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With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

Hyperspectral images (HSIs) have gained high spectral resolution due to recent advances in spectral imaging technologies. This incurs problems, such as an increased data scale and an increased number of bands for HSIs, which results in a complex correlation between different bands. In the applications of remote sensing and earth observation, ground objects represented by each HSI pixel are composed of physical and chemical non-Euclidean structures, and HSI classification (HIC) is becoming a more challenging task. To solve the above problems, we propose a framework based on a deep attention graph convolutional network (DAGCN). Specifically, we first integrate an attention mechanism into the spectral similarity measurement to aggregate similar spectra. Therefore, we propose a new similarity measurement method, i.e., the mixed measurement of a kernel spectral angle mapper and spectral information divergence (KSAM-SID), to aggregate similar spectra. Considering the non-Euclidean structural characteristics of HSIs, we design deep graph convolutional networks (DeepGCNs) as a feature extraction method to extract deep abstract features and explore the internal relationship between HSI data. Finally, we dynamically update the attention graph adjacency matrix to adapt to the changes in each feature graph. Experiments on three standard HSI data sets, namely, the Indian Pines, Pavia University, and Salinas data sets, demonstrate that the DAGCN outperforms the baselines in terms of various evaluation criteria. For example, on the Indian Pines data set, the overall accuracy of the proposed method achieves 98.61% when the training sample is 10%.

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

To efficiently analyze the electromagnetic scattering from composite perfect electric conductor (PEC)-dielectric objects with coexisting closed-open PEC junctions, a modified hybrid integral equation (HIE) is established as the surface integral equation (SIE) part of the volume surface integral equation (VSIE), which employs the combined field integral equation (CFIE) and the electric field integral equation (EFIE) on the closed and open PEC surfaces, respectively. Different from the traditional HIE modeled for the objects whose closed and open PEC surfaces are strictly separate, the modified HIE can be applied to the objects containing closed-open junctions. A matrix equation is obtained by using the Galerkin’s method of moments (MoM), which is augmented with the spherical harmonics expansion-based multilevel fast multipole algorithm (SE-MLFMA), improved by the mixed-potential representation and the triangle/tetrahedron-based grouping scheme. Because in the improved SE-MLFMA, the memory usage for storing the radiation patterns of basis functions is independent of the SIE type in the VSIE, it is highly appropriate for the fast solution of the VSIE that contains the HIE. Various numerical experiments demonstrate that during the calculation of composite objects containing closed-open PEC junctions, the application of the modified HIE in the VSIE can give reliable results with fast convergence speed.

Sensor selection for time-difference-of-arrival (TDOA)-based localization can be fulfilled by minimizing the Cramer–Rao lower bound (CRLB). With an identical range measurement noise variance for different sensors, the CRLB involves the angle information only between the sensors and source. An issue caused by this is that the state-of-the-art sensor selection methods using semidefinite relaxation (SDR) may incorrectly select sensors owing to the ambiguity caused by relaxation, and Gaussian randomization (GR) is usually required to help select the correct sensors, which would cause an additional cost in the implementation. In this article, we develop a sensor selection method for TDOA-based source localization by explicitly incorporating both the angle and the range information between the sensors and the source to resolve the ambiguity caused by SDR. Specifically, the sensor selection problems for the cases of the known and unknown numbers of selected sensors are investigated and formulated as the nonconvex optimization problems. SDR is then utilized to relax the nonconvex problems as the convex semidefinite programs. Simulation results show that the proposed method works well without performing the additional GR procedure.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of transmitting antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.

Providing quality communications under adversarial electronic attacks, e.g., broadband jamming attacks, is a challenging task. Unlike state-of-the-art approaches which treat jamming signals as destructive interference, this paper presents a novel active anti-jamming (AAJ) scheme for a jammed channel to enhance the communication quality between a transmitter node (TN) and receiver node (RN), where the TN actively exploits the jamming signal as a carrier to send messages. Specifically, the TN is equipped with a programmable-gain amplifier, which is capable of re-modulating the jamming signals for jamming modulation. Considering four typical jamming types, we derive both the bit error rates (BER) and the corresponding optimal detection thresholds of the AAJ scheme. The asymptotic performances of the AAJ scheme are discussed under the high jamming-to-noise ratio (JNR) and sampling rate cases. Our analysis shows that there exists a BER floor for sufficiently large JNR. Simulation results indicate that the proposed AAJ scheme allows the TN to communicate with the RN reliably even under extremely strong and/or broadband jamming. Additionally, we investigate the channel capacity of the proposed AAJ scheme and show that the channel capacity of the AAJ scheme outperforms that of the direct transmission when the JNR is relatively high.

In this letter, the residual-energy aware feature of a sensor node in time-varying wireless sensor networks (WSNs) is analyzed and modeled as a Markov chain, upon which the state-transition probability (STP) about the energy level of any node with undetermined and deterministic residual energy can be evaluated. Based on Markov chain and energy-efficient relay search region models, an energy-efficient routing algorithm is proposed to further analyze the impact of STP with known residual energy on extending network lifetime of time-varying WSNs. Simulation results show that the proposed algorithm can effectively extend network lifetime even more than twice while holding a better energy efficiency as compared with the algorithm without considering node-residual energy changes.

Publisher Copyright: IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved. The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods.Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.

The papers in this special section focus on tensor decomposition for signal processing and machine learning. Tensor decomposition, also called tensor factorization, is useful for representing and analyzing multi-dimensional data. Tensor decompositions have been applied in signal processing applications (speech, acoustics, communications, radar, biomedicine), machine learning (clustering, dimensionality reduction, latent factor models, subspace learning), and well beyond. These tools aid in learning a variety of models, including community models, probabilistic context-free-grammars, Gaussian mixture model, and two-layer neural networks. Although considerable research has been carried out in this area, there are many challenges still outstanding that need to be explored and addressed; some examples being tensor deflation, massive tensor decompositions, and high computational cost of algorithms. The multi-dimensional nature of the signals and even “bigger” data, particularly in next-generation advanced information and communication technology systems, provide good opportunities to exploit tensor-based models and tensor networks, with the aim of meeting the strong requirements on system flexibility, convergence, and efficiency.

In this paper, we address the received-signal-strength (RSS) based source localization problem in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments, where the model parameters are unknown. To accommodate the additional path losses incurred by NLOS signal propagations, we introduce a random variable to the outdoor RSS measurement model to represent the additional path loss. Based on this modified model, we propose a robust localization method for the case of unknown model parameters. Specifically, we introduce a balancing parameter and express the additional path loss term as the sum of the balancing parameter and an error term, and then formulate a robust weighted least squares (RWLS) problem to jointly estimate the source location, the unknown model parameters, and the balancing parameter. The RWLS problem is solved in an iterative manner, where the S-Lemma and the semidefinite relaxation technique are used. Both simulated and real experimental data verify that the proposed method works well in both dense and sparse NLOS environments.

The enhancement of light‐defect images such as extremely low‐light, low‐light and dim‐light has always been a research hotspot. Most of the existing methods are excellent in specific illuminations, and there is much room for improvement in processing light‐defect images with different illuminations. Therefore, this study proposes an efficient framework based on deep learning to enhance various light‐defect images. The proposed framework estimates the reflectance component and illumination component. Next, we propose a generator guided by an attention mechanism in the reflectance part to repair the light‐defect in the dark. In addition, we design a colour loss function for the problem of colour distortion in the enhanced images. Finally, the illumination map of the light‐defect images is adjusted adaptively. Extensive experiments are conducted to demonstrate that our method can not only deal with the images with different illuminations but also enhance the images with clearer details and richer colours. At the same time, we prove its superiority by comparing it with state‐of‐the‐art methods under both visual quality comparison and quantitative comparison of various datasets and real‐world images.

In the interference-limited communication scenarios, the interference impairs the data transmission and further degrades the delay performance. In this letter, the stochastic delay guarantee for a wireless dual-hop network with an interference-limited relay is investigated. Considering the randomness of wireless fading in both data links and interference links, the stochastic service process provided by the network is characterized in terms of the moment generation function (MGF). Due to the stochastic service process, the delay should be a random variable (RV). The delay-outage probability is defined as the probability that the actual transmission delay exceeds a threshold value. To evaluate the stochastic delay guarantee, we derive an upper bound on the minimum acceptable delay threshold and a lower bound on the maximum supportable transmitted data size with the constraint of the delay-outage probability, respectively. Simulation results show that the bounds obtained by the theoretical analysis are reasonably tight and well reflect the effect of the interference on the stochastic delay guarantee.

Individuals driving private cars in the urban environments to fulll their travel needs have become one of the major daily activities. In particular, unlike the travel with floating cars such as taxis or ride-hailing vehicles, the travel behaviors of private cars exhibit a certain degree of regularity based on daily travel demands. Understanding such travel behavior facilitates many applications, e.g., intelligent transportation, smart city planning, and location-based services (LBS). In this paper, we focus on extracting the regular travel behavior of private cars based on trajectory data analysis. Specifically, first, we construct a trajectory similarity matrix since the similarity of trajectories reflects regular travel behavior. To achieve this, we introduce the stay time and propose an Improved Edit distance with Real Penalty (IERP) to measure the temporal-spatial distance between trajectories. Then, we employ Kernel Principal Component Analysis (KPCA) to reduce the feature dimension of the similarity matrix. Finally, to identify the travel regularity from large set of unlabeled trajectory data, we propose a classification method based on transfer learning to migrate existing knowledge with the purpose of solving learning problems in the target domain with only a small amount of labeled trajectory data or even no data. Extensive experiments using large-scale real-world trajectory data demonstrate that the proposed method can effectively identify the regular travel pattern of private cars and obtain superior accuracy when compared with the existing methods. Our findings on discovering regular travel behaviors of private cars can be directly applied to applications including destination prediction, PoI recommendation and route planning.

In this letter, we focus on the content delivery efficiency in vehicular communication networks (VCNs), where the content delivery can share the same channel resource with the safety message transmission by adopting non-orthogonal multiple access (NOMA) technology. On the premise of guaranteeing the quality-of-service (QoS) requirement of safety messages, the stochastic delay performance of the content delivery with NOMA transmission is analyzed over Rician fading channels. Then, a lower bound of the maximum supportable content chunk size with delay constraints is derived to measure the content delivery efficiency. Simulation results are provided to validate the tightness of the lower bound, as well as the superiority of NOMA in improving the content delivery efficiency compared with the conventional orthogonal multiple access (OMA).

Proposed by Maddah-Ali and Niesen, a coded caching scheme has been verified to alleviate the load of networks efficiently. Recently, a new technique called placement delivery array (PDA) was proposed to characterize the coded caching scheme. In this paper, we consider a caching system in the scope of ultra dense networks (UDNs). Each base station (BS) has a finite cache and stores some contents. We propose an efficient coded content caching scheme called double coded caching to make the transmission robust to in-and-out wireless network quality. Then the dynamic caching and multicast scheduling are considered to jointly minimize the average delay and power of the content-centric wireless networks. This stochastic optimization problem can be formulated as a Markov decision process (MDP) with unknown transition probabilities and large state space. We propose a deep reinforcement learning approach to deal with the decision problem. Our algorithm uses a variational auto-encoder (VAE) neural network to approximate the state sufficiently, and uses a weighted double Q-learning scheme to reduce variance and overestimation of the Q function. Numerical results demonstrate that the proposed double coded caching scheme increases the probability of the successful transmission, and the caching and scheduling policy can effectively reduce the delay and the power consumption.

Research on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) has received much attention. However, due to the propagation characteristics of mmWave, signals will suffer from a significant loss over long range transmission. The mmWave MIMO system may face the challenge of the near-far problem where the user tries to retrieve the information signal (IS) from the base station under the strong intended jamming signal (JS). This letter proposes a novel hybrid beamforming design to cancel the JS that significantly reduces the computational complexity and incurs less feedback signaling overhead. Simulation results demonstrate that our proposed scheme can significantly suppress the interference from the JS and achieve the array gain in the system.

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose a deep learning framework for a spatiotemporal attention network (STANet) with a neural algorithm logic unit (NALU), the so-called STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. Specifically: 1) we design an improved kernel density estimator (KDE) by defining a log-cosh loss function to calculate the spatial distribution of the aggregation effect with guaranteed robustness and 2) we utilize the stay time of private cars as a temporal feature to represent the nonlinear temporal correlation of the aggregation effect. Next, we propose a spatiotemporal attention module that separately captures the dynamic spatial correlation and nonlinear temporal correlation of the private car aggregation effect, and then we design a gate control unit to fuse spatiotemporal features adaptively. Further, we establish the STANet-NALU structure, which provides the model with numerical extrapolation ability to generate promising prediction results of the private car aggregation effect on weekends. We conduct extensive experiments based on real-world private car trajectories data. The results reveal that the proposed STANet-NALU\pagebreak outperforms the well-known existing methods in terms of various metrics, including the mean absolute error (MAE), root mean square error (RMSE), Kullback-Leibler divergence (KL), and R2.

Achieving reliable intervehicle positioning is one of the most fundamental elements for many vehicular applications, including collision avoidance and autonomous driving. Vehicle position is generally provided by a global navigation satellite system (GNSS), which unfortunately suffers from inaccuracy to varying degrees in challenging environments, for example, GNSS outages. In this article, a reliable fusion technique, called non-Gaussian Redheffer weighted least squares (nGRWLSs), is proposed. This new approach highlights the intervehicle positioning estimation in multi-GNSS outage environments, such as complete, partial, and free GNSS pseudorange outages. The proposed method combines, on the one hand, the benefits of the Gaussian dynamical matrix principle and the Redheffer distribution function for the sparse property in complete GNSS pseudorange outages and, on the other hand, the use of the optimal window size to regulate the data flow generated by both the inertial navigation systems (INSs) and GNSS during a partial GNSS pseudorange outage. During the free GNSS pseudorange outage, the process ignores data from the INS, and instead, GNSS pseudorange information alone will be considered to compute the intervehicle positioning information. Consequently, weighted least squares is used as an intervehicle positioning estimator. To address the pseudorange uncommon and INS measurement noises, the generalized error distribution (GED) is used to estimate the non-Gaussian densities. Finally, road-test experiments are implemented to evaluate the consistency of the proposed approach. The experimental results show that the proposed nGRWLS can accurately estimate the intervehicle positioning under various conditions (free, partial, and complete GNSS pseudorange outages).

The advances in vehicle location service and communication techniques have generated massive spatial-temporal trajectory data, which has caused the crises of storage and communication in the vehicle trajectory data center. In this paper, we propose a novel opportunistic compression and transmission-based long short-term memory method, namely, OCT-LSTM, with aims of reducing trajectory transmission overhead and storage cost. We first present a low-cost vehicle location device for trajectory real-time collection of private cars. Within the proposed OCT-LSTM, we introduced a map-matching method based on MIV-matching which reduces sampling errors of raw trajectories. Then, we present a spatial-temporal transformation method to divide the trajectory data into two parts, i.e., spatial path and time-distance parts, and realize the compression operation separately. Similar movement patterns are repeated and randomly present in trajectories of private cars. In this paper, we train the LSTM model to remember and predict these repetitive movement patterns through historical trajectories. An opportunistic transmission of trajectory data from the vehicle terminal to the data center was designed, which can dramatically decrease the transmission overhead. The proposed OCT-LSTM not only realizes real-time trajectory preprocessing and compressing but also ensures high trajectory compression ratio. To validate the performance of the OCT-LSTM, we collect a large-scale private car trajectory data from real urban environments. The experiments verify the compression ratio effectiveness and time-delay superiority of the proposed methods.

In this letter, we address the differential received signal strength based problem with model parameter errors. To deal with the model parameter errors, we adopt the robust weighted least squares criterion, leading to a minimax optimization problem. By assuming the model parameter errors lie in a ball, the minimax problem is transformed into a tractable reformulation via the S-Lemma. Confronted with the nonconvexity of the reformulated problem, we approximately solve it by applying the semidefinite relaxation. The proposed approach only requires the knowledge of the upper bounds of the model parameter errors, which are practically easy to acquire. Simulation results show that the proposed method is robust to the model parameter errors and outperforms the existing methods.

Non-orthogonal multiple access (NOMA) and millimeter wave (mmWave) are two key enabling technologies for the fifth-generation (5G) mobile networks and beyond. In this paper, we consider uplink communications with a hybrid beamforming structure and focus on improving the spectral efficiency (SE) and energy efficiency (EE) of mmWave multiple-input multiple-output (MIMO)-NOMA systems with enhanced user grouping and power allocation. It is noted that the optimization of the SE/EE is a challenging task due to the non-linear programming nature of the corresponding problem involving user grouping, beam selection, and power allocation. Our idea is to decompose the overall optimization problem into a mixed integer problem comprised of user grouping and beam selection only, followed by a continuous problem involving power allocation and digital beamforming design. Exploiting the directionality property of mmWave channels, we first propose a novel initial agglomerative nesting (AGNES) based user grouping algorithm by taking advantage of the channel correlations. To avoid the prohibitively high complexity of the brute-force search approach and to address the overlapping beam problem, we propose two suboptimal low-complexity user grouping and beam selection schemes, the two-stage direct AGNES (D-AGNES) scheme and the joint successive AGNES (S-AGNES) scheme. We also introduce the quadratic transform (QT) to recast the non-convex power allocation optimization problem into a convex one subject to a minimum required data rate of each user. The continuous problem is solved by iteratively optimizing the power and the digital beamforming. Extensive simulation results have shown that our proposed mmWave-NOMA design outperforms the conventional orthogonal multiple access (OMA) scenario and the state-of-art NOMA schemes.

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.

In this paper, we address the time-of-arrival (TOA) based localization problem in an adverse environment, where line-of-sight (LOS) signal propagation between the source and the sensor is not readily available, in which case we have to resort to non-line-of-sight (NLOS) signals. Two convex relaxation methods, i.e., the semidefinite relaxation (SDR) and the second-order cone relaxation (SOCR) methods, are proposed to mitigate the effect of NLOS errors on the localization performance. We consider two separate cases in which the information of the NLOS status is totally unknown and perfectly known, respectively. The proposed methods can be applied without knowing the distribution of NLOS errors. Moreover, we propose a NLOS error mitigation method that is robust to detection errors, which are generated in the process of detecting NLOS paths. Simulation results show that the proposed convex relaxation methods outperform some existing state-of-the-art methods.

In this paper, we design a new distributed angle estimation method for localization in wireless sensor networks (WSNs) under multipath propagation environment. We employ a two-antenna anchor that can emit two linear chirp waves simultaneously, and propose to estimate the angle of departure (AOD) of the emitted waves at each receiving node via frequency measurement of the local received signal strength indication (RSSI) signal. An improved estimation method is further proposed where multiple parallel arrays are adopted to provide the space diversity. The proposed methods rely only on radio transceivers and do not require frequency synchronization or precise time synchronization between the transceivers. More importantly, the angle is estimated at each sensor in a completely distributed manner. The performance analysis is derived and simulations are presented to corroborate the proposed studies.

In opportunistic selection (OS)-based systems, outdated channel state information (CSI) is encountered due to channel variation between the instants of channel estimation and actual data transmission. This will result in unreliable CSIs employed in OS, which greatly degrades the system performance. To address this issue, a novel metric is proposed to evaluate the transmission quality of a channel suffering from Nakagami- fading, taking both outdated CSI and fading statistics into consideration. Moreover, its suboptimized counterpart is also proposed to significantly reduce computational complexity. We then use this metric to select users (sources) in a multiuser scheduling model and evaluate its outage performance. Both analytical and simulation results show that our proposals outperform the existing alternatives in terms of outage probability.

An unknown-position sensor can be localized if there are three or more anchors making time-of-arrival (TOA) measurements of a signal from it. However, the location errors can be very large due to the fact that some of the measurements are from non-line-of-sight (NLOS) paths. In this paper, we propose a semi-definite programming (SDP) based node localization algorithm in NLOS environment for ultra-wideband (UWB) wireless sensor networks. The positions of sensors can be estimated using the distance estimates from location-aware anchors as well as other sensors. However, in the absence of LOS paths, e.g., in indoor networks, the NLOS range estimates can be significantly biased. As a result, the NLOS error can remarkably decrease the location accuracy. And it is not easy to efficiently distinguish LOS from NLOS measurements. In this paper, an algorithm is proposed that achieves high location accuracy without the need of identifying NLOS and LOS measurement., Comment: submitted to IEEE ICC'10

For realizing robust target tracking with wireless sensor networks in the circumstance where the propagation parameters of the characteristic signal emitted by the target are unknown, a novel tracking algorithm under the particle filter framework is proposed. We propose a scheme to realize particle weight calculation without the prior knowledge about the propagation parameters of the target's characteristic signal. With the use of the monotonic relationship of the distance and the received signal strength, we define the signal characteristic sequence and particle distance sequence and utilize the modified sequence distance between the signal characteristic sequence and the particle distance sequence as the criterion to calculate the particle weight blindly with simple lightweight operations. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

The Chinese remainder theorem (CRT) is an effective tool to solve the phase ambiguity problem in phase-based range estimation. However, existing methods suffer from problems such as requiring special measuring frequency, low spectrum efficiency, noise sensitivity, etc. To overcome these problems, this paper presents a CRT ranging method using two “adjacent” frequencies. As a result, all the frequencies in the given frequency span can be used, which greatly increases spectrum utilization. Moreover, since the same distance is measured by different frequency pairs, statistical processing can be performed on the results, which further improves estimation accuracy. Simulations verify the validity of the proposed method.

We consider a two-way relay network consisting of multiple pairs of single-antenna users and multiple distributed single-antenna relays. The two communication peers in each pair of users transmit simultaneously to the relays in the first time slot, and the relays rebroadcast the received sum signal weighted by a complex gain, in the second time slot. For multi-user systems, the signal arriving at the users contains not only self interference from the back-propagation of user signals, but also inter-pair interferences from other pairs of users. In this paper, we use zero-forcing (ZF) to cancel the inter-user interference, assuming that channel-state information for all relay-peer connections are known at every relay, but no data exchange occurs between relays. We also derive two closed-form expressions for zero-forcing beamforming weights, corresponding to two different relay power constraints, which can be implemented in a distributed manner. The first approach uses standard ZF to null out every inter-pair interference and the second approach sets the total inter-pair interference to zero. We also derive a closed-form upper bound of the achievable sum-rate and show that both methods achieve the same multiplexing gain when the number of relays N is sufficient for perfect zero-forcing, namely 2K2 + K, where K is the number of user pairs. For the case of insufficient number of relays, we also propose two solutions for beamforming weights, i.e., based on diagonal loading and use of the pseudo-inverse, and compare their advantages and weaknesses.

Contention-based geographic forwarding (CGF) is a state-free communication paradigm for data delivery in multihop wireless sensor networks. CGF is robust to frequent topology changes, scalable to large-scale node deployment and applicable to data-centric applications and resource constrained networks. However, CGF may experience significant performance degradation under unreliable links. In this work, we present the intelligent CGF (ICGF) to combat the channel variation. IGCF combines the advantages of both cooperative and contention-based forwarding, involving multiple neighbours of the sender into the local forwarding to improve the transmission reliability. ICGF differs from existing work in that it extends the cooperation scope intelligently, by sending one additional control message on demand. For this reason, the probability of cooperation void in ICGF is decreased and the single-hop packet progress is increased. The authors conduct extensive simulations to study the performance of the proposed ICGF compared with existing protocols. Simulation results demonstrate that ICGF improves the end-to-end data delivery delay, energy efficiency and data delivery ratio.

System-level simulation has been widely used to evaluate the comprehensive performance of different mobile cellular systems. System-level simulation methodologies for different systems have been discussed by different organizations and institutions. However, the framework for a unified simulation methodology and platform has not been established. In this article, we propose a general unified simulation methodology for different cellular systems. Both the design of the simulation structure and the establishment of the simulation platform are studied. Meanwhile, the unified modeling and the realization of various modules related to the system-level simulation are presented. The proposed unified simulation methodology and the general simulation platform can be used to evaluate the performance of multiple mobile communication systems fairly. Finally, the overall performance of LTE and Mobile WiMAX systems is evaluated through the proposed framework. The key simulation results for both Full Buffer and VoIP traffics are presented and discussed. It is shown that the LTE system exhibits better performance than Mobile WiMAX.

We consider the source localization problem using time-difference-of-arrival (TDOA) measurements in sensor networks. The maximum likelihood (ML) estimation of the source location can be cast as a nonlinear/nonconvex optimization problem, and its global solution is hardly obtained. In this paper, we resort to the Monte Carlo importance sampling (MCIS) technique to find an approximate global solution to this problem. To obtain an efficient importance function that is used in the technique, we construct a Gaussian distribution and choose its probability density function (pdf) as the importance function. In this process, an initial estimate of the source location is required. We reformulate the problem as a nonlinear robust least squares (LS) problem, and relax it as a second-order cone programming (SOCP), the solution of which is used as the initial estimate. Simulation results show that the proposed method can achieve the Cramer-Rao bound (CRB) accuracy and outperforms several existing methods.

For two-dimensional (2-D) directions-of-arrival (DOA) estimation problem, both the mutual coupling and the failure in pairing can cause severe performance degradation. In this paper, a new elevation and azimuth direction finding algorithm is developed to overcome the above-mentioned two difficulties in the L-shaped array configuration. The key points of this paper are: (i) constructing several correlation matrices to blindly compensate the effect of unknown mutual coupling using the outputs of properly chosen sensors and (ii) deriving a rank-reduction propagator method to estimate elevation and azimuth angles so as to avoid pairing parameters. Simulation results are presented to validate the performance of the proposed method.

For wireless multiple-input multiple-output (MIMO) communications systems, both channel estimation error and spatial channel correlation should be considered when designing an effective signal detection system. In this paper, we propose a new soft-output MMSE based Vertical Bell Laboratories Layered Space-Time (V-BLAST) receiver for spatially-correlated Rician fading MIMO channels. In this novel receiver, not only the channel estimation errors and channel correlation but also the residual interference cancellation errors are taken into consideration in the computation of the MMSE filter and the log-likelihood ratio (LLR) of each coded bit. More importantly, our proposed receiver generalizes all existing soft-output MMSE V-BLAST receivers, in the sense that, previously proposed soft-output MMSE V-BLAST receivers can be derived as the reduced forms of our receiver when the above three considered factors are partially or fully simplified. Simulation results show that the proposed soft-output MMSE V-BLAST receiver outperforms the existing receivers with a considerable gain in terms of bit-error-rate (BER) performance. Copyright © 2011 John Wiley & Sons, Ltd.

The sensing scheme based on the generalized likelihood ratio test (GLRT) technique has attracted a lot of research interest in the field of cognitive radios (CR). Although its potential advantages in detecting correlated primary signal have been illustrated in prior work, no theoretical analysis of the positive effects of the correlation has appeared in the literature. In this letter, we derive the theoretical false-alarm and detection probabilities of GLRT detector. The theoretical analysis shows that, in the low signal-to-noise ratio (SNR) region, the detector's performance can be improved by exploiting the high correlations between the primary signal samples. The conclusions of the analysis are verified by numerical simulation results.

Passive source localization has wide applications in array signal processing. In the practical applications, the observations collected by an array may be “arbitrary”-field signals, i.e., which are either mixed near-field and far-field signals or multiple near-field signals or multiple far-field signals. With a cross array, a two-stage separated steering vector-based algorithm is developed to localize “arbitrary”field narrowband sources in the spherical coordinates. The key points of this paper are: i) different physical steering vectors of near-field Received 1 November 2010, Accepted 23 December 2010, Scheduled 25 January 2011 Corresponding author: Junli Liang (liangjunli@xaut.edu.cn).