A novel hybrid machine-learning approach is proposed in this work to carry out robust multi-people tracking using a millimeter-wave (mmWave) radar in complex indoor environments. The proposed new system including the adaptive hybrid clustering technique leverages both density-based spatial clustering (DBSCAN) and expectation-maximization (EM) schemes over the received radar point-clouds to separate individual human trajectories reliably. Meanwhile, the Hungarian algorithm incorporation with the Kalman filter is employed for tracking of individual persons. Furthermore, a condition-number-based outlier detector is designed to filter error-prone radar data to improve the tracking accuracy. Realworld experiments are conducted in various indoor scenarios and the results demonstrate that our proposed new system can achieve an average Euclidean-distance error (EDE) of 38.19cm in the presence of a single person and that of 44.70cm in the presence of two persons in our laboratory with the area of 300cm×300cm. Our proposed new multi-subject tracking approach also outperforms the existing methods in terms of EDE.

Orthogonal time-frequency-space (OTFS) modulation, which demonstrates the superior robustness against time-frequency-selective fading over the orthogonal frequency-division multiplexing (OFDM) modulation, has been deemed to be a promising two-dimensional transmission technique for high-mobility wireless communications. Unfortunately, OTFS systems would suffer frominter-frame interference(IFI),inter-Doppler interference(IDoI), andinter-delay interference(IDeI). In this paper, a novel generalized channel model involving IFI, IDoI, and IDeI for wireless OTFS systems has been derived by considering practical factors such as Doppler effect, carrier-frequency drift of local oscillators, multi-path fading, and cyclic prefix coding. Meanwhile, the symbol-error-rate (SER) performance of the PSK-OTFS system is theoretically investigated accordingly. Monte Carlo simulation results demonstrate that our proposed new theoretical SER performance analysis can lead to more accurate results than the existing theoretical analysis involving IDoI only. Moreover, the detailed quantitative analysis of the dominant interference (IFI/IDoI/IDeI) under different conditions is also conducted in this work. Our proposed new interference analysis can be adopted easily to evaluate the reception quality of the future wireless OTFS transceivers.

Routing and task-scheduling in space–air–ground integrated networks (SAGINs) are usually time-dependent due to heterogeneous mobility, intermittent connectivity, and continuously-varying link rates. Existing studies mainly rely on the time-expanded graph (TEG) framework to accommodate mobile dynamics by discretizing continuous link variations into uniform time slots (segments). Consequently, overly coarse slots lead to information quantization loss, while overly fine slots result in a granularity mismatch with the minimum transmission unit and the scalability problem. To overcome these limitations, we propose a novel continuous-time graph (CTG) framework that directly characterizes link-rate functions in continuous time and thus eliminates the stringent dependence on slot granularity. Building upon this new framework, we develop a new CTG event-driven routing (CTG-EDR) algorithm that can perform multi-source, multi-task scheduling through event-driven verification of link and buffer calendars. Monte Carlo simulations demonstrate that our proposed new CTG-EDR scheme can achieve a significantly lower latency and a higher task-completion ratio than the representative baselines. Our simulation results justify that the proposed new CTG-EDR scheme is very promising for robust and scalable routing and task-scheduling in highly dynamic SAGIN environments.

In this paper, we propose a novel cooperative roadside unit (RSU) broadcast approach that can effectively enhance the vehicles’ packet-reception rates of the messages broadcast cooperatively by nearby RSUs. To enable message-aggregation among RSUs, we propose a new vehicular-message format based on the software-defined multiplexing code which can aggregate multiple messages without any need of packet form. To facilitate our proposed new cooperative scheme, RSUs must be organized into clusters. We formulate this RSU clustering problem as a graph partitioning problem, which aims to make individual cluster sizes as close to each other as possible. Furthermore, we design a new heuristic algorithm to efficiently solve this problem. This heuristic algorithm is also compared with the random clustering and minimum-degree clustering schemes in terms of the modified Gini index. The theoretical analysis of our proposed cooperative RSU broadcast scheme is also conducted to guarantee that our proposed new cooperative RSU-broadcast approach can improve the packet reception rate. Furthermore, we simulate the vehicular environment with the simulation tools SUMO and NS-3 as benchmarks. The simulation demonstrates that our proposed new cooperative RSU-broadcast system will increase the packet reception rate and the number of successfully received packets over the conventional non-cooperative broadcast system.

Leveraging breakthroughs in wireless power transfer technology, deploying Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the operational lifetime of wireless sensor networks. Directional Mobile Chargers (DMCs), which integrate directional antennas, can focus radio frequency energy along designated directions, thereby boosting charging efficiency. Most existing works assume that DMCs emit a single energy beam; however, in practice, some DMC platforms can generate multiple sectorial- like energy beams, enabling a novel Multi-Beam DMC (MB-DMC)-based charging paradigm for Wireless Rechargeable Sensor Networks (WRSNs). This paradigm introduces a critical challenge: determining a minimum-size antenna direction set for the DMC while assuring that it is functionally equivalent to the infinite$[0,2\pi )$direction space in maintaining charging schedules with minimal energy loss. This paper systematically investigates the Multi-Sectorial-Beam DMC Charging Scheduling (MSBDCS) problem in MB-DMC-enhanced WRSNs (MB-WRSNs) and proves its NP-hardness. Furthermore, to address this challenge, we propose a Multi-Sectorial Beam Antenna Direction Selection (MSBADS) algorithm by efficiently exploiting geometric properties of local node set distribution, and theoretically prove its optimality in generating a minimum-size charging direction set that is functionally equivalent to the$[0,2\pi )$space. By integrating MSBADS into a four-step charging scheduling framework, we develop our MSBADS-based four-step Charging Scheduling (MSB-4S) algorithm to address the MSBDCS problem in MB-WRSNs. Extensive simulations and testbed experiments demonstrate that MSB-4S outperforms state-of-the-art baseline schemes by over 22% in energy efficiency improvement and more than 14% in scheduling time reduction.

Space-air-ground integrated networks (SAGINs) have emerged as an appealing enabling technology for the next-generation ubiquitous connectivity. By extending terrestrial networks with aerial and space platforms, SAGIN can provide seamless coverage and flexible resource-access across various altitudes. However, dynamic link conditions, intermittent connectivity, and heterogeneous latency constraints would often introduce serious challenges to the service function chain (SFC) migration and orchestration. In this work, we introduce a novel PDO-SFCM (prediction-driven orchestration for SFC migration) approach, which utilizes a fine-tuned large time-series model (LTM) for network status prediction and a deep reinforcement learning (DRL) module for proactive SFC migration in SAGINs. In detail, the fine-tuned LTM predicts multi-horizon estimates of SFC arrivals and per virtual network function (per-VNF) resource demands, which will form the observation space of the DRL agent. The DRL module thus schedules appropriate migration actions on the cost-augmented time-expanded graph (C-eTEG), which can satisfy the feasibility subject to the bandwidth, buffering, and precedence constraints. Extensive simulation results demonstrate that our proposed new PDO-SFCM scheme consistently greatly improves the acceptance rate, reduces the end-to-end delay, and lowers the migration cost in comparison with DRL baselines under different prediction settings. Our proposed new scheme can significantly leverage the SAGIN performance by the devised foundation-level time-series prediction and learning-based orchestration mechanisms.

Millimeter-wave (mmWave) radar has been deemed a key enabling technology for Internet of Things (IoT) applications and offers robust and non-invasive sensing capabilities of intelligent systems. However, conventional Fourier transform (FT)-based range estimation methods suffer from limited resolution, which can restrict performance, especially in dynamic IoT scenarios. To address this, we propose a fractional Fourier transform (FrFT)-based range estimation scheme that achieves higher resolution and accuracy. The proposed method incorporates a recursive clutter removal mechanism to enhance signal quality and employs a random forest regressor to dynamically determine the optimal FrFT angle parameter α for improved performance. Experimental results demonstrate that our proposed new FrFT-based approach achieves a mean absolute percentage error (MAPE) of 16.67%, which significantly outperforms the conventional FT-based method with a MAPE of 37.13%. Furthermore, we validate the effectiveness of our new approach by presenting its application to human height measurement to showcase its potential for real-world applications.

In wireless sensor networks (WSNs), enabling nodes to harvest energy from the environment and facilitating energy sharing among nodes through wireless power transfer (WPT) technology, known as energy cooperation, can alleviate energy scarcity issues and effectively prolong the lifespan of WSNs. Although previous research has investigated various forms of energy cooperation, recent developments have underscored the potential of Multicast Energy Cooperation (M-EC) in supporting efficient multinode energy sharing. This approach leverages the broadcast nature of wireless signals, potentially offering greater efficiency compared to traditional point-to-point style Unicast Energy Cooperation (U-EC). In this article, We focus on the M-EC Assisted Data Collection paradigm for energy harvesting-WSNs (EH-WSNs) and investigate the underlying M-EC assisted data collection scheduling (MECADCS) problem, aiming to minimize the data collection completion time by jointly optimizing the schedule decisions for energy cooperation and data collection. We formulate the MECADCS problem as a mixed integer nonlinear programming (MINLP) problem and establish its NP-hardness. We also simplified the MECADCS problem into a mixed integer linear programming (MILP) formulation via piecewise linear approximation, yet solving it using existing mature MILP solvers is still computationally expensive. To promptly return good solutions, we propose an efficient greedy-based data transmission scheduling algorithm (GDTS),heuristically determines energy cooperation and data transmission schedules and achieves a computational speedup of$10^{4}$times compared to exact solvers. Simulation results demonstrate that GDTS significantly reduces the data collection completion time compared to both algorithms without energy cooperation and those utilizing U-EC.

Nowadays, wireless local-area networks (WLANs) are widely deployed in residential and commercial areas. How to ensure full coverage is always an important challenge during the deployment of access-points (APs) and routers. In this study, we investigate the full Line of Sight (LoS) coverage problem. We will focus on how to achieve the full LoS coverage using a minimal number of APs/routers in an arbitrary polygonal/prismatic field-of-interest, which may be simply or multiply connected subject to a certain restricted link-range. We propose a new visibility-polygon-based approach for doing so. The performance of our proposed new approach is then evaluated in terms of the coverage efficiency, the total number of APs/routers, and the peak link-distance ratio for achieving the full LoS coverage. Meanwhile, we also compare our proposed new approach with the existing schemes. Compared to the existing schemes, our proposed novel visibility-polygon-based approach can achieve the full LoS coverage by requiring the same number of APs/routers but much less computation time.

Mobile edge computing enhances the performance of low-capability end devices by offloading tasks to nearby edge servers, enabling timely responses for delay-sensitive, computation-intensive tasks. However, the rapid and continuous growth of such tasks may soon exceed the capacity of the initially deployed edge server system. This calls for deploying new servers while re-using deployed ones for saving investment, leading to the emergence of a novel paradigm named as Edge Server Expansion Deployment (ESED) here. For this ESED paradigm, aiming to simultaneously minimize the average access delay between end devices and edge servers and the workload deviation among servers, we studied the Budget-Constrained ESED (BC-ESED) problem under the condition of a specified budget constraint. We formulate the problem as a multi-objective optimization problem and prove its NP-hardness. We then propose an algorithm, by combining Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), named GA-PSO. GA-PSO utilizes a four-step iteration framework of selection, crossover, mutation, and correction, where a novel three-party globalbest-localbest-individual crossover operation, inspired by PSO, complements the traditional two-party crossover operation in the crossover step. The convergence and time complexity of GA-PSO are established and analyzed. Simulation results, based on realistic network topologies and workload data from the Shanghai Telecom base station dataset, demonstrate that GA-PSO outperforms other benchmark algorithms in terms of average access delay and workload deviation.

As unmanned aerial vehicles (UAVs) are employed for many applications, UAV identification becomes critical to air traffic control nowadays. Conventional radio-frequency (RF) based UAV identification schemes can correctly recognize a UAV according to the acquired sufficient training RF signal data from a pre-specified training candidate set. However, they may often fail when the model of a test UAV is not included in such a training candidate set and/or the training data are quite limited. To address the aforementioned practical challenges, a novel RF-based open-set few-shot UAV recognition technique is introduced in this work. In our proposed new approach, an RF signal of interest, such as a UAV control signal and a digital data-transmission (DDT) signal, is first sensed and segmented in the UAV frequency band using the corresponding short-time energy-to-spectral entropy ratio (ST-ESER). Then, the cyclic spectrum analysis is applied to the selected RF signal segments to construct the corresponding polyspectra, which are further utilized to establish the cyclic-paw-print (CPP) tensors. Moreover, we design a novel deep-learning (DL) network, namely transformer-attention squeeze-excitation network (TASE-Net), by fusing the transformer-enhanced squeeze-and-excitation (SE) model and the Gaussian mixture model (GMM) into the residual network. The TASE-Net can excel in global feature modeling and unknown class detection simultaneously especially for the open-set and few-shot scenarios. Finally, the constructed CPPs are adopted as the input features of our proposed new TASE-Net to recognize the RF signals (the UAV models). Monte Carlo simulation results demonstrate that our proposed new UAV recognition approach using the TASE-Net can greatly outperform other existing deep-learning methods for open-set few-shot UAV identification.

The well-known soft-focus effect, which relies on either special optical filters or manual post-production techniques, has been intriguing and powerful in photography for quite a while. Nonetheless, how to impose the soft-focus effect automatically simply using sophisticated image-processing (computational photography) algorithms has never been addressed in the literature to the best of our knowledge. In this work, we would like to make the first-ever attempt to design an automatic, optical-filter-free approach to create the appropriate soft-focus effects desired by individual users. Our approach is first to investigate the physical optical filter, namely Kenko Black Mist No. 5, and estimate the corresponding kernel matrix (i.e., the system impulse response matrix) using our proposed novel irradiance-domain kernel-matrix estimation framework. Furthermore, we demonstrate that it is not feasible to find a kernel matrix that precisely characterizes the soft-focus effect by just using a pixel-value-domain image (a regular photo) in post production. To combat the aforementioned problem, we establish a novel pixel-value-to-pseudo-irradiance map such that the pseudo irradiance-domain image can be obtained directly from any pixel-value-domain image. Finally the soft-focus effect can be created from the two-dimensional convolution between the pseudo irradiance-domain image and the estimated kernel. To evaluate our proposed automatic scheme for soft-focus effect, we compare the results from our proposed new scheme and the physical optical filter in terms of the DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) and the conventional PSNR (peak-signal-to-noise ratio). Experiments show that our proposed new scheme can achieve very small DCT-KLDs and very large PSNRs over the ground truth, namely the results from the physical optical filter.

Image mosaic is a prevalent technique to conceal critical content in images. However, conventional mosaic techniques cannot be recovered using a small-sized key, as they require retransmission of the original images for perfect recovery. In this work, we propose a novel, computationally efficient, and effective recoverable image-mosaic technique. A key advantage of our proposed image-mosaic scheme is its robust performance across a range of adjustable key lengths. Our technique effectively conceals original information even with a small-sized key of only a few bits. To evaluate its performance, we introduce a new image-similarity metric based on the magnitude of the discrete cosine transform (DCT). This metric exhibits several advantageous mathematical properties, including the ability to quantify the perceptibility of major content in mosaicked images, invariance under image reflections and 180-degree rotations, and insensitivity to small translations. Finally, numerical experiments demonstrate that our method outperforms existing recoverable image-mosaic techniques and performs consistent across varying key lengths. We also compare the run-times required by our proposed new scheme with those required by other existing recoverable image-mosaic methods and the state-of-the-art image-encryption methods to exhibit the computational efficiency of our proposed new scheme.

Mobile video streaming is an intriguing application for next-generation networks. Wearing the goggles that render two-eye videos, users can enjoy the interactive multimedia experience. Providing high-quality video streams to multiple mobile devices in specific areas will become popular in future cinemas, theme parks, and museums. To ensure quality wireless coverage for a good streaming experience, the next-generation wireless technology (e.g., 5G/6G) employing massive MIMO schemes is a promising solution. Massive MIMO transmissions can improve bandwidth utilization while maintaining acceptable system complexity through numerous transceiving antennae. To incorporate massive MIMO transmissions with mobile video streaming, an innovative cross-layer scheme is needed to flexibly and efficiently manage the antenna array for serving multiple user devices. This allocation mechanism must have low computational complexity and operate stably to prevent demand fluctuations from affecting the quality of service experienced by other users. In this work, we introduce a new problem of provisioning layer-encoded streams to mobile devices (e.g., VR goggles) by allocating antennae in the base stations’ massive MIMO arrays. Given each user’s bitrate demand, the available antennae of each femtocell, and the channel characteristics, the system allocates transmitting antennae to maximize the total system utility. Our theoretical analysis shows that this allocation problem is NP-hard but our proposed scheme provides bounded performance with polynomial-time complexity. We also discuss and justify the stability of our proposed new allocation mechanism. Simulations demonstrate that our scheme outperforms simple heuristic methods. To the best of our knowledge, this is the first attempt to tackle antenna allocation for mobile user devices in immersive video streaming using massive MIMO schemes.

Specific emitter identification (SEI) plays an important role in secure Industrial Internet of Things (IIoT). In recent years, many SEI methods based on machine learning (ML) and deep learning (DL) have been proposed due to their great performance. However, DL-based SEI methods are accompanied by huge computation overhead, which is not suitable for IIoT applications. In addition, the existing ML-based SEI methods rely on feature extraction and a heavy and redundant classifier, which do not ensure optimal feature combination and efficient computation. To solve the above problem, we propose an improved DL-based SEI method using a signal feature embedded knowledge graph (KG) composed of universal features. To the best of our knowledge, this is the first attempt to apply KG for SEI technology. Specifically, we explore an adaptive feature combination (AFC) strategy through the attention mechanism to realize an efficient SEI classifier. The simulation results show that the proposed KG-AFC algorithm outperforms existing SEI methods in identification performance and computation overhead. At the same time, under the optimal compression rate, the average accuracy of the proposed SEI algorithm is higher than 99.2% and can effectively reduce complexity. The code and the data set can be downloaded fromhttps://github.com/Lollipophua/KG-AFC.

Mosaic is a prevalent signal-processing approach to hide or protect critical information from users. The conventional mosaic schemes simply abolish or add artificial noise to the original signal content a sender wants to conceal. Thus, they are not recoverable simply by use of a key which can be represented as a very short sequence compared to the concealed signal content. In this work, we extend our previous effort in recoverable image mosaicing to design a novel audio mosaicing approach using hierarchical permutations. Besides, we establish the mathematical relationship between the popular signal-quality metric, namely, signal-to-noise ratio (SNR), and our previously proposed signal-destructuring metric, namely, Kullback–Leibler divergence of discrete cosine transform (DCT-KLD), so that the mosaicing or signal-destructuring effect in terms of DCT-KLD and the general signal-quality measure in terms of SNR can be translated into each other. As a result, one can easily judge if the mosaicked signal reaches the concealability which is equivalent to the maximum SNR to eliminate the intelligibility of an utterance. The new relationship between DCT-KLD and SNR we develop can thus be very useful to qualify an audio mosaic method without any need of human listening test.

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.

Automatic composite-modulation classification (ACMC) is deemed to be an essential and important cognitive mechanism adopted for the next generation intelligent telemetry, tracking, and command (TT&C), cognitive aerospace communications, and space surveillance as it can automatically recognize the unknown composite-modulation (CM) scheme of the received signal. In this work, we introduce a novel ACMC method using the hybrid feature-vectors based on our proposed new cyclic-paw-print images extracted from the CM signals. In our proposed novel approach, according to the polyspectral analysis of the received CM signals, a received CM signal can be converted to a gray-scale image matrix, named cyclic-paw-print (CPP), which can be very robust against noise. Then, the discrete cosine transform (DCT) and discrete wavelet transform (DWT) are simultaneously applied to the obtained CPP matrix and the hybrid DCT/DWT feature-vector is further constructed thereby. Finally, the sequential minimal optimized support vector machine (SMO-SVM) is adopted as the classifier using such feature vectors. Our proposed new ACMC technique requires a lower computational complexity and leads to a higher recognition-accuracy than other existing ACMC methods according to Monte Carlo simulations and experiments using real CM signal data.

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.

A novel robust package-height estimation and parcel-size classification scheme is proposed in this work. A pertinent prototype system consisting of a millimeter-wave radar, a shipment track, and a stepping motor has been built for proof of concept. Three new schemes using the range-profile data produced by the mmWave radar, namely, the detection based-on cumulative distribution (DBCD) scheme, the detection based-on peak-value clustering (DBPC) scheme, and the DBCD–DBPC hybrid scheme are introduced here. For benchmark evaluation of the proposed new system, two practical scenarios, namely, the undisturbed scenario (no people is close to the system) and the disturbed scenario (some people is close to the system), are tested. This new system can overcome the serious drawback of the existing camera-assisted systems, which have to rely on sufficient lighting conditions, and operate normally even in complete darkness. Meanwhile, the proposed new system can accommodate parcels/packages in arbitrary orientations, which cannot be allowed by the current camera-assisted systems, during the parcel-size measurements. According to the test experiments, the proposed DBCD–DBPC hybrid scheme can reach up to a very high average classification accuracy of 96.4% for both undisturbed and disturbed scenarios. The proposed novel package-height estimation and parcel-size classification technique in this work can be very useful for intelligent inventory management and smart logistics in the future.

The use of mobile chargers (MCs) to charge the nodes in wireless rechargeable sensor networks via wireless power transfer (WPT) has attracted much research effort. Existing works mostly concentrate on path planning whereas neglecting the opportunities to improve charging coverage and efficiency by exploiting the energy redistribution (ERD) process among nodes and an MC’s capability of charging multiple nodes simultaneously via WPT. To exploit such opportunities, we study the underlying ERD-assisted MC charge scheduling (ERAMCCS) problem, i.e., to find a charging schedule satisfying the nodes’ energy demands with minimum energy loss and minimum time span. After proving that the problem is NP-hard, we propose a charge scheduling algorithm based on the greedy idea (CSBGI), which provides a solution by decoupling the problem into two subproblems: 1) ERAMCCS-Energy and 2) ERAMCCS-Time, to minimize the energy loss and the time span, respectively. By partitioning the energy loss into transmission energy loss and moving energy loss, we solve the ERAMCCS-Energy problem by minimizing the two parts, respectively, by formulating and solving some linear programming problems and traveling salesman problem problems based on the charging position set. The charging position set is iteratively refined by identifying and removing redundant charging positions. For the ERAMCCS-Time problem, concurrent energy transmission opportunities are exploited to try to minimize the time span of the schedule. We demonstrate some key properties of CSBGI, such as its approximation ratio in terms of energy loss and its time complexity. Testbed experiments and numerical simulations confirm the superiority of CSBGI over typical algorithms.

Unmanned aerial vehicles (UAVs) are considered to be excellent candidates of airborne relays or base stations for the 3-D ad hoc networks. They can be promptly deployed to serve large bursts of communication traffic in cellular networks or provide timely network support in wireless sensor networks (WSNs). It is desirable but challenging to dynamically find the optimal deployment strategy of UAVs in the air to provide a better Quality of Service (QoS) for a UAV-assisted wireless network. In this article, we propose a novel Gibbs-sampling distributed algorithm (GSDA) to dynamically optimize the UAVs’ locations when they serve as airborne base stations for ground users. In our proposed GSDA, channel capacity is adopted as the objective function and a distributed approach is employed such that each UAV is able to optimize its location independently and asynchronously. Furthermore, we propose a polynomial-regression-based predictor to make use of users’ moving trajectories and take advantage of the predicted users’ future locations to expedite the convergence of the GSDA. Meanwhile, we also compare our proposed GSDA with the existing distributed genetic algorithm. The asynchronization of UAV location updates and the location errors are also investigated to evaluate the robustness of the GSDA. Simulation results demonstrate that our proposed novel GSDA is quite robust and superior to the existing distributed genetic algorithm.

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.

Specific emitter identification (SEI) is a highly potential technology for physical-layer authentication that is one of the most critical supplements for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristics of hardware circuits, which are 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 a complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intracategory distance and separable intercategory 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 code and the dataset can be downloaded fromhttps://github.com/BeechburgPieStar/FS-SEI.

In this letter, we propose a novel automatic-segmentation technique for a dynamic human-posture learning system using skeletal-graph time-series. The focused problem is very challenging as no fixed segment-size is appropriate for capturing precise human postures. Our proposed novel dynamic-segmentation scheme will first estimate the number of segments and then the optimal segmentation can be determined using hidden logistic regression subject to the estimated number of segments. Experimental results from the realworld Kinect data are compared with the well-known dynamic-time-warping (DTW) segmentation method. Based on our experiments, our proposed new scheme greatly outperforms the DTW method in terms of miss-detection probability and miss-alignment percentage.

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.

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.

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.

Underlay device-to-device (D2D) communication network is becoming a promising solution for the fifth generation (5G) and beyond wireless technology. It exploits the proximity of the D2D pairs and improves the overall network’s latency, capacity, and spectral efficiency by sharing/reusing the existing cellular resources. However, due to the frequency-sharing/reusing, the security of the device users (DUs) and the cellular users (CUs) becomes vulnerable. This paper presents a novel physical-layer security (PLS) scheme for the underlay multiple-input multiple-output (MIMO) D2D communications in the presence of multiple eavesdroppers. The proposed new PLS scheme can significantly reduce the information leakage for both CUs and DUs by adopting a null steering scheme at the transmitter. A signal alignment technique is also employed to eradicate the stringent requirement of a larger number of transmitter antennas than that of the receiver antennas. A generalized nonlinear optimization problem has been formulated to improve the PLS performance for MIMO-D2D communications. A closed-form and generalized analytical expression of the secrecy outage probability for CUs and DUs is derived over the imperfect Nakagami-$m$and Norton fading channels. Theoretical and simulation results of our proposed new PLS scheme have shown significant improvement in the secrecy capacity and secrecy outage probability for both CUs and DUs in comparison with the existing methods.

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.

Autonomous vehicles become more and more popular in our daily life. Mobile computing schemes to be installed on these vehicles have drawn a lot of recent research interest. In this paper, we address the important path-planning problem for autonomous vehicles. We introduce and formulate the novelpaintbrush coverage problem. We present a theoretical study on the minimum trajectory length of a paintbrush to cover an arbitrary convex region, which is derived as a function of the area of the region and the size of the cover. Three commonly-used patrolling/scouting methods, namely boustrophedon, spiral, and sector, are manifested in details as the potential solutions to the paintbrush coverage problem. The theoretical minimum trajectory lengths any algorithm can achieve are also demonstrated as the benchmarks for different shapes of regions.

In this paper, a novel blind signal-to-noise-plus-interference ratio (SINR) estimator is proposed based on graph sparsity. The graph representation, which can serve as a new promising alternative to time-, frequency-, and transform-domain waveforms, is capable of extracting sufficient and concise information for communication signals. We discover that the sparsity of the ultimate graph converted from the original signal waveform can be utilized to measure the SINR of the received communication signal without any need of training symbols. For this purpose, a new blind SINR estimation algorithm is introduced in this work. According to Monte Carlo simulations in comparison with the well-known blind SINR estimator using higher-order statistics, our proposed new graph-based SINR estimation scheme has demonstrated the excellent performance, especially for relatively small sample size.

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.

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.

Comprehensive characterization of ligand-binding sites is invaluable to infer molecular functions of hypothetical proteins, trace evolutionary relationships between proteins, engineer enzymes to achieve a desired substrate specificity, and develop drugs with improved selectivity profiles. These research efforts pose significant challenges owing to the fact that similar pockets are commonly observed across different folds, leading to the high degree of promiscuity of ligand-protein interactions at the system-level. On that account, novel algorithms to accurately classify binding sites are needed. Deep learning is attracting a significant attention due to its successful applications in a wide range of disciplines. In this communication, we present DeepDrug3D, a new approach to characterize and classify binding pockets in proteins with deep learning. It employs a state-of-the-art convolutional neural network in which biomolecular structures are represented as voxels assigned interaction energy-based attributes. The current implementation of DeepDrug3D, trained to detect and classify nucleotide- and heme-binding sites, not only achieves a high accuracy of 95%, but also has the ability to generalize to unseen data as demonstrated for steroid-binding proteins and peptidase enzymes. Interestingly, the analysis of strongly discriminative regions of binding pockets reveals that this high classification accuracy arises from learning the patterns of specific molecular interactions, such as hydrogen bonds, aromatic and hydrophobic contacts. DeepDrug3D is available as an open-source program at https://github.com/pulimeng/DeepDrug3D with the accompanying TOUGH-C1 benchmarking dataset accessible from https://osf.io/enz69/.

In this paper, a novel robust 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 random signals. A typical application, namely, target detection within sea clutter, can thus be carried out using our proposed new graph-based signal characterization. According to Monte Carlo simulation results, the proposed graph-based signal (target) detection method leads to the outstanding performance, compared to other existing techniques, especially when the signal-to-noise ratio is rather small (0-6 dB). This new graph-based target detector can be expected to be the future backbone technique for identifying and tracking marine vessels using high-resolution radars.

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%.

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.

More and more new communication scenarios have been emerging, such as heterogeneous, device-to-device (D2D), machine-to-machine (M2M), and unmanned aerial vehicular communications. The existing packet-based protocols cannot serve the individual demands in all of these scenarios effectively. In this paper, we propose a novel evolutional coding approach to govern all of the needs by various communication applications. Superior to our previously proposed software-defined multiplexing coding technique, the new evolutional codes can provide the capacity for fast information retrieval, which is crucial for real-time mapping, routing, and navigation. The fixed and static packet-based communication systems may be replaced by the flexible communication protocol based on the evolutional codes in the future.

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.

This paper presents the optimal physical-layer security scheme of both primary user (PU) and secondary user (SU) in the underlay multiple-input multiple-output cognitive-radio network (MIMO-CRN) by use of a bi-directional zero-forcing beamformer. The proposed new method enables the PU to communicate along with the SUs through a relay node without sacrificing their individual secrecy capacity, i.e., without causing interference to each other even in the presence of eavesdropper(s). In the first phase, a transmitting beamformer at the PU/SU transmitters and a receiving beamformer at the relay have been adopted to separate PU and SU data. In the second phase, a bi-directional beamformer has been applied to eradicate the necessity of involving artificial noise for preventing the active eavesdropper(s). To maximize the total secrecy capacity, a generalized non-linear optimization problem has been formulated and converted to a simplified constrained optimization problem by utilizing the beamformer and the associated subspace restriction. Then, this problem is solved by the Lagrangian method. Both theoretical and numerical analyses of the total and individual ergodic secrecy rates are provided to demonstrate the effectiveness of our proposed method. The secrecy outage probability is also derived to evaluate the performance variations of our proposed scheme when the channel-estimation error occurs.

In this paper, we propose a new hierarchical radio-frequency identification (RFID) tag- recognition method based on the 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 devices). For each modulation scheme, the direct-sequence spread-spectrum strategy is employed to allow simultaneous transmissions of multiple tags. In the first hierarchy, different modulation types are employed 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 first 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, the DSSS scheme is utilized to differentiate the control or sensed data carried by individual tags, the signature sequence set with low cross- correlations is generated from Gold and Kasami sequences. In the third hierarchy, the information data sequences are thus spread by these signature sequences. In our proposed new RFID framework, multiple tags can transmit the signals simultaneously in the same frequency band where the dominant tag signal will be identified and its carried information can be recovered. Monte Carlo simulation results demonstrate the promising performance of our proposed new scheme.

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.

Nowadays, the sufficient quality-of-service (QoS) provision for mobile applications remains a major challenge in any wireless network. Conventional sufficient QoS provision techniques using resource allocation, data scheduling, and cross-layer optimization have been proposed to tackle this problem. Nevertheless, due to the unpredictable nature of wireless channel conditions, the QoS improvements resulting from the aforementioned approaches are often unsatisfactory. In this paper, we would like to follow our previous concept, namely user-placement ushering (UPU), by use of the user's mobility. A mobile user can be relocated to an optimal, or at least a better place to boost the QoS for a particular application. Novel fast algorithms are devised here to usher (guide) the mobile user to an appropriate spot with sufficient QoS, which is nearby. We address the UPU problem to accommodate realistic complex indoor environments, where obstacles are present. Our simulation results have demonstrated that our new fast UPU algorithms are capable of finding new appropriate locations satisfying the required QoSs for different wireless network applications.

In this paper, a novel adequate and concise information extraction approach is explored to provide a promising alternative for manifesting the intrinsic structure of the cyclostationary signals, such as communication signals. A novel graph-based signal representation is proposed to interpret the spectral correlation function into a graph and its adjacency matrix. This graph can represent the proposed adequate and concise information about the communication signals in practice. A typical application, namely modulation classification, can be implemented using our proposed new graph-based approach. According to Monte Carlo simulation results, the proposed graph-based modulation classification method leads to the promising performance in both additive noise channels and difficult multipath fading channels, compared to other existing techniques also using the spectral correlation functions.

The conventional multiplexing approaches for communication systems demand the pre-specified splitting of resources in time, frequency, space, etc. However, these existing techniques are not flexible when variable messages, such as alert or control information generated by other sources, emerges to be included for transmission during a communication session. In this paper, we propose an innovative idea, namely software-defined multiplexing coding, to efficiently accommodate emerging messages from time to time. Such a new multiplexing paradigm does not demand any additional resource and is very easy to be implemented in real time. The computational complexities of the software-defined multiplexing coding schemes are very low and the asymptotic expected code rate approaches very close to one according to our theoretical analysis.

Low-density parity-check (LDPC) codes are favorable in modern telecommunication technologies due to its superior error-correction capability especially when the codeword length is large. The commonly-used LDPC decoders are based on the belief-propagation (BP) algorithms. It is well known that the layered (serial) belief-propagation (LBP) decoding algorithms can reduce the number of iterations by half in comparison with the flooding (parallel) BP decoding algorithms. Further reduction in total number of iterations can be achieved by using the informed dynamic scheduling techniques, such as residual BP (RBP) and node-wise residual BP (NWRBP) methods. However, the incurred additional computation of residuals is far from trivial. In this paper, we propose a novel efficient dynamic scheduling scheme, called a posteriori probability RBP (APPRBP) algorithm, which offers more flexibility to leverage the performance-complexity trade-off using a threshold parameter. The simulation results demonstrate that the average iteration numbers for our proposed APPRBP algorithm can be remarkably reduced especially in the low E_b/N_0 (signal-energy-per-information-bit to noise-power-spectral-density ratio) conditions while the bit-error rate (BER) performance is subject to slight degradation.

The iterative decoding threshold (IDT) estimation for LDPC convolutional codes (LDPC-CCs) may become difficult over additive white Gaussian noise channels, especially when the termination length L gets very large or even approaches infinity. In this paper, we devise a novel fast IDT estimation scheme using the protograph-based extrinsic information transfer (PEXIT) analysis for protograph-based LDPC-CCs. Based on our new analysis, we propose a PEXIT-fast algorithm in which only the mutual information (MI) of a posteriori probability (APP) of the first variable node will be monitored to determine whether the current E_b/N_0 (signal-energy-per-information-bit to noise-power- spectral-density ratio) in evaluation is an upper bound or a lower bound of the IDT. Hence, it is no longer necessary to get through the whole MI evolution and the computational complexity can be greatly reduced thereby. We also design an efficient approach to determine the IDT for an LDPC-CC with an arbitrary large termination length L which can also be allowed to be infinity. The closeness between the known IDTs and the estimated IDTs using our proposed new method for several LDPC-CCs in simulation confirms the effectiveness of our scheme.

Femtocells can be employed as the low-power “green” wireless-access points to extend the macrocell network coverage and enhance the quality-of-service inside homes. In this paper, we focus on the joint subchannel allocation and transmitting-power control for femtocells during the downlink transmissions. Our objective is to minimize the total transmitting power across femtocell access points (FAPs) to facilitate “green” mobile communications. We would like to minimize the following criteria: (1) each user's received signal-to-interference-plus-noise ratio (SINR) per subchannel and (2) the number of subchannels each femtocell user requires to maintain a reliable quality-ofservice (QoS). Meanwhile, the interference of the users in the underlying femtocells to users in other macrocells should also be restricted. Thus, a new pertinent optimization problem is formulated as a mixed integer nonlinear program (MINLP), which is very complicated to solve in practice. In our work, a reformulation-linearization technique (RLT) based on a branchand-bound framework is invoked to simplify the aforementioned MINLP. Finally, simulation results are demonstrated for the effectiveness of our proposed new scheme.

In the technology of smart wind turbine, one of the fundamental requirements is the availability of real-time data of wind speed. The lidar anemometry has several advantages over the conventional ones, especially in the remote marine environment. Thus it may become an important component in the future offshore wind farms. Toward practical applications, the impact of atmospheric interference on optical signals must be well understood first. In this paper, the performance of a generic lidar subject to turbulence and fog is investigated. Several closed-form metrics for evaluating performance are derived. Numerical examples are presented.

In OFDM PMEPR coding, the existing construction method for Asterisk 16QAM is based on the set sum of two QPSKs. However, this approach needs the quaternary Reed-Muller codes in implementation. The high complexity of quaternary codes has restricted the actual deployment of corresponding PMEPR mitigation techniques in cellular phone communications. To reduce the system complexity, in this paper we use two BPSK constellations to construct the Asterisk 16QAM. The proposed scheme only needs the binary Reed-Muller code, so the system complexity is reduced.

In this paper, a novel adaptive coded-modulation scheme, namely M-ary coded modulation, which is spectrally-efficient and can facilitate variable data-rates, is proposed to meet different quality-of-service (QoS) requirements for wireless multimedia applications. An efficient constellation subset selection algorithm incorporated with a novel M-ary coding technique is proposed to generate convolutional codes for different constellation subsets. The studies on the theoretical and practical aspects of our proposed adaptive coded-modulation scheme are presented. Compared with other existing adaptive coded-modulation methods, our scheme offers more flexibilities. According to Monte Carlo simulation results, our new M-ary coded-modulation scheme not only can satisfy the predetermined symbol-error-rate (SER) requirement, but also can significantly enhance the communication quality in terms of bit error rate and throughput.

The transmitter identification (Tx-ID) of digital television (DTV) systems becomes crucial nowadays. However, identification of a weak source signal is quite difficult, and therefore pseudo-random (PN) sequences were proposed to be embedded into the DTV signal for the Tx-ID purpose. Kasami sequences are excellent candidates adopted for the Tx-ID PN sequences as they provide a large family of nearly-orthogonal codes. We envision that, in the future, different broadcasting companies may use different Tx-ID sequences (codes) just as different telephone companies have owned different code prefixes or “area codes”. Tx-ID sequences may be grouped separately for different companies and hence there is an emerging need to categorize Tx-ID sequences just as ZIP codes are used to categorize postal addresses. In this paper, we are particularly interested in the Tx-ID sequences made of Kasami sequences. Our main objective is to efficiently categorize and address Kasami sequences. We design a new algorithm that can efficiently classify Kasami sequences within O(n1.5log2n)-time, where n is the sequence length. Simulations show that our proposed algorithm is very fast in practice. Our proposed new blind detection method for determining the addressing parameters for an arbitrary Kasami sequence can save the required huge memory storage space by the conventional correlation-based Kasami sequence classifier (detector).

Nowadays, it is well-known that the LTE (Long Term Evolution) networks have greatly tackled the Doppler effect problem at the physical layer since they are capable of achieving a 100 Mbps data throughput on a high-speed moving vehicle up to 350 km/h within the same cell (i.e. no handover is allowed). However, there are still cases where vehicles are moving at very high speeds and thus frequent handovers across cells are inevitable, such as high-speed trains, which have been constructed rapidly all over the world in recent years. Therefore, how to maintain good link quality and schedule multimedia services optimally on high-speed trains remains challenging. In this paper, we propose a novel optimal LTE-based multimedia-service-scheduling and resource-allocation mechanism for highspeed trains, which could maximize the service rate and maintain a good service quality at the same time. This new scheme makes use of the seamless handover mechanism by carefully organizing the cell array along the railroad and aggregating the data (at the femtocell) from different users within the train cabins. Besides, we also project to schedule the multimedia services and allocate the network resources as fair as possible for all the users on the train. The simulation results justify the effectiveness of our proposed new scheme.

In this letter, we propose new frequency-domain pilot-multiplexing techniques (FDPMTs) for the channel estimation and equalization in orthogonal frequency-division multiplexing (OFDM) systems. A robust and effective pilot insertion and detection scheme is devised thereby. These pilot positions are optimally selected to minimize the distortion of the transmitted time-domain signal caused by the subcarrier-removal at the corresponding pilot positions. Besides, three different blind pilot-detection techniques are designed at the receiver without any a priori knowledge of the pilot positions, and the distorted data symbols can thus be iteratively reconstructed. Rigorous theoretical analysis and Monte Carlo simulation results both demonstrate that our proposed new OFDM system using dynamical pilot positions is more robust than the conventional OFDM system using the fixed pilot positions over multipath fading channels.

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.

This paper tackles the low-density parity-check (LDPC) encoder identification problem encountered in the time-varying flat-fading channels which are modeled as finite-state Markov chains. The in-phase and quadrature-phase components of the channel coefficients are both represented by a number of states. To greatly simplify the computation of the channel observation probabilities, each channel-state region is further quantized to an interior point. Based on our proposed finite-state Markov model, the Viterbi algorithm is thus invoked to blindly estimate the unknown channel-state sequence from each received signal segment. To mitigate the phase ambiguity which is inherent in the channel-state estimation process, the pilot-aided channel estimation method is also proposed here. The LDPC encoder is finally identified in the framework of the log-likelihood ratio of syndrome a posteriori probability. The performance of our proposed LDPC identification scheme is investigated for different normalized Doppler rates and different mechanisms to reconstruct the channel-state information. Monte Carlo simulation results suggest that pilot symbols are necessary for leading to a satisfactory identification performance for time-varying flat-fading channels.

As a promising theory to recover sparse signal from data samples acquired below the Nyquist rate, compressive sensing (CS) has been drawing pervasive interest in the past decade. In this paper, we explore the compressive sensing potentials for the near-field multiple acoustic-source localization. A novel localization scheme is designed by introducing the optimization of the measurement matrix to enforce the restricted isometry property (RIP) and maximize the signal-to-noise ratio (SNR). Monte Carlos simulations have been carried out to demonstrate the effectiveness of our proposed new scheme. Compared to other existing localization techniques, our scheme exhibits superior performances.

Orthogonal frequency-division multiplexing (OFDM) has been widely used for many communication technologies nowadays due to its high spectral efficiency, robustness against inter-symbol-interference and multipath fading, simplicity for implementation, and so on. In this paper, we propose the frequency-domain pilot multiplexing techniques (FDPMTs) for the channel estimation and equalization in OFDM systems. A robust and effective pilot insertion and detection scheme is devised thereby. The information signal sequence resulting from the constellation mapper is spread over all subcarriers by a precoder and certain subcarriers can be nulled for the insertion of training pilots. These pilot positions are optimally selected to minimize the distortion of the transmitted time-domain signal (OFDM modulated signal) caused by the aforementioned subcarrier-removal at the corresponding pilot positions. The associated new receiver structure is also presented, where three different blind pilot-detection techniques are designed without any a priori knowledge of the pilot positions (based on sample variance, subspace decomposition, and Jarqur-Bera (JB) statistics, respectively), and the distorted data symbols can thus be iteratively reconstructed. Besides, rigorous theoretical analysis and Monte Carlo simulation results both demonstrate that our proposed new OFDM system using dynamical pilot positions is more robust than the conventional OFDM system using the fixed pilot positions over multipath fading channels.

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.

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.

In this work, we study the statistical behavior of the peak-to-mean-envelope-power ratio (PMEPR) for orthogonal frequency-division multiplexing (OFDM) signals both theoretically and empirically. First, the relationship is mathematically established between the PMEPR upper-bound of OFDM-QPSK (quadrature phase-shift keying) sequences and the clipping probability for moderate sequence lengths n (i.e. n < 100). Then, the empirical study of the PMEPR distributions is carried out to rectify the preliminary theoretical results. Finally, we theoretically prove that the analysis for OFDM-QPSK sequences can be extended to OFDM-QAM (quadrature amplitude modulation) sequences as well.

The transmitter identification (Tx-ID) technique has been addressed in the modern ATSC digital television standards. Kasami sequences are adopted as the Tx-ID sequences due to the favorable code capacity. However, the conventional cross-correlation based Tx-ID technique cannot combat the realistic multipath channel impairment. Therefore, in this paper, we propose a new least-square Tx-ID approach, which is demonstrated to be very robust when the multipath channels are encountered. Besides, we also establish the new Hessian analysis to derive the constraint of the injection level. Finally, we devise a fast Tx-ID scheme when the channel length is set to be equal to the Tx-ID sequence length.

Nonbiliary low-density parity-check (LDPC) codes can lead to excellent error performance while the codewords are of short or moderate length. However, the high decoding complexity of nonbiliary LDPC codes inevitably depreciates their practical values. The computational bottleneck arises from the check node processing in the iterative message passing (MP) algorithms which terminate when either all parity checks are satisfied or the maximum iteration number is reached. We have observed that for undecodable blocks, the MP algorithms always run up to the maximum iteration limit and therefore cannot generate the correct codeword. Thus, it would be better to terminate the algorithms early so as to save the unnecessary computational time and reduce the extra power consumption when undecodable blocks are experienced. In this paper, we propose a new T-tolerance stopping criterion for LDPC decoders by exploiting the fact that the total a posteriori probability (APP) should increase as the iteration number grows. Simulation results demonstrate that our proposed new T-tolerance criterion can greatly reduce the average iteration number (complexity) while restricting the decoding performance degradation within 0.1 dB in low bit-energy-to-noise ratio scenarios.

Adaptive modulation and coding (AMC) is commonly used in wireless systems to dynamically change the modulation and coding schemes (MCSs) in subsequent frames such that the spectral efficiency can be adapted to various channel conditions. The spectrum and energy efficiency would decrease if the adopted MCS option at the transmitter needs to be dynamically transmitted to the receiver through a secure control channel. To combat this problem, in this paper, we would like to propose a novel blind channel-encoder identification scheme with low computational complexity for Reed-Solomon (RS) codes over Galois field GF(q), which could also be applied to other similar non-binary channel codes as well. Our proposed new scheme involves the estimation of the channel parameters using the expectation-maximization (EM) algorithm, the calculation of the log-likelihood ratio vectors (LLRVs) of the syndrome a posteriori probabilities over GF(q), and the identification of the non-binary RS encoder in use subject to the maximum average log-likelihood ratio (LLR) over the pre-selected candidate encoder set. Simulation results justify the effectiveness of this new mechanism.

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.

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.

In this work, we derive the PMEPR upper bound for the concatenation of OFDM (orthogonal frequency-division multiplexing) sequences. This new study enables people to construct the longer OFDM sequences from the shorter ones. We investigate how the PMEPR (peak-to-mean-envelope-power ratio) upper bound changes as sequences of different lengths and PMEPRs concatenate. Our new theoretical results are crucial for the PMEPR control of OFDM sequences since it is harder to construct long sequences possessing good properties and concatenating several shorter sequences can be an effective alternative solution in practice.

In this paper, we propose a novel scheduling scheme which can be employed for multi-way relay systems. This new scheme is based on the physical-layer network coding (PNC). To evaluate our proposed scheme, the bit-error-rate (BER) and throughput metrics are analyzed for the multi-way relay systems enduring both channel fading and additive white Gaussian noise (AWGN). Moreover, numerous Monte Carlo experiments are undertaken to compare our proposed new scheduling scheme with other existing strategies. The simulation results demonstrate that our proposed scheme is much more efficient than other existing methods.

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 (ROI). 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 pursuit 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 (AP) algorithm and expectation maximization (EM) algorithm, which can combat the energy-based localization problem for multiple sources.

The optimal data fusion rule for multiple sensor detection systems based on the Bayesian criterion has been derived by Chair and Varshney in 1986. However, most of the following works are focused on how to implement such a fusion rule, since the probability of false alarm and the probability of miss detection are hard to evaluate in practice. Till now, although more and more satisfactory data-fusion implementation schemes are available, most of the cooperative spectrum sensing techniques are based on the simple energy-detection algorithm, which only relies on the energy of the received signal. However, when noise is relatively large or the time-varying characteristics of the signal are conspicuous, the energy-detection spectrum sensing algorithm is more prone to fail. Thus, in this paper, we propose a new adaptive cooperative spectrum sensing scheme, which is based on a novel detection algorithm involving JB (Jarque-Bera) statistic. The ROC (receiver-operating characteristic) curves show that our new cooperative spectrum sensing scheme is more robust than that based on the energy-detection spectrum sensing algorithm. Besides, the performance comparison also implies that the optimal data-fusion rule in our new cooperative spectrum sensing scheme is superior to the commonly adopted “OR” and “AND” rules in the existing literature.

In this paper, we design a novel preprocessor involving signal selection and blind pose estimation such that our previously proposed iterative weighted least-mean-square (IWLMS) blind equalization algorithm only for BPSK and QPSK signals can be adopted for any arbitrary PSK signal constellation. The simulation results show that our proposed new BPE-IWLMS method greatly outperforms the conventional constant-modulus algorithm (CMA).

Nowadays, there is urgent demand for wireless sensor network applications. In these applications, usually a base station is responsible for monitoring the entire network and collecting information. If emergency happens, it will propagate such information to all other nodes. However, quite often the message source is not a fixed node, since there may be base stations in charge of different regions or events. Therefore, how to propagate information efficiently when message sources vary from time to time is a challenging issue. None of conventional broadcast algorithms can deal with this case efficiently, since the change of message source incurs a huge implementation cost of rebuilding a broadcast tree. To deal with this difficult problem, we make endeavor in studying multiple source broadcast, in which targeted algorithms should be source-independent to serve the practical need. In this paper, we formulate the Minimum-Latency Multisource Broadcast problem. We propose a novel solution using a fixed shared backbone, which is independent of the message sources and can be used repeatedly to reduce the broadcast latency. To the best of our knowledge, our work is deemed the first attempt to design such a multisource broadcast algorithm with a derived theoretical latency upper bound.

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.

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.

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.

Orthogonal frequency division multiplexing (OFDM) is a prevalent telecommunication technology to mitigate the common multi-path problem and it is widely adopted with high-order modulations such as quadrature amplitude modulation (QAM) in many communication systems. However, uncoded OFDM systems have a serious drawback of high peak-to-mean-envelope-power ratio (PMEPR). On the other hand, coded OFDM systems, although mitigating the PMEPR problem, often lead to low code rates. Obviously, there exists a tradeoff between PMEPR and code rate in the design of OFDM systems. Nevertheless, hardly exists any approach to address this tradeoff so far in the literature. In this paper, a new general design framework for OFDM M-QAM sequences is proposed, in which the constellation size M can be any arbitrary radix-4 number (i.e. M = 4h) and the sequence length n can be any radix-2 number (i.e. n = 2m). Theoretical studies on PMEPR and code rate are presented so that both metrics can be determined for arbitrary radix-4 M and radix-2 n. This new general approach can be used to construct numerous families of sequences achieving low PMEPR, high code rate, or balancing these two metrics. Our proposed novel general design framework can be deemed very promising for the future OFDM transmission systems.

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 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).

In this paper, the DTV (digital television) spectrum sensing problem is studied, which plays a key role in the cognitive radio. In contrast to the existing higher-order-statistics (HOS) approach, we propose a novel robust spectrum-sensing method, which is based on the JB (Jarqur-Bera) statistic. In our studies, the existing detector may often not be robust when the sample size is small. Our proposed JB detector is heuristically justified to be superior for the simulated microphone signals as well as the real DTV signals. Moreover, the computational complexity analysis for our proposed new JB detector and the HOS detector is also presented. Ultimately, the normality test and the spectral analysis are provided to justify the advantage of our proposed spectrum sensing method.

The digital imaging technology has grown explosively for multimedia applications in recent years. The need for the copyrighted digitalized media becomes urgent nowadays. An approach for the digital copyright protection is to employ advanced watermarking techniques, where watermarks can reveal the ownership identities. Generally speaking, the watermarks are embedded into image or video signals. In this paper, we will investigate digital watermarking techniques and propose a new optimal watermarking scheme. When multiple embedded watermarks are considered, a new analysis for the signal-to-interference-plus-noise-ratios (SINRs) with respect to the subject signal and the watermark signals is carried out. The objective quality measure for the digital watermarking applications should essentially consist of both signal-to-interference-plus-noise-ratio for the subject signal and similarity coefficients for the watermarks. In order to optimize the aforementioned objective measure, we design a novel efficient scale-factor optimization scheme, which can lead to the maximum overall SINR for both subject signal and watermarks. Simulation results are also demonstrated to illustrate the effectiveness of our proposed method.

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.

ENG is a broadcasting industry acronym which stands for electronic news gathering in digital terrestrial television systems. It usually means that a television crew take an ENG truck on location to do a live report for a newscast. The ENG transmitter identification becomes important in digital video broadcasting nowadays. However, the transmitter identification could be quite difficult in a distributed transmission network. A pseudo-random sequence was proposed to be embedded into the digital television (DTV) signal prior to 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. Kasami sequences are commonly used as the transmitter ID (Tx-ID) sequences as they provide a large family of nearly-orthogonal codes. In order to investigate the sensitivity of the transmitter identification performance to the arbitrary topologies and the Kasami sequence lengths, we present the new analysis here for Tx-ID over random geometric layouts. In our analysis, the lowest received co-channel signal-to-interference ratio is considered as the crucial factor for the multiple-transmitter identification. Moreover, the transmitter location uncertainties are also considered in this paper. The effect of such uncertainties on the minimum required Kasami sequence lengths is investigated. It turns out to be that the larger the Kasami sequence length, the larger the received signal-to-interference ratio. Our new analysis can be used to determine the required Kasami sequence length for an arbitrary ENG-transmitter topology.

Orthogonal frequency division multiplexing (OFDM) is a prevalent telecommunication technology to mitigate multipath distortion with high-order modulations such as quadrature amplitude modulation (QAM). However, uncoded OFDM systems also have a serious drawback of high peak-to-mean envelope power ratio (PMEPR). On the other hand, coded OFDM systems can reduce the PMEPR problem but often lead to low code rates. There is thus a tradeoff between PMEPR and code rate in the design of OFDM systems. In this paper, PMEPR reduction for OFDM 64- and 256-QAM sequences is comprehensively studied. Four new families of 64-QAM sequences and seven new families of 256-QAM sequences are proposed to achieve the lowest PMEPR, the highest code rate, or the tradeoffs between these two metrics. Through the comparison with all other OFDM 16- or 64-QAM sequences, these new families of OFDM sequences can facilitate higher code rates. Furthermore, many of these new sequences have lower PMEPR than other OFDM sequences. Adjustment of the tradeoff between PMEPR and code rate can be made to meet the stringent demand in low PMEPR or the need for high code rate subject to various system requirements. Moreover, the construction method of the proposed new sequences is quite simple.

In this paper, the DTV (digital television) spectrum sensing problem is studied, which plays a key role in the cognitive radio. In contrast to the existing higher-order-statistics (HOS) approach, we propose a novel robust spectrum-sensing method, which is based on the JB (Jarqur-Bera) statistic. In our new studies, the existing HOS spectrum sensing technique may often not be robust. Our proposed JB-statistic based detector has been justified to be superior for the simulated microphone signals as well as the real DTV signals.

In this paper, we propose a novel blind equalizer which can deal with high-order modulated QAM (quadrature amplitude modulation) signals. This new scheme is based on the signal selection (SS) and the iterative weighted-least-mean-square (IWLMS) algorithm. The incurred additional complexity by the SS scheme is linear with respect to the sample size of the received signal and the IWLMS method is also very efficient. We employ numerous Monte Carlo experiments to compare our proposed blind equalization method with the popular constant modulus algorithm (CMA). The simulation results demonstrate that our proposed scheme is more robust than CMA especially when the QAM modulation order is large.

In this paper, a fundamental but important statistical signal processing characteristic, namely the Gaussianity or normality, is studied. In contrast to the existing conventional Gaussianity measures, we propose a novelmeasure, 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 the conventional tests for different PDFs, especially for the symmetric PDFs.

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.

Coding techniques for reducing peak-to-mean-envelope-power-ratio (PMEPR) have been intensively investigated for the orthogonal frequency division multiplexing (OFDM) systems adopting PSK, square QAM, and rectangular QAM constellations since the PMEPR is the crucial problem in OFDM. Recently, we designed a new coding method for reducing the PMEPR in OFDM built on star 16QAM constellations. In this paper, we present a generalized paradigm for the novel asterisk 16QAM constellations and facilitate the new PMEPR controlling technique for OFDM systems. In this new framework of the asterisk 16QAM (A16QAM) coded OFDM systems, we establish the theoretical development and show that the maximum PMEPR is bounded by 4.0 and 2.0, if the information symbols are coded using Golay sequences and Golay complementary pairs, respectively.

Blind equalization for high-order modulated signals is difficult to combat since the corresponding probability density functions are very complex. The conventional constant modulus algorithm (CMA) is simple but not robust when the modulation order is high. In this paper, we design a novel preprocessor involving signal selection and blind pose estimation such that our previously proposed iterative weighted least-mean-square (IWLMS) blind equalization algorithm only for BPSK and QPSK signals can be adopted for any arbitrary PSK signal constellation. The simulation results show that our proposed new BPE-IWLMS method greatly outperforms the CMA.

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.

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.

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.

Expectation-Maximization (EM) algorithms have been widely adopted in a variety of areas such as clustering, hidden Markov modeling, channel estimation and equalization, etc. The EM-based approaches to resolve the likelihood maximization involving latent variables are usually very complicated for signal processing and communication applications. To combat this problem, we construct an alternative metric for likelihood or log-likelihood, namely auxiliary function, which results in a novel EM hill-climbing (EM-HC) optimization procedure. We extend our previous efforts along this line of the auxiliary function research to generalize the EM-HC scheme and solve the blind equalization for the complex-valued signals. In this paper, our new EM-HC method, namely efficient Iterative Weighted LeastMean Squared (IWLMS) algorithm is extended for QPSK and QAM signals. The new version of the IWLMS algorithm greatly outperforms the prevalent blind equalization algorithms based on the constant-modulus and kurtosis criteria according to Monte Carlo simulations.

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.

An adaptive Orthogonal Frequency Division Multiplexing (OFDM) system, having precoded cyclic prefix (PCP) was proposed earlier as a way to carry the control signaling parameters to provide much needed flexible transmission techniques in cognitive radio (CR) communications. The ultimate goal of the PCP-OFDM system is to achieve concurrent transmission of CR control signaling parameters with data information. Therefore, hardware and computational efficient demodulation of PCP is of huge importance to recover the signaling parameters and for the design of fair spectrum sharing mechanism in cognitive radio scenario. But due to the hardware and computational complexity of the conventional optimal matched filter, recently a demodulator was proposed in paper which has reduced complexity than the conventional approach. However, the complexity is still high which is often very difficult to implement and also not cost-effective. In this paper, we propose a low complexity demodulator for the efficient demodulation of the signaling information carried by the PCPs. Design of this demodulator is presented and performance is evaluated through the simulations in different channel scenarios. Along with a peak combining technique, impact of the multipath impairment can be mitigated to a reasonable extent. More importantly, the hardware and computational complexity is reduced substantially compared with the other techniques proposed earlier. It is also found from the numerical simulation results that this demodulation scheme provides the same performance as the conventional optimal matched filter, with significantly reduced hardware and computational complexity, therefore reducing the overall complexity and cost of the PCP-OFDM receiver.

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.

An adaptive orthogonal frequency division multiplexing (OFDM) system, with a preceded cyclic prefix (PCP) as a signaling link was proposed earlier to address the recent need of flexible transmission techniques in cognitive radio (CR) communications. The flexibility of the PCP-OFDM system relies on the concurrent transmission of OFDM signal and PCP signaling which represents OFDM system parameters including bandwidth, modulation, coding schemes etc. Efficient demodulation of PCP signaling is therefore of great importance for OFDM data recovery and reduce the delay from the system adaption. In this paper, we propose to use a three-stage demodulator to recover the signaling information carried by the PCPs. Design of this three-stage demodulator are analyzed and performance is evaluated through the simulations in different channel conditions. In addition, with proposed peak combining technique, impact of the multipath impairment is mitigated to a great extent. It is observed from the simulation results that this signaling demodulation scheme provides the same performance as the conventional optimal matched filter but with significantly reduced hardware and computational complexity.

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.

Nowadays, the source localization has been widely applied for wireless sensor networks. The Gaussian mixture model has been adopted for maximum-likelihood (ML) source localization schemes. However, this model does not match the statistics of the real data in practice. In this paper, we study the probability density function of the sensor signals and demonstrate that the distribution is not Gaussian. We propose to employ the Gaussianity test based on the bootstrap algorithm to quantify the departure of Gaussianity for the received signals added with different kinds of noise. Our proposed Gaussianity test can be used as the robustness figure for evaluating the prevalent ML source localization schemes.

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).

Recent development in cognitive radio (CR) brings significant technical challenges in the design of robust and flexible transmission technique in hostile communication environment with varying channel condition. An adaptive orthogonal frequency division multiplexing (OFDM) system, with a precoded cyclic prefix (PCP), is proposed in this paper to address these challenges. Besides the basic function as a guard interval for the OFDM systems, the PCP provides an efficient way of sending the transmission system parameters of the cognitive radio simultaneously with the data carrying OFDM signal. These parameters may include the bandwidth, total number of OFDM subcarriers, modulation and coding schemes used. Overall spectrum efficiency can be improved due to the elimination of the preambles and handshaking signaling required when there is any change in the CR transmission parameters. The receiver design particularly a hybrid domain equalizer for the PCP-OFDM system is presented in this paper. Implementation related issues including exploitation of the PCP structure, interference cancellation and complexity reduction are investigated. The performance of the proposed OFDM systems and the channel estimators are analyzed and verified through numerical simulations.

A new multicarrier system, termed multi-symbol encapsulated orthogonal frequency division multiplexing (MSE-OFDM), was proposed earlier, in which one cyclic prefix (CP) is used for multiple OFDM symbols. The motivation of MSE-OFDM is to address the disadvantages of OFDM system, i.e., the sensitivity to carrier frequency offset and high peak to average power ratio at the same time. In this paper, we propose a new time slicing technique by replacing the cyclic prefix with preceded pseudo random sequence for MSE-OFDM system. With the proposed time slicing technique, multiple multimedia data streams with dynamic data rates can be easily multiplexed. In addition, this system overcomes the high sensitivity to frequency offset inherited with OFDMA system and can be used for both uplink and downlink for mobile multimedia communications with variable data rates. The proposed time slicing system also enables the power saving for mobile receivers, through which the batter life can be substantially extended.

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.

Wideband source localization using acoustic sensors 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 in 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.

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.

Kurtosis minimization has been applied for the existing blind equalization schemes but the corresponding optimization procedures are very sensitive to the channel conditions and the initial conditions. In this paper, we introduce a new cognitive receiver front-end, which includes a novel switching blind equalizer and an automatic modulation classifier. We design a switching criterion based on the kurtosis/normalized moment ratio threshold to select the better signal between the raw data and the equalized sequence. Simulations demonstrate that our proposed robust switching blind equalization scheme can significantly outperform the existing blind equalizer and would not degrade the subsequent modulation classification accuracy.

The voice activity detection (VAD) is crucial in all kinds of speech applications. However, almost all existing VAD algorithms suffer from the nonstationarity of both speech and noise. To combat this difficulty, we propose a new voice activity detector, which is based on the Mel-energy features and an adaptive threshold related to the signal-to-noise ratio (SNR) estimates. In this paper, we first justify the robustness of the Bayes classifier using the Mel-energy features over that using the Fourier spectral features in various noise environments. Then, we design an algorithm using the dynamic Mel-energy estimator and the adaptive threshold which depends on the SNR estimates. In addition, a realignment scheme is incorporated to correct the sparse-and- spurious noise estimates. Numerous simulations are carried out to evaluate the performance of our proposed VAD method and the comparisons are made with a couple existing representative schemes, namely the VAD using the likelihood ratio test with Fourier spectral energy features and that based on the enhanced time-frequency parameters. Three types of noise, namely white noise (stationary), babble noise (nonstationary) and vehicular noise (nonstationary) were artificially added by the computer for our experiments. As a result, our proposed VAD algorithm significantly outperforms other existing methods as illustrated by the corresponding receiver operating curves (ROCs). Finally, we demonstrate one of the major applications, namely speech waveform compression, associated with our new robust VAD scheme and quantify the effectiveness in terms of compression efficiency.

A new cross-layer signaling interface using overlay watermarks for orthogonal frequency division multiplexing (OFDM) system is proposed in this paper. The major advantage of the new proposal is the multiple functionalities the overlay watermark provides, which includes a cross-layer signaling interface, a transceiver identification for position-aware cross-layer design, as well as its basic role as a training sequence for channel estimation. Location information of a mobile can be easily derived for efficient routing when a unique watermark is associated with each individual mobile transceiver. In addition, a new data pipe can be created by modulating the overlay watermarks for the transmission of cross-layer signaling and other interlayer interactive information. We also study the channel estimation and watermark removal techniques at the physical layer for the proposed overlay OFDM. Our channel estimator iteratively estimates the channel impulse response and the combined signal vector from the overlay OFDM signal. Cross-layer design that leads to low power consumption and more efficient routing are investigated.

We propose a new Multi-Symbol Encapsulated Orthogonal Frequency Division Multiplexing (MSE-OFDM) system with reduced implementation complexity in this paper, by replacing the traditional cyclic prefix (CP) with one pseudo random sequence. Although robust performance and high bandwidth efficiency are achieved, the receiver implementation of the previous MSE-OFDM system is substantially more complicated than traditional OFDM. In this paper, we propose a new cyclic prefix for MSE-OFDM using pseudo random sequences. With the new introduced cyclic prefix, inter-symbol interference (ISI) between OFDM symbols within one MSE-OFDM frame can be easily canceled in time domain. An iterative intercarrier interference (ICI) cancellation algorithm is also proposed in this paper. Equalization of the OFDM signal can therefore be achieved on every OFDM symbol basis, based on our new hybrid domain equalizer. It is shown that the implementation complexity of the new MSE- OFDM receiver with the pseudo random sequence cyclic prefix is comparable to the traditional OFDM. The performance of the proposed MSE-OFDM is also analyzed and verified through numerical simulations.

Phase noise is a critical factor that degrades the synchronization performance of a wireless communication receiver. Hot-carriers (HCs), found in the CMOS synchronization devices, are high-energy charge-carriers that can degrade the MOSFETs performance by damaging the internal device structure and lead to the phase noise increase therein. Such incremental phase noise can be related to the essential parameter, namely the MOSFET threshold voltage due to the HC effect, which is particularly evident in the short-channel MOSFET devices. In this letter, we analyze the impact of the phase noise arising from the HC effect on the wireless systems in terms of the bit-error-rate (BER) and the signal-to-interference-plus-noise ratio (SINR).

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

Due to the superior spectral efficiency and robustness over the multipath channels, orthogonal frequency division multiplexing (OFDM) has served as one of the major modulation schemes for the modern communication systems. In the future, the wireless OFDM systems are expected to operate at high carrier-frequencies, high speed and high throughput mobile reception, where the fast time-varying fading channels are encountered. The channel variation destroys the orthogonality among the subcarriers and leads to the intercarrier interference (ICI). ICI poses a significant limitation to the wireless OFDM systems. In this paper, we develop a pilot-aided minimum mean square error (MMSE) ICI coefficient estimation algorithm for combating the rapid time-variant channels. Our MMSE ICI estimator utilizes the channel correlation properties. Since the channel statistics are usually unknown, we also investigate the mismatch between the underlying channel model and the actual channel statistics. Monte Carlo simulation results show that the achieved mean square ICI estimation error mostly depends on the number of pilots instead of such a mismatch. In addition, a MMSE equalizer incorporated with our ICI coefficient estimation scheme can greatly improve the symbol error rate in the rapid time-variant channels with multiple Doppler frequencies over the conventional one-tap equalizer.

A computationally efficient algorithm for multiple source localization, using the expectation-maximization (EM) algorithm, for the wideband sources in the near field of a sensor array/area, is presented. The basic idea is to decompose the observed sensor data, which is a superimposition of multiple sources, into individual components in the frequency domain and then estimate the corresponding location parameters associated with each component separately. Instead of the conventional alternating projection method, we propose to adopt the EM algorithm in this paper; our method involves two steps, namely Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum likelihood estimates of the source location parameters are obtained. These two steps are executed iteratively and alternatively until the pre-defined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing alternating projection scheme is investigated. It is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the conventional alternating projection algorithm.

We propose a new orthogonal frequency division multiplexing (OFDM) system embedded with overlay training watermarks in this paper. The major advantage of the proposed overlay watermark is the elimination of the in-band OFDM pilots while keeping the peak to average power ratio (PAPR) low for the overlay OFDM signal. As a result, the corresponding spectral efficiency and the transmitter amplifier efficiency are both improved over the existing OFDM systems. The adopted Kasami sequence encrypted overlay watermark is known to the receiver and has very minimal impact on the data detection performance once the channel impulse response is estimated. The proposed channel estimation technique is based on an iterative estimation of the channel impulse response, and the combined signal vector from the overlay pilot sequence and the desired OFDM signal. We also investigate the crucial interfering effect of the overlay pilot on the OFDM signal demodulation. The PAPR of the overlay OFDM signal is analyzed and it is shown that the PAPR of our proposed new scheme is less than the OFDM systems using the frequency domain overlay sequences. Moreover, according to our PAPR analysis, a new PAPR reduction technique via the selective watermark insertion is also designed in this paper.

Intercarrier interference is deemed as one of the crucial problems in the wireless orthogonal frequency division multiplexing (OFDM) systems. The conventional ICI mitigation schemes involve the frequency-domain channel estimation or the additional coding, both of which require the spectral overhead and hence lead to the significant throughput reduction. Besides, the OFDM receivers using the ICI estimation rely on a large- dimensional matrix inverter with high computational complexity especially for many subcarriers such as digital video broadcasting (DVB) systems and wireless metropolitan-area networks (WMAN). To the best of our knowledge, no semi-blind ICI equalization has been addressed in the existing literature. Thus, in this paper, we propose a novel semi-blind ICI equalization scheme using the joint multiple matrix diagonalization (JMMD) algorithm to greatly reduce the intercarrier interference in OFDM. However, the well-known phase and permutation indeterminacies emerge in all blind equalization schemes. Hence we also design a few OFDM pilot blocks and propose an iterative identification method to determine the corresponding phase and permutation variants in our semi-blind scheme. Our semi-blind ICI equalization algorithm integrating the JMMD with the additional pilot-based iterative identification is very promising for the future high- throughput OFDM systems. Through Monte Carlo simulations, the QPSK-OFDM system with our proposed semi-blind ICI equalizer can achieve significantly better performance with symbol error rate reduction in several orders-of-magnitude. For the 16QAM-OFDM system, our scheme can also improve the performance over the plain OFDM system to some extent.

Since the modulation type is unknown at a cognitive receiver front-end, the training sequence or pilot symbols are not available and the robust blind equalization is in demand to combat the fading channel problem and improve the symbol detection performance. Kurtosis minimization has been applied for the existing blind equalization schemes but the corresponding optimization procedures are very sensitive to the channel conditions and the initial conditions. In this paper, we introduce a new cognitive receiver front-end, which includes a novel switching blind equalizer and an automatic modulation classifier. We design a switching criterion based on the kurtosis/normalized moment ratio threshold to select the better signal between the raw data and the equalized sequence. Monte Carlos simulations show that our proposed robust switching blind equalization scheme can significantly outperform the existing blind equalizer and would not degrade the latter modulation classification accuracy.

Phase noise is a critical factor that degrades the synchronization performance of a wireless communication receiver. Hot-carriers (HCs), found in the CMOS synchronization devices, are high-energy charge-carriers that can degrade the MOSFET's performance by damaging the internal structure and lead to an increase in the phase noise therein. The increase in the phase noise can be related to a critical parameter, the MOSFET threshold voltage. The HC effect is particularly evident in the short-channel MOSFET devices. In this paper, we analyze the impact of the phase noise arising from the HC effect on the single-carrier QAM systems in terms of the symbol-error-rate (SER) and the signal-to-interference-plus-noise ratio (SINR). We provide an exact analysis for the SER and SINR degradation for the QPSK systems in the presence of the phase noise, and verify our analytical results through computer simulations. In addition, we illustrate the performance degradation of the single-carrier 16-QAM and 64-QAM systems via Monte Carlo simulations. Through our analysis and simulations, we can show that the SER performance of the QPSK systems can deteriorate by two orders-of-magnitude at an SNR of 10 dB. Our analysis can help the design of receivers for single-carrier wireless systems built on short-channel MOSFET devices

Orthogonal frequency division multiplexing (OFDM) employs a set of subcarriers for the information symbol transmission in parallel via the multipath fading channels. Due to its spectral efficiency and robustness over multipath channels, OFDM has been adopted for broadband communications. However, for high-speed mobility, the time-varying channels pose a performance limitation to the wireless OFDM systems. In this paper, we investigate the OFDM system performance in the time-varying channels where the intercarrier interference (ICI) occurs; we study the impacts of the time-varying multipath fadings together with the multiple Doppler spreads. According to our analysis and simulation results, the maximum OFDM symbol normalized Doppler frequency must be less than 0.04 to achieve the signal-to-interference ratio (SIR) = 20 dB or larger. As the maximum OFDM symbol normalized Doppler frequency increases, the OFDM system performance degrades dramatically. Different irreducible symbol error rate (SER) floors about 10-2for QPSK-OFDM schemes and 10-1for 16QAM-OFDM schemes would arise in case that the maximum OFDM symbol normalized Doppler frequency is fixed at 0.06

The intercarrier interference (ICI) matrix for the orthogonal frequency division multiplexing (OFDM) systems usually has a fairly large dimension and changes over time for mobile communications. Obviously, the traditional least-square solution for the ICI matrix estimation based on the pseudo-inverse operation has its limitation. In addition, the provision of a sufficiently long training sequence to estimate the complete ICI matrix is not feasible, since it will result in severe throughput reduction. In this paper, we design padded maximum-length shift-register sequences (pm-sequences) to serve as the training sequences for the ICI estimation. Such new training sequences will induce the diagonal correlation matrices which can lead to the optimal estimation. We propose a new distributive allocation scheme of short pm-sequences for the ICI estimation with a linear ICI estimate interpolation, which can lead to the multi-ICI-vector estimation to combat the realistic non-circulant ICI matrix problem, where almost all other existing schemes such as the ICI matrix estimation using the Hadamard sequences, the convolutional coder/decoder and the ICI self-cancellation coder/decoder could not perform effectively

Automatic modulation classification (AMQ is a scheme to automatically identify the modulation types of the transmitted signals by observing the received data samples in the presence of noise and fading channels. Nowadays, 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. The advantage of our new algorithm is that, by carefully designing the essential features needed for AMC, we don't 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

Phase noise is the major cause of performance degradation in OFDM systems. Hot carriers (HCs), found in the CMOS-based synchronization circuits, are high-mobility charge carriers that affect the MOSFETs' stability by increasing the required operating threshold voltages. The HC effect manifests itself as the phase noise, which increases with the continued MOSFET operation and results in the performance degradation of the voltage-controlled oscillator (VCO) built on the MOSFETs. The MOSFET instability impacts on the OFDM system performance, by inducing intercarrier interference (ICI) and common phase error (CPE) on the subcarriers. In this paper, we evaluate the effect of ICI self-cancellation coding on the phase noise induced by the hot-carrier effect in OFDM systems.

We propose a new joint multiple matrix diagonalization algorithm for robust blind beamforming. This new algorithm is based on the iterative eigen-decomposition of cumulant matrices. Therefore it can avoid the stability and misadjustment problems arising among the conventional steepest-descent approaches for constant-modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the JADE algorithm based on the Givens rotation for PSK source signals in terms of signal-to-interference-and-noise ratio for a wide variety of signal-to-noise ratios.

The intercarrier interference (ICI) matrix for the orthogonal frequency division multiplexing (OFDM) systems usually involves a fairly large dimension. The traditional least-square solution based on the pseudo-inverse operation therefore has its limitation. In addition, a sufficiently long training sequence to estimate the complete ICI matrix is not feasible since it results in the severe throughput reduction. In this paper, we derive a lower bound for the mean-square estimation error among the least-square ICI matrix estimators using different training sequences and prove that the minimum mean-square error (MMSE) optimality is attained when the training sequences carried by different OFDM blocks are orthogonal to each other, regardless of the sequence length. Then we propose to employ the training sequences based on the padded maximal-length shift-register sequences (m-sequences) so as to achieve a highly efficient and optimal ICI matrix estimator with the minimum mean-square estimation error among all least-square ICI matrix estimators. Besides, our new scheme involves only linear complexity while other existing least-square methods require the complexity proportional to the cube of the ICI matrix size. Both analytical and experimental comparisons between our new scheme using padded m-sequences and the existing method using conventional m-sequences justify the significant advantages of our new ICI matrix estimator.

A novel method for indoor orthogonal frequency division multiplexing (OFDM) quality of service (QoS) analysis using the robust sparse channel estimator is proposed in this paper. Through the procedures of radiofrequency propagation modeling, sparse channel estimation and interference analysis, OFDM quality-of-service measures, such as signal-to-interference-and-noise ratios (SINR) and bit error rates (BER) can be generated using this computer simulation tool. The geometry-dependent QoS measure contours from the simulations can be used by service providers and OFDM communication system designers to quantify the system performance and determine the optimal access locations indoors.

The paper considers the design of optimal precoders which minimize the bit error rate (BER) for multiple-input multiple-output (MIMO) downlink communication channels under the perfect reconstruction (zero-forcing) condition. The closed form solutions are derived and an efficient algorithm is proposed. Numerical simulations are provided to illustrate the proposed design technique, and to demonstrate the performance of the optimal precoders.

OFDM has been applied in the current wireless communications since it has the advantage over the conventional single-carrier BPSK, QPSK, QAM and MSK modulation schemes when enduring the nonflat fading. However, intercarrier-interference (ICI) and interblock interference (IBI) due to the Doppler effect, noncoherent phase and carrier frequency and multipath fadings limit the capability of OFDM systems. In this paper, a new generalized mathematical model for intercarrier and interblock interferences is derived for wireless OFDM systems, in which Doppler effect, carrier frequency drift, multipath fading, and cyclic prefix coding are all considered.

Blind equalization draws a lot of attention. Several statistical objective functions such as kurtosis and constant modulus were based on the noise-free model and hence their ISI cancellers were sensitive to noise. In this paper, a unifying adaptive blind equalization method is proposed, which can be robust to noise than the current cumulant-based adaptive methods. Our new objective functions can be applied for wired or wireless i.i.d. communication symbols with noise. The simulation shows that our new method outperforms the kurtosis-based method when the background noise exists.

The information-theoretic framework for source separation is highly suitable. However the choice of the nonlinearity or the estimation of the multidimensional joint probability density function are nontrivial. We propose here a generalized Gaussian model to construct a generalized blind source separation network based on the minimum entropy principle. This new separation network can suppress the interference to a significant amount compared to the traditional LMS-echo-canceler. The simulation is given to show the disparity of the performance as a varies. Finally how to choose the appropriate a in our generalized anti-Hebbian rule is discussed.

Reverberant signals recorded by multiple microphones can be described as sums of sources convolved with different parameters. Blind source separation of this unknown linear system can be transformed to a set of instantaneous mixtures for every frequency band. In each frequency band, we may use the simultaneous diagonalization algorithms to separate the sources. In addition to our previous simultaneous diagonalization to minimize the Frobenius norm, we now propose another set of efficient simultaneous diagonalization algorithms based on Hadamard's inequality to make the source separation feasible in the frequency domain.

In adaptive signal processing, the least-mean squares (LMS) algorithm has long been used in signal enhancement and noise cancellation but it cannot overcome the difficulty caused by the signal leakage into the reference input. Hence we have to explore more general statistical properties about the observed signals. This view corresponds to a statistical modeling of the signals using statistical measures such as a loss function, which is different from the mutual information. This paper proposes a new loss function based on generalized Gaussian distribution family, and derives new simple adaptive learning rules. Our separator based on the new generalized "anti-Hebbian rules" is also justified by the simulation on both artificial and real data with good performance.

This paper explores the different frequency contents in short time segments (temporal microstructure) of speech to identify the mixing matrix in blind source separation. We propose a new method based on the eigenspread in different frequency bands to identify the segments which contain only one of the mixtures. It is much simpler to accurately estimate the mixing matrices from these segments. This short-time subband analysis trains very fast and estimates reliably the column vectors of the linear mixture. Simulation results show that our proposed method outperforms the existing model-based and competitive learning approaches in the identification of the mixing matrix for both sensor-sufficient (as many sensors as sources) and sensor-deficient (less sensors than sources) cases.

Measures of independence (and dependence) are fundamental in many areas of engineering and signal processing. Shannon introduced the idea of information entropy which has a sound theoretical foundation but sometimes is not easy to implement in engineering applications. In this paper, Renyi's entropy is used and a novel independence measure is proposed. When integrated with a nonparametric estimator of the probability density function (Parzen Window), the measure can be related to the "potential energy of the samples" which is easy to understand and implement. The experimental results on blind source separation confirm the theory. Although the work is preliminary, the "potential energy" method is rather general and will have many applications.

This article presents a novel, on-line, local learning algorithm to obtain generalized eigenvalues and their corresponding eigenvectors in descending order with a linear adaptive filter. The filter is composed of a set of forward linear projections constrained by lateral inhibitions. Equilibrium points of the adaptation process and their stability are briefly analyzed. Simulations and experimental comparisons are given to verify the validity and effectiveness of the proposed algorithm.