Dual-output look-up tables (LUTs) are supported by modern commercial Field-Programmable Gate Array (FPGA) architectures. Existing technology mapping usually generates multi-output LUTs by merging single-output LUTs. However, these mapping solutions often fail to fully leverage the multi-output capability. This paper proposes a novel FPGA mapping scheme for multi-output logic units, aiming to improve synthesis performance significantly. By introducing new metrics functions, k-input l-output cut (kl-cut) generation method, and methods to add secondary LUT outputs, the proposed approach enables thorough exploration of the design space. Results show that the proposed dual-output LUT synthesis method achieves 30.2% and 34.7% reduction in LUT usage and depth, respectively, outperforming leading-edge dual-output mapping tools. In addition, the proposed mapping scheme is applied to an emerging field-programmable unit, MCluster, proving compatibility and efficiency on non-LUT logic blocks. For the first time, we perform a system-level design flow to demonstrate the benefit of multi-output LUT at the system level and provide critical insights and design trade-offs.

Understanding the cause-and-effect connections in intricate systems like telecommunication networks is essential for enhancing and optimizing their performance. This article, in its novelty, extends causal discovery to the context of 5G new radio (NR) Networks by leveraging established technique of the greedy equivalent search (GES) algorithm to real-world 5G network data. Our research uncovers causal relations among several network characteristics represented in the form of a directed acyclic graph (DAG). A specific causal link between latency and downlink throughput is further analyzed unveiling a robust correlation between network utilization and latency, manifesting the factors of packet loss and retransmissions especially when the network infrastructure becomes overwhelmed. Building on these findings, the second significant contribution of our study involves the introduction of a novel enhancement to the NR proportional fair (PF) scheduling algorithm. This enhancement incorporates retransmission considerations to improve network resource utilization. Our experimental results show notable gains in network efficiency and resource allocation, highlighting the potential for real-world enhancements based on the causal insights uncovered in our research. This research broadens the horizons of causal discovery within 5G NR networks and presents a tangible pathway for enhancing network performance and resource allocation, with implications for the broader field of network optimization.

This study introduces a novel approach to cylindrical antenna array deployment in cellular networks, emphasizing a method that reduces infrastructure costs while enhancing spectral efficiency through noninteger array configurations. Traditional cylindrical arrays, based on integer spacing, often require several antenna elements, leading to increased costs in materials, installation, and maintenance. Our approach leverages noninteger spacing between elements, both vertically and horizontally, which allows for a significant reduction in the total number of antennas required without compromising beam strength or focus. This reduction in hardware not only cuts costs but also simplifies the overall deployment process. Simulations reveal that noninteger spaced arrays maintain, and in some cases, improve beam characteristics, while simultaneously enhancing sum-rate capacity and spectral efficiency. These improvements are particularly notable under the multipath channel conditions prevalent in cellular networks, where reduced mutual coupling and lower signal correlation contribute to better performance. By minimizing the number of antennas needed for effective operation, this approach offers a cost-effective alternative for cellular network design, positioning it as a key enabler for the development of next-generation wireless communication technologies, including 6G networks.

The demand for efficient and high-performing antenna arrays in next-generation wireless networks has led to the exploration of innovative design strategies. Traditional integer-spaced cube arrays often face challenges such as elevated mutual coupling and increased costs from dense deployments. This paper addresses these challenges by introducing a novel extension of non-integer spacing configurations to three-dimensional cube antenna arrays, supported by derived array factor and sum-rate capacity formulations. Comprehensive performance evaluations across multiple communication metrics demonstrate the consistent advantages of non-integer arrays, even under diverse channel conditions. Additionally, a bottom face-off configuration is explored, reducing element count while maintaining competitive capacity and beamforming performance. These findings position non-integer cube arrays as scalable, cost-effective solutions to meet the rigorous demands of fifth generation (5G) and future wireless networks.

Reconfigurable devices are gaining increasing attention as a viable alternative and supplementary solution to the traditional CMOS technology. In this article, we develop a more efficient field-programmable gate array (FPGA) based on the reconfigurable field-effective transistor (RFET). We use the multi-gate characteristics of RFET to redesign the key components of FPGAs, namely SRAM-controlled multiplexer and look-up tables. The compact structure of the proposed design requires fewer transistors and leads to reduced delay of the overall FPGA system. In addition, we develop a comprehensive technology/system co-design and co-optimization framework to thoroughly investigate the design space, including various device- and system-level design parameters. A series of benchmark tests show that under the optimal design, up to 32% and 13% reduction can be achieved in delay and energy-delay product (EDP), respectively, compared to the traditional CMOS FPGAs.

Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.

The future Internet comprising multimodal mobile wireless sensors (MMWSs) dramatically increases the data dimension and complexity compared to the traditional homogeneous networks. It consists of various types of nodes that can be deployed in different spatial domains to form the space–air–ground–ocean-integrated network (SAGOI-Net). A key challenge to Internet of Things (IoT) with SAGOI-Net is harnessing the real-time data from these sensors to obtain target characteristics and realize target tracking, especially in rough environment with obstacles and enemy threats. In this article, we propose an approach—fuzzy logic and flocking control under an extended Kalman filter (EKFFL) to provide sensor nodes trajectory formation. With the tools from control systems and signal processing, this approach improves the commonly adopted flocking control algorithm and fully employs multi modality of data. Compared with the modified Kalman consistency filter (KCF) approach, the proposed EKFFL achieves higher target tracking accuracy with a shorter time period of trajectory formation. The tradeoff between the data delay and tracking accuracy is also analyzed. Based on its advantages, the EKFFL approach can be applied in broad scenarios, such as unmanned border awareness systems, environmental monitoring, and intelligent transportation.

Person reidentification (re-ID), which is a significant and potential application in the Internet of Things (IoT), aims to retrieve pedestrians of interest given a labeled image in a camera network. Now, it is still existing many challenges that severely influence feature representation in practical scenarios. Many methods adopt the attention mechanism in convolutional neural network (CNN) to improve the ability of feature learning. Although they only apply 1-D attention block in the popular deep learning architecture, the learned features are not discriminative for the feature representation. In this work, we investigate a self-focusing network (SFNet) that considers both the channel-dimensional attention and spatial-dimensional attention to adaptively learn more discriminative features. Namely, we embed the new attention module into the common backbone network, which can focus on the salient region by inhibiting the redundant features. Specifically, we design eight variants of the channel-dimensional attention and spatial-dimensional attention throughout the entire network and explore the most powerful feature representation. The heatmaps of different layers are visualized to intuitively present the performance of SFNet. Furthermore, we compare SFNet with the prior work on three popular person re-ID benchmarks by abundant experiments.

The parametrization of recurrent neural network (RNN) to solve the gradient vanishing and exploding problem is critical for sequential learning. The reason lies on eigenvalue of the gradient of loss function against the recurrent weight matrix in recurrent weight matrix. To control the eigenvalues, the orthogonal constraint is imposed on the recurrent weight matrix. In this article, we analyze the designing mechanism of decomposition methods of three orthogonal constrained RNNs (OCRNNs) and derive their corresponding training algorithms. We compare the performance of the four OCRNNs and the standard long short-term memory (LSTM) network on the synthetic baseline copying and adding tasks. We find out that the coordinate descent-based iteration of the two OCRNNs proposed by us can achieve comparative or better testing error and convergence speed than the rest model. The above two OCRNN models are more cost-effective in terms of total number of parameters, which save the storage of the model. Furthermore, we introduce the pseudospectrum to visualize and monitor the evolving of the orthogonality of the recurrent weight matrix and find that the orthogonality is a empirically useful condition but not rigorously theoretical guarantee for convergence speed. Finally, we deploy the three OCRNNs on the acoustic polyp detection problem based on the and formulate the detection problem as the sequential binary classification. We also demonstrate that our proposed two OCRNNs can achieve comparative accuracy with less total number of network parameters. The quick inference time demonstrates its ability for Internet of Things (IoT) application.

Person reidentification (re-ID), which aims at recognizing the pedestrians captured by multiple nonoverlapping cameras, has attracted more interest due to its significant and potential application in the Internet of Things like intelligent visual surveillance. However, person reID is still a challenging problem in the situations of various pose, similar appearances, partial occlusion, etc. To handle these obstacles, in this article, we investigate an innovative exciting-inhibition network (EINet) that is a two-branch network composed of the exciting branch and the inhibition branch. The channel-spatial attention block that recalibrates the relationship between channels and highlights features at different spatial positions is used in the exciting branch. A novel Soft Batch DropBlock that randomly selects a continuous region of the intermediate feature maps at the same location is applied in the inhibition branch to inhibit the trivial by an inhibitive mask and reinforce learning the remaining regions. We integrate the comprehensive features from both branches for evaluation and show the performance of EINet intuitively using the visualization method. Abundant experiments demonstrate the state-of-the-art performance by comparing with the previous methods on three popular person re-ID benchmarks. For example, our method obtains 95.64% Rank-1 and 88.75% mean average precision (mAP) on Market-1501, and 77.00% Rank-1 and 74.51% mAP on CUHK03-Detect in the single query mode, respectively.

Sixth-generation (6G) wireless communications and networks will continue to move to higher frequency and wider bandwidth, with much higher data rate and spectral efficiency. Given the heterogeneity and densification of Internet of Things (IoT), 6G wireless network may need to be extended to modern random access (RA) for IoT applications, which can be achieved via smart protocol design and advanced signal processing and communications technologies. The modern RA techniques, such as massive multiple-input–multiple-output (MIMO), OFDMA, nonorthogonal multiple access (NOMA), sparse signal processing, or new orthogonal design techniques provide good candidacy for 6G-enabled IoT. In 6G, grant-free transmission should be designed for distributed IoT applications. IoT applications are often involved in self-organizing decision making. 6G-enabled IoT will take advantage of the recent development of artificial intelligence (AI) techniques. It will generate new knowledge and understanding and accelerate discovery and innovation in IoT. Each of these efforts is designed to amplify the intrinsically multidisciplinary nature of the emerging field of IoT. The 6G-enabled IoT will establish theoretical, technical, and ethical frameworks that will be applied to tackle many challenges in IoT, advancing technology for humanity.

The learning automaton (LA) that simulates the interaction between an intelligent agent and a stochastic environment to learn the optimal action is an important tool in reinforcement learning. Being confronted with an unknown environment, most learning automata have more than one parameters to be tuned during a pretraining process in which the LA interacts with the environment. Only after the parameters are tuned properly, an LA can act most properly during the training procedure to obtain the optimal behavior. The cost of parameter tuning can be enormous, e.g., possibly millions of interactions are required to seek the best parameter configuration. Therefore, the parameter-free LA that uses identical parameters for every environment and saves further tuning has become the hot spot of this research. This article proposes an efficient parameter-free learning automaton (EPFLA) that depends on a separating function (SF). Taking advantage of both frequentist inference and Bayesian inference, the SF plays a dual role in the proposed scheme: 1) evaluating the difference in performance between actions in the environment and 2) exploring actions by coining an action selection strategy. A proof is provided to ensure the ϵ -optimality of EPFLA. Comprehensive comparisons verify the privileges of EPFLA over both parameter-based schemes and existing parameter-free schemes.

The machine-to-machine (M2M) communications, which achieve the implementation of Internet of Things (IoT), can be carried over wireless cellular networks. The massive random access (RA) in M2M communications will cause radio access network congestion in the base station (BS), leading to sharp deterioration in access delay and access probability. Access class barring (ACB) that can directly control the flow of machine-type communication (MTC) devices by an ACB factor is an efficient scheme to prevent the BS from traffic overload. In wireless cellular networks, the RA resources (i.e., preambles) are shared by M2M and human-to-human (H2H) devices, and research on ACB scheme ordinarily assumes that a restricted number of preambles are assigned to M2M traffic. However, when suffering from massive access in M2M communications, it is desirable to rapidly satisfy the access requests from MTC devices using all available preambles, especially in time-sensitive IoT scenarios. In this paper, we study the massive access problem in M2M traffic centered scenarios where M2M and H2H traffic can apply for all available preambles without distinction. Utilizing the self-adaptive learning property of learning automata, we further propose a novel learning automata-based ACB (LA-ACB) scheme. Simulation results show that the LA-ACB scheme achieves the performance close to theoretical optimality. The BS equipped with the LA-ACB scheme can effectively control the M2M traffic by dynamically adjusting the ACB factor under the interference of H2H traffic and provide quality services for both M2M and H2H traffic.

The Internet of Things (IoT) provides enormous connections of devices and sensors with different applications. It is an enabling technology for smart city, intelligent transportation systems, environmental monitoring, security surveillance, smart homes, satellite and space information network, ocean monitoring, and unmanned border awareness systems, just to name a few. IoT as a high-density network will take the burden of massive data generated by different kinds of terminals and sensors. Dramatic growth in IoT has created a shortage in the available radio spectrum. Wireless communications services in IoT such as cellular phones, tablets, and wireless Internet access have to compete with existing users in radar, government and military communications, environmental monitoring, and other IoT applications. The strategy to increase the efficiency of spectrum sharing among the enormous users in IoT. Besides, the IoT applications demand more and better functionality and performance from new electronic devices; these demands translate into greater energy consumption demands. The gap between energy storage and demand continues to grow and the battery technologies for energy storage are not expected to increase tremendously in the coming years. Furthermore, reducing signal transmission power can lessen interference among devices in IoT. Energy-efficient protocols and network architectures will further reduce the number of transmissions and extend the battery life of IoT devices (IoTDs). Therefore, it is essential to pursue fundamental research on new components, techniques, and architectures to achieve energy-efficient sensing, communications, and networking in a shared spectrum environment for IoT.

Internet of Things is coming to critical missions such as battlefield, border patrol, search and rescue, critical structure monitoring and surveillance, etc. Internet of Things (IoT) in Critical Missions or Internet of Mission-Critical Things (IoMCT) is propelled by the convergence of sensing, communication, computing, and control. To support IoMCT, the mission-critical networks will need to be flexible and interactive, and still work despite limited bandwidth, intermittent connectivity and with a large number of devices on the network. The focus of IoMCT is to improve surveillance utilizing a network, not fusion of disparate sensor products. Such adaptation, management, and re-organization of information sources, devices, and networks must be accomplished almost entirely autonomously, in order to avoid imposing additional burdens on the humans, and without much reliance on support and maintenance services.

In this paper, two new structures of sparse cylindrical sensor network are proposed, which are 1-D nested cylindrical sensor network (1-D NCSN) and 2-D NCSN. Considering the development of Internet of Mission Critical Things, numerous of devices need to be interconnected and sharing information. Sparse structures of sensor networks will not only reduce the cost of deployment, but also decrease the quantity of data. The reason of choosing cylindrical sensor network is that cylindrical array has been widely used in wireless communication and underwater target detection. According to the characteristic of cylindrical array, the 1-D and 2-D nested array is extended to build the 1-D and 2-D sparse cylindrical sensor networks, respectively. Comparing with the 1-D NCSN, the 2-D structure could save more elements, while it is hard to derive the beampattern expression of the 2-D NCSN. Simulation results show that the 1-D NCSN has a higher resolution than the uniform cylindrical sensor network (UCSN). In addition, through augmented matrix MUSIC approach, 2-D NCSN could detect all targets in the range, even if the number of targets is larger than the number of real elements in the network. However, both the UCSN and the 1-D NCSN could not find all the sources.

(First paragraph) In the first edition of the special issue titled “Wireless Sensor Networks for Smart Communications”, a total of 22 manuscripts were received and 6 of these were accepted. This issue demonstrated that network congestion, user mobility, and adjacent spectrum interference are the main reasons for the degradation ofcommunication quality inWireless Sensor Networks (WSNs).

In traditional cellular networks, cell edge users suffer from severe interference. This paper considers a cell free network scenario that multiple base stations (BSs) serve a group of users without cell boundary. Each user can only be served by its own BS according to the traditional cellular network paradigm, while in the user centric cell free network, users can be associated with multiple nearby BSs. With the cell free model, interference could be converted into useful signal. Several BSs close to the users can form virtual cell and serve the cell edge users, thus eliminating the severe interference. We try to achieve the potential of the cell free network by optimization based on channel statistics. Due to the coupled nature of interference, it is difficult to get the global optimal solution. We aim to adopt a heuristic precoder that balances signal and interference to maximize the network sum rate. However, the optimization of the problem requires instantaneous channel state information. In order to reduce the information exchange in massive MIMO system, an effective coordinated power allocation algorithm only based on statistical channel state information is proposed by deriving the large system asymptotic optimization problem. In this way, the problem can be optimized in a long-term timescale and signaling overhead and computational cost can be reduced significantly. Numerical results validate the effectiveness of the proposed asymptotic optimization algorithm and show that the cell free network model can efficiently boost the throughput and maintain good fairness performance.

In recent years, 4G LTE networks have been deployed by some operators and some of them also sell Femto Cell service in the market. As a kind of Heterogeneous network in cellular communication, lots of work have been published in last few years. However, considered in next generation cellular communication system, some challenging features have been planned like: A super-efficient and fast mobile network. But current system cannot satisfy users and operator. In the next generation wireless communication system, at least three layers of Heterogeneous Networks (HetNets) are required to provide high efficiency bandwidth usage and high speed data throughput. The problem is the users' distribution and their quality of service (QoS) request are random, and the number of users may vary through the time. In order to deal with these problems, this paper builds two games' models to optimize the resource allocation corresponding to different situations. Analyze the spectrum efficiency and made a comparison between two games. By playing those games, cells can serve more users inside one cell, all users are fair to share the bandwidth according to their request and location. The whole system becomes more flexible and performance has been enhanced.

Security of data is an issue that is of significant interest. In this paper, we propose a new compressive sensing-based data encryption system that can represent the original signal with far fewer samples than the conventional Nyquist sampling-based system. Compressive sensing could also be treated as an encryption algorithm with good secrecy. As an application example, we apply it to sense-through-wall ultra-wideband UWB noise radar that requires enormous storage space and high security. Interestingly, a random Gaussian matrix is sufficient to capture the information of UWB noise radar signal; no knowledge of UWB signal is required in advance. Simulation results indicate only one-third of the original samples are needed to perfectly recover UWB noise radar signal, and compressive sensing provides good secrecy as an encryption algorithm. It is impossible to retrieve the original message without the entire sensing matrix. Copyright © 2013 John Wiley & Sons, Ltd.

This paper considers the problem of how the ergodic throughput capacity scales in hybrid wireless networks under the infrastructure mode. As opposed to the existing hybrid wireless networks in which the base station only serves individual cell area, the concept of distributed base stations (DBS) is introduced to achieve transmit/receive diversity in this paper. We model the hybrid wireless network with DBS as a multi- cell virtual MIMO system. In addition, a successive interference cancelation (SIC) strategy and the frequency reuse scheme are employed to limit the intra-cell interference and other-cell interference (OCI), respectively. The ergodic throughput capacity, which is an indicator of spectrum efficiency, is investigated over the independent but not identically distributed composite fast fading channels. It is analytically shown that compared to the traditional hybrid wireless network, the ergodic throughput capacity of hybrid wireless networks with DBS scales with gain with N×NBS.

This paper proposes a novel deployment for multi-cell cooperative cellular networks based on the two-dimensional (2D) nested co-array, and analyzes its sum-rate capacity and spectrum efficiency. The system model is based on the traditional hexagonal cellular array, in which each hexagon represents a marcocell. We take advantage of the invariance in the difference co-array so that the 2D nested array is able to calculate all elements in the covariance matrix of channel fading coefficients. Based on this premise, we demonstrate that the derivation procedure of average sum-rate capacity for the cooperative cellular networks is still valid for the nested distributed base stations (BSs) in the non-fading and Rayleigh fading channels. In numeric simulations, the derived formulas are consistent with the results from previous references. More importantly, given the same number of BSs, the proposed distribution significantly increases the sum-rate capacity of the system.

Underwater target detection has been widely used nowadays. In this paper, we show that the 3-D nested-array system can provide O(N2) degree of freedom(DOF) by using only N physical sensors when the second order statistics of the received data is used, which means we can use less sensors to get a better performance. A maximum likelihood (ML) estimation algorithm for underwater target size detection is also introduced. Theoretical analysis illustrates that our underwater sensor network can greatly reduce the variance of target estimation. We show that our maximum likelihood estimator is unbiased, also the Cramer-Rao lower bound can be achieved when estimating the variance of parameter. Simulations further validate these theoretical results.

In this paper, we consider massive MIMO in two-layer Heterogeneous Networks. The system has a large self-interference and co-channel interference due to operating in full-duplex mode. By using Game Theory, an optimized sum-rate is achieved. We investigate the potential sum-rate before and after the optimization under the power constraints. It is shown that the game theoretical method performed a very good access scheduling. Compared to non-optimized model, game theoretical method can achieve higher sum-rate.

Abstract Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two‐way data transmission between electric devices and the smart meter, and thus deteriorates the reliability of the in‐home communication of Smart Grid. On the basis of this consideration, this paper incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency‐division multiplexing‐based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Simulation results validate the feasibility of the proposed hybrid architecture, and furthermore, as a receiver diversity scheme, selection combining is preferred to maximum ratio combining. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. This paper presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying (BFSK) signal. When the sampling spacing pairs are big enough, the spectrum of BFSK signal performs like frequency hopping. This property has great advantage in the security of big data collection and transmission using FH/BFSK, as it could achieve low error probability. With the same multitone interfering signal added to FH/BFSK, the error probability becomes much lower using nested sampling and coprime sampling compared with the original FH/BFSK signal. This proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. Copyright © 2013 John Wiley & Sons, Ltd.

The phrase ‘Big Data’ refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future, as defined by U.S. National Science Foundation in its recent solicitation. The research of Big Data will accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible, encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security. Big Data presents critical requirements for security in data collection and transmission of selected data through a communication network. This special issue contains 11 papers selected from submissions to the open call for papers on Security in Big Data. These papers highlight some of the current research interests and achievements in the area of security in Big Data. The wide use of high-performance image acquisition devices and powerful image-processing software has made it easy to tamper images for malicious purposes. The paper by Zhang et al. proposes an effective framework for revealing image-splicing forgery. The experiment results show that the proposed method can perform better than some state-of-the-art methods in terms of the detection performance over the Columbia image-splicing detection evaluation data set. Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy attack. The paper by Du et al. proposes a weakly secure scheme from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node, and the performance of advanced scheme is better. Traditionally, jamming to the wireless system is a fatal threat to the security of home area networks (HANs), which impedes the two-way data transmission between electric devices and the smart meter and thus deteriorates the reliability of the in-home communication of Smart Grid. The paper by Li et al. incorporates the power line system into the HAN and proposes a hybrid architecture of orthogonal frequency-division multiplexing-based wireless communication and power line communication for the Smart Grid security application. With this new solution, the channel diversity of the HAN is realized, and the communication reliability is still guaranteed even when the wireless channel suffers from jamming. Information of multi-cells is big data because of the enormous quantities of various cells as well as their parameters and status. To securely and efficiently integrate all the cells' information and trace multi-cells are challenging because of varying number of the multi-cells, as well as the complicacy of the multi-cells' movement. The paper by Yin and Sun proposes an automatic big data integration algorithm based on the optical transfer function. The experimental results show that the algorithm can securely and efficiently integrate all the cell information and simultaneously track a large quantity of cells. Real-time digital video presents great challenges on processing and storage and is a typical example in Big Data. How to secure and efficiently transmit digital video is critical. The paper by Zhang et al. uses the distributed compressed sensing to deal with video coding. To reduce the orthogonal matching pursuit algorithm computational complexity, quantum-behaved particle swarm optimization algorithm is used to reconstruct video signal. Simulation results demonstrate that it can obtain the better reconstructed video with low sample value and it can guarantee safety performance. Wireless image sensor network generates a large number of images from the distributed camera sensors. The image data need to be delivered securely and efficiently to the sink in many circumstances. The current node-disjoint multipath and dispersive routings cannot provide enough security and efficiency for the image data collection and transportation. The paper by Su and Hu proposes an ellipse batch dispersive routing algorithm to address the secure and efficient data collection issue in wireless image sensor network. The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. The 24/7 information collection and transmission in smart grid is a good example of Big Data. The transmission of Big Data in smart grid needs wireless network, which introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. The paper by Wang et al. aims to present a general overview of the physical layer security in wireless smart grid and covers the effective countermeasures proposed in the literature of smart grid to date. Security is a very broad topic; particular attention has been paid in communications, networking on security issues. However, in practical applications, providing security services increases the computation and the occupation of system resources. This problem is particularly important when energy is a limited resource for mobile communication devices operating on battery. Thus, energy-efficient security devices are very necessary for the communication. The paper by Yuan and Liang designed a new low voltage, low power consumption comparator for successive approximation register analog to digital converter to improve the energy efficiency in the problem of secure communication. Big data presents critical requirements for security in data collection and transmission of selected data through a communication network. The paper by Chen et al. presents a new secure transmission for big data based on nested sparse sampling and coprime sampling. With nested sampling and coprime sampling, besides the advantage of higher spectrum efficiency, big data could also achieve higher power spectral density for binary frequency shift keying signal. It proves that both nested sampling and coprime sampling could be used in big data transmission to resist interference, while guaranteeing the transmission performance. With the rapid adoption of cloud storage services, a great deal of data is being stored at remote servers, so a new technology, client-side deduplication, which stores only a single copy of repeating data, is proposed to identify the client's deduplication and save the bandwidth of uploading copies of existing files to the server. It was recently found, however, that this promising technology is vulnerable to a new kind of attack in which by learning just a small piece of information about the file, namely, its hash value, an attacker is able to obtain the entire file from the server. The paper by Yang et al. proposes a cryptographically secure and efficient scheme for a client to prove to the server his ownership on the basis of actual possession of the entire original file instead of only partial information about it. The paper by Wang et al. presents the definitions of big data and anomaly detection. The theory of ultra-wideband radar and the through-wall detection of a human model based on ultra-wideband radar are briefly introduced. The target criterion with wavelet packet transform is deduced, and the procedure for the through-wall human detection with statistical process control is constructed. The radar echo signals are collected at stationary and moving statuses of a human being for three types of walls. The experimental results demonstrate the effective of through-wall target detection based on the proposed algorithm. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Security and Communication Networks for their support in editing this special issue. Qilian Liang is a University Distinguished Scholar Professor in the Department of Electrical Engineering, University of Texas at Arlington. He received the BS degree from Wuhan University in 1993, MS degree from Beijing Uni- versity of Posts and Telecommunica- tions in 1996, and PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining UTA in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, signal processing, information theory, radar systems, and wireless networks. Dr. Liang has published more than 270 journal and conference papers. He received 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, 2003 U.S. Office of Naval Research (ONR) Young Investigator Award, 2005 UTA College of Engineering Outstanding Young Faculty Award, 2007, 2009, 2010 U.S. Air Force Summer Faculty Fellowship Program Award, 2012 UTA College of Engineering Excellence in Research Award, 2013 UTA Outstanding Research Achievement or Creative Activity Award, and was inducted into UTA Academy of Distinguished Scholars in 2015. Jian Ren received the BS and MS degrees both in mathematics from Shaanxi Normal University and received the PhD degree in EE from Xidian University, China. He is an Associate Professor in the Department of ECE at Michigan State University. His current research interests include cryptography, network security, energy efficient sensor network security protocol design, privacy-preserving communications, secure and efficient cloud computing, and cognitive networks. He is a recipient of the US National Science Foundation Faculty Early Career Development (CAREER) award in 2009. Dr. Ren is a senior member of the IEEE. Jing Liang received the BS and MS degrees from Beijing University of Posts and Telecommunications, China in 2003 and 2006, respectively, and PhD degree from University of Texas at Arlington in August 2009, all in Electrical Engineering. She is currently a Professor in the Department of Electrical Engineering at University of Electronic Science and Technology of China. Her current research interests include radar sensor networks, collaborative and distributed signal processing, wireless communications, wireless networks, and fuzzy logic systems. Baoju Zhang is a Professor at the College of Physical and Electrical Information, Tianjin Normal Uni- versity. She received the BS degree from Tianjin Normal University in 1990, MS degree from Tianjin Nor- mal University in 1993, and PhD degree from Tianjin University in 2002. She was a Postdoctoral Fellow at Tianjin University from 2002 to 2004. Her research interests include radar sensor networks, digital audio and video technology, image compressing and coding, and compressive sensing. Yiming Pi was born in 1968 in China. He obtained PhD degree in Electronic Engineering from University of Electronic Science and Technology of China in 1993. Since 2002, he has been a Professor of Department of EE, University of Electronic Science and Technology of China. He is a councilor of Signal Processing Society in the Chinese Institute of Electronics and has served in organizing several international conferences in the field of Signal Processing and Radar Systems. He became IEEE Senior Member in 2011. He had been the leaders of some Natural Science Funding of China. He has more than 100 publications in the conferences and journals of IEEE/IET. His research interests are radar imaging, signal processing and terahertz technology, and so on. Chenglin Zhao received his BS degree in Tianjin University in 1986, MS degree and PhD degree in Beijing University of Posts and Telecommu- nications in 1993 and 1997, respec- tively. He is a Professor of the Key Lab of the ubiquitous wireless of Education Ministry, Information and Telecommunication engineering college, Beijing University of Post and Telecommunication. His main research areas include radar sensor networks, wireless broadband interconnection, wireless sensor network, and digital signal processing and its applications.

Abstract The smart grid system is composed of the power infrastructure and communication infrastructure and thus is characterized by the flow of electric power and information, respectively. Although there is no doubt that the wireless communication architecture will play a significant role in smart grid, the wireless network introduces additional vulnerabilities, given the scale of potential threats. Therefore, the physical layer security issue is of first priority in the study of smart grid and has already attracted substantial attention in the industry and academia. In this paper, we aimed to present a general overview of the physical layer security in wireless smart grid and cover the effective countermeasures proposed in the literature of smart grid to date. We first investigate the security challenges from malicious attacks. Specifically, two typical forms of malicious attack in smart grid, namely, jamming and bad data injecting, are studied. In addition, the related countermeasures against these malicious attacks are illustrated. Further, we analyze the state of the art of the privacy issues in smart grid. The private information and privacy concerns are introduced, and then the effective solutions to privacy security are provided. Finally, voltage regulation, a security topic that has been hardly studied in the wireless smart grid domain, is presented. We expect that the work presented here will advance the research on smart grid security. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract As a critical infrastructure element, smart grid is facing security and reliability challenges. In this paper, we have investigated the real‐time communication between a smart meter and the central control system, a component that provides centralized monitoring and performs control operations for the power grid. To combat severe communication impairments induced by malicious attacks or link failures, we propose to implement space–time block coding over multiple antennas at the transmitter and the receiver. We first investigated the security challenges from malicious attacks, in which the malicious attacks of additive pattern were primarily addressed. Reliability challenges from link failure were then considered. Finally present were extensive numerical studies on multiple‐link attacks, multiple‐link failures and receiver with detection scenarios. Simulations showed that additive malicious attacks and link failures could be effectively mitigated in virtue of space–time block coding. Meanwhile, it is verified that more antennas will give more diversity gain and better performance. Copyright © 2012 John Wiley & Sons, Ltd.

This paper studies higher-order statistics of non-Gaussian signal using temporal co-prime sampling. We extend co-prime sampling to pairwise co-prime sequence (PCS) in order to derive higher-order statistics (HOS). We also examine the computational complexity of PCS-HOS algorithm for both parametric and nonparametric methods. Compared to the existing HOS algorithms, the proposed approach vastly reduces the complexity by several orders in terms of the length of segmentation window. Besides, we apply PCS-HOS to estimate both the orders and coefficients of fading channels, which are modeled as autoregressive moving average (ARMA) processes. In particular, we analyze the variance of third-order cumulants as the output of the model, and use it to determine the orders of autoregressive and moving average processes. In simulations, the proposed algorithm decreases the computational complexity to 17% of the existing HOS algorithms with negligible performance loss in high noise-to-signal ratio (SNR) environment. In low SNR scenario, it is more sensitive to order mismatch in terms of the variances of expectations, which makes it a more reliable indicator to confirm the correct order. Furthermore, we also develop the overdetermined matrix representation for moving average (MA) system, and apply PCS-HOS algorithm to calculate its parameters only based on the outputs. The results of our simulations show that 3rd-order PCS-HOS achieves 80% performance gain versus the existing HOS with the same computational complexity, or it has 12% performance loss with 85% reduction in complexity compared to the counterpart processing the same length of signal.

This paper studies the optimization problem of total downlink capacity in heterogeneous multiuser Home Area Network (HAN) for Smart Grid application, where beamforming technique is employed at the smart meter. Based on the proposed system model, an optimal downlink power allocation scheme is proposed under the constraints that 1) each user should satisfy individual signal to interference noise ratio (SINR) requirement for successful heterogeneous communication; 2) the sum of transmit power allocated to each user is equal to the permissible total transmit power at the smart meter; 3) allocated transmit power to each user is feasible. By employing the theorem that the sum of independent exponentially distributed random variables follows Erlang distribution, the probability distribution function (PDF) of the smallest allocated transmit power is mathematically obtained from the properties of downlink indoor Saleh-Valenzuela (S-V) channels. Numerical results show that the total downlink capacity is greatly enhanced by the proposed power allocation scheme. Furthermore, numerical results validate the contribution of beamforming technique at the smart meter on capacity optimization.

This paper studies higher-order statistics based on nested sampling. We propose multilevel nested sampling (MNS) algorithm to obtain higher-order statistics (HOS), and analyze the computational complexity of the MNS-HOS algorithm for both parametric and nonparametric methods. Compared to the existing HOS algorithms, the proposed algorithm vastly reduces the complexity by several orders in terms of the length of segmentation window. We also apply MNS-HOS algorithm to estimate the coefficients of a simplified LTE spatial channel model blindly without using any training sequences. Our simulations show that compared with pairwise coprime sampling HOS algorithm, MNS-HOS produces less variance and converges faster in estimating higher-order cumulants, and achieves 17% performance gain for channel estimation. The proposed MNS-HOS algorithm is also able to reduce computational complexity by 98% with a tradeoff of 22% performance loss in contrast with the HOS algorithm without sparse sampling.

This paper proposes an algorithm deriving higher-order statistics (HOS) from co-prime sampled sequences and implements it in the order determination of the parametric approach for modeling ultra-wide bandwidth indoor channel. We extend the co-prime sampling to pairwise co-prime sequences, and apply singular value decomposition in the matrix formed by third-order cumulants for the order determination of autoregressive model. We also analyze the variance of third-order cumulants and use their basic characteristic to determine the order of moving average model. In simulations, the proposed algorithm reduces the computational complexity to 17% of the existing HOS algorithms with negligible performance loss in high noise-to-signal ratio (SNR) environment. In low SNR scenario, the proposed algorithm is more sensitive to order mismatch in terms of the variances of expectations, which makes it a more reliable indicator to confirm the correct order.

This paper studies higher-order statistics of non-Gaussian signal using temporal co-prime sampling. We extend co-prime sampling to pairwise co-prime sequences (PCS) to derive higher-order statistics (HOS), and analyze the computational complexity of PCS-based HOS algorithm for both parametric and nonparametric methods. Compared to the existing HOS algorithms, the proposed algorithm vastly reduces the computation complexity by several orders in terms of the length of segmentation window. We also apply PCS-based HOS algorithm to estimate the coefficients of simplified LTE spacial channel model only based on its outputs. Our simulations show that 3rd-order PCS-based HOS achieves 80% performance gain compared to the existing HOS with the same computational complexity, or it has 12% performance loss with 15% complexity compared to the counterpart processing the same length of signal.

In real world, wireless heterogeneous sensor network (HSN) design and information integration are necessary in different applications. Traditionally, wireless sensor networks information integration is set up to passively fuse all received data. Such an approach is computationally challenging and operationally ineffective because improvements in information accuracy are not guaranteed. Opportunistic Sensing (OS) refers to a paradigm for signal and information processing in which a network of sensing systems can automatically discover and select sensor platforms based on an operational scenario. In this paper, we propose theory and algorithms of OS to simplify the HSN design and promote more efficient information integration. We propose an information theoretical criterion for opportunistic sensing in HSN, and show that HSN with correlated modalities needs less number of codewords than that with independent modalities. Our OS algorithm advances autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance. We apply our OS algorithm to radar sensor networks for surveillance and monitoring, and show that our approach works very well and much better than other approaches.

The current and future communication and sensing systems such as cognitive radio, radar, or sensor network include a network of multiple communication modules/devices or sensors. The network of communication modules/devices or sensors should operate with multiple goals managed by an intelligent platform network that can manage the dynamics of each device/sensor to meet the common goals of the platform, rather than each device/sensor to operate as an independent system. Therefore, it is significant to perform cooperative communication and sensing within and between platforms of sensors and their communication modules/devices. This special issue contains 14 papers selected from submissions to the 6th International Conference on Communications and Networking in China (CHINACOM 2011), with further extension and satisfactory external peer reviews. These papers highlight some of the current research interests and achievements in the area of cooperative communications and sensing. The topics include cognitive radio, spectrum sensing, MIMO system, radar, compressive sensing, and localization. The paper by Huang et al. analyzed the performance bonds of a wireless relay system that consists of several relays stations working on both amplifier-and-forward (AF) and decode-and-forward (DF) algorithms. The outage probability behavior of the proposed mixed AF and DF relay systems under independent Nakagami-m fading channels was studied. The trade-off between the complexity and performance issues was also discussed. The paper by Sun et al. presented a review on evolution from symbol-level space-time coded MIMO to chip-level space-time coded MIMO. It discussed the characteristics and limitations of traditional symbol-level space-time coding schemes. The proposed chip-level space-time coded MIMO works well even in a fast fading channel in addition to its flexibility to achieve diversity and multiplexing gains simultaneously in varying channel environments. The paper by Liu et al. studied cognitive radio and proposed a double cooperative detection scheme based on weighed combination in order to improve the global detected performance of the cooperative detection. The paper by Peng and Adachi analyzed the performance of a single-carrier frequency domain adaptive antenna array (SC-FDAAA) in a distributed antenna network (DAN) system. In order to make it clear whether the performance of SC-FDAAA can benefit from the distributed nature of DAN system or not, they made a comparison between DAN system and the traditional cellular system, that is, central antenna network system. The paper by Li et al. proposed a conception on the terahertz communication system for plasma sheath penetration. An idea based on the capability of radiation from the terahertz communication system to penetrate the plasma sheath was proposed to provide the theoretical basis for real-time communication with the nearcraft. The paper by Zhang et al. studied single-carrier frequency-domain equalization (SC-FDE). The feasibility of building SC-FDE system was explored by comparing performance with orthogonal frequency division multiplexing (OFDM). It studied on a dual-mode (DD-LMS+MCMA) blind equalization algorithm applied in a single-carrier system. The simulation model of image transmission in SC-FDE system was built with better result through the comparative analysis of image. The paper by Wu et al. proposed a new compressive sensing-based compression and recovery ultra-wideband (UWB) communication system. Real-world UWB signal was collected and processed to evaluate the performance of our proposed system. Simulation results showed that the data could be perfectly recovered if the compression ratio does not exceed 2.5:1 when Bernoulli matrix is chosen as the sensing matrix. The paper by Zou et al. focused on improving the system capacity of 60GHz wireless personal area networks and put forward an effective timeslot allocation scheme for spatial reuse, which combines the existing exclusive-region-based scheduling algorithm with simple power control. The paper by Huang and Pi proposed a GPS-based positioning and navigation with aid of wireless network positioning scheme, which utilizes the existing resources of the wireless communication network to provide the cheaper and more accurate urban positioning service with the higher survivability. The paper by Arianpoo et al. presented a survey on applications of network coding to improve TCP performance over wireless mesh networks (WMNs). It provided a thorough survey of TCP performance issues over WMNs and the available solutions to address these issues. Among the existing methods, network coding and the ways that TCP interacts with network-coded systems were focused. Some open research areas in this field were suggested. The paper by Yu et al. proposed a combined code reuse scheme with two-dimensional orthogonal variable spreading factor codes assignment algorithm for uplink multi-user/multi-rate block spread multi-cellular code division multiple access (CDMA). It was confirmed by the computer simulation that the proposed code assignment algorithm improves the code reuse efficiency while achieving lower blocking probability than traditional CDMA. The paper by Yang et al. proposed a relatively complete and robust optimization model under the scenario where multi-secondary users cooperatively sense multi-channels. The objective of this model was to maximize the system throughput, while aiming to jointly optimize the parameters including the sensing time and the weight coefficients of the sampling results. The results showed the performance advantage of the proposed model on improving the system throughput by comparing with other state-of-the-art optimization models. The paper by Shi et al. proposed a new linear fractional shift invariant system structure associated with the fractional Fourier transform, and the linear time-invariant systems were noted as special cases. The eigenfunctions and eigenvalues of the new LFSI systems were presented. The paper by Wu et al. studied channel estimation algorithms for cooperative spectrum sensing in AF cooperative system. The influence of channel estimation error to system performance was firstly deduced, and then linear minimum mean-square error (LMMSE) channel estimation algorithm with delay time-domain windowing technique (LMMSE-DTW) and modified singular value decomposition based LMMSE algorithm (SVD-LMMSE) were proposed to improve the channel estimation performance for CDMA system and OFDM system, respectively. We would like to thank all authors for contributing papers to the special issue. We appreciate the staff of Wireless Communications and Mobile Computing for their support in editing this special issue. Qilian Liang is a Professor at the Department of Electrical Engineering, University of Texas at Arlington (UTA). He received the BS degree from Wuhan University in 1993, the MS degree from Beijing University of Posts and Telecommunications in 1996, and the PhD degree from University of Southern California (USC) in May 2000, all in Electrical Engineering. Prior to joining the faculty of the University of Texas at Arlington in August 2002, he was a Member of Technical Staff in Hughes Network Systems Inc. at San Diego, California. His research interests include wireless sensor networks, wireless communications, compressive sensing, smart grid, signal processing for communications, fuzzy logic systems and applications, and so on. Dr Liang has published more than 250 journal and conference papers and seven book chapters. He received the 2002 IEEE Transactions on Fuzzy Systems Outstanding Paper Award, the 2003 US Office of Naval Research (ONR) Young Investigator Award, the 2005 UTA College of Engineering Outstanding Young Faculty Award, the 2007, 2009, 2010 US Air Force Summer Faculty Fellowship Program Award, the 2012 UTA College of Engineering Excellence in Research Award, and the 2013 UTA Outstanding Research Achievement Award. Victor C. M. Leung received the BASc (Hons) degree in electrical engineering from the University of British Columbia (UBC) in 1977 and was awarded the APEBC Gold Medal as the head of the graduating class in the Faculty of Applied Science. He attended graduate school at UBC on a Natural Sciences and Engineering Research Council Postgraduate Scholarship and completed the PhD degree in electrical engineering in 1981. From 1981 to 1987, Dr Leung was a Senior Member of Technical Staff and satellite system specialist at MPR Teltech Ltd., Canada. In 1988, he was a Lecturer in the Department of Electronics at the Chinese University of Hong Kong. He returned to UBC as a faculty member in 1989 and currently holds the positions of Professor and TELUS Mobility Research Chair in Advanced Telecommunications Engineering in the Department of Electrical and Computer Engineering. Dr Leung has co-authored more than 700 technical papers in international journals and conference proceedings, 29 book chapters, and co-edited eight book titles. Several of his papers had been selected for best paper awards. His research interests are in the areas wireless networks and mobile systems. Dr Leung is a registered professional engineer in the Province of British Columbia, Canada. He is a Fellow of IEEE, the Royal Society of Canada, the Engineering Institute of Canada, and the Canadian Academy of Engineering. He was a Distinguished Lecturer of the IEEE Communications Society. He is a member of the editorial boards of the IEEE Wireless Communications Letters, Computer Communications, and several other journals and has previously served on the editorial boards of the IEEE Journal on Selected Areas in Communications—Wireless Communications Series, IEEE Transactions on Wireless Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Computers, and Journal of Communications and Networks. He has guest-edited many journal special issues and contributed to the organizing committees and technical program committees of numerous conferences and workshops. He was a recipient of the IEEE Vancouver Section Centennial Award and 2012 UBC Killam Research Prize. Weixiao Meng received the BEng, MEng, and PhD degrees from Harbin Institute of Technology (HIT), Harbin, China, in 1990, 1995, and 2000, respectively. From 1998 to 1999, he worked at NTT DoCoMo on adaptive array antenna and dynamic resource allocation for beyond 3G as a senior visiting researcher. He is now a full professor at the School of Electronics and Information Engineering of HIT. His research interests include broadband wireless communications and networking, MIMO, GNSS receiver, and wireless localization technologies. He has published three books and over 190 papers on journals and international conferences. He is the Chair of IEEE Communications Society Harbin Chapter, a senior member of the IEEE ComSoc, IET, the China Institute of Electronics, and the China Institute of Communication. He has been an editorial board member for Wiley's WCMC Journal since 2010 and the area editor for PHYCOM journal since 2014. He acted as leading TPC co-chair of ChinaCom2011, leading Services and Applications track co-chair of IEEE WCNC2013, Awards co-chair of IEEE ICC2015, and Wireless Networking Symposia co-Chair. In 2005, he was honored provincial excellent returnee and selected into New Century Excellent Talents (NCET) plan by Ministry of Education, China, in 2008. Fumiyuki Adachi received the BS and DrEng degrees in electrical engineering from Tohoku University, Sendai, Japan, in 1973 and 1984, respectively. In April 1973, he joined the Electrical Communications Laboratories of Nippon Telegraph & Telephone Corporation (now NTT) and conducted various types of research related to digital cellular mobile communications. From July 1992 to December 1999, he was with NTT Mobile Communications Network, Inc. (now NTT DoCoMo, Inc.), where he led a research group on wideband/broadband CDMA wireless access for IMT-2000 and beyond. Since January 2000, he has been with Tohoku University, Sendai, Japan, where he is a distinguished Professor of Communications Engineering at the Graduate School of Engineering. His research interest is in the areas of wireless signal processing and networking including broadband wireless access, equalization, transmit/receive antenna diversity, MIMO, adaptive transmission, channel coding, and so on. From October 1984 to September 1985, he was a United Kingdom SERC Visiting Research Fellow in the Department of Electrical Engineering and Electronics at Liverpool University. Dr Adachi is an IEEE Fellow and a VTS Distinguished Lecturer for 2011 to 2013. He is a co-recipient of the IEEE Vehicular Technology Transactions Best Paper of the Year Award 1980 and again 1990 and also a recipient of Avant Garde award 2000. He is a Fellow of Institute of Electronics, Information and Communication Engineers of Japan (IEICE) and is a recipient of IEICE Achievement Award 2002 and a co-recipient of the IEICE Transactions Best Paper of the Year Award 1996, 1998, and again 2009. He is a recipient of Thomson Scientific Research Front Award 2004, Ericsson Telecommunications Award 2008, Telecom System Technology Award 2009, Prime Minister Invention Prize 2010, British Royal Academy of Engineering Distinguished Visiting Fellowship 2011, and KDDI Foundation Research Award 2012.

ABSTRACT In this paper, we propose a new compressive sensing‐based compression and recovery ultra‐wideband (UWB) communication system. Compared with the conventional UWB system, we can jointly estimate the channel and compress the data, which can also simplify the design of hardware. No information about the transmitted signal is required in advance as long as the channel follows autoregressive process. As an application example, real‐world UWB signal is collected and processed to evaluate the performance of our proposed system. The compression procedure is so simple that we just multiply random Gaussian or Bernoulli matrix with the original data to capture all the information we want. Simulation results show that the data could be perfectly recovered if the compression ratio does not exceed 2.5:1 when Bernoulli matrix is chosen as the sensing matrix. Copyright © 2012 John Wiley & Sons, Ltd.

In this paper, we analyzed the performance of noisy compressive sensing theoretically, and derived both the lower bound and upper bound of the probability of error for compressive sensing, with the assumption that both the original information and the noise follow Gaussian distribution. Both the lower bound and upper bound of the probability of error for the general case without special requirement of the measurement matrix Φ are provided. It has been shown that under some condition, perfect reconstruction of the information vector is impossible, as there will always be certain error. Specially, when the Bernoulli matrix is chosen as the measurement matrix, the corresponding lower bound and upper bound of the probability of error are given with a much neat and clear expression. The corresponding Cramer-Rao lower bound is also provided. These results provide some theoretical reference of the probability of error of compressive sensing.

Cooperative diversity techniques have emerged as promising solutions to combat the deleterious effects caused by multipath fading. The main advantage of this technique is that the diversity gain can be achieved without installing multiple antennas at the transmitter or the receiver. In this paper, we study the ergodic capacity performance of cooperative communication systems with decode-and-forward (DF) relaying protocol over Nakagami-m fading channels. Specifically, the ergodic capacity over both independent identically distributed (i.i.d.) fading channels and independent non-identically distributed (i.n.d.) fading channels are investigated. We first explore the ergodic capacity over i.i.d. fading channels, and then derive the tight bounds for the ergodic capacity at the regime of low signal-to-noise (SNR) and high SNR, respectively. Further, we obtain an exact closed-form analytical expression of the ergodic capacity over i.n.d. fading channels. Finally, the Monte-Carlo simulations are provided to verify the tightness of the proposed bounds. The theoretical results obtained are instrumental to the future cooperative-diversity network modeling and design.

In this paper, we investigate the theoretical per-node transmission limit of hybrid wireless networks over fast fading channels. We first formulate a hybrid wireless network model, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. Two types of transmission mode in hybrid wireless network, the so called intra-cell transmission mode and the infrastructure transmission mode are considered. In this study, we define a feasible ergodic throughput capacity as the criterion for throughput performance analysis. To effectively overcome the fast fading impairments, we introduce an optimal multiple access technique allowing opportunistic sources to transmit concurrently with the scheduled source. A successive interference cancelation (SIC) strategy is then applied at the receiver to limit the intra-cell interference and achieve the maximum throughput. In addition, the frequency reuse scheme is employed to minimize the intercell interference. Finally, we derive closed-form analytical expressions for the ergodic throughput capacity and further explore the scaling laws for the ergodic throughput capacity at low signal-to-interference-plus-noise ratio (SINR) and high SINR scenarios, respectively. The theoretical bounds obtained are instrumental to the future energy-limited network modeling and design.

As a paradigm of the incoming smart grid, vehicle-to-grid (V2G) has been proposed as a solution to increase the adoption rate of plug-in hybrid electric vehicles (PHEVs). In this work, we investigate the energy management strategies for PHEVs via bidirectional V2G. We first follow a cost-conscious approach. To minimize the daily energy cost, we formulate the energy management problem through dynamic programming. However, the “well-known” complexity in solving dynamic programming poses a computational challenge even for a small number of iterations. Therefore, we prove that a state-independent four-threshold (s, S, s', S') feedback policy is optimal for PHEV battery charging/discharging based on stochastic inventory theory. A backward iteration algorithm is further developed to practically implement the above (s, S, s', S') policy. Second, aiming to minimize the peak load and flatten the overall load profile, we propose an optimal PHEV charging scheme and derive a reminiscent “water-filling” solution for this scenario. Realistic PHEV battery models, time-of-use (TOU) electricity pricing rate and real data of household demand are integrated into our formulated PHEV model. The theoretical analysis and proofs are instrumental to the future large-scale PHEV adoption in smart grid.

In this paper, we focus on the hybrid wireless network, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. Two types of transmission mode in hybrid wireless network, the so called intra-cell transmission mode and the infrastructure transmission mode are considered. Then what is the throughput capacity of hybrid wireless network over fading channels and how the throughput capacity varies with different network setup? To study these open questions, we first define a feasible outage throughput capacity as the criterion for throughput performance analysis. Aiming to effectively overcome the fading impairments, we introduce an optimal multiple access technique allowing opportunistic sources to transmit concurrently with the scheduled source. A successive interference cancelation (SIC) strategy is then applied at the receiver to limit the intra-cell interference and achieve the maximum throughput. We derive closed-form analytical expressions for the outage throughput capacity and further explore the scaling laws for the outage throughput capacity under different transmission modes. Finally, we provide the detailed performance analysis in terms of the per-node average error probability (AEP). The theoretical bounds obtained are instrumental to the future network modeling and design.

The measurement of an emitter's position using electronic support passive sensors is termed passive localization and plays an important part both in electronic support and electronic attack. The emitting target could be in terrestrial or underwater environment. In this paper, we propose a time difference of arrival (TDoA) algorithm for passive localization in underwater and terrestrial environment. In terrestrial environment, it is assumed that a Rician flat fading model should be used because there exists line of sight. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. We propose the TDoA finding algorithm via estimating the delay of two correlated channels, and compare it with the existing approach. Simulations were conducted for terrestrial and underwater environment, and simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and root-mean-square error (RMSE). Compared to the TDoA performance in terrestrial environment, the TDoA performance in underwater environment is much worse. This is because the underwater channel has clusters and rays, which introduces memory and uncertainties. For the two scenarios in underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

In this paper, we investigate the theoretical per-node transmission limit of hybrid wireless networks over fading channels. We formulate a hybrid wireless network model, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. Two types of transmission mode, the so called intra-cell transmission mode and the infrastructure transmission mode are considered. Aiming to effectively overcome the fading impairments, we introduce an optimal multiple access technique allowing opportunistic sources to transmit concurrently with the scheduled source. A successive interference cancelation (SIC) strategy is then applied at the receiver to limit the intra-cell interference and achieve the maximum throughput. In addition, the frequency reuse scheme is employed to minimize the inter-cell interference. We first study the scaling laws for outage throughput capacity in the slow fading scenario and provide the closed-form analytical expressions for the outage throughput capacity at the regime of high signal-to-interference-plus-noise ratio (SINR). The ergodic throughput capacity, which serves as the performance criterion for fast fading situation, is then explored. We derive the tight bounds for ergodic throughput capacity at low SINR and high SINR scenarios, respectively. Finally, we provide the detailed quality of service (QoS) performance analysis in terms of the per-node average error probability (AEP). The theoretical bounds obtained are instrumental to the future energy-limited network modeling and design.

In this paper, we investigate the problem of how much information can be transported over hybrid wireless networks, in which a wired network of base stations is deployed to support long-range communications between wireless nodes. The main focus of this paper is the effect of fading on such hybrid wireless networks. To effectively overcome fading impairment, we introduce optimal multiple access technique allowing opportunistic sources to transmit concurrently with the scheduled source. All these sources share the entire bandwidth, much like a wideband CDMA system. A successive interference cancellation (SIC) strategy is then applied at the receiver to limit the intra-cell interference and achieve the maximum capacity. Close-form solutions for outage throughput capacity are derived under intra-cell transmission mode and infrastructure transmission mode, respectively. It is showed that, with opportunistic sources, the intra-cell mode effectively combats fading as wireless nodes increases; however, the infrastructure mode is bottlenecked by the downlink transmission since base station is the only transmitter in the cell during the downlink phase. The theoretical bounds obtained and proofs are instrumental to the future network modeling and design.

Integration of distributed energy resources (DERs) into microgrid makes power supply more reliable and reduces the cost. However, the connection of a large number of DERs among the load on middle voltage/low voltage (MV/LV) feeders may result in a severe voltage regulation problem. These challenges motivate the application of wireless sensor and actuator networks (WSANs) into smart grid to coordinate DERs with the traditional voltage regulators, such as onload tap changer (OLTC), line regulators and switched shunt capacitors. Thus, to achieve proper voltage regulation it is absolutely necessary to improve the voltage measurement accuracy and assure a long voltage sensor network lifetime. In this paper, two sensor selection schemes are proposed to improve the voltage measurement performance. We first propose an opportunistic sensor selection scheme under equal power allocation and investigate the asymptotic behaviors. Then we address sensor selection scheme under optimal power allocation and derive a reminiscent of the “water-filling” solution for this scenario. Finally present are numerous studies on improving the power efficiency using sensor selection. The theoretical analysis and proofs are instrumental to the future sensor network design in smart grid.

This work addresses the use of compressive sensing to compress real Synthetic Aperture Radar (SAR) raw data. Due to the low computational resources of the acquisition platforms and the steadily increasing resolution of SAR systems, huge amounts of data are collected and stored, which cannot generally be processed on board and must be transmitted to the ground to be processed and archived. Although compressive sensing (CS) has been proposed and studied by a lot of researchers, almost none of them touches the real application of it. While, in this paper, we test the sparsity of the real SAR raw data (obtained by University of Kansas in Greenland, 2010), compress it using compressive sensing, and then recover the original signal using several CS recovery algorithms (Basis Pursuit, Matching Pursuit and Orthogonal Matching Pursuit), and compare these methods' performance. Simulation results are presented to prove the successful application of CS to real SAR raw data. When proper sparsity matrix is chosen, the real SAR data could be transformed to sparse signal. Using our designed algorithm, the positions and the exact values of the SAR raw data can be almost perfectly recovered with a very low MSE at a compression ratio of 1/8. This is of great significance to help us perform further research in the applications of CS to real SAR raw data.

Motivated by recent advances on Compressive Sensing (CS), we study the sparsity of sense-through-foliage radar signals. Based on CLEAN method, we obtain the impulse response for sense-through-foliage communication channels for three different radars, 200MHz, 400MHz, and UWB radars. Channel impulse responses for the above three different kinds of channels demonstrate that the sense-through-foliage signals are very sparse, which means CS is possible to be applied to sense-through-foliage radar signals to tremendously reduce the sampling rate. We apply CS and linear programming to sparse signal compression and recovery, and it turns out that we could achieve compression ratio of 32:1 with perfect recovery for the UWB radar signals.

The measurement of an emitter's position using electronic support passive sensors is termed passive geolocation. The emitting target could be in underwater environment. In this paper, we propose a Time Difference of Arrival (TDoA) algorithm for passive geolocation in underwater environment. In underwater environment, we apply a modified UWB Saleh-Valenzuela (S-V) model to characterize the underwater acoustic fading channel. To estimate the delay for TDoA of two correlated channels, we propose an TDoA finding algorithm, and compare with existing approach. Simulation results show that our TDoA algorithm performs much better than the cross-correlation-based TDoA algorithm with a lower level of magnitude in terms of average TDoA error and Root-Mean-Square-Error (RMSE). In underwater environment, the performance in rich scattering underwater environment is worse than that in less scattering underwater environment, because the latter has less clusters and rays, which would cause less uncertainties in TDoA.

In this paper, we propose, one novel information theoretic method for target detection through foliage, using ultra wide-band (UWB) radars. This method is based on mutual information between subsequent radar return that makes use of the fact, that there is more random phenomenon in the targeted region. Mutual information is a robust method to extract dependent information and insensitive to nonlinearities. When echoes are in good quality, the detection of target can be achieved by applying our method. Finally, we propose radar sensor network (RSN) and rake structure for poor signal quality, which will integrate the information in the fusion center. Results show that accurate detection can be achieved by this method. The performance of the algorithm was evaluated, based on real world data.

Inspired by recent advances in compressive sensing (CS), we introduce CS to the radar sensor network (RSN) using the pulse compression technique in order to efficiently compress, restore and recover the radar data. For the sake of simplicity but without losing generality, we study an RSN consisting of a number of transmit sensors and one receive sensor. Our idea is to employ a set of Stepped-Frequency waveforms as pulse compression codes for transmit sensors, and to use the corresponding Stepped-Frequency (SF) waveforms as the sparse matrix in the receive sensor due to the orthogonality of the basis. We conclude that the signal samples along the time domain could be largely compressed so that they could be recovered by a small number of measurements. In addition, a diversity gain could also be obtained at the output of the set of the matched filters in the receive sensor. Simulation results show that even if the signal could not be perfectly reconstructed, the probability of miss detection of target could be kept zero. In the future, the reconstructed signal would further be applied to the study of target recognition.

Although there are some work focusing on rate distortion analysis of compressive sensing (CS), some studied the distortion performance at a particular data rate and some only show the related figures without an expression. Different from all the others, in this paper, we study the performance of rate distortion function R(D) of CS with a distortion D and give a clear relationship between rate and distortion. Besides, we specify both uniform and non-uniform quantization for CS, and get the corresponding rate distortion functions in these two cases. Then, we compare their performance and analyze the results. In addition, we get the rate distortion function R(D) in the case of combining channel coding with CS, as channel coding process can make our final recovery of the original signal more accurate.

Opportunistic Sensing (OS) refers to a paradigm for signal and information processing in which a network of sensing systems can automatically discover and select sensor platforms based on an operational scenario. In this paper, we propose theory and algorithms of OS for advancing autonomous sensing that not only ensures effective utilization of sensing assets but also provides robust optimal performance. We propose an information theoretical criterion for opportunistic sensing in wireless sensor networks and derive an algorithm for efficient OS. We apply our OS algorithm to radar sensor networks for sense-through-foliage target detection, and show that our OS algorithm works very well and much better than other approach.

In this paper, we design a network of small unmanned aerial vehicles (SUAVs) for passive location of RF emitters. Each small UAV is equipped with multiple electronic surveillance (ES) sensors to provide local mean distance estimation based on received signal strength indicator (RSSI). Fusion center will determine the location of the target through UAV triangulation. Different with previous existing studies, our method is on a basis of an empirical path loss and log-normal shadowing model, from a wireless communication and signal processing vision to offer an effective solution. The performance degradation between UAVs and fusion center is taken into consideration other than assuming lossless communication. We analyze the geolocation error and the error probability of distance based on the proposed system. The result shows that this approach provides robust performance for high frequency RF emitters.

In this paper, we propose a novel compressive sensing (CS) based approach for sparse target counting and positioning in wireless sensor networks. While this is not the first work on applying CS to count and localize targets, it is the first to rigorously justify the validity of the problem formulation. Moreover, we propose a novel greedy matching pursuit algorithm (GMP) that complements the well-known signal recovery algorithms in CS theory and prove that GMP can accurately recover a sparse signal with a high probability. We also propose a framework for counting and positioning targets from multiple categories, a novel problem that has never been addressed before. Finally, we perform a comprehensive set of simulations whose results demonstrate the superiority of our approach over the existing CS and non-CS based techniques.

In this paper, we design a network of distributed radar sensors that work in an ad hoc fashion, but are grouped together by an intelligent clusterhead. This system is named Radar Sensor Network (RSN). A RSN not only provides spatial resilience for target detection and tracking compared to traditional radars, but also alleviates inherent radar defects such as the blind speed problem. This interdisciplinary area offers a new paradigm for parallel and distributed sensor research. We propose both coherent and noncoherent RSN detection systems applying selection combination algorithm (SCA) performed by clusterhead to take the advantage of spatial diversity. Monte Carlo simulations show that proposed RSN can provide much better detection performance than that of single radar sensor for fluctuating targets, in terms of probability of false alarm and miss detection. We also analyze the impact of Doppler shift on both coherent and noncoherent RSN detection systems at the presence of clutter. The result is that the coherent system is more robust to the noncoherent RSN.

In this paper, we investigate the capacity of wireless hybrid networks, in which a wired network of base stations is used to support long-range communications between wireless nodes. We introduce the multiple access technique by allowing more than one source node to transmit simultaneously and utilizing successive interference cancellation to decode information at the destination node. Our results show that, with the multiple access concept, the destination or the nearest base stations can receive information from the source nodes at rate O(b/n log (n/b)). But, when data is delivered to node, because the base station is the only transmitter in the cell, it can forward the message to each node only at rate Θ(b/n) and causes a bottleneck.

Motivated by recent advances on Compressive Sensing (CS) and high data redundancy among radars in radar sensor networks, we study CS for radar sensor networks. We demonstrate that the sense-through- foliage UWB radar signals are very sparse, which means CS could be applied to radar sensor networks to tremendously reduce the sampling rate. We propose to apply SVD-QR and maximum likelihood algorithms to CS for radar sensor networks. SVD-QR could vastly reduce the number of radar sensors, and CS is applied to the selected radar sensors for data compression. Simulations are performed and our compression ratio could be 192:1 overall.

We investigate the use of information theory to design waveforms for the measurement of extended radar targets in radar sensor networks (RSN). The different channel gains for transmitting signals from different radar sensors are introduced to model the radar system channel. Given the transmitting signals, we optimized the estimation waveforms that maximize the mutual information between a target ensemble and the received signal within additive Gaussian noise given the transmitting signals so that characteristics of the target could be well recognized. We also study the maximum mutual information with the constraints of the number of radar sensors, waveform energy and duration, which could be taken into consideration when waveforms are designed for RSN.

Inspired by recent advances in MIMO radar, we introduce orthogonal pulse compression codes to MIMO radar system in order to gain better target direction finding performance. We propose the concept and the design methodology for the optimized ternary pulse compression codes called the optimized punctured Zero Correlation Zone sequence-Pair Set (ZCZPS). According to codes property analysis, our proposed codes are able to provide the optimized autocorrelation and cross correlation properties during ZCZ. We also present a generalized MIMO radar system model using our proposed codes as pulse compression codes and simulate the target direction finding performance of the fluctuating and nonfluctuating system. The simulation results show that the more antennas used, the better target direction finding performance could be provided.

In this paper, we present new developed ternary code - punctured binary sequence-pair, give its definitions and the autocorrelation properties. We also investigate Doppler shift performance of the proposed code. The significant advantages of this ternary codes over conventional pulse compression codes, such as the widely used Barker codes, are zero autocorrelation sidelobes and the longer length of the code which can be as long as 31 so far. We apply our new ternary codes to radar system for target detection and observe that our codes outperform some other conventional pulse compression codes.

In multi-hop wireless networks, the use of virtual backbone can greatly simplify routing, broadcasting, as well as energy/bandwidth saving. However, constructing a virtual backbone is costly and time-consuming because of the inevitable transmission interference during the process of the construction. In the literature, most of virtual backbone construction algorithms did not take the interference issue into consideration. To the best of our knowledge, our proposed algorithm is the first fully-distributed, interference-aware virtual backbone construction algorithm that has a proven bound on the construction latency. Besides, our proposed algorithm can be applied to the leader election problem, and such application results in a fully-distributed and interference-aware leader election algorithm of time complexity O(n \log n) (where n is the number of nodes). This new leader election algorithm is practical in wireless networks because interference has already been dealt with; is also results in the fastest interference-aware leader election algorithm to the best of our knowledge.

Humans use multiple sources of sensory information to estimate environmental properties and has innate ability to integrate information from heterogeneous data sources. How the multi-sensory and multimodal information are integrated in human brain? There is consensus that it depends on the prefrontal cortex (PFC). The PFC has top-down control (favor weak) and rule-based mechanisms, and we incorporate the favor weak mechanism into rule-based fuzzy logic systems (FLS) via using upper and lower membership functions. The inference engine of favor weak fuzzy logic system is proposed under three different categories based on fuzzifiers. We apply the favor weak FLS to situation understanding based on heterogeneous sensor network, and it shows that our favor weak fuzzy logic system has clear advantage comparing to the type-1 FLS. The favor weak FLS can increase the probability of threat detection, and provides timely indication & warning (I&W).

In this paper, we study target detection in foliage environment. When radar echoes are in good quality, the detection of target can be achieved by applying short time Fourier transform (STFT) to the received UWB radar waveform. We compare our approach in case of no target as well as with target against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Results show that our approach can detect target more easily. When radar echoes are in poor condition and single radar is unable to carry out the detection, we employ both Radar Sensor Networks (RSN) and RAKE structure to combine the echoes from different radar members and finally detect the target.

In radar sensor network (RSN), interference with each radar can be effectively reduced when waveforms are properly designed. In this paper, we firstly perform some theoretical studies on co-existence of phase coded waveforms in RSN. Then we give the definition of a new set of triphase coded waveforms called optimized punctured zero correlation zone sequence-pair set (optimized punctured ZCZPS) and analyze their properties especially their optimized cross correlation property of any two sequence-pairs in the set. Furthermore, we apply our newly provided triphase coded waveforms and equal gain combination technique to the system simulation, and study the performances versus different number of radars in RSN, with Doppler shift or not. Simulation results show that detection performances of multiradars (utilizing our optimized punctured ZCZPS and equal gain combination), either under the Doppler shift condition or not, are superior to those of singler radar.

In this paper, we propose a maximum-likelihood (ML) estimation algorithm for underwater target size detection using underwater acoustic sensor networks. Theoretical analysis demonstrates that our underwater sensor network can tremendously reduce the variance of target size estimation. We show that our ML estimator is unbiased and the variance of parameter estimation matches the Cramer-Rao lower bound. Simulations further validate these theoretical results.

In this paper, the foliage penetration measurement data is provided by Air Force Office of Scientific Research (AFOSR). When radar echoes are in good quality, the detection of target can be achieved by applying our differential based technology on received single UWB radar waveform. We compared our approach in case of no target as well as with target against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Results show that our approach can work much better. When radar echoes are in poor condition and single radar is unable to carry out the detection, we employ both radar sensor networks (RSN) and RAKE structure to combine the echoes from different radar members and successfully detect the target.

In this paper, we propose that the foliage clutter follows log-logistic model using maximum likelihood (ML) parameter estimation as well as the root mean square error (RMSE) on PDF curves between original clutter and statistical model data. The measured clutter data is provided by Air Force Office of Scientific Research (AFOSR). In addition to investigating the log-logistic model, we also compare it with other popular clutter models, namely log-normal, Weibull and Nakagami. We show that the log-logistic model not only achieves the smallest standard deviation (STD) error on estimated model parameters, but also has the best goodness-of-fit and smallest RMSE. Further, the performance of detection at presence of foliage clutter is theoretically analyzed.

In this paper, we propose a discrete-cosine-transform (DCT)-based approach for sense-through-foliage target detection when the echo signal quality is good, and a radar sensor network (RSN) and DCT-based approach when the echo signal quality is poor. A RAKE structure which can combine the echos from different cluster-members is proposed for clusterhead in the RSN. We compared our approach with the ideal case when both echos are available, i.e., echos with target and without target. We also compared our approach against the scheme in which 2-D image was created via adding voltages with the appropriate time offset. Simulation results show that our DCT-based scheme works much better than the existing approach, and our RSN and DCT-based approach can be used for target detection successfully while even the ideal case fails to do it.

In this paper, we introduce a new method for packet transmission delay analysis and prediction in mobile ad hoc networks. We apply a fuzzy logic system (FLS) to coordinate physical layer and data link layer. We demonstrate that type-2 fuzzy membership function (MF), i.e., the Gaussian MFs with uncertain variance is most appropriate to model BER and MAC layer service time. Two FLSs and one neural network: a singleton type-1 FLS, an interval type-2 FLS and back-prop neural network (NN) are designed to predict the packet transmission delay based on the BER and MAC layer service time. Simulation results show that the interval type-2 FLS performs much better than the type-1 FLS in transmission delay prediction. And FLSs performs better than back-prop NN. We use the forecasted transmission delay to adjust the transmission power, and it shows that the interval type-2 FLS performs much better than a type-1 FLS, and FLSs are performs better than back-prop NN in terms of energy consumption, average delay and throughput. Besides, we obtain the performance bound based on the actual transmission delay.

Extending the lifetime of the energy constrained wireless sensor networks is a crucial challenge in sensor network research. In this paper, we present a novel approach based on fuzzy logic systems to analyze the lifetime of a wireless sensor network. We demonstrate that a type-2 fuzzy membership function (MF), i.e., a Gaussian MF with uncertain standard deviation (std) is most appropriate to model a single node lifetime in wireless sensor networks. In our research, we study two basic sensor placement schemes: square-grid and hex-grid. Two fuzzy logic systems (FLSs): a singleton type-1 FLS and an interval type-2 FLS are designed to perform lifetime estimation of the sensor network. We compare our fuzzy approach with other nonfuzzy schemes in previous papers. Simulation results show that FLS offers a feasible method to analyze and estimate the sensor network lifetime and the interval type-2 FLS in which the antecedent and the consequent membership functions are modeled as Gaussian with uncertain std outperforms the singleton type-1 FLS and the nonfuzzy schemes.

In this paper, we propose an efficient MAC protocol: the throughput maximized MAC protocol (TM-MAC), inspired by the availability that a number of ultrawideband (UWB) transmission parameters can be tuned to better match the requirements of data flow. In TM-MAC, we implement a concurrent multiuser access scheme instead of a mutual exclusion method such as TDMA and random access. For multiuser interference, we establish a model to adaptively adjust the data transmission rate to generate the expected signal to interference noise ratio (SINR) at the receiver side for reliable communications. We also analyze the relationship among the theoretical maximum channel capacity, achievable maximum channel capacity, and data transmission rate. According to network topology, TM-MAC redivides each piconet into several subsets in which communication pairs can make communication simultaneously and achieve the maximum throughput using the highest data rate. In subset formation, we propose a general analytical framework that captures the unique characteristics of shared wireless channel and throughput variance, as well as allows the modeling of a large class of systemwide throughput maximization via the specification of the per-link utilization function. For algorithm essential parameters design, we consider the influence of traffic type on the system performance. Heavy tailed distribution, compared to Poisson distribution for most existing work, is exploited to accurately model the real traffic to achieve the adaptation of our algorithm. Simulation results show that our algorithm can maximize throughput to achieve short latency.

Motivated by the observation that channel assignment for multiradio multi-channel mesh networks should support both unicast and local broadcast 1, should be interference-aware, and should result in low overall switching delay, high throughput, and low overhead, we propose two flexible localized channel assignment algorithms based on s-disjunct superimposed codes. These algorithms support the local broadcast and unicast effectively, and achieve interference-free channel assignment under certain conditions. In addition, under the primary interference constraints 2, the channel assignment algorithm for unicast can achieve 100 % throughput with a simple scheduling algorithm such as the maximal weight independent set scheduling, and can completely avoid hidden/exposed terminal problems under certain conditions. Our algorithms make no assumptions on the underlying network and therefore are applicable to a wide range of MR-MC mesh network settings. We conduct extensive theoretical performance analysis to verify our design.

In this paper, we design a transmit power control system using fuzzy logic system to provide cognitive radios the ability to coexist with primary (licensed) users in the same frequency band. With our built-in fuzzy power controller, a cognitive radio is able to opportunistically adjust its transmit power in response to the changes of the interference level to primary user (PU), the distance to PU and its received power difference at the base station (BS) while satisfying the requirement of sufficiently low interference to PU. We increase the reliability of our power control scheme by using linguistic knowledge of transmit power control (TPC) obtained from a group of network experts rather than a single expert. Simulation results show that using our proposed fuzzy power control scheme, we can decrease average transmit power consumption and achieve lower average outage probability compared with the fixed-step power control scheme.

In this paper we propose orthogonal waveforms and equal gain combination algorithm in radar sensor network (RSN) to alleviate blind speed problem. We also design a fuzzy logic system (FLS) to optimize the number of radars in RSN. Simulation results show that our FLS-based RSN can not only balance the number of radars and QoS in terms of probability of miss detection (PMD), but to some extent achieve constant PMD with different system configuration.

In this paper, extending our previous works on query processing, we propose two methods to substitute cosine measure for vector similarity: cosine-length measure (CLM) and joint-deference measure (JDM). Through considering the impact of vector length on vector similarity, CLM alleviates the disadvantage of traditional vector space model (VSM), in which the confidence of query answer is degraded since truly similar nodes cannot be elected according to users' requirement. JDM upgrades the accuracy and degrades the complexity for the computation on similarity coefficient through simplifying the measure from vector domain to scalar domain. In addition, with the distributions of measurement error and environment noise known and/or unknown respectively, we formulate the criteria to determine the optimum sample size to meet users' pre-specified target precision. Through simulation, we checked the validity and sensitivity of cosine measure, CLM and JMD methods on answer quality and network lifetime. Furthermore, our simulation results, in this paper, form a set of criteria for method selection basing on specific applications.

In existing works on MIMO for wireless sensor networks, virtual MIMO schemes based on the space-time block codes (STBC) have been widely used, and it is demonstrated that the full diversity and full rate are achieved which enhances power/bandwidth efficiency and reliability. However, all the existing works have generated substantial insight into the applications of MIMO to wireless sensor networks design for certain simplified models of the communication environment, such as complete channel state information (CSI), absence of interference generated by other users in the network, and persistent, homogeneous traffic from the physical layer point of view. In this paper, the authors study channel capacity of virtual MIMO-based wireless sensor networks with imperfect CSI. The authors compare the channel capacities of using equal power allocation and water-filling strategy and consider different channel models with imperfect CSI.

Delay and energy efficiency are two important parameters to evaluate the wireless sensor networks (WSN) quality. In the WSN, the interference will affect the packets transmission. When a sensor needs to send a packet, we choose the parameter signal-to-interference ratio (SIR) as the threshold to decide whether send or not. SIR thresholds selection show SIR could manage the tradeoff between the average delay and energy consumption. We propose fuzzy logic system (FLS) in the optimization of SIR threshold selection. Average delay and distance of a node to the source node are selected as antecedents for the FLS. The output of FLS provides adjusting factor for the SIR threshold. Simulation result show the fuzzy optimization could achieve a better network efficiency, reduce the average delay and extend the network lifetime.

A novel framework is proposed to introduce the game theory into power control in UWB network. Firstly, the power control problem is modeled as a cooperation potential game. Secondly, a novel utility function and potential function are introduced for UWB network. Finally, the process of power control is expressed as the process of maximum potential function for each active link. For the application of the framework, a new algorithm is also proposed. The algorithm converges to an exact Nash equilibrium by the theoretical proving. Through the simulation which compares the performance of our algorithm and the traditional scheme, the result shows that our algorithm performed better in both convergence and fairness, and also saved power consumed.

In this paper, we propose a MAC protocol: throughput-maximized MAC protocol (TM-MAC), based on the characteristics of ultra wideband (UWB) technology. In TM- MAC, we implement concurrent multiuser access instead of mutual exclusion method, such as TDMA and random access. For multiuser interference, we establish a model to adaptively adjust the data transmission rate to ensure a satisfied signal to noise ratio (SNR) at receiver side. We also analyze the relationship among the theoretical maximum channel capacity, the achievable maximum channel capacity and the maximum data transmission rate. According to the network topology, TM-MAC re-divides network into subsets, in which communication pairs can make communication simultaneously to enhance throughput and to exploit as fast as possible data transmission rate for reliable communication. For subset formation, we propose a general analytical framework, which captures the unique characteristics of shared wireless channel and allows to model a large class of systemwide throughput maximization issue via the specification of per-link utilization functions. Simulation results demonstrate that TM-MAC can implement throughput maximization to shorten latency and to enhance network processing capability.

In this paper, we introduce a new method for packet transmission delay analysis and prediction in mobile ad hoc networks. We apply a fuzzy logic system (FLS) to coordinate physical layer and data link layer. We demonstrate that type- 2 fuzzy membership function (MF), i.e., the Gaussian MFs with uncertain variance is most appropriate to model BER and MAC layer service time. Two FLSs: a singleton type-1 FLS and an interval type-2 FLS are designed to predict the packet transmission delay based on the BER and MAC layer service time. Simulation results show that the interval type-2 FLS performs much better than the type-1 FLS in transimission delay prediction. We use the forecasted transimission delay to adjust the transmission power, and it shows that the interval type-2 FLS performs much better than a type-1 FLS in terms of energy consumption, average delay and throughput. Besides, we obtain the performance bound based on the actual transmission delay.

In this paper, we perform some theoretical studies on constant frequency (CF) pulse waveform design and diversity in radar sensor networks (RSN): (1) the conditions for waveform co-existence, (2) interferences among waveforms in RSN, (3) waveform diversity combining in RSN. As an application example, we apply the waveform design and diversity to automatic target recognition (ATR) in RSN and propose maximum-likelihood (ML)-ATR algorithms for nonfluctuating target as well as fluctuating target. Simulation results show that our waveform diversity-based ML-ATR algorithm performs much better than single-waveform ML-ATR algorithm for nonfluctuating targets or fluctuating targets. Conclusions are drawn based on our analysis and simulations

Query processing has been studied extensively in traditional database systems. But few of existed methods can be directly applied to wireless sensor database systems due to their characteristics, such as decentralized nature, limited computational power, imperfect information recorded, and energy scarcity of individual sensor nodes. In this paper, we extend our previous work: quality-guaranteed and energy-efficient algorithm (QGEE) for wireless sensor database systems. We introduce radius of covering disk from point spread function (PSF) aspect and sample size for query quality and energy consumption control. PSF introduces ambiguity into query answers, since the sensitivity of nodes is nonuniform within monitoring region. Sample size determination refers to the process of determining exactly how many samples should be measured in order that the sampling distribution of estimators meets users' pre-specified target precision. In this paper, we formulate the criteria to determine the optimum radius and sample size according to users' requirements on query answers. Simulation results demonstrate that the impact of sample size and monitoring coverage on query answers in terms of root mean square error (RMSE)

We model the end-to-end distance for a given number of hops in dense planar wireless sensor networks in this paper. We derive that the closed-form formula for single-hop distance and postulate Beta distribution for 2-hop distance. When the number of hops increases beyond three, the multi-hop distance approaches Gaussian. The Gaussian approximation model is also applied to ranging, which achieves less distance error than Hop-TERRAIN and APS (ad hoc positioning system). Our error analysis also shows the distance error is be minimized by using our model

In wireless sensor networks, security and energy efficiency are two important topics. In multipath routing, threat can come from compromised nodes, which might relay incorrect information (packet) to the next node during routing. Detection of such incorrect information is very difficult. In this paper, we propose a secure and energy efficient multipath-routing (SEEM) scheme aided with channel coding and interleaver. The M-path in multipath routing are selected using existing routing algorithm and fuzzy logic system considering the average remaining battery capacity and mobility of associated nodes. Simulation results show that even if certain paths are compromised, the receiver node is still able to recover the transmitted message from errors with very low bit error rate

Energy-efficient MAC protocols applied for WSNs can be classified into two main categories: con tendon-based and schedule-based protocols, according to their wireless media access strategy. In this paper, we propose an asynchronous energy-efficient MAC protocol: A-MAC from contention perspective. Through applying free-running method and rescheduling scheme, A-MAC does not require network time synchronization any more, which is necessary for existing energy-efficient MAC protocols. So our algorithm is more suitable for the system which is difficult to ensure high quality network time synchronization. Moreover, we present a traffic-intensity- and network-density-based model to yield essential algorithm parameters: power on/off duration and interval of schedule broadcasts. Simulation results show that our algorithm can acquire optimal values of these parameters successfully to increase energy utility, ensure successful transmission rate and decrease data packet average waiting time. Therefore, network performance can be improved and network lifetime can be extended by using our algorithm

Query processing methods have been studied extensively in traditional database systems. But few of them can be directly applied into sensor database systems due to the characteristics of sensor networks: decentralized nature of sensor networks, limited computational power, imperfect information recorded, and energy scarcity of individual sensor nodes. In this paper, we propose a quality-guaranteed and energy-efficient algorithm (QGEE) for sensor database systems. We employ an in-network query processing method to task sensor networks through declarative queries. Given a query, our QGEE will adaptively form an optimal query plan in terms of energy efficiency and query quality. The goal of our approach is to reduce interference coming from measurements with extreme errors and to minimize energy consumption by providing service that is considerably necessary and sufficient for the requirement of applications. Moreover, we employ probabilistic method to formulate the distribution of imperfect information sources in terms of probability distribution function (PDF), and acquire probabilistic query answers on uncertain data. The probability to an answer allows users to place appropriate confidence in it. Simulation results demonstrate that our algorithm can reduce resource usage and supply quality satisfied query answers to users

A wireless sensor network (WSN) is designed to perform various information processing tasks such as event detection, target tracking and data classification. Comparing with traditional centralized networks, networked sensing offers unique advantage in improved robustness and scalability. Measures of performance for these tasks are well defined, including detection of false alarms or misses, classification errors, and track quality. In this paper, we present a fundamental performance analysis of event detection in wireless sensor networks. Our performance analysis is based on a new detection scheme - double sliding window (DSW) even detection. We compare it theoretically against the fixed threshold event detection approach. Results show that the DSW detection scheme performs better than the fixed threshold detection under certain circumstances

In this paper, we introduce a novel method for cross-layer design in mobile ad hoc networks. We use fuzzy logic system (FLS) to coordinate physical layer, data-link layer and application layer for cross-layer design. Ground speed, average delay and packets successful transmission ratio are selected as antecedents for the FLS. The output of FLS provides adjusting factors for the AMC (adaptive modulation and coding), transmission power, retransmission times and rate control decision. Simulation results show that our cross-layer design can reduce the average delay, increase the throughput and extend the network lifetime. The network performance parameters could also keep stable after the cross-layer optimization

Increasing the lifetime of energy constrained wireless sensor networks is one of the key challenges in sensor network research. In this paper we present a bottom-up approach to evaluating the lifetime performance of networks employing two basic sensor placement schemes: square-grid and hex-grid. Lifetimes of individual sensor node as well lifetimes of networks are modeled as random variables and their probability density functions (pdf) are obtained theoretically. Reliability theory is used for the network lifetime analysis, and provides a methodology for similar studies. Simulation results show that the actual pdf's match very closely to those obtained theoretically. The theoretical results provided in this paper will serve as a basis for other related research such as analysis of other sensor placement schemes, lifetime and sensor density tradeoff study, and performance of energy efficiency related algorithms.

In this paper, an asynchronous energy-efficient MAC protocol, ASCEMAC, is proposed for wireless sensor networks. In ASCEMAC, by applying free-running method and fuzzy logic rescheduling scheme, time synchronization which is necessary in existing energy-efficient MAC protocols is not required any more. Moreover, we present a traffic-strength- and network-density-based model to determine essential algorithm parameters, such as power on/off duration, interval of schedule broadcast and super-time-slot size and order. Simulation results show that our algorithm can ensure the average successful transmission rate, decrease the data packet average waiting time, and reduce the average energy consumption. Therefore, network performance is improved and network lifetime is extended by using our algorithm.

In our proposed multipath routing scheme, remaining battery capacity, mobility, and distance to the destination location of candidate sensors in the local communication range are taken into consideration for next hop relay node selection, and a fuzzy logic system is applied to the decision making. Simulation results show that this scheme can extend the network lifetime longer than the original geographic routing scheme which only considers distance to the destination location, and this scheme can reduce the frame loss rate and link failure rate, since mobility is considered.

The effectiveness of distributed wireless sensor networks depends highly on the deployment of sensors. Given a finite number of sensors, optimizing the sensor deployment enhances the field coverage of a wireless sensor network. Network lifetime and quality of communication in terms of outage probability are, as a result, greatly ameliorated as the topology fast approaches uniformity. We propose a fuzzy optimization algorithm (FOA) to adjust the sensor placement efficiently after an initial random deployment. We apply fuzzy logic theory to handle the uncertainty in the sensor deployment problem. Simulation results show that our approach achieves fast and stable deployment and maximizes the field coverage. Outage probability, as a measure of communication quality, is effectively decreased.

We study the demodulation problems for enhanced general packet radio services (EGPRS) wireless systems, where Rician flat fading channels are considered. A linear interpolation with decision feedback combined with a modified self-organizing-map (LIDF-MSOM) demodulator is implemented for such systems. Simulation results show that the performance of the proposed demodulator is much better than that of LIDF alone.

In this paper, we utilize clustering to organize wireless sensors into an energy-efficient hierarchy. We take a fully distributed approach to energy-efficient clustering and propose an expellant self-organization (ESO) scheme that uses expellant election to achieve local energy efficiency. As a distributed scheme using only local information, ESO achieves comparable performance in terms of effective lifetime and data/energy ratio compared with native LEACH that relies on another routing algorithm to access global information. An exponential data correlation model is also introduced to simulate the variation extent of the data aggregation effect.

We study the demodulation problem in time division multiple access (TDMA) wireless asynchronous transfer mode (ATM) networks, where Rician flat fading channels are considered. A linear interpolation with decision feedback combined with a modified version of the self-organizing-map (LIDF-SOM) demodulator is proposed for such a system. We obtain the training sequence by exploiting medium access control (MAC) and data link control (DLC) protocols such that a semi-blind adaptive demodulator is implemented. Simulation results show that LIDF-SOM obtains 0.4-1.0 dB gain over Rician fading channels as compared to LIDF alone.

The ad hoc wireless sensor network is usually deployed in severe environment with limit power supply, whose topology is dynamic and flexible. We developed an access-based energy efficient clustering protocol (ABEE), whose clustering algorithm is based on the outcome of the multiple access to the public channel. ABEE retains the advantage of access-based scheme such as self-organization, flexibility and stability. Also by considering the energy dissipation during the compression and transmission of the collected data, ABEE can provide uniformly distributed and energy efficient infrastructure for the upper lever protocols. We also propose to use coverage to judge the robustness of the network as well as the number of active nodes. Simulation is used to compare ABEE with the exclusively access-based clustering protocol in terms of rationality of the cluster structure, system lifetime and the coverage area. Our result shows that the ABEE can partition the large scale of nodes more rationally, efficaciously improve the system lifetime and the coverage of the whole network.

This paper studies the co-channel interference (CCI) problem for time-division-multiple-access (TDMA) cellular mobile communication systems with burst transmission. We present a method using learn vector quantization (LVQ) to cancel CCI for such systems. The model of the overall CCI is significantly simplified based on that of the individual CCI. The LVQ is realized as a classification equalizer with a decision feedback adaptive filter. An extremely small number of unique words (UWs) is utilized to initialize the LVQ equalizer. Simulation results show that the bit error rate (BER) of our proposed method is much better than that of the recently proposed nearest neighbor classification (NNC) equalizer.

In wireless sensor networks, the bandwidth and energy are limited, and some link failures may happen during data transmission. We propose a fault tolerant multipath transportation aided with channel coding and interleaver (MTACCI) scheme for wireless sensor networks. Simulation results show that this scheme works much better than the dual transportation scheme in static channel and diversity combining scheme in fading channel when link failures exist. Further analysis demonstrates that this scheme can solve the bandwidth and energy constraint problems, and makes wireless sensor networks survivable and resilient.

Current works on MAC protocols in wireless sensor networks(WSN) mainly concentrate on optimizing the efficiency and fairness of common channel access. We propose a secure MAC protocol improve the security of WSN, which is based on the RTS(request to send)/CTS(clear to send) mechanism. Our major contributions include: analysis of the security problems of RTS/CTS based MAC protocols; design of the intrusion detection method using soft decision theory; discussion of the intrusion strategies and attack and defense simulation models.

Performance evaluation is one of the most important research topics for the wireless sensor networks (WSN). Latency-aware and energy efficiency are two important parameters to evaluate the networks quality. In order to meet different performance requirements of the service, we classify the packets into high priority and low priority. We consider the latency and energy tradeoffs in WSN. The latency of the high priority packets is small but the network cost more energy. The latency of the packets of low priority packets is large but the network cost less energy. We solve this problem by transmitting redundant packets in the WSN. We assume that the network has a cell-partitioned structure, and sensor moves according to one-step Markov path model with constant speed. Simulation shows that the scheduling algorithm with/without redundancy can realize the latency/energy tradeoffs in WSN.

We utilize clustering to achieve energy efficiency for an on-off wireless sensor network, whose member nodes alternate between active and inactive states. In the proposed distributed and energy efficient self-organization (DEESO) scheme, the head election is adjusted adaptively to the energy reserve of local active nodes. Furthermore, we apply adaptive channel assignment to address the on-off topology changes. Simulation results show that DEESO delivers 184% amount of data to the base station compared to LEACH (low energy adaptive clustering hierarchy) for the same amount of dissipation and the effective network lifetime is extended by around 56%.

We present a nonlinear equalization scheme for a Rician multipath fading channel - a fuzzy logic-based approach. We show that each channel state of a multipath Rician channel follows a Gaussian distribution, which means a Bayesian equalization can be implemented. The parameters of the Bayesian equalization are determined using an unsupervised clustering method - fuzzy c-means (FCM) method. An extremely small number of training symbols (about 1% of a burst) are used to determine the category of each channel state with the aid of data mining. Simulation results show that our Bayesian equalizer performs much better than the recently proposed nearest neighbor classifier-based equalizer at moderate to high signal-to-noise ratio (SNR).

In this paper, we propose a Bayesian (known as optimal) demodulation scheme for satellite-based time-division-multiple-access (TDMA) wireless asynchronous transfer mode (ATM) networks. Rician flat fading is assumed for the satellite channel (no multipath). Because it is asynchronous between the transmission and receiver in wireless ATM networks, it is most important to focus on the demodulator design. The parameters for the Bayesian demodulator are determined using an unsupervised (blind) clustering method - fuzzy c-means (FCM). Our simulation results show that our demodulator performs better than the block phase estimation demodulator in terms of uncoded bit error rate (BER) with a gain of 0.2 to 0.3 dB.

Channel equalization has been revealed to be a classification problem by some recent applications of clustering and neural network techniques. In this paper, a new unsupervised learning (clustering) algorithm, adaptive nearest neighbor classifier (ANNC) is presented for channel equalization. ANNC can mine more channel characteristics that the recently proposed nearest neighbor (NNC) classifier. The proposed method is applied to a time-division-multiple-access (TDMA) satellite communication system with burst digital transmission. The improvement of the proposed algorithm over the recently reported NNC approach is clearly demonstrated.

In this paper, we study the demodulation problem in time-division-multiple-access (TDMA) wireless asynchronous transfer mode (ATM) networks. We propose a self-organizing-map (SOM) based demodulator. A linear interpolation with decision feedback (LIDF) combined with SOM algorithm (LIDF-SOM) is developed, in which the SOM network is initialized with some known symbols. Such known symbols are obtained by exploiting the medium access control (MAC) and data link control (DLC) protocols. Our scheme has three advantages, it: (1) avoids training symbols for uplink data bursts, (2) has reduced computational costs compared with common blind demodulators, and (3) blocks propagation estimation error through cells. Simulation results show that LIDF-SOM has better performances (0.2-0.4 dB gain) over time-varying channel as compared to the LIDF algorithm.

Selecting an appropriate clusterhead can save power for the whole ad hoc wireless network. Generally, clusterhead election for mobile ad hoc network is based on the distance to the centroid of a cluster, and the closest one is elected as the clusterhead; or pick a node with the maximum battery capacity as the clusterhead. In this paper, we present a clusterhead election scheme using fuzzy logic system (FLS) for mobile ad hoc wireless networks. Three descriptors are used: distance of a node to the cluster centroid, its remaining battery capacity, and its degree of mobility. The linguistic knowledge of clusterhead election based on these three descriptors is obtained from a group of network experts. 27 FLS rules are set up based on the linguistic knowledge. The output of the FLS provides a clusterhead possibility, and node with the highest possibility is elected as the clusterhead.

In mobile wireless personal area networks (WPAN), the position of each node changes over time. A new network protocol that is able to dynamically update its links in order to maintain strong connectivity is said to be "self-reconfiguring". In this paper, we propose mobile wireless personal area networks' (WPAN) design method with self-reconfiguring protocol for power efficiency. The WPAN is self-organized to clusters using an unsupervised clustering method, fuzzy c-means. A fuzzy logic system is applied to master/controller election for each cluster. A self-reconfiguring topology is proposed to manage the mobility and recursively update the network topology. We also modify the mobility management scheme with hysteresis to overcome the ping-pong effect.

In mobile wireless personal area networks (WPAN), the position of each node changes over time. A network protocol that is able to dynamically update its links in order to maintain strong connectivity is said to be "self-reconfiguring." We propose a mobile wireless personal area networks (WPAN) design method with self-reconfiguring protocol for power efficiency. The WPAN is self-organized to clusters using an unsupervised clustering method, fuzzy c-means. A fuzzy logic system is applied to master/controller election for each cluster. A self-reconfiguring topology is proposed to manage the mobility and recursively update the network topology. We also modify the mobility management scheme with hysteresis to overcome the ping-pong effect. Simulation results show that our scheme performs much better than the existing algorithm.

In this paper, we present a channel equalization scheme for time-division multiple access (TDMA) satellite communications with burst digital transmission. We show that each channel states of the multipath satellite channel follows a Gaussian distribution, which means a Bayesian equalization can be implemented. The parameters of the Bayesian equalization are determined using an unsupervised clustering method - the fuzzy c-means (FCM) method. An extremely small number of training symbols (about 1% of a burst) are used to determine the category of each channel state with the aid of data mining. Simulation results show that our Bayesian equalizer performs much better than the recently proposed nearest neighbor classifier-based equalizer at moderate to high signal-to-noise ratio (SNR).

This paper presents a method for eliminating adjacent-channel interference (ACI) in satellite-based General Packet Radio Service (GPRS) systems using a new fuzzy adaptive filters (FAF). We validate the reasons for using such a filter by analyzing the Bayesian detector in which some parameter uncertainties exist. A clustering method is used to adaptively design the parameters of the FAF. Simulation results show that the detector based on the new FAF perform better than the maximal-likelihood block phase estimation detector.

Most existing works on handover for mobile cellular networks are based on the assumption that the network traffic is uniformly-loaded for every cell, i.e., all cells in the network are equally loaded. In this paper, we present a handover initiation scheme using a fuzzy logic system (FLS) for nonuniformly loaded mobile cellular networks. Bandwidth utilization is combined into the handover decision making. The linguistic knowledge of handover based on bandwidth utilization and signal strength is obtained from a group of network experts. The FLS rules are set up based on the linguistic knowledge, and the number of rules is reduced from 81 to 18 using union-rule configuration. The output of the FLS provides a four-dimension handover possibility surface which is used to make a hard decision. We compared our scheme against the relative signal strength with hysteresis method.

In this paper, a fuzzy adaptive filter (FAF) is applied to satellite packet data system demodulation. This approach is much simpler than the existing demodulator. it achieves better performance (0.3 dB gain) than the existing schemes such as linear interpolation with decision feedback. This algorithm breaks the limitation of the number of unique words (training sequences) and nonuniform pattern of the unique word. The parameters are determined via mining and extracting knowledge based on the unique word.

We present an approach for MPEG variable bit rate (VBR) video modeling and classification using fuzzy techniques. We demonstrate that a type-2 fuzzy membership function, i.e., a Gaussian MF with uncertain variance, is most appropriate to model the log-value of I/P/B frame sizes in MPEG VBR video. The fuzzy c-means (FCM) method is used to obtain the mean and standard deviation (std) of T/P/B frame sizes when the frame category is unknown. We propose to use type-2 fuzzy logic classifiers (FLCs) to classify video traffic using compressed data. Five fuzzy classifiers and a Bayesian classifier are designed for video traffic classification, and the fuzzy classifiers are compared against the Bayesian classifier. Simulation results show that a type-2 fuzzy classifier in which the input is modeled as a type-2 fuzzy set and antecedent membership functions are modeled as type-2 fuzzy sets performs the best of the five classifiers when the testing video product is not included in the training products and a steepest descent algorithm is used to tune its parameters.

We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs using the concept of upper and lower membership functions (MFs). We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting, when a non-stationary time-series is corrupted by additive noise where SNR is uncertain, and demonstrate improved performance over type-1 FLSs.

Presents a decision feedback equalizer (DFE) that uses type-2 fuzzy adaptive filters (FAFs). These FAFs are realized using an unnormalized type-2 TSK fuzzy logic system. We apply our DFE to a nonlinear time-varying channel, and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Our DFE vastly reduces computational complexity as compared to a transversal equalizer. Simulation results show that our type-2 FAF-based DFE performs much better than nearest neighbor classifiers or a DFE based on type-1 FAFs.

A type-2 fuzzy logic system (FLS) can handle numerical and linguistic uncertainties, but, like a type-1 FLS, rule explosion is one of its major disadvantages. In this paper, we present a design method which can tremendously reduce rule number for interval type-2 fuzzy logic systems using an SVD-QR method. The SVD-QR method is performed after extracting two fuzzy basis function expansions from the interval type-2 FLS. We evaluate this method by applying it to a time-series forecasting problem in conjunction with back-propagation training, and demonstrate that tremendous rule number reduction ratio is achieved with very little performance degradation. © 2000 John Wiley & Sons, Inc.

We present the theory and design of interval type-2 fuzzy logic systems (FLSs). We propose an efficient and simplified method to compute the input and antecedent operations for interval type-2 FLSs: one that is based on a general inference formula for them. We introduce the concept of upper and lower membership functions (MFs) and illustrate our efficient inference method for the case of Gaussian primary MFs. We also propose a method for designing an interval type-2 FLS in which we tune its parameters. Finally, we design type-2 FLSs to perform time-series forecasting when a nonstationary time-series is corrupted by additive noise where SNR is uncertain and demonstrate an improved performance over type-1 FLSs.

Presents a kind of adaptive filter: type-2 fuzzy adaptive filter (FAF); one that is realized using an unnormalized type-2 Takagi-Sugeno-Kang (TSK) fuzzy logic system (FLS). We apply this filter to equalization of a nonlinear time-varying channel and demonstrate that it can implement the Bayesian equalizer for such a channel, has a simple structure, and provides fast inference. A clustering method is used to adaptively design the parameters of the FAF. Two structures are used for the equalizer: transversal equalizer (TE) and decision feedback equalizer (DFE). A decision tree structure is used to implement the decision feedback equalizer, in which each leaf of the tree is a type-2 FAF. This DFE vastly reduces computational complexity as compared to a TE. Simulation results show that equalizers based on type-2 FAFs perform much better than nearest neighbor classifiers (NNC) or equalizers based on type-1 FAFs.

This paper presents a connection admission control (CAC) method that uses a type-2 fuzzy logic system (FLS). Type-2 FLSs can handle linguistic uncertainties. The linguistic knowledge about CAC is obtained from 30 computer network experts. A methodology for representing the linguistic knowledge using type-2 membership functions and processing surveys using type-2 FLS is proposed. The type-2 FLS provides soft decision boundaries, whereas a type-1 FLS provides a hard decision boundary. The soft decision boundaries can coordinate the cell loss ratio (CLR) and bandwidth utilization, which is impossible for the hard decision boundary.

We introduce a type-2 fuzzy logic system (FLS), which can handle rule uncertainties. The implementation of this type-2 FLS involves the operations of fuzzification, inference, and output processing. We focus on "output processing," which consists of type reduction and defuzzification. Type-reduction methods are extended versions of type-1 defuzzification methods. Type reduction captures more information about rule uncertainties than does the defuzzified value (a crisp number), however, it is computationally intensive, except for interval type-2 fuzzy sets for which we provide a simple type-reduction computation procedure. We also apply a type-2 FLS to time-varying channel equalization and demonstrate that it provides better performance than a type-1 FLS and nearest neighbor classifier.

In the original paper (IEEE Trans. Fuzzy Syst., vol.6, p.1-11, 1998), Combs and Andrews proved the following logical equivalence (stated here for two antecedents p and q and one consequent r, but easily generalize to an arbitrary number of antecedents and consequents): [(p/spl and/q)/spl rArr/r]/spl hArr/[(p/spl rArr/r)V(q/spl rArr/r)]. This is a very significant result because it suggests that we can replace multi-antecedent rules with an interconnection of single antecedent rules, which eliminates the rule explosion that is associated with multi-antecedent rules. Combs and Andrews refer to the left-hand side of this equivalence as an intersection rule configuration (IRC) and to its right-hand side as a union rule configuration (URC). Their result gives rise to two distinctly different paths for the design of fuzzy logic systems; IRC, which leads to rule explosion, and URC, which does not. The authors discuss four points about the IRC/spl hArr/URC relation. The original authors reply, acknowledging some of the points and stating that they would present their results differently if starting now.