NOMA) communication systems is proposed in this letter. Different from the conventional pulse-shaping schemes, this work exploits multiwavelets for constructing pulse-shaping filters so different users’ information can be multiplexed using the power diversity within the same frequency band. Hence, all users’ transmitted signals can be combined through the superposition by various power-allocation coefficients. At the proposed MW-NOMA receiver, after the successive interference cancellation (SIC) is carried out, the corresponding MW matched filters (shaping pulses) will be invoked to extract each individual user’s transmitted signal. A link-level performance evaluation of the proposed new NOMA scheme is also conducted in this letter. The simulation results demonstrate that our proposed new NOMA technique can significantly improve the system capacity and the bit error rate (BER) performance over the conventional NOMA scheme. When the power-allocation ratio is set to 0.4:0.6 in the proposed NOMA system for two users, the remarkable transmitting $E_{ \text {b}}/\mathcal {N}_{0}$ gain of 15 dB can be achieved by both users.

How to combine multiple data-streams for transmission in aggregate is a very interesting problem, especially for the emerging software-defined networks nowadays. The conventional packet-based protocols cannot provide the flexibility for combining data-streams in the ad hoc nature. If the number of data-streams changes over time, the existing packet formats cannot handle the transmission of multiple data-streams effectively. The effectiveness is measured by two performance metrics, namely coding efficiency and data-transmission intermittency. We propose a new software-defined multiplexing code (SDMC) approach, which can combine (multiplex) multiple data-streams easily and is much more effective than the conventional packet-based method. Three SDMC schemes (distributed, hierarchical, and hybrid) are compared theoretically and by simulation. A trade-off between these two performance metrics can be found when one selects one of the three SDMC schemes for combining multiple data-streams. The hierarchical SDMC scheme brings about the highest coding efficiency while the hybrid SDMC scheme suffers from the smallest overall intermittency.

A novel robust pilot-tone spotting (PTS) technique for software defined radio (SDR) is introduced in this letter. This proposed new technique is designed upon the new idea of spectrum clustering. Given the spectrum of a band-limited signal, the pilot appears to be a spectral impulse located at the valley between two signal-spectrum segments. Then, the pilot-tone search is carried out thereby to first characterize two spectral segments around the pilot in the frequency domain and then find the pilot-tone frequency within the middle spectral segment. Based on the new PTS algorithm, the pilot signal embedded in the frequency-modulation (FM) stereo signal can be spotted accurately, even in the very low signal-to-noise-ratio conditions. The software-defined radio platform has been utilized to undertake real-world experiments for performance evaluation. Experimental results show that the performance of the proposed new PTS scheme is very promising.

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

Proper deployment of roadside units (RSUs) is of crucial importance to vehicular ad hoc networks (VANETs). However, our understandings to the simple one-dimensional RSU Deployment (D1RD) problem with nonuniform profit density is still seriously limited. In this paper, we analyze the D1RD problem and try to design optimal algorithms for it. We first analyze the properties of the optimal solutions of the D1RD problem involving a single RSU, and then extend to multiple RSUs. Next, we propose an efficient technique named Dynamic Limiting (DynLim), which reduces the solution search space size considerably by adjusting search space limits dynamically. Finally, an optimal algorithm named OptDynLim is proposed based on the DynLim technique, and its optimality is proved. Numerical simulations validate the correctness of our analyzes and show that DynLim can usually reduce solution search space size by more than 99%.

An innovative automatic modulation classification via graph-based constellation analysis for ${M}$ -ary QAM signals is presented. In our framework, a unified mesh model for the constellation diagrams of the ${M}$ -QAM signals within the modulation candidate set is first constructed and exploited to transform the received ${M}$ -QAM signal into graph domain. The concise graph representation of the received ${M}$ -QAM signal is established from its constellation according to the positions of the recovered symbols in the mesh model. Then, the modulation feature vector is built from the eigenvector(s) corresponding to the maximum eigenvalue of its adjacency matrix. The modulation type can be identified by measuring the angle between the feature vectors resulting from the training data and the test data. Monte Carlo simulation results and theoretical analysis demonstrate that the proposed method with lower computational complexity can provide superior performance to the existing subtractive clustering technique, and is robust to the residual phase and timing offsets.

In order to perform big-data analytics, regression involving large matrices is often necessary. In particular, large scale regression problems are encountered when one wishes to extract semantic patterns for knowledge discovery and data mining. When a large matrix can be processed in its factorized form, advantages arise in terms of computation, implementation, and data-compression. In this work, we propose two new parallel iterative algorithms as extensions of the Gauss–Seidel algorithm (GSA) to solve regression problems involving many variables. The convergence study in terms of error-bounds of the proposed iterative algorithms is also performed, and the required computation resources, namely time- and memory-complexities, are evaluated to benchmark the efficiency of the proposed new algorithms. Finally, the numerical results from both Monte Carlo simulations and real-world datasets are presented to demonstrate the striking effectiveness of our proposed new methods.

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) play the crucial roles in the fifth generation wireless communication systems. However, the physical-layer security still remains quite challenging. In this letter, a downlink cascaded transmitting zero-forcing-beamforming (ZFBF) technique is proposed to secure communications in a two-cell multiple-input multiple-output (MIMO) NOMA-based CRN. The proposed technique protects the information from illegitimate users (eavesdroppers) within the same and adjacent cells. The restriction on the number of at the transmitter can also be relaxed by applying the signal alignment for the ZFBF technique. This proposed new method can maximize the total secrecy rate of the MIMO-NOMA-based CRNs in the lack of conventional transceiver beamforming techniques. Numerical and simulation evaluations have demonstrated the effectiveness of the proposed method.

Today, it is essential to provide the sufficient quality-of-service (QoS) for real-time mobile applications over wireless channels. Researchers have proposed a variety of solutions, thereby, including resource allocation, data scheduling, and cross-layer design, etc. However, hardly any previous study has investigated the advantage of users’ mobility. This paper proposes a novel QoS-oriented navigation approach to usher the mobile device(s) to the appropriate location, which can enable a sufficient QoS. The proposed mechanism will establish a two-dimensional geographical packet-success-rate (PSR) map, which can help to guide the mobile user(s) to locate the spot providing the QoS sufficing the application(s). With the help of the indoor positioning system and this PSR map, an innovative ushering method can be facilitated for the appropriate mobile users’ placement to reach the satisfactory QoS. The corresponding performance for our proposed scheme has been evaluated through the experiments over the femtocell network. Experimental results demonstrate the effectiveness of our proposed method to provide the sufficient QoS by ushering the user's placement for three networked multimedia applications.

Power-line communications (PLCs) endure a severe transmission channel distortion due to Gaussian noise, impulsive noise, and multipath self-interference. Conventional transmission models with white Gaussian noise would be very inappropriate and impair the necessary channel estimation drastically to the PLC receivers. In this study, a statistical model for impulsive noise, namely Middleton Class A model, is employed for Gaussian information sources. Robust channel estimation algorithms based on a novel parameter estimation scheme are established thereby. Superior to the conventional robust-least-square estimation algorithms, the authors' proposed new adaptive robust-least-square estimation methods can take advantage of the statistical parameter estimation for impulsive noise. The studies on the theoretical and practical aspects of their proposed new parameter estimator are also presented.

In this letter, a novel graph-based adequate and concise information representation paradigm is explored. This new signal representation framework can provide a promising alternative for manifesting the essential structure of the communication signals. A typical application, namely, band-limited signal detection, can thus be carried out using our proposed new graph-based signal characterization. According to Monte Carlo simulation results, the proposed graph-based signal detection method leads to the outstanding performance, compared with other existing techniques especially when the signal-to-noise ratio is rather small.

In this study, the authors tackle the problem of carrier aggregation (CA) in downlink of long-term evolution advanced (LTE-A) femtocell networks. They propose a novel approach in a new perspective: namely, user navigation, to improve the CA performance of an LTE-A system in the indoor environment. The proposed indoor user navigation (IUN) algorithm exploits the a priori knowledge of radio interferences between femtocells to build a geometric quality-of-service (QoS) map, which can be utilised to navigate users toward the locations suitable for performing CA to satisfy the QoS requirements. The simulations demonstrate the effectiveness of the proposed IUN algorithm to improve the CA performance in terms of the aggregate throughput for the LTE-A femtocell networks.

A novel automatic modulation classification method based on the graph presentation of the cyclic spectrum is proposed. In our proposed scheme, the periodicity and the symmetry of the cyclic spectrum will be exploited to establish a concise feature representation of multiple graphs. The modulated signal is first transformed from the cycle-frequency domain into the graph domain. Consequently, the concise graph-presentation, namely, a set of weighted directed rings, will be formulated as the robust features of the original signal. Those features can be easily expressed by the corresponding adjacency matrices. It can be verified that the adjacency matrices are sparse and the non-zero entries therein can be registered as the efficient feature parameters. Through the Hamming distance measure to enumerate the difference between the feature parameters resulting from the training data and the test data, one can perform the modulation classification. Monte Carlo simulation results demonstrate that our proposed method can achieve much better classification accuracy than the existing technique when the cyclic spectrum is used.

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.

Low-density parity-check (LDPC) codes are favorable in low bit-error-rate and high code-rate applications. However, the decoding complexity for LDPC codes is quite large, especially for nonbinary LDPC codes. In this paper, we propose a new T-tolerance stopping criterion for LDPC decoders by exploiting the fact that the total aposteriori probability (APP) should increase across iterations when message passing (MP) algorithms are employed. Simulation results demonstrate that our proposed new T-tolerance criterion can greatly reduce the average iteration number (complexity) while the decoding performance degradation is controlled within 0.1 dB in the low bit-energy-to-noise-spectral-density ratio (Eb/N0) scenarios.

Source localization using acoustic sensor networks has been drawing a lot of research interest recently. In a sensor network, there are a large number of inexpensive sensors which are densely deployed in a region of interest. This dense deployment enables accurate intensity (energy)-based target localization. The maximum likelihood is the predominant objective which leads to a variety of source localization approaches. However, the investigation on the energy-based localization for multiple sources has been very rare. The corresponding robust and efficient algorithms are still being pursued by researchers nowadays. In this paper, we would like to combat the energy-based multiple-source localization problem. We propose two new algorithms, namely, alternating projection algorithm and expectation-maximization algorithm, which can combat the energy-based localization problem for multiple sources. Furthermore, we derive the Cramer-Rao lower bound for these two new methods. Through Monte Carlo simulations and theoretical analysis, we also compare the robustness and the computational complexity of these two algorithms.

The articles in this special section focus on point-to-multipoint communications and broadcasting. In the last few years, since the Internet, streaming video, and mobile devices such as tablets and smartphones have come to play prominent roles in modern daily life, the communication industry has started working on new, more effective digital broadcast systems that can simultaneously deliver signals to both fixed and mobile devices. Digital broadcast systems are suitable for large-volume broadband information delivery, especially for multimedia communications. In fact, the rapid global growth of mobile data traffi c is primarily driven by the massive deployment of mobile video services on modern large-screen devices.

In this paper, the problem involving joint blind frame synchronization and encoder identification for binary low-density parity-check (LDPC) codes of binary phase-shift keyed (BPSK) signals is tackled. The unknown encoder and time-delay can be blindly estimated simultaneously using the average log-likelihood ratios (LLRs) of syndrome a posteriori probability (APP). A new two-stage search method with a search step of size q by exploiting the quasi-cyclic nature of the parity-check matrix is devised to reduce the complexity of the blind frame synchronization algorithm. Numerous Monte Carlo simulations are evaluated to demonstrate the effectiveness of the new joint blind frame-synchronization and encoder-identification scheme for multi-path situations.

The optimal data-fusion rule was first established for multiple-sensor detection systems in 1986. Most subsequent works have been focused on the corresponding implementation aspects. The probability of false alarm and the probability of miss detection used in this data-fusion rule are quite difficult to precisely enumerate in practice. Although the improved data-fusion implementation techniques are now 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 this paper, we propose a novel adaptive cooperative spectrum-sensing scheme based on our recently proposed single-reception spectrum-sensing technique. We also found that the commonly-used sample-average estimator for the cumulative weights in the data-fusion rule becomes unreliable in time-varying environments. To overcome this drawback, 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 both stationary and time-varying environments.

Prevalent adaptive modulation and coding (AMC) techniques can facilitate the flexible strategies subject to the dynamic channel quality. It would be quite intriguing for one to build a blind encoder identification technique without spectrum-efficiency sacrifice for AMC transceivers. In this paper, we make the first-ever attempt to tackle the blind nonbinary low-density parity-check (LDPC) encoder identification given a predefined encoder candidate set over the Galois field GF(q) for q-ary quadrature amplitude modulation (q-QAM) signals. Our proposed method establishes the log-likelihood ratios (LLRs) of syndrome a posteriori probabilities (APPs), which specify the potential correctness of the underlying parity-check relations, and identifies the nonbinary LDPC encoder leading to the maximum average LLR over the candidate set. Monte Carlo simulation results verify the effectiveness of our proposed new scheme.

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.

A novel blind parameter-estimation method, which identifies a turbo encoder, is proposed in this paper. The blind estimator is designed using an iterative expectation-maximization (EM) algorithm. To facilitate this innovative blind estimation scheme, we transform the recursive systematic convolutional (RSC) encoder into a non-systematic convolutional encoder preceded by a feedback encoder. The effect of the separate feedback encoder on the state sequence of the forward convolutional encoder will be studied. Besides, the effectiveness of our proposed new scheme will be evaluated by Monte Carlo simulations.

Source localization for wideband signals using acoustic sensor networks has drawn much research interest recently. The maximum-likelihood is the predominant objective for a wide variety of source localization approaches, and we have previously proposed an expectation-maximization (EM) algorithm to solve the source localization problem. In this paper, we tackle the source localization problem based on the realistic assumption that 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. We also derive the Cramer-Rao lower bound (CRLB) analysis and the computational complexity study for the aforementioned source localization schemes. Through Monte Carlo simulations and our derived CRLB analysis, it is demonstrated that our proposed DEB algorithm significantly outperforms the previous EM method in terms of both source localization accuracy and computational complexity.

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.

The transmitter identification (TxID) of digital television (DTV) systems becomes crucial nowadays. 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 pseudorandom sequences are proposed to be embedded into the DTV signals before transmission. 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 study, the authors investigate the important unsolved optimisation problem for injection level. The authors 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, the authors design a novel efficient injection-level optimisation 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).

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.

Wireless networking plays an extremely important role in civil and military applications. However, security of information transfer via wireless networks remains a challenging issue. It is critical to ensure that confidential data are accessible only to the intended users rather than intruders. Jamming and eavesdropping are two primary attacks at the physical layer of a wireless network. This article offers a tutorial on several prevalent methods to enhance security at the physical layer in wireless networks. We classify these methods based on their characteristic features into five categories, each of which is discussed in terms of two metrics. First, we compare their secret channel capacities, and then we show their computational complexities in exhaustive key search. Finally, we illustrate their security requirements via some examples with respect to these two metrics.

In the last year or so, significant momentum has started to build around the idea of a fifth generation (5G) for wireless communications technology. New research projects have started internationally, and research centers devoted to 5G technology have begun to open. At the ICC 2013 conference in Budapest, there were a number of keynote talks and special invited sessions addressing some of the key concepts around 5G technology. This Special Issue was created by IEEE Communications Magazine to help readers understand the current perspectives on 5G technology from both industry and academic standpoints.

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.

Wideband source localization using acoustic sensor networks has been drawing a lot of research interest recently in wireless communication applications, such as cellular handset localization, global positioning systems (GPS), and land navigation technologies, etc. The maximum-likelihood is the predominant objective which leads to a variety of source localization approaches. However, the appropriate optimization (search) algorithms are still being pursuit by researchers since different aspects about the effectiveness of such algorithms have to be addressed on different circumstances. In this paper, we focus on the two popular source localization methods for wideband acoustic signals, namely the alternating projection (AP) algorithm and the expectation maximization (EM) algorithm. We explore the respective limitations of these two methods and design a new hybrid approach thereupon. Through Monte Carlo simulations, we demonstrate that the trade-off can be achieved between the computational complexity and the localization accuracy using our newly proposed scheme. Moreover, we present the new robustness analysis for the source localization algorithms. We derive the Cramer-Rao lower bound (CRLB) involving the source spectral estimation error and thus prove that the new hybrid algorithm is more efficient than the EM algorithm. By employing the Gaussianity test, we also quantify the statistical mismatch between the actual statistics of the sensor signals and the underlying Gaussian model. We show that the Gaussianity measure can be a reliable robustness figure for source localization.

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.

The intercarrier interference (ICI) due to the Doppler frequency shift, sampling clock offset, time-varying multipath fading and local oscillator frequency offset becomes the major difficulty for the data transmission via the wireless orthogonal frequency division multiplexing (OFDM) systems. The existing ICI mitigation schemes involve the frequency-domain channel estimation/equalization or the additional coding and therefore require the pilot symbols which reduce the throughput. The frequency-domain channel estimation/equalization relies on the huge matrix inversion with high computational complexity especially for the OFDM technologies possessing many subcarriers such as digital video broadcasting (DVB) systems and wireless metropolitan-area networks (WMAN). In our previous work, we proposed a semi-blind ICI equalization scheme using the joint multiple matrix diagonalization (JMMD) algorithm and empirically showed that the proposed method significantly improved the symbol error rates for QPSK- and 16QAM-OFDM systems. In this paper, we discuss the sufficient condition for the theoretical ICI equalizability and also propose an alternative semi-blind ICI equalization method based on the joint approximate diagonalization of eigen-matrices (JADE) algorithm, which is much more computationally efficient than our previous method.

Nowadays, automatic modulation classification (AMC) plays an important role in both cooperative and non-cooperative communication applications. Very often, multipath fading channels result in the severe AMC performance degradation or induce large classification errors. The negative impacts of multipath fading channels on AMC have been discussed in the existing literature but no solution has ever been proposed so far to the best of our knowledge. In this paper, we propose a new robust AMC algorithm, which applies higher-order statistics (HOS) in a generic framework for blind channel estimation and pattern recognition. We also derive the Cramer-Rao lower bound for the fourth-order cumulant estimator when the AMC candidates are BPSK and QPSK over the additive white Gaussian noise channel, and it is a nearly minimum-variance estimator leading to robust AMC features in a wide variety of signal-to-noise ratios. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we do not really have to acquire the complete channel information and therefore it can be feasible without any a priori information in practice. The Monte Carlo simulation results show that our new AMC algorithm can achieve the much better classification accuracy than the existing AMC techniques.

Biological systems present remarkable adaptation, reliability, and robustness in various environments, even under hostility. Most of them are controlled by the individuals in a distributed and self-organized way. These biological mechanisms provide useful resources for designing the dynamical and adaptive routing schemes of wireless mobile sensor networks, in which the individual nodes should ideally operate without central control. This paper investigates crucial biologically inspired mechanisms and the associated techniques for resolving routing in wireless sensor networks, including Ant-based and genetic approaches. Furthermore, the principal contributions of this paper are as follows. We present a mathematical theory of the biological computations in the context of sensor networks; we further present a generalized routing framework in sensor networks by diffusing different modes of biological computations using Ant-based and genetic approaches; finally, an overview of several emerging research directions are addressed within the new biologically computational framework.

The intercarrier interference (ICI) matrix for the orthogonal frequency division multiplexing systems usually has a fairly large dimension and changes over time for mobile communications. In this paper, we design the modified maximum-length shift-register sequences and propose a new distributive allocation scheme together with a linear interpolation for both pilot-aided and non-pilot-aided ICI vector estimates; this new method can combat the realistic non-circulant ICI matrix problem, where almost all other existing schemes could not perform effectively. Monte Carlo experiments show the significant advantages of our new ICI matrix estimator especially when the signal-to-noise ratio is larger than 15 dB