Distance-based localization using Time-of-Arrival (ToA) estimation is fundamentally constrained by the accuracy of channel parameter tracking in non-stationary, high-mobility environments. In such scenarios, the estimation landscape becomes highly non-convex and multi-modal, rendering conventional gradient-driven trackers inadequate. This paper proposes a Gated Recurrent Unit (GRU)-enhanced Whale Optimization-based Tracking Algorithm (WoTA), a robust framework that synergistically fuses meta-heuristic global optimization with recursive filtering. Specifically, an Opposition-based Learning Whale Optimization Algorithm (OBL-WOA) is integrated to adaptively explore the complex cost function surface, effectively initializing the tracker and preventing local optima entrapment in multi-modal distributions. To overcome the limitations of manual parameter tuning in traditional WOA and Kalman Filters (KF), a GRU-based adaptive mechanism is introduced. This mechanism learns the temporal dependencies of optimization residuals to dynamically recalibrate the filter’s noise covariance and optimization hyperparameters, thereby enhancing resilience against model uncertainties. By coupling OBL-WOA’s global exploration with the KF’s exploitation of temporal kinematics, WoTA achieves smoothed and precise state transitions. Comprehensive simulations and real-world channel sounding measurements demonstrate that WoTA consistently outperforms state-of-the-art methods, such as SAGE and KEST. These results demonstrate that the proposed integration of heuristic exploration and recursive refinement offers a scalable and accurate framework for channel parameter tracking in dynamic multipath environments, thereby supporting reliable 6G wireless systems.

In multi-intersection traffic signal timing (TST) the use of a uniform cycle time reduces efficiency at low-volume intersections and signal changes which can alter vehicle flows and degrade communication quality. This paper presents a solution to this problem introducing a Twin-Timescale model whereby a multi-intersection TST method is jointly considered with communication resource allocation (CRA) strategies. For each intersection, a Twin-Timescale Reinforcement Learning (RL) algorithm is proposed, by considering both CRA and TST as the Short- and the Long- Timescale models, respectively, and jointly formulating their rewards. Based on the characteristics of this Twin-Timescale model, the reward function is constructed as a singularly perturbed model (SPM), and a state feedback controller that can ensure asymptotic stability is designed. Furthermore, a novel asynchronous time Multi-Agent Coordinated RL algorithm is proposed, which allows each intersection to generate each own joint parameters for its decision strategy which are based upon information obtained from neighboring intersections at asynchronous time. In addition, the use of Federated Learning (FL) in conjunction with the proposed algorithms is investigated, and its advantages in enhancing the resistance to malicious attacks are discussed. Using real traffic data, various performance evaluation results obtained by means of extensive computer simulation experiments have confirmed that the proposed TST-CRA joint optimization approach significantly improves traffic indicators and maintains the quality of the overall communication performance. Through numerical calculations, the state feedback controller coefficients have been determined.

This paper presents a novel framework for object-oriented integrated sensing and communications (ISAC) channel modeling in urban environments over low-altitude 3D space, which jointly considers unmanned aerial vehicle (UAV)-based and ground-level measurements in conjunction with ray-tracing (RT) technology. Two types of object-oriented channels are considered, namely the static background channel, e.g., buildings, and the dynamic channel, e.g., cars and UAVs. Firstly, a systematic measurement campaign was conducted in the 4 GHz band at 80 m height using a UAV and at ground level using a vehicle. Additionally, photographs of the measurement area are taken by the UAV for an object-oriented 3D model. Based upon channel measurement data and employing RT techniques, the EM parameters of the considered coverage area are accurately calibrated and validated at both heights. This has allowed us to obtain accurate simulations across the vertical airspace of both sensing- and communication-background channels, followed by a comprehensive channel characteristics analysis. By subtracting the background channel from the dynamic channel, experimental results have demonstrated that the angle and distance information of objects can be accurately estimated. Through a case study, it has been shown that the acquired prior scene information can be utilized as a reference to improve object-oriented coverage.

Radio environment maps (REMs) have been used to visualize the information of invisible electromagnetic spectrum. Although in the past there have been many research activities dealing with the reconstruction of static REMs, they did not consider the time variation of the dynamic spectrum operational environment. In this article, we present a novel time-variant REM reconstruction methodology based on sparsely distributed sensors which jointly considers sensor layout optimization, propagation model improvement, and missing spectrum data recovery. First, a low complexity and computationally efficient method is proposed to improve the sampling efficiency. The proposed method jointly employs the gradient descent method and an upgraded greedy matching algorithm to optimize the sensor positions even when large-scale scenarios are considered. Then, by using the sampled spectrum data obtained from these sensors, the accuracy of commonly employed propagation models is improved and subsequently used to construct a channel dictionary for such time-varying environments. By exploring the heterogeneity of dynamic spectrum operational environments, an improved optimal reconstruction method is designed to recover the spectrum data using their spatial-temporal correlation. By considering a typical university campus environment as a case study, simulation and measurement data are obtained to reconstruct the time-variant REM. Through the simulation data, the reconstruction performance results are compared with those obtained from other state-of-the-art methods showing that the proposed methodology outperforms the others with respect to the sampling scheme and missing rate. Additionally, field measurement results have demonstrated that the proposed approach can effectively reconstruct time-variant REMs under dynamic scenarios.

Infrared imaging possesses characteristics such as long visual distance and strong anti-interference capabilities, enabling it to provide clear target images in low light conditions. However, due to the difficulty in obtaining a large amount of labeled data, recognizing infrared targets under few-shot conditions remains a challenge. To address these challenges, this paper proposes an infrared time-sensitive target recognition method based on model-agnostic meta-learning (MAML). We utilize the lightweight network Lite-HRNet as the backbone, and incorporate the scale-aware squeeze-and-excitation (SASE) module to achieve multi-scale feature extraction and fusion. Simultaneously, the MAML algorithm is used to optimize the parameter update process, enabling the model to quickly obtain optimal parameters through fine-tuning on few-shot datasets in the target domain. The experiments were conducted on a public dataset of time-sensitive targets. The results demonstrate that the proposed method outperforms other comparative algorithms in terms of recognition precision, achieving a precision of 72.58%.

Heterogeneous image change detection involves identifying changes on the Earth’s surface using different imaging data from satellites. In this paper, we propose a multilayer convolutional adversarial network model based on transfer learning for detecting changes in heterogeneous images under unsupervised conditions. Firstly, we construct an adversarial network consisting of generators and discriminators, which is used to build a heterogeneous image transformation network and an approximate network that introduces change recognition factors. The generator is designed with a feedback-connected multi-convolutional layer structure to repair and reuse image features. Additionally, based on the idea of transfer learning, the transformation and approximate networks are alternately trained to strengthen the network’s ability to capture potential changes between images. Finally, the algorithm is validated on a public heterogeneous dataset to enhance the accuracy of image change detection, particularly for detecting small changes in images.

This article presents a new sensing channel modeling approach by jointly considering ray-tracing (RT) and stochastic methods, to accurate and efficient model sensing channels for vehicle-to-everything (V2X) applications. For the former, moving targets are modeled through accurate RT simulations while for the latter a statistical approach is used for generating complex environmental clutter by emphasizing for the first time individual object modeling. This approach is used to form a feature library of objects which ensures space-time consistency while significantly improving the modeling speed. The channel transfer functions generated by RT and stochastic methods are jointly considered through coherent superposition to form a more complete sensing channel which includes both clutters and targets. To verify its effectiveness and accuracy, a comprehensive experimental study has been conducted taking systematic measurements using a 77-GHz mmWave radar as it is the prevalent equipment for sensing used for intelligent driving applications. We have considered a typical V2X scenario, with the radar deployed on vehicles traveling along roads at an urban intersection. The experimental results obtained have demonstrated that the accuracy in target distance detection and velocity estimation has improved leading to errors of less than 0.5 m and less than 0.2 m/s, respectively, while for clutter modeling, the error of power is 3 to 6 dB. Moreover, compared to traditional RT methods, the proposed approach is 20 times faster. Through the proposed approach, realistic sensing channel data can be obtained in a systematic, effective, and accurate manner, facilitating research of sensing-assisted communication applications.

This paper presents systematic experimental and related analytical studies for the accurate channel modeling of vehicle-to-vehicle (V2V) radio channels in mountainous plateau environments (MPEs) at the 376MHz band which is allocated for emergency communications. The measurement campaign has been conducted in the Ailao Mountains (Yunnan Province of China) and the influence of this mountainous environment on the radio channel characteristics in the power domain has been thoroughly investigated. The time-varying characteristics of the measured V2V radio channels have identified the stationarity time based on the local scattering function as a prerequisite for accurate modeling. Furthermore, Akaike’s information criteria and Kolmogorov-Smirnov tests have revealed that the Weibull probability distribution function is the best fit for the measured data. With the Weibull shaping parameter, the line of sight (LOS) and non-LOS (NLOS) channels can be conveniently and accurately classified. As large variations of the LOS probability and the shape parameter over long communication distances and LOS/NLOS conditions have been observed, an analytical approach using a 3rd order sum of sinusoid functions has been used to model them. Through this model, the maximum distance for LOS communications has been derived directly from the measured data. The large-scale propagation properties, including path loss, shadow fading, and shadow fading correlation, have been thoroughly studied, and accurate path loss models specifically for the MPEs have been proposed. Moreover, a new shadow fading correlation model has been presented. Compared to previously known models, this model is more accurate and more suitable for long-distance emergency communication scenarios.

A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.

Change detection based on synthetic aperture radar (SAR) images is an important application in the remote-sensing technology field. However, the lack of labeled data has been a difficult problem in SAR image detection, especially for pixel-level change detection. In this letter, we propose a novel unsupervised change detection algorithm, which improves the detection accuracy by exploring features from both spatial and frequency domains of SAR images. In particular, first clustering is used as preclassification to obtain pseudo-labels and then by incorporating classifiers and pseudo-labels in terms of feature learning, a novel unsupervised detection algorithm is proposed. To improve the sensitivity of the algorithm to changed details and enhance the antinoise ability of the change detection network, the attention mechanism (AM) is integrated into the network to fully extract important spatial structure information. Moreover, a multidomain fusion module is proposed to integrate spatial and frequency domain features into complementary feature representations. This module contains multiregion features weighted by the channel-spatial AM and deep features filtered out by the gated linear units (GLUs) in the frequency domain. To verify the effectiveness of the proposed algorithm, it is compared against the other four SAR image change detection algorithms using three real datasets. The experimental results show that the proposed method outperforms the other four algorithms in terms of percent correct classification (PCC) and Kappa coefficient (KC).

Urban functional zones (UFZs) contain abundant landscape information that can be adopted to better understand the surroundings. Various landscape compositions and configurations reflect different human activities, which may affect the particulate matter (PM2.5) concentrations. The very high-resolution (VHR) image features can reflect the physical and spatial structures of the UFZs. However, the existing PM2.5 estimation methods neither have been based on the scale of UFZs, nor have the VHR image features of UFZs as independent variables. Hence, this article proposes a spatiotemporal interpolation graph convolutional network (STI-GCN) model and introduces VHR image features to achieve PM2.5 estimation in UFZs. First, UFZs are split, and VHR image features are extracted by the visual geometry group 16 (VGG16). Subsequently, meteorological factors, aerosol optical depth (AOD), and VHR image features are used to estimate the PM2.5 concentrations at the scale of the UFZs. The two metropolises, Beijing and Shanghai, are chosen to assess the validity of the STI-GCN model. As for Beijing and Shanghai, the overall accuracy${R^{2}}$of the STI-GCN model can reach 0.96 and 0.89, the root-mean-square errors (RMSEs) are 8.15 and 6.40$\mu \text {g}/{\text {m}^{3}}$, the mean absolute errors (MAEs) are 5.51 and 4.78$\mu \text {g}/{\text {m}^{3}}$, and the relative prediction errors (RPEs) are 18.53% and 17.38%, respectively. Experiments show that the STI-GCN consistently outperforms other models. What’s more, the PM2.5 values are relatively high in commercial and official zones (COZs) and relatively low in urban green zones (UGZs).

A scalable cell-free massive MIMO (SCF-mMIMO) system where all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) over correlated Rician fading is presented and analysed in this paper. A closed-form expression for the uplink (UL) spectral efficiency (SE) using maximal-ratio combining (MRC) detection for centralized scheme is first derived. Moreover, a novel low complexity partial MMSE (P-MMSE) detector is proposed, which achieves very similar SE performance and maintains much less computational complexity in comparison with the original partial MMSE (P-MMSE) detector. In addition, a joint algorithm consisting of AP cluster formation, pilot assignment, and power control policy is proposed, which yields much higher SE performance than random pilot assignment and user-group based pilot assignment policies do, and meanwhile improves the quality of service (QoS) fairness for all accessing UEs as compared to the equal power transmit policy.

Land use mapping (LUM) of a coal mining subsidence area (CMSA) is a significant task. The application of convolutional neural networks (CNNs) has become prevalent in LUM, which can achieve promising performances. However, CNNs cannot process irregular data; as a result, the boundary information is overlooked. The graph convolutional network (GCN) flexibly operates with irregular regions to capture the contextual relations among neighbors. However, the global context is not considered in the GCN. In this paper, we develop the deep global context construction with enabled boundary (DGCC-EB) for the LUM of the CMSA. An original Google Earth image is partitioned into nonoverlapping processing units. The DGCC-EB extracts preliminary features from the processing unit that are further divided into nonoverlapping superpixels with irregular edges. The superpixel features are generated and then embedded into the GCN and vision transformer (ViT). In the GCN, the graph convolution is applied to superpixel features; therefore, the boundary information of objects can be preserved. In the ViT, the multihead attention blocks and positional encoding build the global context among the superpixel features. The feature constraint is calculated to fuse the advantages of the features extracted from the GCN and ViT. To improve the LUM accuracy, the cross-entropy (CE) loss is calculated. The DGCC-EB integrates all modules into a whole end-to-end framework and is then optimized by a customized algorithm. The results of case studies show that the proposed DGCC-EB obtained acceptable OA (89.06%/88.68%) and Kappa (0.86/0.87) values for Shouzhou city and Zezhou city, respectively.

In this paper, a novel jointly adaptive transmission strategy for buffer-aided multiple-relay non-orthogonal multiple access (NOMA) networks is proposed. By leveraging time diversity brought by the data storage buffer, and multiple relays’ beamforming gains, the source node broadcasts data in the first hop and all relays perform distributed beamforming to transmit information to users with NOMA in the second hop. Our focus is on maximizing the average network throughput (ANT) by jointly optimizing the mode selection, power and rate allocation at the source node, as well as the rate allocation and distributed beamforming coefficients at the relays along the time. As a solution to this complex problem, we propose an online scheduling scheme, which first adopts the Lyapunov optimization methodology to convert the ANT maximization problem into the drift-plus-penalty function minimization problem at each time slot. Secondly, focusing on the non-convexity of the transformed problem at each time slot, a novel two-loop algorithm is proposed as an effective solution. Extensive performance evaluation results have revealed the superiority of the proposed scheme as compared to other benchmark access schemes, namely, buffer-aided single relay NOMA, maximum ratio transmission based NOMA and buffer-aided time division multiple access schemes.

This paper presents novel moment generating function (MGF)- and characteristic function (CHF)-based frameworks for the EC performance analysis of a generic Lp-norm diversity combining scheme under adaptive transmission policies. The considered system operates over generalized fading channels, a maximum delay constraint and under various channel state information (CSI) conditions. Depending upon the operational CSI, four policies are studied, namely: i) Constant power with optimal rate adaptation (ORA); ii) Optimal power and rate adaptation (OPRA); iii) Channel inversion with fixed rate (CIFR); and iv) Truncated CIFR (TIFR). The Lp-norm diversity is a generic diversity structure which includes as special cases various well-known diversity schemes, such as equal gain combining (EGC), maximal ratio combining (MRC) and amplify-and-forward (AF) relaying. Under the ORA and OPRA policies, we derive single integral expressions for evaluating the EC of Lp-norm diversity reception directly from the MGF or the incomplete MGF of the Signal-to-Noise-Ratio (SNR) at the receiver, respectively. For the EC performance evaluation of the EGC and AF relaying systems operating under the OPRA policy, a CHF-based approach, which is computationally more efficient as compared to the MGF-based approach, is further presented. It is shown that the EC for the CIFR and TIFR policies can be directly evaluated from the MGF or the CHF of the SNR at the receiver, respectively. For the ORA policy, a novel analytical approach for the asymptotic EC performance analysis is also developed and evaluated, revealing how important system operation parameters affect the overall system performance. The mathematical formalism is validated with selected numerical and equivalent simulation performance evaluation results, thus confirming the correctness of the proposed analytical methodology.

Due to the large intraclass variances and complicated object distribution, recognizing objects with complex appearances and arbitrary orientations has been an active research topic and a challenging task in remote sensing fields. In this article, we formulate object recognition as a high-level feature-learning problem, and a novel supervised method is proposed to learn high-level feature representations from high-resolution remote sensing images for object recognition. Our method simultaneously and coherently achieves high-level feature learning and classifier training, which improves the recognition performance. Two constraints that enforce the label consistencies of group images and label consistencies of single images are introduced in a deep learning framework to obtain the high-level feature space. The high-level feature and a multiclass linear classifier are finally learned by an effective optimization algorithm. Experimental results demonstrate the superior performance of the proposed method over many state-of-the-art techniques in object recognition.

Homomorphic Encryption (HE) allows processing cipher-text data, but it is a challenge to enable complex methods such as multimedia decompression in the HE domain. In this paper, we propose a novel scheme to enable FLAC (Free Lossless Audio Codec) decompression in the HE domain. FLAC applies linear prediction to predict the current sample and Golomb coding to encode residuals. FLAC decoding relies heavily on dynamic controls that HE does not support due to unknown values of control variables after encryption. Our scheme regularizes dynamic controls in FLAC decoding with static controls by calculating an encrypted matching bit for each possible value of a control variable and producing candidate results as if it were a match. The summation of each possible value’s candidate results multiplied by its matching bit is equivalent to selecting the results of the matched control value. Our FLAC decoding scheme enables Single-Instruction Multiple-Data (SIMD): multiple (e.g., 256) plaintexts are packed and encrypted into a single ciphertext, and decoding one encrypted frame corresponds to decoding multiple plaintext frames. Our scheme is applicable to other audio compression standards based on similar technologies. Experimental results are also reported.

A secure downlink transmission system which is exposed to multiple eavesdroppers and is appropriate for Internet of Things (IoT) applications is considered. A worst case scenario is assumed, in the sense that, in order to enhance their interception ability all eavesdroppers are located close to each other, near the controller and collude to form joint receive beamforming. For such a system, a novel cooperative nonorthogonal multiple access (NOMA) secure transmission scheme for which an IoT device with a stronger channel condition acts as an energy harvesting relay in order to assist a second IoT device operating under weaker channel conditions, is proposed and its performance is analyzed and evaluated. A secrecy sum rate (SSR) maximization problem is formulated and solved under three constraints: 1) transmit power; 2) successive interference cancellation; and 3) quality of service. By considering both passive and active eavesdroppers scenarios, two optimization schemes are proposed to improve the overall system SSR. On the one hand, for the passive eavesdropper scenario, an artificial noise-aided secure beamforming scheme is proposed. Since this optimization problem is nonconvex, instead of using traditional but highly complex, brute-force 2-D search, it is conveniently transformed into a convex one by using an epigraph reformulation. On the other hand, for the active multiantennas eavesdroppers’ scenario, the orthogonal-projection-based beamforming scheme is considered, and by employing the successive convex approximation method, a suboptimal solution is proposed. Furthermore, since for single antenna transmission the orthogonal-projection-based scheme may not be applicable a simple power control scheme is proposed. Various performance evaluation results obtained by means of computer simulations have verified that the proposed schemes outperform other benchmark schemes in terms of SSR performance.

This paper introduces a novel methodology for residential building contouring from large-scale airborne point clouds. Unlike other methods that handle linearization and regularization of the linear primitives separately by imposing rigid constraints, we propose an optimization-based linearization and global regularization to form accurate, topologically error-free, and lightweight polygons. To this end, we enhance the classic density-based spatial clustering of applications with noise algorithm to segment individual building entities at the instance level. The initial contours of each individual building are then delineated and further decomposed by a novel topologically aware propagation process and a global optimization technique. The decomposed linear primitives are fed into the global regularization step, from which the regular shapes are learned and enforced hierarchically by imposing constraints, such as parallelism, homogeneity, orthogonality, and collinearity. Based on the concept of hybrid representation, the regularized and unaltered linear primitives are jointly connected in an esthetic way. Various experiments using representative buildings and large-scale residential scenes from the Dutch AHN3 data set have shown that the proposed methodology generates meaningful building contouring representation in terms of accuracy, compactness, topology, and levels of detail abstraction while being robust and scalable.

In order to fix a bug or install a new application, disseminating a code to specific target sensors is an important management task in wireless sensor networks. In order to disseminate a code to specific target sensors in an energy efficient manner, it is required to select the minimum required number of forwarders with a small number of control messages. In this paper, we propose an energy efficient target specific code dissemination scheme with a forwarder selection algorithm. The proposed scheme consists of two steps, (i) a forwarder selection algorithm with a control message suppression, (ii) unnecessary forwarders reduction with a sleep mechanism. In order to select the minimum required forwarders with a small number of control messages, we propose an algorithm to select a forwarder where more target sensors are reachable, through a single control message by leveraging overhearing of control messages. In addition, in order to reduce the number of forwarders, we propose a sleep mechanism to put unnecessary forwarders to sleep where any target sensor is not reachable. A performance analysis has shown that the number of control messages of the proposed scheme is always theoretically smaller than that of a previously published forwarder selection scheme under the same condition. In addition, we have shown that the proposed scheme reduces the energy consumption compared with the previous scheme.

The effective capacity (EC) has been introduced as a performance metric that takes into account the delay constraints imposed by real-time emerging applications. This paper develops a novel moment generating function (MGF)-based approach for the effective capacity analysis of equal gain combining (EGC) diversity receivers over generalized fading channels under a maximum delay constraint. Selected numerical and simulation performance evaluation results are presented, verifying the validity of the proposed unified approach.

Massive connectivity and low latency are two important challenges for the Internet of Things (IoT) to achieve the quality of service provisions required by the numerous devices it is designed to service. Motivated by these challenges, in this paper we introduce a new millimeter-wave nonorthogonal multiple access (mmWave-NOMA) transmission scheme designed for cellular machine-to-machine (M2M) communication systems for IoT applications. It consists of one base station (BS) and numerous multiple machine type communication (MTC) devices operating in a cellular communication environment. We consider its down-link performance and assume that multiple MTC devices share the same communication resources offered by the proposed mmWave-NOMA transmission scheme, which can support massive connectivity. For this system, a novel MTC pairing scheme is introduced the design of which is based upon the distance between the BS and the MTC devices aiming at reducing the system overall overhead for massive connectivity and latency. In particular, we consider three different MTC device pairing schemes, namely: 1) random near and the random far MTC devices; 2) nearest near and the nearest far MTC devices (NNNF); and 3) nearest near and the farthest far MTC device. For all three pairing schemes, their performance is analyzed by deriving closed-form expressions of the outage probability and the sum rate. Furthermore, performance comparison studies of the three MTC device pairing schemes have been carried out. The validity of the analytical approach has been verified by means of extensive computer simulations. The obtained performance evaluation results have demonstrated that the proposed cellular M2M communication system employing the mmWave-NOMA transmission scheme improves outage probability as compared to equivalent systems using mmWave with orthogonal multiple access schemes.

In this paper, a novel three-dimensional (3-D) indoor positioning scheme is proposed for millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Its operation is based upon a hybrid received signal strength and angle of arrival (RSS-AoA) positioning scheme, which employs only a single access point equipped with a large-scale uniform cylindrical array. To reduce the high computational complexity imposed by the large number of antennas used in mmWave massive MIMO (M3-MIMO) systems, we firstly propose a novel channel compression method. By proper quantization and selection of the received mmWave signals, which exhibit quasioptical and sparse multipath characteristics, the channel compression method reduces the dimension of the received signal space while maintaining the accuracy of the position estimation. Then, we propose a beamspace transformation approach to transform signal vectors in the element space to the beamspace, and thus the computational complexity of the angle estimation is significantly reduced. Finally, a novel hybrid RSS-AoA positioning scheme is designed for the computations of the 3-D coordinates of the target mobile terminal. Simulation results have shown that the proposed indoor positioning scheme is capable of achieving high accuracy as well as significantly lower computational complexity as compared to other previously known indoor positioning techniques.

Although Bitcoin is one of the most successful decentralized cryptocurrency, recent research has revealed that it can be used as fraudulent activities such as HYIP (High Yield Investment Program). To identify such undesired activities, it is important to obtain Bitcoin addresses related with fraud. So far, the identification of such activities is based upon relating Bitcoin addresses with graph mining procedures. In this paper, we follow a different approach for identifying Bitcoin addresses related with HYIP by analyzing transactions patterns. In particular, based on the individual inspection of HYIP activity in Bitcoin, we propose a number of features that can be extracted from transactions. In particular, a signed integer called pattern is assigned to each transaction and the frequency of each pattern is calculated as key features. By evaluating the classification performance with more than 1,500 labeled Bitcoin addresses, it is shown that about 83% of HYIP addresses are correctly classified while maintaining false positive rate less than 4.4%.

This paper deals with the detection of malicious attacks in a Gaussian two-hop relay network, in which the source transmits information to the destination with the assistance of an amplify and forward (AF) relay node with unreliable channel state information (CSI). We consider the case where the potentially malicious relay node attempts to corrupt the network by malicious forwarding. In addition, dishonest feedback results in reliable CSI is not available at the source and/or destination. By modeling the relay behavior with two kinds of operations, namely detectable and undetectable operations, we propose a novel detection method which can deal with any detectable malicious attacks and works effectively in the presence of unreliable CSI. For the detectable operation, it is shown that the malicious attacks can be, with high probability, successfully detected. Furthermore, our research has shown that, although for the undetectable operation each attack can fool the proposed detection method, these undetectable attacks do not affect the improvements cooperative diversity. The effectiveness of our method has been validated by numerical results obtained by means of computer simulations.

The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Modeling 3-D trees from terrestrial laser scanning (TLS) point clouds remains a challenging task for several well-known reasons, including their complex structure and severe occlusions. In order to accurately reconstruct 3-D tree models from TLS point clouds that typically suffer from significant occlusions, in this paper, a novel local structure and direction-aware approach is presented to successfully complete missing structures of trees. In this method, we first extract the coarse tree skeleton from the input point cloud, and thus, the branch dominant direction and the point density of each branch are obtained. By a skeleton-based Laplacian algorithm, the point cloud is further shrunk into a skeleton point cloud to highlight the branch dominant direction of each branch. For obtaining even more accurate point densities, a dictionary-based algorithm is utilized to learn and reconstruct the local structure. Finally, the branch dominant direction and point density are integrated into an iterative optimization process to recover the missing data. Extensive experimental results have shown that the proposed method is very robust to incomplete data sets, and it is capable of accurately reconstructing 3-D trees, which are partially, or even to a large extent, missing from the input point cloud.

With the emergence of huge volumes of high-resolution remote sensing images produced by all sorts of satellites and airborne sensors, processing and analysis of these images require effective retrieval techniques. To alleviate the dramatic variation of the retrieval accuracy among queries caused by the single image feature algorithms, we developed a novel graph-based learning method for effectively retrieving remote sensing images. The method utilizes a three-layer framework that integrates the strengths of query expansion and fusion of holistic and local features. In the first layer, two retrieval image sets are obtained by, respectively, using the retrieval methods based on holistic and local features, and the top-ranked and common images from both of the top candidate lists subsequently form graph anchors. In the second layer, the graph anchors as an expansion query retrieve six image sets from the image database using each individual feature. In the third layer, the images in the six image sets are evaluated for generating positive and negative data, and SimpleMKL is applied to learn suitable query-dependent fusion weights for achieving the final image retrieval result. Extensive experiments were performed on the UC Merced Land Use-Land Cover data set. The source code has been available at our website. Compared with other related methods, the retrieval precision is significantly enhanced without sacrificing the scalability of our approach.

Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects.

In this paper, we study the physical-layer security of amplify-and-forward relaying networks under a spectrum-sharing mechanism over independent non-identically distributed Rayleigh fading channels. Relay selection is presented to select the best relay, which can guarantee the security performance by minimizing the received signal-to-noise ratio at the eavesdropper. In order to guarantee the quality-of-service of primary networks, both the maximum tolerable peak interference power at the primary users and maximum allowable transmit power at secondary users are considered. Closed-form lower and upper bounds as well as asymptotic expressions for the intercept outage probability (OP) are derived. From the asymptotic expressions, it can be observed that the diversity order of intercept OP equals to two. Our analysis results are validated by Monte-Carlo simulation.

The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars.

A 3-D tree structure plays an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3-D spatial arrangements and structure of trees as a point cloud. In the past, several methods to reconstruct 3-D trees from the TLS point cloud were proposed. However, in general, they fail to process incomplete TLS data. To address such incomplete TLS data sets, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian function. To reconstruct 3-D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The developed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable of effectively reconstructing 3-D tree models from grossly incomplete TLS point clouds with significant amounts of missing data.

In this study, the authors deal with the problem of how to effectively approximate the max* operator when having n>2 input values, with the aim of reducing implementation complexity of conventional Log-MAP turbo decoders. They show that, contrary to previous approaches, it is not necessary to apply the max* operator recursively over pairs of values. Instead, a simple, yet effective, solution for the max* operator is revealed having the advantage of being in non-recursive form and thus, requiring less computational effort. Hardware synthesis results for practical turbo decoders have shown implementation savings for the proposed method against the most recent published efficient turbo decoding algorithms by providing near optimal bit error rate (BER) performance.

Adaptive M-QAM systems with maximal-ratio diversity (MRD) over composite arbitrarily correlated Nakagami-m fading and log-normal shadowing channels are analyzed. The slow adaptive modulation (SAM) technique is considered which adapts the constellation signaling to slow channel variations and achieves good performance in terms of spectral efficiency (SE) and bit error outage (BEO) despite its low feedback rate. Non-ideal channel estimation (CE) based on pilot symbols is assumed. Analytical expressions and tight bounds for the bit error probability are obtained which are used for deriving analytical expressions and tight bounds for the BEO and achieved SE (ASE). Performance evaluation results have shown that fading correlation and CE errors degrade the performance of SAM systems. The partition of resources for CE and information data requires a careful definition of the ASE and results have shown the tradeoff between CE quality and ASE.

In the paper, a modified Log-MAP algorithm based on a novel approximation of the max*operator derived from the Jensen inequality is proposed. In this approach, the max*operation is performed on n≥2 arguments, thus the recursive calculations are avoided and significant reduction in the number of operations performed per decoding step, as compared with the conventional Log-MAP, is achieved. Simulation results of BER performance for both turbo codes and turbo trellis-coded modulation (TTCM) employing the new algorithm are given, showing the near-optimal performance of the proposed algorithm.

The performance of multibranch selection diversity (SD) receivers over L arbitrarily correlated generalised Gamma (GG) fading channels is analysed and evaluated. Following a statistical approach previously proposed for the multivariate Nakagami-m distribution, a closed-form upper bound for the joint GG probability density function (PDF) and an infinite series upper bound for the joint GG cumulative distribution function (CDF) are obtained. For the special case of the trivariate GG distribution (L=3), exact expressions in the form of infinite series for the PDF and CDF are presented. The derived bounds are used to obtain tight upper bounds for the distribution of the SD output signal-to-noise ratio (SNR). Furthermore, based on the derived analysis, novel analytical expressions that accurately approximate the distribution of the SD output SNR are presented. The bounds and approximations are used to study important performance criteria of SD receivers such as outage and average symbol error probability. The accuracy of the derived expressions has been verified, on the one hand, analytically for triple-branch (L=3) and, on the other hand, by means of computer simulations for multibranch (L>3) SD receivers.

This paper addresses the problem of robust frame synchronization for TDM/TDMA systems in the presence of frequency errors, accomplished through data-aided recognition of the Unique Word (UW) preamble in the transmission flow. Robust detection design is performed applying, through approximations, the Maximum Likelihood (ML) criterion coupled to Post Detection Integration (PDI), to obtain a novel detector identified as Balanced-GPDI (B-GPDI). This new approach considerably outperforms other schemes available in the literature, at the cost of a moderate complexity increase. This is possible thanks to an optimized use of coherent accumulation, which enhances the correlation term in the decision variable, along with highly accurate modeling of the energy correction factor. To limit complexity increase and memory requirements, several approximations of the exact B-GPDI are proposed and discussed in the paper, to provide the designer with practical solutions that are still able to outperform other approaches in specific application scenarios. In particular, approximations for low and high Signal-to-Noise Ratio (SNR) are presented. Different application scenarios are considered in the paper for numerical analysis. In particular, the cases of forward link Continuous Transmission (CTX) and return link Burst Transmission (BTX) are addressed.

A unified analytical framework for evaluating the performance of maximal ratio combining (MRC) and orthogonal space-time block coding (OSTBC) over generalized fading channels is presented. The basic motivation for developing such a framework pertains to analyzing the distribution of a sum of squared random variables (RVs) belonging to different families of fading distributions. Following a novel analytical approach stemming from the definition of a common moment generating function (MGF) model for these families of distributions, the probability density function (PDF) and the cumulative distribution function (CDF) of a general sum of squared RVs are expressed by simple infinite Gamma series expansions. Based on these expressions, the capacity and error probability of MRC/OSTBC over generalized fading channels is thoroughly studied. The developed theory is used to evaluate the performance of OSTBC for a mixed Nakagami-m/Rice fading model and novel analytical results are presented. The accuracy of the derived expressions is validated by extensive Monte Carlo simulations.

A novel method for approximating the optimal max* operator in Log-MAP decoding of turbo and turbo trellis-coded modulation (TTCM) codes is presented. The max* operation is performed on n>2 rather than on only two inputs, as it is in the conventional approach. The approximation uses a very simple max operation and a look-up table (LUT) with only eight stored values. Complexity comparison reveals a significant reduction in the number of operations required per decoding step for the presented approximation, as compared with the conventional Log-MAP decoding. Performance evaluation results are presented for both turbo and TTCM codes, showing the near-optimal performance of the novel approach.

A new constellation shaping technique has been proposed, which efficiently and effectively reduces the peak-to-average power ratio (PAPR) of orthogonal-frequency-division-multiplexing (OFDM) systems. Its novelty is based upon a simple transformation in the time domain instead of the commonly used frequency domain. This transformation is shown to ensure PAPR reduction regardless of the OFDM system input. As compared with previously known PAPR reduction methods, the proposed technique requires minimal implementation complexity, while it offers considerable performance gains. Closed form analytical expressions for the distribution of the PAPR and the bit error rate are derived. The accuracy of these analytical expressions is verified via equivalent performance evaluation results obtained by means of Monte Carlo computer simulations. Furthermore, performance comparisons made with other competitive techniques show that the proposed technique is an attractive alternative for PAPR reduction.

The correlated bivariate generalized-K (KG) distribution, with not necessarily identical shaping and scaling parameters, is introduced and studied. This composite distribution is convenient for modeling multipath/shadowing correlated fading environments when the correlations between the signal envelopes and their powers are different. Generic infinite series expressions are derived for the probability density function (PDF), the cumulative distribution function (CDF) and the joint moments. Assuming identical shaping parameters, simpler expressions for the PDF, CDF and the characteristic function (CF) are provided, while the joint moments are derived in closed form. Furthermore, the PDFs of the product and ratio of two correlated KGrandom variables are obtained. Capitalizing on these theoretical expressions for the statistical characteristics of the correlated KGdistribution, the performance analysis of various diversity reception techniques, such as maximal ratio combining (MRC), equal gain combining (EGC) and selection diversity (SD), over bivariate KGfading channels is presented. For the SD, the outage probability is studied, while for the MRC and EGC the average bit error probability is obtained. The proposed analysis is accompanied by numerical results, clearly demonstrating the usefulness of the theoretical approach as well as the appropriateness of the KGdistribution to model multipath/shadowing fading channels.

The statistical characteristics of the trivariate and quadrivariate Weibull fading distribution with arbitrary correlation, non-identical fading parameters and average powers are analytically studied. Novel expressions for important joint statistics are derived using the Weibull power transformation. These expressions are used to evaluate the performance of selection combining (SC) and maximal ratio combining (MRC) diversity receivers in the presence of such fading channels.

Abstract In this paper, an efficient cross‐layer design that performs joint adaptation of the physical (PHY) and application layers of a mobile WiMAX network is proposed. The design takes into account channel state and performance information from the PHY and medium access control (MAC) layers, respectively. It uses a decision algorithm to evaluate this information, specify unfavorable conditions regarding low channel quality and increased congestion, and take measures by coordinating modulation order, transmission power, and media encoding rate, toward improved overall quality of service (QoS) offered to the user. Extensive simulation results show that the proposed design achieves considerably reduced packet loss and power consumption, combined with increased throughput as compared to a typical mobile WiMAX system. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper the Shannon capacity of generalized-gamma (GG) fading channels is studied, under different adaptive transmission techniques. The GG is a generic distribution, which is considered to be appropriate for modeling fading/shadowing phenomena. Hence, assuming single (i.e., no diversity), as well as, dual-branch selection diversity receivers, novel expressions for the capacity under three adaptive transmission policies, namely optimal power and rate adaptation (OPRA), channel inversion with fixed rate (CIFR) and truncated CIFR (TIFR), are obtained. The derived expressions are in closed form and generalize previously presented results. The proposed analysis is accompanied by numerical results, clearly demonstrating the usefulness of the theoretical approach.

The correlated bivariate K-distribution with arbitrary and not necessarily identical parameters is introduced and analyzed. Novel infinite series expressions for the joint probability density function and moments are derived for the general case where the associated bivariate distributions, i.e., Rayleigh and gamma, are both arbitrary correlated. These expressions generalize previously known analytical results obtained for identical parameter cases. Furthermore, considering independent gamma distributions, the cumulative distribution and characteristic functions are analytically obtained. Although the derived expressions can be used in a wide range of applications, this letter focuses on the performance analysis of dual branch diversity receivers. Specifically, the outage performance of dual selection diversity receivers operating over correlated K fading/shadowing channels is analytically evaluated. Moreover, for low normalized outage threshold values, closed-form expressions are obtained.

In this paper, the statistics of quadratic forms in normal random variables (RVs) are studied and their impact on performance analysis of wireless communication systems is explored. First, a chi-squared series expansion is adopted to represent the probability density function of a quadratic form in normal RVs and novel series truncation error bounds are derived, which are much tighter compared to already known ones. Secondly, it is theoretically shown that when an orthogonal space time block coding (OSTBC) transmission scheme is used, the signal to noise ratio (SNR) at the receiver under various fading conditions can be expressed as a quadratic form in normal RVs. Capitalizing on these results, a thorough error probability and capacity analysis is presented for the performance of OSTBC systems over Nakagami-q (Hoyt) fading channels. For all error probability and capacity performance criteria considered, simple, closed-form truncation error bounds expressions are derived, which avoid the use of infinite sums and complicated functions. The proposed theoretical analysis is validated through extensive Monte Carlo simulations.

In this paper, a novel error probability analysis of orthogonal space-time block coding (OSTBC) over independent but not necessarily identical Hoyt (Nakagami-q) fading channels is presented. First, it is shown that for a precise performance analysis the derivation of the distribution of a quadratic form in normal random variables is necessary. After representing this distribution using a chi-squared series expansion a new, tight upper bound on the series truncation error is obtained. The proposed bound has a simple form, which allows the derivation of closed-form truncation error bound expressions for several performance analysis criteria. Based on the chi-squared series formulation, closed-form error probability expressions are also provided. Simulation results show that the proposed bounds are much tighter compared to previously derived ones and verify our analytical results.

The authors deal with the sum–product algorithm (SPA) based on the hyperbolic tangent (tanh) rule when it is applied for decoding low-density parity-check (LDPC) codes. Motivated by the finding that, because of the large number of multiplications required by the algorithm, an overflow in the decoder may occur, two novel modifications of the tanh function (and its inverse) are proposed. By means of computer simulations, both methods are evaluated using random-based LDPC codes with binary phase shift keying (BPSK) signals transmitted over the additive white Gaussian noise (AWGN) channel. It is shown that the proposed modifications improve the bit error rate (BER) performance up to 1 dB with respect to the conventional SPA. These results have also shown that the error floor is removed at BER lower than 10−6. Furthermore, two novel approximations are presented to reduce the computational complexity of the tanh function (and its inverse), based on either a piecewise linear function or a quantisation table. It is shown that the proposed approximations can slightly improve the BER performance (up to 0.13 dB) in the former case, whereas small BER performance degradation is observed (<0.25 dB) in the latter case. In both cases, however, the decoding complexity is reduced significantly.

A generic and novel distribution, referred to as Nakagami, constructed as the product ofNstatistically independent, but not necessarily identically distributed, Nakagami-mrandom variables (RVs), is introduced and analyzed. The proposed distribution turns out to be a very convenient tool for modelling cascaded Nakagami-mfading channels and analyzing the performance of digital communications systems operating over such channels. The moments-generating, probability density, cumulative distribution, and moments functions of theN*Nakagami distribution are developed in closed form using the Meijer's G -function. Using these formulas, generic closed-form expressions for the outage probability, amount of fading, and average error probabilities for several binary and multilevel modulation signals of digital communication systems operating over theN*Nakagami fading and the additive white Gaussian noise channel are presented. Complementary numerical and computer simulation performance evaluation results verify the correctness of the proposed formulation. The suitability of theN*Nakagami fading distribution to approximate the lognormal distribution is also being investigated. Using Kolmogorov--Smirnov tests, the rate of convergence of the central limit theorem as pertaining to the multiplication of Nakagami-mRVs is quantified.

A detailed performance analysis for the most important diversity receivers operating over a composite fading channel modeled by the generalized-K (Kg) distribution is presented. The Kg distribution has been recently considered as a generic and versatile distribution for the accurate modeling of a great variety of short term fading in conjunction with long term fading (shadowing) channel conditions. For this relatively new composite fading model, expressions for important statistical metrics of maximal ratio combining (MRC), equal gain combining (EGC), selection combining (SC) and switch and stay combining (SSC) diversity receivers are derived. Using these expressions and by considering independent but not necessarily identical distributed fading channel conditions, performance criteria, such as average output signal-to-noise ratio, amount of fading and outage probability are obtained in closed form. Moreover, following the moments generating function (MGF) based approach for MRC and SSC receivers, and the Pade approximants method for SC and EGC receivers, the average bit error probability is studied. The proposed mathematical analysis is complemented by various performance evaluation results which demonstrate the accuracy of the theoretical approach.

The reverse link capacity of a cellular code division multiple access (CDMA) network employing group successive interference cancellation (GSIC) is studied. By extending a model previously applied to successive interference cancellation (SIC), we analytically evaluate the outage performance of GSIC. Numerical and simulation results demonstrate that without a large reduction in reverse link capacity the GSIC scheme can significantly reduce detection delay and hardware complexity as compared to conventional SIC

The end-to-end performance of a dual-hop relaying system operating over independent, non-identical Nakagami-m fading channels, is analyzed and evaluated. Closed-form expressions for the cumulative distribution function, the probability density function, the moments and the moment generating function of the end-to-end signal-to-noise ratio (SNR), are derived. Using these results, closed-form expressions for the outage probability are presented for both channel state information and fixed-gain relays. Furthermore, for the case of fixed-gain relay, the average end-to-end SNR, the amount of fading and the average bit-error rate are also expressed in closed-form. The proposed mathematical analysis is complemented by numerical examples, including the effects on the overall performance of the SNRs unbalancing as well as the fading severity

The symbol error probability (SEP) performance of time-hopping (TH) ultra-wideband (UWB) systems in the presence of multiuser interference (MUI) and timing jitter is considered without making the usual Gaussian approximation (GA). Contrary to previous studies restricted to binary modulation formats, we analyze and evaluate the performance of such multiuser TH-UWB systems employing M-ary pulse position modulation (PPM) and a correlation receiver. Using the Gaussian quadrature rules (GQR) method and by considering the correlator outputs to be statistically independent, a semi-analytical method for the SEP evaluation is proposed. Furthermore, by modeling the timing jitter as a Gaussian process, we study the degradation it causes on the overall system performance (SEP and capacity). The accuracy of the proposed approach has been verified by means of computer simulations. Furthermore, a comparison with equivalent performance results obtained using the GA has provided precise conditions where this approximation should not be used.

This paper presents a new approach in analyzing the reverse link capacity performance of cellular CDMA systems employing successive interference cancellation (SIC). Due to the controlled power disparity present in such systems previous methods cannot precisely model their inter-cell interference. We first introduce a novel model which allows the accurate analysis and evaluation of the inter-cell interference of cellular CDMA systems with controlled power disparity. Its validity is verified by means of theoretical analysis and computer simulations. Secondly, by using this new inter-cell interference model, a theoretical analysis of the CDMA reverse link capacity is presented. In terms of evaluation results, we focus on the performance improvements SIC offers as compared to single user detection (SUD). Analytical results complemented by equivalent computer simulated performance evaluation results have shown that SIC significantly increases the reverse link capacity of cellular CDMA systems operating in realistic controlled power disparity environments.

A versatile envelope distribution which generalizes many commonly used models for multipath and shadow fading is the so-called generalized Gamma (GG) distribution. By considering the product of N GG random variables (RV)s, novel expressions for its moments-generating, probability density, and cumulative distribution functions are obtained in closed form. These expressions are used to derive a closed-form union upper bound for the distribution of the sum of GG distributed RVs. The proposed bound turns out to be an extremely convenient analytical tool for studying the performance of TV-branch equal-gain combining receivers operating over GG fading channels. For such receivers, first the moments of the signal-to-noise (SNR) at the output, including average SNR and amount of fading, are obtained in closed form. Furthermore, novel union upper bounds for the outage and the average bit error probability are derived and evaluated in terms of Meijer's G-functions. The tightness of the proposed bounds is verified by performing comparisons between numerical evaluation and computer simulations results

The exact analysis of dynamic channel allocation (DCA) with first-in/first-out (FIFO) queuing of handover (QH) requests is highly complex, due to the dynamic nature of channel allocation to different cells. This letter presents an approximate but accurate analytical method to evaluate the performance of DCA in conjunction with FIFO-QH in low earth orbit mobile satellite systems. The accuracy of the proposed analysis has been verified by means of computer simulation.

In this paper, optimum and suboptimum diversity combining schemes for coherent and differential M-ary phase-shift keying (M-PSK) transmission impaired by general Ricean fading and impulsive Class-A noise are derived and analyzed. The proposed suboptimum coherent combining (SCC) and suboptimum noncoherent combining (SNC) schemes yield similar performance as the corresponding optimum combining schemes but require a lower computational complexity. In addition, the novel SCC and SNC strategies achieve large performance gains over conventional maximum ratio combining (MRC) and equal gain combining (EGC), respectively. For MRC and EGC, respectively, we also provide a performance analysis for coherent and differential M-PSK transmissions over general Ricean fading channels with Class-A noise. Furthermore, tight performance upper bounds for the proposed optimum and suboptimum combining schemes are derived.

Efficient performance bounds for multihop wireless communications systems with non-regenerative fixed-gain relays operating over non-identical generalized fading channels, are presented. More specifically, the end-to-end signal-to-noise ratio (SNR) is formulated and upper bounded by using the well-known inequality between harmonic and geometric mean of positive random variables. Based on this bound, the moments of the end-to-end SNR for Rayleigh, Nakagami-m, and Rice fading channels, are obtained in simple closed-forms. Furthermore, the outage performance and the average error probability for coherent and non-coherent modulation schemes are also studied using the moment-generating function (MGF) approach. The proposed method for the evaluation of the MGF is based on the Pade approximants theory. Moreover, new expressions are derived for the gain of previously proposed "semi-blind" relays. These expressions are used in numerical and computer simulations examples, to verify the accuracy and to show the tightness of the proposed bounds.

Motivated by the suitability of the Weibull distribution to model multipath fading channels, the performance of L-branch selection combining (SC) receivers, with non-identical statistics, is presented. Deriving a useful expression for the probability density function of the SC output signal-to-noise ratio (SNR), important performance metrics are studied. More specifically, a closed-form expression is derived for the moments of the combiner output SNR, which is used to study the average output SNR and the amount of fading. The average symbol error probability for several coherent and non-coherent, binary and multilevel modulation schemes is also obtained in closed-form.

In this paper, optimum (ONC) and suboptimum (SNC) noncoherent diversity combining schemes for M-ary differential phase-shift keying (MDPSK) transmission over general Rician fading channels with impulsive class-A noise are derived and analyzed. The proposed SNC scheme yields a similar performance to ONC, but requires a lower computational complexity, and achieves a large performance gain over conventional equal gain combining (EGC). We provide a performance analysis for MDPSK transmission with EGC over general Rician fading channels with class-A noise, and derive a tight lower bound on the bit error rate of ONC.

The performance of dual-branch SC receivers over correlated Weibull fading channels is studied in this paper. Exact closed-form expressions are derived for the probability/cumulative density functions and the moments of the output signal-to-noise ratio (SNR). Important performance criteria, such as average output SNR, amount of fading (AoF), outage probability and average bit error probability (ABEP) for several modulation schemes, are studied and novel closed-form analytical expressions are derived. The proposed analysis is complemented by various performance evaluation results, including the effects of input SNRs unbalancing, fading severity and fading correlation on the overall system performance. Computer simulations results verify the validity and the accuracy of the proposed analysis.

Motivated by the suitability of the Weibull distribution to model multipath fading channels, the second order statistics and the spectral efficiency (SE) of L-branch SC receivers are studied. Deriving a novel closed-form expression for the probability density function (pdf) of the SC output SNR, the average level crossing rate (LCR), the average fade duration (AFD) and the Shannon's average SE, at the output of the SC, are derived in closed-forms. Our results are sufficiently general to handle arbitrary fading parameter and dissimilar branch powers.

Ascertaining the importance of the dual selection combining (SC) receivers and the suitability of the Weibull model to describe mobile fading channels, we study the performance of a dual SC receiver over correlated Weibull fading channels with arbitrary parameters. Exact closed-form expressions are derived for the probability density function, the cumulative distribution function, and the moments of the output signal-to-noise ratio (SNR). Important performance criteria, such as average output SNR, amount of fading, outage probability, and average bit-error probability for several modulation schemes are studied. Furthermore, for these performance criteria, novel closed-form analytical expressions are derived. The proposed analysis is complemented by various performance evaluation results, including the effects of the input SNR's unbalancing, fading severity, and fading correlation on the overall system's performance. Computer simulation results have verified the validity and accuracy of the proposed analysis.

The variable bit rate nature of compressed video remains a major challenge to the transmission of real-time video over mobile CDMA cellular networks. Indeed, H.263 and MPEG-4 video bitstreams can exhibit high peak rates and frequent rate variations, which are difficult to support in 2.5G and 3G mobile networks. In this paper, we consider smoothing schemes to minimize the peak rate, variance, and average bandwidth of the transmitted signal, in order to obtain a lower bit-error-rate and thus, a higher received video quality than if unsmoothed video were transmitted. By introducing a delay in the decoding process, the video output stream can be buffered at the video decoder end: this buffering capability is used in order to perform real-time smoothing of the video streams. The transmitter chooses among the set of rates provided by the IS-95B or cdma2000 standards, in a mobility scenario, in order to support the rate variations. We simulate two end-to-end video communication systems based on IS-95B and cdma2000. They consist of an H.263 codec, an H.223 multiplexer, a rate management system and the physical layer components of the IS-95B and cdma2000 standards. The transmitted signal is subject to multipath fading and multiple-access interference, obtained by simulating all the other users in the cell. Results show a significant gain in the video quality obtained through smoothing of the video streams.

This paper presents the performance analysis and simulation of a multicode direct-sequence code-division multiple-access system with noncoherent M-ary modulation, in a multipath fading environment. This type of transceiver is specified for the reverse link of the IS-95B and cdma2000 (radio configurations 1 and 2) systems and is intended to serve high-rate applications such as data transfer and video communications. While previous studies considered the analytical error performance of coherent multicode systems, little attention has been devoted in the literature to the noncoherent case. We provide concise and useful expressions for the interference terms as a function of the commonly used aperiodic cross correlation functions. After a statistical characterization of these terms, we make use of the Gaussian approximation (GA) in order to obtain the bit-error rate (BER). However, unlike some other analyses (for coherent detection) relying on the GA, in our derivation, we take into account the fact that all the codes transmitted by a mobile user fade in unison. As demonstrated via computer simulations, this fact is crucial to obtain a reliable estimate of the BER, especially when equal-gain combining (EGC) is used at the receiver. The analysis is also extended to include a simple closed-loop power control algorithm and hard handoff between multiple cells. In particular, we verify-for the multicode case-previous observations that the use of EGC allows improvement only for a certain range of values of the total interference seen at the receiver: When either the number of interfering users is too large, or too many codes are assigned to the high-rate user, the noncoherent combining loss becomes such that the use of many diversity branches can decrease the performance as compared to a system with little or no diversity.

In this paper, important statistical parameters, such as the k-moment and the k-central moment, of the output signal-to-noise ratio (SNR), in predetection equal-gain combining (EGC) systems operating over correlated Nakagami-m fading channels are studied. Simple closed-form expressions for the mean and variance of the output SNR are presented. Furthermore, it is shown that our general results reduce to the specific noncorrelative fading case previously published. Moreover, significant performance criteria for the EGC such as amount of fading (AoF) and spectral efficiency in the low power regime are investigated. Numerical and simulation results are presented validating the proposed mathematical analysis. These results also point out the impact of the fading correlation, the input SNRs unbalancing as well as the fading severity, on the EGC performance.

A useful formulation for the multivariate Rayleigh probability density function (PDF) with an exponential form for its correlation matrix is presented. Furthermore, the corresponding multivariate Rayleigh cumulative distribution function (CDF) is derived in a nested infinite series representation, which converges rapidly and can be efficiently used in many wireless applications with diversity reception. Finally, bounds on the error resulting from truncation of the infinite series are also derived.

The variable bit rate nature of compressed video remains a challenge to the transmission of real-time video over mobile CDMA cellular networks. Indeed, H.263 and MPEG-4 video bitstreams are characterized. by high peak rates and frequent rate variations, which are difficult to support in 2.5G and 3G mobile networks. In this paper, we propose a simple scheme which minimizes the peak transmission rate and its variance, which leads to a lower average bit-error rate (BER) and thus a higher received video quality. By introducing a small delay in the decoding process, the video output stream can be buffered at the video decoder end. We propose an algorithm which uses this buffering capability to perform real-time smoothing of the video stream. The transmitter then varies the number of codes assigned to the stream in order to support the remaining smaller rate variations. We simulate an end-to-end video communication system for the IS-95B uplink. It consists of an H.263 codec, an H.223 multiplexer and the physical layer components of the IS-95B standard. The transmitted signal is subject to multipath fading and multiple-access interference, obtained by simulating all the other users in the cell. Our joint smoothing/rate adaptation algorithm leads to a significant gain in the video quality scheme, as compared to previous approaches.

This paper presents the performance analysis and simulation of a multicode DS/CDMA system with noncoherent M-ary modulation, operating over a Rayleigh or Nakagami fading environment. This type of transceiver is specified for the reverse link of the IS-95B and cdma2000 (Radio configurations 1 and 2) systems, and is intended to serve high-rate applications such as data transfer and video communications. After a statistical characterization of the interference terms, we make use of the Gaussian approximation (GA) in order to obtain the bit error rate (BER). However, unlike other analyses relying on the GA, in our derivation we take into account the fact that all the codes transmitted by a mobile user fade in unison. As demonstrated via computer simulations, this fact is crucial to obtain a reliable estimate of the BER, especially when equal-gain combining (EGC) is used at the receiver.

Using a unified methodology, we derive analytical expressions for the level crossing rates (LCR) and average fade durations (AFD) for three diversity reception techniques and a general Nakagami fading channel. We provide novel analytical expressions for selection combining (SC) and equal-gain combining (EGC), and rederive, in a more general manner, the case of maximal-ratio combining (MRC). Our general results reduce to some specific cases previously published. These results are used to examine the effects on the concerned quantities of the diversity technique, the number of receiving branches and the severity of fading. It is observed that as the Nakagami m-parameter and the diversity order increase, the behavior of the combined received envelope for EGC follows closely the one for MRC, and distances itself from SC.

We propose a software radio based distributed base station (DBS) architecture which replaces conventional base stations with a cluster of DBS and a central processing station (CPS) which are connected by a high speed data communication network. As most signal processing is carried out in the CPS, the DBS is compact and lightweight, and requires low power for operation, so it can be deployed economically and virtually everywhere. Furthermore, since the radio signals received by different DBS are processed in one CPS, signal combining techniques (SCT) can be employed to improve the reverse link capacity of the 3G UMTS. Analytical expressions for this improvement are derived using the concept of percentage of coverage area with and without the SCT. Complementary computer simulation results for a cdma2000 system have also shown that by employing the SCT, the proposed software radio based DBS architecture can achieve significant reverse link capacity improvement.

The level crossing rates (LCRs) and average fade durations (AFDs) of a fading channel find diverse applications in the evaluation and design of wireless communication systems. Analytical expressions for these quantities are available in the literature for certain diversity reception techniques, but are generally limited to the Rayleigh fading channel, with few exceptions. Moreover, the methods employed are usually specific to a certain channel/diversity pair, and thus cannot be applied to all cases of interest. Using a unified methodology, we derive analytical expressions for the LCRs and AFDs for three diversity reception techniques and a general Nakagami (1960) fading channel. We provide novel analytical expressions for selection combining (SC) and equal-gain combining (EGC), and rederive in a more general manner the case of maximal-ratio combining (MRC). It is shown that our general results reduce to some specific cases previously published. These results are used to examine the effects of the diversity technique, the number of receiving branches and severity of the fading on the concerned quantities. It is observed that as the Nakagami m-parameter and the diversity order increase, the behavior of the combined received envelope for EGC follows closely the one for MRC, and distances itself from SC.

The effects on the performance of differentially detected Gaussian minimum shift keying (GMSK) signals operated in the presence of adjacent channel interference (ACI), modulator impairments, amplifier nonlinearities and additive white Gaussian noise (AWGN) is investigated. By means of computer simulation, the bit error rate (BER) performance of 1- and 2-bit conventional and decision feedback differentially detected (C-DD and DF-DD) GMSK systems in the presence of static and Rayleigh faded ACI is obtained. It is found that the best performance is achieved by the 2-bit DF-DD receiver and has resulted in BER performance improvements for the static ACI channel and error floor reductions for the Rayleigh faded ACI channel.

This paper deals with the problem of efficient transmission of video signals over generalized fading channels in direct sequence-spread spectrum (DS-SS) code division multiple access (CDMA) systems. We first propose a modified version of the H.263 video codec incorporating a selective forward error correction (FEC) coding scheme combined with a forced intra-frame update mechanism. The modified codec results in the improvement of the average video and frame-to-frame performance. We further consider a coherent DS-CDMA system for the forward link (base-to-mobile) in both single-cell and multiple-cell environment. We provide performance evaluation results by both analysis, employing the Gaussian approximation, and computer simulations, using Monte Carlo error counting techniques. By integrating the proposed video codec with a coherent DS-CDMA system based upon the IS-95 standard, we investigate the performance of the video transmission over frequency-selective, correlated Nakagami fading forward-link channels employing a RAKE receiver. To simulate the fading channel, we have implemented in software a correlated Nakagami fading simulator based upon the principle of superposition of complex partial waves, an approach which replicates the wave propagation process in actual physical situations. A variety of performance evaluation results, both in single-cell and multiple-cell environment, are presented for a different number of resolving paths, cell user capacity, signal propagation characteristics, as well as for the presence of channel estimation errors. Heuristic explanations and interpretations of the trend of the obtained results are also given.

Differential encoding is often used in conjunction with noncoherent demodulation to overcome carrier phase synchronization problems in communication systems employing M-ary phase-shift keying (M-PSK). It is generally acknowledged that differential encoding leads to a degradation in performance over absolutely encoded M-PSK systems with perfect carrier synchronization. In this paper, we show that when differential encoding is combined with convolutional encoding and interleaving, this degradation does not necessarily occur. We propose a novel noncoherent receiver for differentially encoded M-PSK signals that is capable of significantly outperforming optimal coherent receivers for absolutely encoded M-PSK using the same convolutional code. This receiver uses an iterative decoding technique and is based on a multiple differential detector structure to overcome the effect of the carrier phase error. In addition, to better illustrate the benefits of the powerful combination of convolutional encoding, interleaving, and differential encoding, we also present an iterative coherent receiver for differentially encoded M-PSK.

A new technique for iterative decoding of parallel concatenated convolutional (turbo) codes (PCCCs) for the correlated fast Rayleigh fading channel is proposed and evaluated. This technique is based upon the use of a multiple differential detector (MDD) receiver structure which exploits the statistical characteristics of the fading process to overcome the effects of the rapid phase and amplitude variations. Since traditional MDD receivers cannot be used with PCCCs because they do not produce soft output and are not compatible with channel interleaving, a novel MDD receiver structure is derived which overcomes these shortfalls. In addition, with careful use of extrinsic information related to the a posteriori probability distribution function of the transmitted symbols, the receiver is designed in such a fashion as to allow channel estimation to improve with each iteration. Evaluation of the proposed receiver by means of computer simulation has shown dramatic performance improvements in fast Rayleigh fading channels as compared to long constraint-length conventional convolutional codes using both single and traditional MDD receiver structures.

This comment points out some papers published before the paper of Vitetta and Taylor (see ibid., vol.43, no.11, p.2750-58, 1995). These early papers have dealt with the general problem of maximum-likelihood sequence estimation of coded/uncoded phase-shift keying (PSK) and quadrature amplitude modulation (QAM) signals in correlated Rician and Rayleigh-fading channels. These publications did not assume use of interleaving or use of optimal codes, designed for interleaved systems. The statistical properties of the multiplicative fading process have been included in the design of the receivers which were proposed, analyzed, and evaluated. The research contributions documented in these papers have also shown for the first time in the open literature the link between conventional detection techniques and the maximum-likelihood detection of signals in this type of fading channels.

Performance upper bounds for noncoherent receivers employed in conjunction with single and multi-amplitude/-phase signals, transmitted over time dispersive and Gaussian noise channels are derived. Based upon a metric which has been previously derived by the authors, we present analytical expressions and computer generated results for the performance of asymptotically optimal noncoherent detection over such channels. As a typical application of the developed theoretical analysis, we consider wideband telecommunication systems. Where time dispersion resulting in intersymbol interference (ISI) is one of the significant sources of system performance degradation. Numerical evaluation of the optimal noncoherent decoding algorithms, shows the proposed bounds to be an effective and efficient means of evaluating the performance of the noncoherent receivers under investigation. Using the derived bounds, performance evaluation results for modulation schemes such as /spl pi//4-shift DQPSK (differential quadrature phase shift keying), 8- and 16-DQAM (differential quadrature amplitude modulation), at very low bit-error rates (BER), which would otherwise pose impractically high computational loads when using Monte-Carlo error counting techniques, are readily obtained. At BER>10/sup -4/ evaluation results generated via computer simulation have verified the tightness of the bounds.>

The maximum likelihood sequence estimator for the reception of coded digital phase modulated signals with single or multiamplitude constellations, transmitted over a multiplicative, frequency-nonselective (i.e., flat) correlated fast fading Rayleigh or Rician channel and corrupted by additive white Gaussian noise (AWGN), is derived. Due to this correlation, the errors caused by fading tend to occur in bursts. In the analysis, no assumption simplifying the problem is made. For fast fading the authors consider the most general case where both phase and amplitude distortion resulting from the fading process could change significantly and thus cannot be assumed to be constant over a number of transmitted symbols. It is shown that the estimator's hardware structure consists of a combination of envelope, multiple differential and coherent detectors. With multiple differential detectors they define a receiver structure consisting of a combination of more than one distinct differential detectors each of them employing a progressively increasing (by the symbol duration) time-delay element. The outputs of these detectors are jointly processed by means of an algorithm which is presented in a recursive form. The derivation of this new receiver is general enough to accommodate trellis coded phase shift keying (PSK) and quadrature amplitude modulated (QAM) systems. Differentially encoded signals, such as the /spl pi//4-shift differential quadrature phase shift keying (DQPSK) scheme can also be incorporated. In order to reduce the overall receiver implementation complexity, several reduced complexity, near-optimal versions of the algorithm are presented. These reduced complexity receivers are based on the use of only a few multiple differential detectors. Performance evaluation results for reduced complexity trellis coded /spl pi//4-shift DQPSK, /spl pi//4-shift 8-DQAM (differential quadrature amplitude modulation) and 8-DPSK (differential phase shift keying) systems have demonstrated that the proposed receivers significantly reduce the error floors caused by fading.>

The performance of multilevel quadrature partial-response systems (QPRS) in the presence of an additive combination of Gaussian and impulsive noise is analytically evaluated by means of the Fourier-Bessel series expansion method. The model of the impulsive noise used, which closely models atmospheric and/or man-made electromagnetic interference, consists of a stream of Poisson impulses with areas distributed according to a power Rayleigh probability density function. Analytical performance evaluation results are presented for 9, 49, and 225 QPR systems. The results indicate severe system performance degradation, especially for highly bandwidth-efficient systems (e.g. 225 QPRS), mostly due to the long error bursts caused by the impulsive noise tails. However, a qualitative comparison between these results and the corresponding performance results of equivalent QAM schemes demonstrates that 49 QPRS could be a suitable choice for microwave radio or HDTV satellite links operating in pure impulsive noise environments.>

Closed-form expressions are derived for the error rate performance of coherent M-ary quadrature amplitude modulated (QAM) systems in the presence of an additive combination of Gaussian and highly impulsive noise, using the Fourier-Bessel series expansion method. Analytical as well as computed simulated results are presented for 16, 64, and 256 QAM systems. The analytical results demonstrate a negligible truncation error and an accuracy ranging from approximately 1 dB for the 16 QAM scheme up to 3.5 dB for the 256 QAM scheme. The numerical evaluation of the closed-form expressions has been performed much faster than the corresponding computer simulation procedure. Hence, the validity of Fourier-Bessel analysis as a fast and accurate performance evaluation tool for high-level digital modulation schemes in complex interference environments is verified.>