Categorical attributes with qualitative values are ubiquitous in cluster analysis of real datasets. Unlike the Euclidean distance of numerical attributes, the categorical attributes lack well-defined relationships of their possible values (also called categories interchangeably), which hampers the exploration of compact categorical data clusters. Although most attempts are made for developing appropriate distance metrics, they typically assume a fixed topological relationship between categories when learning distance metrics, which limits their adaptability to varying cluster structures and often leads to suboptimal clustering performance. This paper, therefore, breaks the intrinsic relationship tie of attribute categories and learns customized distance metrics suitable for flexibly and accurately revealing various cluster distributions. As a result, the fitting ability of the clustering algorithm is significantly enhanced, benefiting from the learnable category relationships. Moreover, the learned category relationships are proved to be Euclidean distance metric-compatible, enabling a seamless extension to mixed datasets that include both numerical and categorical attributes. Comparative experiments on 12 real benchmark datasets with significance tests show the superior clustering accuracy of the proposed method with an average ranking of 1.25, which is significantly higher than the 5.21 ranking of the best-performing methods. Code and extended version with detailed proofs are provided online.

Pre-trained vision-language (V-L) models exhibit promising performance across various general-domain tasks. However, they fall short in medical pathology due to the critical need for fine-grained semantic alignment, which is essential for distinguishing subtle visual patterns across categories. This limitation is not merely due to domain gaps but stems from the inability to capture detailed, pathology-specific semantics. Previous efforts leveraging large language models (LLMs) or cross-modal training often introduce redundant or ambiguous cues, ultimately weakening generalization. To explicitly enhance fine-grained alignment, we propose a Variational Distillation framework tailored for Medical Pathology V-L models. This method introduces a dual-loop optimization mechanism that jointly distills and aligns semantic signals from both textual inputs and external LLM knowledge. Specifically, we use variational latent distributions to model semantic ambiguity and apply a KL-based loss to reduce differences between signals. This encourages the model to retain robust and generalizable features, enabling improved sensitivity to subtle semantic variations critical in pathology image understanding. During cross-modal alignment, the proposed method further amplifies modality-shared semantics while suppressing modality-specific noise and task-irrelevant factors, yielding more precise and pathology-aware image-text matching. Extensive experiments on five pathology benchmarks across three settings, including class generalization, few-shot learning, and cross-organ transfer, demonstrate that the proposed method consistently outperforms the existing approaches.

Spatial transcriptomics (ST) technologies have transformed our understanding of tissue biology by capturing gene expression with spatial context, enabling systematic analysis of cell-cell interactions (CCIs) and spatial domains in complex tissues. However, existing computational approaches often rely on fixed proximity graphs, curated ligand-receptor (LR) databases, or deep graph neural networks that are prone to over-smoothing and lack principled uncertainty quantification. These limitations hinder the discovery of heterogeneous, directional, and long-range CCIs essential for interpreting tissue organization and disease mechanisms. Here, we present B-HGME (Bayesian Hyperspherical Graph Mixture of Experts), a scalable, unsupervised framework that jointly delineates spatial domains and infers CCI networks from ST data with principled uncertainty estimates. B-HGME integrates spatial and gene-regulatory graphs into a dual-scale structure, encodes cell representations on a unit hypersphere via coupled message passing, and decodes edges using a Bayesian mixture-of-experts governed by a Dirichlet-regularized gating network. This design enables the model to capture multi-scale, directional, and biologically coherent interactions while avoiding the over-smoothing and posterior collapse of conventional models. The hyperspherical embedding geometry ensures angular similarity is preserved in high dimensions, and edge-level credibility is derived from the Bayesian posterior, facilitating interpretable and confident CCI inference. Across multiple datasets from six major ST platforms, B-HGME consistently achieves state-of-the-art spatial clustering accuracy and uncovers biologically coherent and diverse CCIs, including novel interactions beyond curated ligand-receptor pairs. B-HGME's hyperspherical embeddings accurately localize canonical astrocytic and laminar markers (e.g.,Gfap,Pcp4,Calb1, andCamk2a) to their expected spatial niches, confirming biochemical fidelity at single-gene resolution. Inferred ligand-receptor circuits not only recover known pathways but also reveal previously uncharacterized interactions (e.g., Astro1-eL2/3, VIP-Oligo), furnishing mechanistic hypotheses for cortical layer formation and tumor-stroma crosstalk. Together, these results demonstrate that B-HGME offers a powerful tool for spatial systems biology and hypothesis generation in development, immunity, and cancer. The source code of our model is available athttps://github.com/zxj8806/B-HGME.

The rapid integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies has led to the pro liferation of AIoT applications, significantly escalating demands for computing and communication resources in multi-user, multitask scenarios. A critical challenge lies in efficiently managing resource allocation to ensure Quality of Service (QoS) for diverse Deep Neural Network (DNN) inference tasks. Existing edge cloud collaborative inference approaches partially address this by hierarchical resource management; however, these methods often overlook the joint optimization of computation, communication, and data quality, and neglect long-term system stability in dynamic environments. To address these limitations, we propose EdgeManager, an online adaptive resource management frame work for hierarchical DNN inference in collaborative heterogeneous edge environments. Specifically, we formulate a Mixed Integer Nonlinear Programming (MINLP) optimization problem aimed at balancing inference accuracy and latency. Leveraging Lyapunov optimization, we transform the complex, multi-stage dynamic optimization problem into manageable deterministic sub-problems for each time slot, ensuring long-term stability. Furthermore, we introduce HyDRL-MO, a hybrid approach integrating model-free Deep Reinforcement Learning (DRL) and model-based multi-decision optimization techniques to achieve efficient and stable resource allocation. Extensive experimental evaluations demonstrate that EdgeManager significantly improves system performance, achieving up to 42.08% enhancement in average system benefits compared to state-of-the-art solutions.

Person detection in videos is vital for area admission and public safety. Existing studies have made significant progress in improving the accuracy of this task on the cloud. Meanwhile, with people's increasing awareness of privacy protection, there is a surging demand for privacy not being transmitted and processed by the cloud. Thus, providing services on edges becomes a promising solution. The dilemma is that edges are typically resource-constrained and cannot support the deployment of large models. However, tiny models that fit resource-constrained edges generally have unsatisfactory performance in accuracy and efficiency. To this end, we propose a Logical Correction Enabled Collaborative Person Detection Inference (LC-CPDI) framework for resource-constrained edges. First, we formulate the problem studied with a delay minimization objective. Second, we design a logical correction scheme to perceive abnormal predictions and perform corrections to improve accuracy. Third, a hybrid position prediction algorithm is proposed to replace time-consuming inference for simple scenarios. Finally, we design a collaborative inference scheme that enables frame outsourcing to idle edges to reduce the inference delay. We implemented LC-CPDI on a testbed designed with commercial edges. The experiments on real-world datasets show the effectiveness of LC-CPDI with up to 41.8% delay reduction on average and near 2% recall improvement.

Link prediction (LP) is fundamental to graph-based applications, yet existing graph autoencoders (GAEs) and variational GAEs (VGAEs) often struggle with intrinsic graph properties, particularly the presence of negative eigenvalues in adjacency matrices, which limits their adaptability and predictive performance. To address this limitation, we propose Hyperspherical Kolmogorov-Arnold Networks for LP (HKANLP), a novel framework that combines multiple graph neural network (GNN)-based representation learning strategies with Kolmogorov-Arnold networks (KANs) in a hyperspherical embedding space. Specifically, our model leverages the von Mises-Fisher (vMF) distribution to impose geometric consistency in the latent space and employs KANs as universal function approximators to reconstruct adjacency matrices, thereby mitigating the impact of negative eigenvalues and enhancing spectral diversity. Extensive experiments on homophilous, heterophilous, and large-scale graph datasets demonstrate that HKANLP achieves superior LP performance and robustness compared to state-of-the-art baselines. Furthermore, visualization analyses illustrate the model's effectiveness in capturing complex structural patterns. The source code of our model is publicly available at https://github.com/zxj8806/HKANLP/.

Existing studies on federated learning (FL) are mostly focused on system orchestration forstatic snapshotsof the network and makingstatic control decisions(e.g., spectrum allocation). However, real-world wireless networks are susceptible totemporal variationsof wireless channel capacity and users’ datasets. In this paper, we study the impacts of the dynamics: 1) wireless channels and 2) users’ datasets on the FL execution. The former is captured by introducing a set of discrete time events while the latter is characterized by a novelordinary differential equationand the metric ofdynamic model drift, formulated via apartial differential inequality, drawing concrete analytical connections between the dynamics of users’ datasets and FL accuracy. We then proposedynamiccooperative FLwith dedicatedMAC schedulers (DCLM), exploiting the unique features of open radio access network (O-RAN) to execute FL.DCLMentails: 1) a hierarchical device-to-device (D2D)-assisted model training; 2) dynamic control decisions through dedicated O-RAN MAC schedulers; and 3) asymmetric user selection. We provide extensive theoretical analysis to study the convergence ofDCLMand then aim to optimize its degrees of freedom (e.g., user selection and spectrum allocation) through a non-convex optimization problem. We develop a systematic and generic approach to obtain the solution for this problem. We finally show the efficiency ofDCLMvia numerical simulations and provide a series of future directions.

Medical AI models have achieved remarkable progress in various tasks, including medical image classification and segmentation. However, a critical issue frequently overlooked in clinical applications is the fairness of these models across different subgroups. Existing data collection strategies typically prioritize balancing disease categories while neglecting the importance of subgroup balance based on factors such as age and gender. This imbalance leads to the model's inconsistent performance across subgroups, thereby limiting its clinical applicability. This issue is particularly pronounced in skin cancer diagnosis due to the entanglement and complexity of feature patterns in skin cancer images. In this article, we approach the problem from a new perspective: making the feature patterns extracted by the model more independent and enhancing the representational ability of subgroups. We propose a novel framework that enhances feature independence across channels and improves subgroup representation. Central to our design is the Feature Pattern Separating Module (FPSM), combined with dual subgroupspecific classifiers and an auxiliary subgroup-type predictor. A diverse objective optimization model was introduced to guide the joint optimization of classification accuracy and subgroup fairness. Additionally, we proposed a novel classification loss function to dynamically balance the loss between subgroups. Experiments on two public skin cancer datasets demonstrate that our method improves both overall performance and fairness across subgroups, outperforming existing methods and offering a promising solution for fairer medical AI.

Deep neural networks (DNNs) have demonstrated exceptional performance across a variety of applications, yet they require substantial computing and power resources. In contrast, Spiking Neural Networks (SNNs) offer significant potential for energy-efficient computing due to their binary, event-driven properties. However, existing deep generative SNNs often struggle to produce high-quality low-dimensional representations in latent space, adversely affecting the quality of their generated samples. To address this limitation, we introduce a novel generative model that integrates the principles of variational autoencoders (VAEs) with adversarial training techniques. Our model consists of a generative module based entirely on an SNN-structured VAE and a discriminator employing artificial neural networks. This discriminator enhances the training process of our SNN-based generative model by applying adversarial principles similar to those used in Generative Adversarial Networks (GANs). Additionally, we have developed a conditional generation capability within our model, enabling the controlled production of specific images based on label inputs. Experimental evaluations on multiple datasets demonstrate that our model achieves superior image quality compared to existing top-performing SNN-based generative models. The source code of our model is accessible at https://github.com/zxj8806/SGM-VaGAN.

In extreme scenarios such as nighttime or low-visibility environments, achieving reliable perception is critical for applications like autonomous driving, robotics, and surveillance. Multi-modality image fusion, particularly integrating infrared imaging, offers a robust solution by combining complementary information from different modalities to enhance scene understanding and decision-making. However, current methods face significant limitations: GAN-based approaches often produce blurry images that lack fine-grained details, while AE-based methods may introduce bias toward specific modalities, leading to unnatural fusion results. To address these challenges, we propose DAE-Fuse, a novel two-phase discriminative autoencoder framework that generates sharp and natural fused images. Furthermore, We pioneer the extension of image fusion techniques from static images to the video domain while preserving temporal consistency across frames, thus advancing the perceptual capabilities required for autonomous navigation. Extensive experiments on public datasets demonstrate that DAE-Fuse achieves state-of-the-art performance on multiple benchmarks, with superior generalizability to tasks like medical image fusion.

Temporal graph link prediction aims to predict future interactions between nodes in a graph based on their historical interactions, which are encoded in node embeddings. We observe that heterogeneity naturally appears in temporal interactions, e.g., a few node pairs can make most interaction events, and interaction events happen at varying intervals. This leads to the problems of ineffective temporal information encoding and forgetting of past interactions for a pair of nodes that interact intermittently for their link prediction. Existing methods, however, do not consider such heterogeneity in their learning process, and thus their learned temporal node embeddings are less effective, especially when predicting the links for infrequently interacting node pairs. To cope with the heterogeneity, we propose a novel framework called TAMI, which contains two effective components, namely log time encoding function (LTE) and link history aggregation (LHA). LTE better encodes the temporal information through transforming interaction intervals into more balanced ones, and LHA prevents the historical interactions for each target node pair from being forgotten. State-of-the-art temporal graph neural networks can be seamlessly and readily integrated into TAMI to improve their effectiveness. Experiment results on 13 classic datasets and three newest temporal graph benchmark (TGB) datasets show that TAMI consistently improves the link prediction performance of the underlying models in both transductive and inductive settings. Our code is available at https://github.com/Alleinx/TAMI_temporal_graph.

Semi-supervised learning (SSL) has been widely used to learn from both a few labeled images and many unlabeled images to overcome the scarcity of labeled samples in medical image segmentation. Most current SSL-based segmentation methods use pixel values directly to identify similar features in labeled and unlabeled data. They usually fail to accurately capture the intricate attachment structures in the left atrium, such as the areas of inconsistent density or exhibit outward curvatures, adding to the complexity of the task. In this paper, we delve into this issue and introduce an effective solution, CORAL(Correlation-Aligned)-Correlation Consistency Network (CORN), to capture the global structure shape and local details of Left Atrium. Diverging from previous methods focused on each local pixel value, the CORAL-Correlation Consistency Module (CCM) in the CORN leverages second-order statistical information to capture global structural features by minimizing the distribution discrepancy between labeled and unlabeled samples in feature space. Yet, direct construction of features from unlabeled data frequently results in "Sample Selection Bias", leading to flawed supervision. We thus further propose the Dynamic Feature Pool (DFP) for the CCM, which utilizes a confidence-based filtering strategy to remove incorrectly selected features and regularize both teacher and student models by constraining the similarity matrix to be consistent. Extensive experiments on the Left Atrium dataset have shown that the proposed CORN outperforms previous state-of-the-art semi-supervised learning methods.

To accommodate the explosive growth of the Internet of Things (IoT), incorporating interference alignment (IA) into existing multiple access (MA) schemes is under investigation. However, when it is applied in MIMO networks to improve the system capacity, the new problem regarding information delay arises which does not meet the requirement of low-latency. Therefore, in this paper, we first propose a new metric named degree of delay (DoD) to quantify the issue of information delay. By analyzing DoD with classical transmission schemes, it can be seen that the information latency does affect the performance of the system. To cope with this issue, hybrid antenna array based partial interference elimination and retrospective interference regeneration scheme (HAA-PIE-RIR) is first proposed. It achieves optimal performance in 2-user MIMO scenarios, but suffers a performance loss in$K$-user MIMO scenarios. Then, the improved HAA-PIE-RIR scheme (HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme (HAA-CIE-RIR) are proposed. The former achieves optimal performance in$K$-user MIMO scenarios, but requires heavy computational cost. The latter is a trade-off scheme considering performance and computational cost comprehensively. Overall, our proposed schemes can obtain lower DoD and higher DoF than that of traditional IA schemes.

Recent rapid interest in renewable energy generation, especially high penetration of grid-connected renewable energy system, is imposing new challenges to the harmonic model and propagation in power system. In practice, there is a potential risk of harmonic interaction resonance and harmonic propagation. It is of great importance to identify which kind of harmonic model and harmonic propagation are for further harmonic analysis. This review will help reader to understand the following important questions: How to model the harmonics generation from power converters? How to model the transportation of harmonics in power system? How the harmonic affect the power quality. It will provide a useful reference to researchers and engineers who concern about harmonic problems resulted from the grid-connected renewable energy systems.

Spectral efficiency, energy efficiency, and security are cornerstones for the upcoming 5G systems. In this study, the issue of how the energy and spectral efficiency of multiuser massive multiple‐input multiple‐output (Ma‐MIMO) systems are affected in the presence of a secrecy constraint is addressed. The performance of the two most prominent linear precoding techniques, the matched filter (MF) and zero‐forcing (ZF) precoders, for secure downlink multiuser Ma‐MIMO in the presence of multi‐antenna passive eavesdropper is investigated. The authors consider three performance metrics, namely, the achievable ergodic secrecy rate, the secrecy spectral efficiency (SSE), and the secrecy energy efficiency (SEE), assuming perfect and imperfect channel state information. The tradeoff between SSE and SEE is also studied. Moreover, the authors derive tight lower bounds on the achievable ergodic secrecy rate for MF and ZF precoding techniques. The derived lower bounds provide insights on the tradeoff between the SSE and SEE. It is shown that ZF precoder outperforms MF precoder at high transmit power, whereas at very low transmit power, MF outperforms ZF. Moreover, it is shown that using large number of transmit antennas can improve the SSE and the SEE with orders of magnitude compared to a single‐input single‐output system.

In this paper, an optimal power and time assignment strategy is developed for hybrid automatic-repeat-request (H-ARQ) communication protocol over quasi-static Rayleigh fading channels. For any given average total time duration and energy budget, we try to find the sequences of power values and time durations for H-ARQ retransmission rounds that minimize the outage probability of the H-ARQ protocol. We solve the joint optimization of power and time assignment for the H-ARQ protocol and derive a set of equations that describe the optimal transmission power and time duration sequence which enable a recursive calculation. Numerical results show that the performance of the proposed optimal-time and optimal-power assignment scheme is substantially better than that of the conventional equal-time and equal-power scheme as well as the equal-time and optimal-power scheme. For example, when the maximum transmission number is L=2 and the target data amount per packet is D_0=2000 bits, the optimal-time and optimal-power assignment scheme saves about 28dB in terms of the average energy consumption.

It is known that copy number variations (CNVs) are associated with complex diseases and particular tumor types, thus reliable identification of CNVs is of great potential value. Recent advances in next generation sequencing (NGS) data analysis have helped manifest the richness of CNV information. However, the performances of these methods are not consistent. Reliably finding CNVs in NGS data in an efficient way remains a challenging topic, worthy of further investigation. Accordingly, we tackle the problem by formulating CNVs identification into a quadratic optimization problem involving two constraints. By imposing the constraints of sparsity and smoothness, the reconstructed read depth signal from NGS is anticipated to fit the CNVs patterns more accurately. An efficient numerical solution tailored from alternating direction minimization (ADM) framework is elaborated. We demonstrate the advantages of the proposed method, namely ADM-CNV, by comparing it with six popular CNV detection methods using synthetic, simulated, and empirical sequencing data. It is shown that the proposed approach can successfully reconstruct CNV patterns from raw data, and achieve superior or comparable performance in detection of the CNVs compared to the existing counterparts.

This paper proposes novel transmission schemes for a class of interference networks that can achieve new tradeoff regions between the sum of degrees of freedom (sum-DoF) and channel state information (CSI) feedback delay with distributed and temperately-delayed CSI at the transmitter (CSIT). A significant impact of the results is they reveal that distributed and temperately-delayed CSIT contributes to achieve better sum-DoF than that without CSIT in a certain class of interference networks. Specifically, a distributed space-time interference alignment (STIA) scheme is proposed for the two-user multiple-input multiple-output (MIMO) X channel via a novel precoding method called Cyclic Zero-padding. The achieved sum-DoFs herein for certain antenna configurations are greater than the best known sum-DoFs in literature with delayed CSIT. Furthermore, we propose a distributed retrospective interference alignment (RIA) scheme that achieves more than 1 sum-DoF for the K-user single-input single-output (SISO) X network. Finally, we extend the distributed STIA to the M×N user multiple-input single-output (MISO) X network, where each transmitter has N - 1 antennas and each receiver has a single antenna, yielding the same sum-DoF as that in the global and instantaneous CSIT case. The discussion and result of the MISO X network can be extended to the MIMO case due to the spatial scale invariance property.

We design, implement, and evaluate an acoustic receiver structure for distributed multi-input and multi-output (MIMO) underwater systems that accounts for multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs) encountered in real deployments of underwater communication systems. We focus on challenging practical issues that arise in underwater acoustic sensor network setups where co-located multi-antenna sensor deployment is not feasible due to power, computation, and hardware limitations. In this paper, we utilize distributed underwater sensors to form virtual MIMO underwater systems without requiring frequency or time synchronization. The proposed receiver consists of a bank of matched filters (one per effective CFO) at each receive antenna, followed by an information symbol detector. Each filter in the bank is sampled at the symbol rate with sampling timing selected according to the corresponding TO. We evaluate in real-time the performance of our algorithmic developments in a software-defined underwater testbed that utilizes in-house built software-defined acoustic modems (SDAMs). Experimental studies in both indoor, lab-controlled (tank) and outdoor (lake) real-world environments demonstrate superior bit-error-rate (BER) receiver performance compared to receiver designs that are not able to accommodate multiple CFOs and multiple TOs.

We propose a novel receiver design for distributed MIMO systems that accounts for multiple carrier frequency offsets (CFOs) and multiple timing offsets (TOs). The proposed structure utilizes a bank of pulse matched filters (one per effective CFO) at each receive antenna, followed by an information symbol detector. Each filter in the bank is sampled at the symbol rate with sampling timing selected according to the corresponding TO. For the proposed receiver configuration, we derive the maximum likelihood (ML) detector. Our theoretical developments are illustrated through extensive simulation studies and indicate that the proposed receiver structure together with the optimal ML detection offers significant performance gains compared to the current state of the art.

We present Q2P, a system that discovers query templates from search engines via their query autocompletion services. Q2P is distinct from the existing works in that it does not rely on query logs of search engines that are typically not readily available. Q2P is also unique in that it uses a trie to economically store queries sampled from a search engine and employs a beam-search strategy that focuses the expansion of the trie on its most promising nodes. Furthermore, Q2P leverages the trie-based storage of query sample to discover query templates using only two passes over the trie. Q2P is a key part of our ongoing project Deep2Q on a template-driven data integration on the Deep Web, where the templates learned by Q2P are used to guide the integration process in Deep2Q. Experimental results on four major search engines indicate that (1) Q2P sends only a moderate number of queries (ranging from 597 to 1,135) to the engines, while obtaining a significant number of completions per query (ranging from 4.2 to 8.5 on the average); (2) a significant number of templates (ranging from 8 to 32 when the minimum support for frequent templates is set to 1%) may be discovered from the samples.

Differential amplify-and-forward (DAF) cooperative relaying appears to be an attractive strategy for wireless networks where channel estimation is not feasible or it is rather avoided. In this work, we intend to determine the optimum relay location in order to minimize the outage probability of the DAF cooperative relaying in case that the system is allowed to deploy the relay. We consider two scenarios. First, we optimize the relay location for the DAF cooperative relaying with any given power assignment at the source and the relay. Second, we further consider relay location optimization with optimum power assignment in which the performance of the DAF relaying is further enhanced with the price of higher complexity. In order to obtain the optimum relay location, we develop a recursive algorithm based on the fix-point theorem and show that the relay location has a unique optimum solution which can be determined recursively from the sequence developed in our theorem. We also study the impact of the path-loss exponent to the optimal relay location and found that the relay should be deployed closer to the source in lossy environment such as near-ground environment than that in the free space environment. Extensive numerical results are provided to validate and illustrate the theoretical development.

BACKGROUND: Classifying cancers by gene selection is among the most important and challenging procedures in biomedicine. A major challenge is to design an effective method that eliminates irrelevant, redundant, or noisy genes from the classification, while retaining all of the highly discriminative genes. RESULTS: We propose a gene selection method, called local hyperplane-based discriminant analysis (LHDA). LHDA adopts two central ideas. First, it uses a local approximation rather than global measurement; second, it embeds a recently reported classification model, K-Local Hyperplane Distance Nearest Neighbor(HKNN) classifier, into its discriminator. Through classification accuracy-based iterations, LHDA obtains the feature weight vector and finally extracts the optimal feature subset. The performance of the proposed method is evaluated in extensive experiments on synthetic and real microarray benchmark datasets. Eight classical feature selection methods, four classification models and two popular embedded learning schemes, including k-nearest neighbor (KNN), hyperplane k-nearest neighbor (HKNN), Support Vector Machine (SVM) and Random Forest are employed for comparisons. CONCLUSION: The proposed method yielded comparable to or superior performances to seven state-of-the-art models. The nice performance demonstrate the superiority of combining feature weighting with model learning into an unified framework to achieve the two tasks simultaneously.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e. equal time duration is assigned to each source and each relay, in this work we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. We jointly optimize the power and time allocations for the cooperative relaying protocol such that the outage probability of the protocol is minimized. Specifically, for any given time allocation, we are able to determine the corresponding optimum power allocation analytically with a closed-form expression. We also show that in order to minimize the outage probability of the protocol, one should always allocate more energy and time to the source than the relay. Extensive numerical and simulation studies illustrate our theoretical developments.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e. equal time duration is assigned to each source and each relay, we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. In our recent work [18], we explored an ideal non-equal-time cooperative relaying protocol where the system can use arbitrary re-encoding methods at the relay and adjust time allocation arbitrarily, in which we showed that with optimum power and time allocation the cooperative relaying protocol has significant performance improvement compared to the equal-time relaying protocol. In this paper, with more realistic consideration, we design a practical cooperative relaying protocol based on linear mapping, i.e. using linear mapping as the re-encoding method at the relay and considering integer time slots in the two phases. Furthermore, we develop an optimum linear mapping to minimize the outage probability of the linear-mapping based cooperative relaying protocol. Numerical and simulation results show that the performance of the proposed cooperative relaying protocol based on the optimum linear mapping is close to the performance benchmark of the ideal cooperative protocol.

In this paper, we systematically analyze the outage probability performance of the distributed cooperative multicast with randomized orthogonal space-time codes (OSTC) for wireless multimedia networks. The distributed cooperative multicast protocol with randomized OSTC enables users who successfully decode signals from base station or access point serving as relays to forward the signals to other users, and consequently the overall performance of wireless multimedia multicast can be greatly improved. We derive a closed-form expression for the outage probability for the distributed cooperative multicast with randomized OSTC. To get more insight, we further develop an asymptotically tight approximation for the outage probability which reveals the diversity order of the protocol as K + 1, where K is the size of the randomized OSTC used in the protocol. Based on the asymptotically tight approximation of the outage probability, we are able to determine an optimum power allocation for the distributed cooperative multicast protocol. It shows that in a statistic sense, we should allocate 1/(K + 1) of the total power budget to the BS/AP and evenly distribute the rest for the users as relaying power. Numerical and simulation results validate our theoretical development.

Cooperative communication has emerged as a new wireless network communication concept, in which parameter optimization such as power budget and time allocation plays an important role in cooperative relaying protocol designs. While most existing works on cooperative relaying protocol designs considered equal-time allocation scenario, i.e., equal time duration is assigned to each source and each relay, in this work we intend to design and optimize cooperative communication protocols by exploring all possible variations in time and power domains. We consider a cooperative relaying network in which no channel state information (CSI) is available at the transmitter side and the protocol optimization is based on channel statistics (i.e., mean and variance) and it does not depend on instantaneous channel information. First, we consider an ideal cooperative relaying protocol where the system can use arbitrary re-encoding methods at the relay and adjust time allocation arbitrarily. We obtain an optimum strategy of power and time allocations to minimize the outage probability of the ideal cooperative protocol. Specifically, for any given time allocation, we are able to determine the corresponding optimum power allocation analytically with a closed-form expression. We also show that to minimize the outage probability of the protocol, one should always allocate more energy and time to the source than the relay. Second, with more realistic consideration, we design a practical cooperative relaying protocol based on linear mapping, i.e., using linear mapping as the re-encoding method at the relay and considering integer time slots in the two phases. The theoretical results from the ideal cooperative protocol serve as a guideline and benchmark in the practical cooperative protocol design. We also develop an optimum linear mapping to minimize the outage probability of the linear-mapping based cooperative protocol. Extensive numerical and simulation studies illustrate our theoretical developments and show that the performance of the proposed cooperative relaying protocol based on the optimum linear mapping is close to the performance benchmark of the ideal cooperative protocol.

We develop a new 4 × 4 Hadamard-precoded quasi-orthogonal space-time block code (QO-STBC) that enables highly effective near-maximum-likelihood (near-ML) reliability-based prioritized symbol detection using linear filters. Approximate block-error-rate minimization is being used to optimize the code rotation angle. Detailed computational complexity evaluation of the decoder in terms of real multiplications and additions shows significant complexity reduction for symbol alphabet sizes of interest. Numerical and simulation studies demonstrate negligible bit-error-rate degradation compared to the state-of-the-art in bit-error-rate by ML decoded 4 × 4 codewords.

In this paper, the capacity of airborne multiple-input-multiple-output (MIMO) wireless communication systems with arbitrary alignments of linear transmit and receive antenna arrays is systematically analyzed and the maximum achievable capacity is determined. Based on a general three-dimensional (3D) airborne MIMO communication model, we are able to approximate the airborne MIMO capacity as a function of the transmit and receive antenna array geometry in the 3D space. The capacity approximation is asymptotically tight as the distance between the transmit and receive antenna arrays large compared to their size. Based on the asymptotically tight capacity approximation, we derive an upper bound as well as a lower bound of the airborne MIMO capacity. Interestingly, both the upper and lower bounds are achievable. We also derive a necessary and sufficient condition for airborne MIMO communication systems to achieve the capacity upper bound for any given 3D transceiver antenna array geometry. The necessary and sufficient condition allows us to properly select the system parameters and design airborne MIMO communication systems that reach the best possible performance in terms of system capacity. We prove that when the distance between the transmit and receive antenna arrays is within a certain range, there exists a set of system parameter values (e.g. antenna element separation) for which the capacity of the MIMO communication system achieves the theoretical upper bound and this capacity value is larger than the average capacity of the corresponding conventional MIMO communication system under Rayleigh fading. Finally, we prove that the airborne MIMO capacity converges to the capacity lower bound when the distance between the transmit and receive antenna arrays goes to infinity. Extensive numerical studies included in this paper illustrate and validate our theoretical developments.

Users submit queries to an online database via its query interface. Query interface parsing, which is important for many applications, understands the query capabilities of a query interface. Since most query interfaces are organized hierarchically, we present a novel query interface parsing method, StatParser (Statistical Parser), to automatically extract the hierarchical query capabilities of query interfaces. StatParser automatically learns from a set of parsed query interfaces and parses new query interfaces. StatParser starts from a small grammar and enhances the grammar with a set of probabilities learned from parsed query interfaces under the maximum-entropy principle. Given a new query interface, the probability-enhanced grammar identifies the parse tree with the largest global probability to be the query capabilities of the query interface. Experimental results show that StatParser very accurately extracts the query capabilities and can effectively overcome the problems of existing query interface parsers.

Location-based social networks, such as Foursquare and Facebook Places, are bridging the gap between the physical world and online social networking services through acquired user locations. Some social networks released check-in services that allow users to share their visiting locations with their friends. In this paper, users' interests are modeled by check-in actions. We propose a new spatial-aware interest group (SIG) query that retrieves a user group of size k where every user is highly interested in the query keyword and also spatially close to each other. An efficient algorithm AIR based on the IR-tree is proposed for the processing of SIG queries. Furthermore, an optimization is developed and achieves a much better performance than the baseline algorithm.

In this paper, a penalty-based distributed algorithm is proposed to design beamforming for multi-source multi-destination (MSMD) networks. The objective of the beamforming design is to minimize the transmission power of each source under the constraint that the signal-to-interference-plus-noise ratio (SINR) requirements of all source-destination pairs are satisfied. The proposed beamforming design has better power-efficiency compared to the egoism, altruism, and balancing algorithms. The convergence of the proposed algorithm is also discussed and a sufficient condition is determined for the algorithm to converge to a Pareto optimal solution. Simulation results show that the proposed algorithm has higher probability of convergence compared to the egoism, altruism, and balancing algorithms, which is close to a centralized performance benchmark.

In this paper, we develop a methodology to analyze systematically the capacity of airborne MIMO wireless communication systems with linear transmit and receive antenna arrays. Our methodology is based on a general three-dimensional airborne MIMO communication model that we develop to accommodate arbitrary alignments of the transmit and receive antenna arrays. Specifically, we determine an upper bound and a lower bound for the airborne MIMO capacity. Then, we derive a necessary and sufficient condition for airborne MIMO communication systems to achieve the capacity upper bound. The necessary and sufficient condition allows us to properly select the system parameters and design airborne MIMO communication systems that reach the best possible performance in terms of system capacity. We prove that when the distance between the transmit and receive antenna arrays is within a certain range, there exists a set of system parameter values for which the capacity of the MIMO communication system achieves the theoretical upper bound and this capacity value is larger than the average capacity of the corresponding conventional MIMO communication system with Rayleigh fading. We also show that asymptotically, as the distance between the transmit and receive antenna arrays goes to infinity, the capacity of the airborne MIMO system converges to the capacity lower bound. Extensive numerical studies included in this paper illustrate and validate our theoretical developments.

In this paper, the optimal power assignment strategy is determined for hybrid automatic-repeat-request (H-ARQ) protocols such that the average delay of the protocol is minimized for any given total transmission power budget and any targeted outage probability. A set of equations is derived that describe the optimal transmission power sequence and its optimality is shown based on the Karush-Kuhn-Tucker (KKT) Theorem. The set of equations enables an exact recursive calculation of the optimal transmission power per round, and the calculation complexity is fixed regardless of the maximum number of (re)transmission rounds allowed in the H-ARQ protocol. Compared to the conventional equal power assignment strategy, the optimal power assignment scheme achieves the same average delay with much less total transmission power. More importantly, for certain power budget levels, the optimal power assignment can make the H-ARQ protocol work while the equal power assignment cannot. Extensive numerical results are presented to illustrate the theoretical development.

We present a new 4×4 Hadamard-precoded Quasi-Orthogonal Space-Time Block Code (QO-STBC) that enables reliability-based prioritized symbol detection using linear filters. Approximate block-error-rate analysis is carried out and used to optimize the code rotation angles. For benchmarking purposes, the maximum-likelihood (ML) code detector is also derived with complexity of the order of joint two-real-symbol decoding. Numerical and simulation studies compare linear filter decoding against ML decoding.

Web databases generate query result pages based on a user's query. Automatically extracting the data from these query result pages is very important for many applications, such as data integration, which need to cooperate with multiple web databases. We present a novel data extraction and alignment method called CTVS that combines both tag and value similarity. CTVS automatically extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table, in which the data values from the same attribute are put into the same column. Specifically, we propose new techniques to handle the case when the QRRs are not contiguous, which may be due to the presence of auxiliary information, such as a comment, recommendation or advertisement, and for handling any nested structure that may exist in the QRRs. We also design a new record alignment algorithm that aligns the attributes in a record, first pairwise and then holistically, by combining the tag and data value similarity information. Experimental results show that CTVS achieves high precision and outperforms existing state-of-the-art data extraction methods.

In this paper, we consider the problem of designing transceiver beamforming vectors for multi-source multi-destination (MSMD) wireless networks such that the transmission power of each source is minimized while the signal-to-interference plus noise ratio (SINR) requirements of all source-destination pairs are satisfied. We propose an efficient iterative algorithm to design the transceiver beamforming vectors and address the convergence of the algorithm. We determine a necessary condition as well as a sufficient condition for the algorithm to converge to a generalized Nash equilibrium solution. Especially, if each destination has only one antenna, we obtain a necessary and sufficient condition for the algorithm to converge to a unique generalized Nash equilibrium solution. Simulation results show that the proposed iterative algorithm has higher probability of convergence compared to an iterative waterfilling (IWF) approach. For example, for a system with three source-destination pairs and SINR requirement of 2dB, the probability of convergence is 84% with the proposed algorithm while it is only 66% with the IWF algorithm.

In this work, we consider the problem of assigning optimal transmission power sequence for cooperative hybrid automatic-repeat-request (H-ARQ) relaying protocol over quasi-static Rayleigh fading channels. We try to determine the optimal power sequence by minimizing the average total transmission power that we analyzed in our previous work. However, the closed-form expression of the average total power consumption of the cooperative H-ARQ relaying protocol is complicated in general, so we develop first in this work a simple approximation of the average total transmission power that is tight at high SNR. Then, based on the asymptotically tight approximation, we are able to identify the sequence of power values that minimizes the average total power consumption of the cooperative H-ARQ relaying protocol for any given targeted outage probability. In particular, we derive a set of equations that describe the optimal power level in each (re)transmission and enable its recursive calculation with fixed searching complexity. When the maximum number of (re)transmissions allowed in the protocol is L = 2, we have a closed-form result for the optimal transmission power sequence. The optimal power assignment solution reveals that conventional equal power assignment scheme is not optimal in general. Extensive simulation and numerical results are provided to illustrate and validate the theoretical results.

We address the fundamental problem of identifying the optimal power assignment sequence for hybrid automatic-repeat-request (H-ARQ) communications over quasi-static Rayleigh fading channels. For any targeted H-ARQ link outage probability, we find the sequence of power values that minimizes the average total expended transmission power. We first derive a set of equations that describe the optimal transmission power assignment and enable its exact recursive calculation. To reduce calculation complexity, we also develop an approximation to the optimal power sequence that is close to the numerically calculated exact result. The newly founded power allocation solution reveals that conventional equal-power H-ARQ assignment is far from optimal. For example, for targeted outage probability of 10-3with a maximum of two transmissions, the average total transmission power with the optimal assignment is 9 dB lower than the equal-power protocol. The difference in average total power cost grows further when the number of allowable retransmissions increases (for example, 11 dB gain with a cap of 5 transmissions) or the targeted outage probability decreases (27 dB gain with outage probability 10-5and transmissions capped at 5). Interestingly, the optimal transmission power assignment sequence is neither increasing nor decreasing; its form depends on given total power budget and targeted outage performance levels. Extensive numerical and simulation results are presented to illustrate the theoretical development.

In this paper, the total transmission power of a multi-source multi-destination relay network is minimized under the constraint that the signal to interference plus noise ratio (SINR) requirement of each source-destination pair is satisfied. The optimization problem involves K power variables, where K is the number of source-destination pairs in the network, and an exhaustive search is prohibitive for large K. In this work, we develop an asymptotically tight approximation of the SINR that allows us to reformulate the original optimization problem to a single-variable optimization problem, which can be easily solved by numerical search of the single variable. Then, the corresponding optimal transmission power at each source and relay can be calculated directly. The proposed optimization scheme is scalable and leads to a power assignment algorithm that exhibits the same optimization complexity for any number (K) of source-destination pairs in the network. Moreover, for the special case of transmission over orthogonal channels, we derive analytically the solution to the optimization problem. Extensive numerical studies illustrate our theoretical developments.

In this paper, we present the lowest-computational-complexity maximum-likelihood (ML) decoder known to-date for 4×4 full-diversity Quasi-Orthogonal Space-Time Block Codes (QO-STBC) with symbols from square or rectangular quadrature amplitude modulation (QAM) constellations. The complexity savings come from a simplified quadratic ML decoding statistic that is being presented and the utilization of the signal points of the QAM constellation. Comparative computational complexity analysis is carried out and a simulation study demonstrates the theoretical equivalence of the proposed and original ML implementation.

In this paper, the average total power consumption per information packet is investigated for a cooperative hybrid automatic-repeat-request (H-ARQ) protocol in a quasistatic Rayleigh fading environment. Specifically, a closed-form expression of the average total transmission power is obtained for the cooperative H-ARQ relay protocol, in which the source may use different transmission power level in different (re-)transmission rounds. The closed-form expression is valid for any maximum number of (re-)transmission rounds L allowed by the protocol and may play a key role thereafter in optimizing power allocation. Since the closed-form expression is complicated for large L, an approximation of the average total transmission power is developed which is asymptotically tight at high SNR. Extensive simulation and numerical results are also provided herein to illustrate and validate the theoretical results.

In this paper, a new analytical approach is developed for the evaluation of the outage probability of decode-and-forward (DF) automatic-repeat-request (ARQ) relaying under packet-rate fading (fast fading) channels. Based on this approach, a closed-form asymptotically tight (as SNR → ∞) approximation of the outage probability is derived, and the diversity order of the DF cooperative ARQ relay scheme is shown to be equal to 2L - 1, where L is the maximum number of ARQ retransmissions. The closed-form expression clearly shows that the achieved diversity is partially due to the DF cooperative relaying and partially due to the fast fading nature of the channels (temporal diversity). Numerical and simulation studies illustrate the theoretical developments.

In this work, a cognitive cooperative communication protocol is proposed for cognitive ad-hoc networks, in which primary users and cognitive radio (CR) users may cooperate for mutual benefit. The new cooperation protocol allows active cooperation between primary users and CR users in which CR users assist to relay primary users' signals in exchange for some spectrum released from the primary users. While conventional cognitive radios do not guarantee continuous operation of CR users (they use the spectrum only when primary users do not), the protocol proposed in this work provides continuous service for CR users. The proposed cognitive cooperation protocol is optimized in terms of maximizing the primary user's energy savings and the CR user's own data transmission rate. It turns out that the primary users have significantly average energy savings from cooperation (e.g. up to 50% when compared to a non-cooperation case at the same transmission power level), which provides a good incentive for they to cooperate.

For wireless multicast applications where a group of users subscribe to the same service and receive the same data, a promising solution to combat channel fading is to explore the cooperative diversity and let users help each other forward packets. This paper investigates a distributed cooperative multicast scheme that uses a maximal ratio combiner to enhance the received signal-to-noise ratio (SNR), and provides a thorough performance analysis. We derive a close-form formulation of the average outage probability, examine its asymptotic behavior in the high SNR regime, and investigate the optimal power allocation. Our analytical and simulation results show that cooperative multicast performs better in denser networks with more relays helping, and user cooperation can significantly reduce the outage probability, especially in the high SNR region.

Differential Amplify-and-Forward (DAF) relaying can be viewed as an attractive cooperative communication strategy for wireless networks where channel estimation is not feasible or it is rather avoided. A new exact outage probability expression is presented herein for DAF relaying that involves only a single integral. Then, an asymptotically tight closed-form approximation is obtained which enables power allocation optimization for the outage behavior of DAF relaying. Numerical and simulation studies validate the theoretical analysis.

We address the fundamental problem of identifying the optimal power allocation sequence for hybrid automatic-repeat-request (H-ARQ) communications over quasistatic Rayleigh fading channels. For any targeted H-ARQ link outage probability, we find the sequence of power values that minimizes the average total expended transmission power. The newly founded power allocation solution reveals that conventional equal-power H-ARQ assignment is far from optimal. For example, for targeted outage probability of 10-3with a maximum of two transmissions, the average total transmission power with optimal assignment is 9dB lower than the equal-power protocol. The difference in average total power cost grows further when the number of allowable retransmissions increases (for example, lldB gain with a cap of 5 transmissions) or the targeted outage probability decreases (27dB gain with outage probability 10-5and transmissions capped at 5).

In this letter, the outage probabilities of selection relaying protocols are analyzed and compared for cooperative wireless networks. It is assumed that both source and relay use equal allocated time in transmission. Depending on the quality of the source-relay channel, the relay may choose either Decode-and-Forward (DF), Amplify-and-Forward (AF), or Direct-Transmission (DT) to forward signals. It turns out that in terms of outage probability, two selection relaying schemes are better than others: selecting between DF and AF protocols (DF-AF) or selecting between DF and DT protocols (DF-DT). It is shown that with an equal power allocation, both of the DF-AF and DF-DT selection relaying protocols have the same asymptotic outage probability. However, with an optimum power allocation strategy, the DF-AF selection scheme is in general better than the DF-DT selection scheme. Note that the optimum power allocations depend on channel variances, not on instantaneous channel gains. When the quality of the relay-destination link is much better than that of the source-relay link, observed from simulation, the outage probability of the DF-AF selection protocol with its optimum power allocation is 1.5 dB better than that of the DF-DT selection with its own optimum power allocation. Extensive simulations are presented to validate the analytical results.

The Alamouti orthogonal space-time block code for two transmit antennas was designed primarily for QAM and PSK modulations, and we have previously generalized it for the continuous phase modulation (CPM), denoted as OST-CPM, by maintaining the orthogonality (for the fast ML decoding/demodulation) and the phase continuity of two signals from two transmit antennas. In this paper, we design orthogonal-like space-time coded CPM systems for three and four transmit antennas based on orthogonal and quasi-orthogonal space-time codes. Although the signals from transmit antennas in the proposed orthogonal-like space-time coded CPM systems are not orthogonal, the fast decoding/demodulation is maintained like the two transmit antenna case. Simulation results show that the performance of the proposed orthogonal-like space-time coded CPM systems for four transmit antennas is much better than that of the OST-CPM systems for two transmit antennas.

Record matching, which identifies the records that represent the same real-world entity, is an important step for data integration. Most state-of-the-art record matching methods are supervised, which requires the user to provide training data. These methods are not applicable for the Web database scenario, where the records to match are query results dynamically generated on-the-fly. Such records are query-dependent and a prelearned method using training examples from previous query results may fail on the results of a new query. To address the problem of record matching in the Web database scenario, we present an unsupervised, online record matching method, UDD, which, for a given query, can effectively identify duplicates from the query result records of multiple Web databases. After removal of the same-source duplicates, the ¿presumed¿ nonduplicate records from the same source can be used as training examples alleviating the burden of users having to manually label training examples. Starting from the nonduplicate set, we use two cooperating classifiers, a weighted component similarity summing classifier and an SVM classifier, to iteratively identify duplicates in the query results from multiple Web databases. Experimental results show that UDD works well for the Web database scenario where existing supervised methods do not apply.

In this paper we develop a new analytical methodology for the evaluation of the outage probability of cooperative decode-and-forward (DF) automatic-repeat-request (ARQ) relaying under packet-rate fading (fast fading or block fading) channels, where the channels remain fixed within each ARQ transmission round, but change independently from one round to another. We consider a single relay forwarding Alamouti-based retransmission signals in the cooperative ARQ scheme. In particular, (i) we derive a closed-form asymptotically tight (as SNR → ∞) approximation of the outage probability; (ii) we show that the diversity order of the DF cooperative ARQ relay scheme is equal to 2L-1, where L is the maximum number of ARQ (re)transmissions; and (iii) we develop the optimum power allocation for the DF cooperative ARQ relay scheme. The closed-form expression clearly shows that the achieved diversity is partially due to the DF cooperative relaying and partially due to the fast fading nature of the channels (temporal diversity). With respect to power allocation, it turns out that the proposed optimum allocation scheme depends only on the link quality of the channels related to the relay, and compared to the equal power allocation scheme it leads to SNR performance gains of more than 1 dB. Numerical and simulation studies illustrate the theoretical developments.

The popularity of multimedia multicast/broadcast applications over wireless networks makes it critical to address the error-prone, heterogeneous and dynamically changing nature of wireless channels. A promising solution to combat channel fading is to explore the cooperative diversity in which users may help each other forward packets. This paper investigates cooperative multicast schemes that use a maximal ratio combiner to enhance the received signal-to-noise ratio (SNR), and provides a thorough performance analysis. Two relay selection schemes are considered: the distributed and the genie-aided cooperation schemes. We derive the closed-form formulation and the approximations of their average outage probabilities.We also analyze the optimal power allocation and relay location strategies, and show that allocating half of the total transmission power to the source minimizes the average outage probability. Our analysis and simulation results show that cooperative multicast gives better performance when more relays help forward signals. Cooperative multicast helps achieve diversity order 2, and user cooperation can significantly reduce the outage probability, especially in the high SNR region. Finally, we compare the two cooperation strategies, and show that distributed cooperative multicast is preferred since it achieves a lower outage probability without introducing extra overhead for control messages.

Orthogonal designs have received considerable attention in the development of efficient modulation and coding methods for future multi-antenna wireless communication systems due to their special properties. In this paper, we propose a class of space-time block codes constructed by combining orthogonal designs with sphere packing for an arbitrary number of transmit antennas. The structure of the orthogonal designs is exploited to guarantee full diversity, and sphere packing is used to improve the coding advantage. Space-time block code construction from block-orthogonal designs is also considered: the full-diversity property is ensured by rotating the sphere packing underlying the code, and the optimal rotation angle is determined for a class of sphere packing. Code design examples are provided for two and four transmit antennas and various transmission rates. The simulation results show that by jointly designing the symbols in the orthogonal designs, the performance of the block codes can be significantly increased.

Online databases respond to a user query with result records encoded in HTML files. Data extraction, which is important for many applications, extracts the records from the HTML files automatically. We present a novel data extraction method, ODE (Ontology-assisted Data Extraction), which automatically extracts the query result records from the HTML pages. ODE first constructs an ontology for a domain according to information matching between the query interfaces and query result pages from different Web sites within the same domain. Then, the constructed domain ontology is used during data extraction to identify the query result section in a query result page and to align and label the data values in the extracted records. The ontology-assisted data extraction method is fully automatic and overcomes many of the deficiencies of current automatic data extraction methods. Experimental results show that ODE is extremely accurate for identifying the query result section in an HTML page, segmenting the query result section into query result records, and aligning and labeling the data values in the query result records.

This paper addresses the problem of joint optimization of transmit beamforming and space-frequency (SF) coding for MIMO-OFDM systems with spatial correlation feedback in broadband communications. This problem is challenging in the sense that the transmitter should be designed to beamform across multiple eigenspaces associated with the multipath environment simultaneously. With arbitrary transmit spatial correlation, the performance analysis for SF-coded MIMO-OFDM systems with beamforming is provided, and a general optimization problem for the beamforming design is formulated. Three suboptimal approaches to design the beamformer based on the derived design criteria are proposed: i) Eigenvalue selection scheme; ii) Eigenspace selection scheme; and iii) Per-subcarrier approach based on decoding at each subcarrier. The proposed schemes take into account the multiple eigenspace information associated with the multipath-delay channel. Improvement in the performance over SF coding without beamforming is shown through simulations in terms of bit error rate. The Eigenvalue selection scheme provides the best performance among the proposed algorithms. This scheme locates the subspace associated with the largest eigenvalues in the eigenspace of the covariance matrices. With the Eigenvalue selection scheme, the performance improvement is about 3 dB over the SF coding without beamforming for highly correlated channels as shown in our simulations.

An 8times8 two-symbol decodable quasi-orthogonal space-time block code (QO-STBC) is presented which can be transmitted across either 8 or 4 antennas with full rate and the same full diversity order. For the 8-transmit-antenna system, a new expression is developed to identify rotation angles that maximize the diversity (eigenvalue) product. In addition, it is shown that the previously proposed sum-eigenvalue maximization criterion for the design of rotation angles is not relevant/applicable and an alternative minimum eigenvalue maximization criterion is suggested. Finally, new optimal rotation angles are obtained by working directly with a pairwise-error-probability (PEP) upperbound expression. For 4-transmit-antenna systems and correlated channel fading conditions, the PEP-upper-bound is modified accordingly to take into account the channel correlation. Using the new PEP-upper-bound we obtain rotation angles that maximize the diversity product and find, contrary to previous results, that the optimized angles are independent of the correlation coefficient. Simulation studies initiated herein demonstrate the advantage of using the proposed codeword across 4 transmit antennas when compared with other 4times4 QO-STBC transmission schemes. For 8 transmit antennas, the studies compare the three selected rotation angle optimization criteria (diversity product, minimum eigenvalue, PEP-upper-bound).

In this paper; we propose a new cooperative communication protocol, which achieves higher bandwidth efficiency while guaranteeing the same diversity order as that of the conventional cooperative schemes. The proposed scheme considersrelayselectionvia the available partial channel state information (CSI) at the source and the relays. In particular, we discuss the multi-node decode-and-forward cooperative scenarios, where arbitrary N relays are available. The source determines when it needs to cooperate with one relay only, and which relay to cooperate with in case of cooperation, i.e., "Whentocooperate?" and "Whomtocooperatewith?". An optimal relay is the one which has the maximum instantaneous scaled harmonic mean functionof its source-relay and relay-destination channel gains. For the symmetric scenario, we derive an approximate expression of the bandwidth efficiency and obtain an upper bound on the symbol error rate (SER) performance. We show that full diversity is guaranteed and that a significant increase of the bandwidth efficiency is achieved. Moreover, we present the tradeoff between the achievable bandwidth efficiency and the corresponding SER. Finally, the obtained analytical results are verified through computer simulations.

In this paper, a closed-form bit-error-rate (BER) performance analysis is presented for a suboptimum maximum- likelihood (ML) receiver in decode-and-forward (DF) cooperative wireless communications. The suboptimum ML receiver assumes that an average probability of decoding error at the relay is available at the destination, which depends only on the variance of the source-relay channel link. A by-product of the analysis is that we can obtain a BER analytical result for the DF cooperative protocol using an equal-gain-combining receiver at the destination. Simulation results are presented to validate the theoretical analvsis.

We derive a new expression for the rotation angles that maximize the diversity (eigenvalue) product of the 8×8 two-symbol decodable quasi-orthogonal space-time block code (QO-STBC). We show that the previously proposed sum-eigenvalue maximization criterion for the design of rotation angles is not relevant/applicable to the 8-transmit-antenna QO-STBCs and we suggest, instead, minimum eigenvalue maximization. Finally, working directly with the pairwise-error-probability (PEP) upper bound expression, we obtain new true PEP-upper-bound optimal rotation angles. Simulation studies demonstrate and compare the error rate of the three design criteria (diversity product, minimum eigenvalue, PEP upper bound).

In this paper, the performance of optimum maximum-likelihood (ML) receiver is analyzed for the decode-and-forward (DF) cooperative communication protocol in wireless networks. A closed-form bit-error-rate (BER) analysis is presented for the DF cooperative protocol with BPSK modulation and with ML detection at the destination. To further understand the result, we develop an approximation for the BER analysis in a special scenario. Simulation results are presented to validate both the closed-form BER expression and the approximation.

In this letter, a novel approach for outage probability analysis of the multinode amplify-and-forward relay network is provided. It is shown that the harmonic mean of two exponential random variables can be approximated, at high signal-to-noise ratio (SNR), to be an exponential random variable. The single relay case considered before is a special case of our analysis. Based on that approximation, an outage probability bound is derived which proves to be tight at high SNR. Based on the derived outage probability bound, optimal power allocation is studied. Simulation results show a performance improvement, in terms of symbol error rate, of the optimal power allocation compared to the equal power-allocation scheme

To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.

In this paper, we propose a new cooperative communication protocol, which achieves high bandwidth efficiency while guaranteeing full diversity order. The proposed scheme considers relay selection via the available partial channel state information (CSI) at the source and the relays. More precisely, the source determines when it needs to cooperate with one relay only among arbitrary N relays and which relay to cooperate with in case of cooperation, i.e., "When to cooperate?" and "Whom to cooperate with?". In case of cooperation, the source employs the optimal relay, which has the maximum instantaneous scaled harmonic mean function of its source-relay and relay-destination channels' gains. For the symmetric scenario, we prove that full diversity is guaranteed and that a significant increase of the bandwidth efficiency is achieved. Furthermore, we show the tradeoff between the achievable bandwidth efficiency and the corresponding error rate. Finally, the obtained analytical results are verified through computer simulations.

In this paper, we analyze and compare the outage probabilities of selection relaying protocols for cooperative communications in wireless networks. Depending on the quality of the source-relay channel, the relay may choose either decode-and-forward (DF), amplify-and-forward (AF), or direct-transmission (DT) to forward signals. It turns out that in terms of outage probability, two selection relaying protocols are better than others: either selection between DF and AF protocols (DF-AF) or selection between DF and DT protocols (DF-DT). We show that with an equal power allocation at the source and the relay, both the DF-AF and DF-DT selection protocols have the same asymptotic outage probability. However, with an optimum power allocation strategy, the DF-AF selection is in general better than the DF-DT selection. Simulations are also presented to validate the analytical results.

Using query interfaces of different Web databases, we propose a new complex schema matching approach, Parallel Schema Matching (PSM). A parallel schema is formed by comparing two individual schemas and deleting common attributes. The attribute matching can be discovered from the attribute-occurrence patterns if many parallel schemas are available. A count-based greedy algorithm identifies which attributes are more likely to be matched. Experiments show that PSM can identify both simple matching and complex matching accurately and efficiently.

In this paper, we propose a multi-node differential amplify-and-forward scheme for cooperative communications. The proposed scheme efficiently combines signals from the direct and multiple relay links to improve communication reliability. Bit-error-rate (BER) analysis for M-ary differential phase shift keying is provided as performance measure of the proposed scheme, and optimum power allocation is investigated. While the exact BER formulation of the proposed scheme is not available currently, we provide as a performance benchmark a tight BER formulation based on optimum combining weights. A simple BER upper bound and a tight BER approximation show that the proposed scheme can achieve the full diversity which equals to the number of cooperating nodes. We further provide simple BER approximation in order to provide analytical result on power allocation scheme. A closed-form optimum power allocation based on the tight simple BER approximation is obtained for single-relay scenario. An approximate optimum power allocation scheme is provided for multi-relay systems. The provided BER formulations are shown to closely match to the simulation results. Moreover, simulation results show that the optimum power allocation scheme achieves up to 2 dB performance gain over the equal power allocation scheme.

This paper proposes a threshold-based differential decode-and-forward cooperative scheme that efficiently exploits the cooperative relay channels via the use of a pre-determined decision threshold. In the proposed scheme, the source information is forwarded by the relay only if it is correctly decoded. The properly-designed threshold enables the destination to decide whether the received signal from the relay contains information such that the received signals from the source and the relay can be efficiently combined and jointly decoded. The bit error rate (BER) analysis of the proposed scheme is analyzed in case of differential M-ary phase shift keying signals. A tight BER approximation is established, and BER upper bound and lower bound are determined. Based on the tight BER approximation, joint optimum decision threshold and power allocation is numerically evaluated. Both analytical and simulation results reveal that the decision threshold and the power allocation depend on channel link qualities. Interestingly, when the link quality between the relay and the destination is very good, the effect of the threshold dominates the effect of the power allocation at high signal-to-noise ratio. Extensive simulation results are provided to validate the merit of the proposed scheme and confirm the theoretical analysis

In this paper, we consider the outage probability analysis of multi-node amplify-and-forward relay network with N relay nodes helping the source. We consider a system in which each relay node amplifies the source signal only. We obtain an approximation for the outage probability which is tight at high signal-to-noise ratio (SNR). This tight outage approximation shows that the system can achieve a maximum diversity of order N+1. For the case of N = 1, our approach gives the same result obtained previously by Laneman et al. for the single relay scenario.

This paper proposes a threshold-selection relaying scheme, in which each relay decides whether to forward the source information by comparing the received signal power with a decision threshold. The challenging problem is to design the threshold such that the relay is able to forward only correctly decoded information. In this work, bit-error-rate (BER) performance analysis is provided for the proposed scheme with BPSK signals. We develop a BER upper bound which is tight over the entire range of signal-to-noise ratio values. Based on the established BER formulation, we jointly determine optimum decision threshold and power allocation. An interesting result is that the effect of optimum threshold dominates that of optimum power allocation, especially when the relay is close to the destination. For example, in case of equal power allocation, the proposed scheme with optimum threshold yields 10 dB performance improvement over the fixed relaying scheme without Q threshold at a BER of 10-2. If both the power allocation and the threshold are jointly optimized, then the performance of the proposed scheme can be further improved by 2 dB.

Due to limitation on transmitted power level, any UWB system faces major design challenges in achieving wide coverage while assuring an adequate system performance. In this paper, an employment of cooperative communications in UWB is proposed to enhance the performance and the coverage of UWB by exploiting the broadcasting nature of wireless channels and the cooperation among UWB devices. Symbol-error-rate (SER) performance analysis and optimum power allocation are provided for cooperative UWB multiband OFDM systems with decode-and-forward cooperative protocol. To capture the multipath-clustering phenomenon of UWB channels, the SER performance is characterized in terms of cluster and ray arrival rates. An optimum power allocation is determined based on two different objectives, namely minimizing the overall transmitted power and maximizing the system coverage. Furthermore, an improved cooperative UWB multiband OFDM scheme is proposed to take advantage of unoccupied subbands. Simulation results are shown to validate the theoretical analysis.

This paper provides a novel performance analysis for UWB systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize the pairwise error probability and the outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, the effect of random-clustering phenomenon on the performance of UWB-MIMO systems is analyzed. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded OFDM symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis.

This paper provides a novel performance analysis for ultra-wideband (UWB) systems that successfully captures the unique multipath-rich property and multipath-clustering phenomenon of UWB channels. Using the Saleh-Valenzuela model, we characterize pairwise error probability and outage probability for UWB systems employing multiband OFDM based on the cluster arrival rate, the ray arrival rate within a cluster, and the cluster and ray decay factors. Furthermore, an approximation technique is established, which allows us to obtain closed-form performance formulations that provide insightful understanding of the effect of channel characteristics on the performances of UWB systems. Finally, we characterize the effect of random-clustering phenomenon on the performance of UWB-multiple-input-multiple-output systems. The theoretical results reveal that regardless of the clustering behavior of UWB channels, the diversity gain can be improved by increasing the number of jointly encoded subcarriers, the number of jointly encoded orthogonal frequency-division multiplexing symbols, or the number of antennas. The coding gain on the other hand, depends heavily on the cluster-arriving channels. Extensive simulation results are provided to support the theoretical analysis.

In this paper, the effect of a general spatial and temporal fading correlation structure on the performance of coded multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems is studied. The analysis handles an arbitrary joint transmit-receive spatial correlation model, including the non-Kronecker model. An upper bound on the maximum achievable diversity order for frequency-selective MIMO-OFDM systems with general temporal and spatial correlation is derived. Furthermore, a space-time-frequency code design that can achieve the upper bound for any arbitrarily correlated channel scenario is provided. The general framework of the analysis includes space-frequency (SF)-coded systems as a special case. For the SF-coded MIMO-OFDM system, it is shown that any SF code designed to achieve full diversity in the independent fading channel can achieve full diversity in an arbitrary spatially correlated channel. The derived analytical results are consistent with those in the existing literature for special correlation structures. Extensive simulation results are provided to confirm the theoretical analysis.

In this paper, we propose a differential encoding and decoding scheme for multiband UWB systems. The proposed scheme incorporates frequency-domain differential en/decoding with the hopping multiband OFDM modulation. To capture the effect of multipath-rich clustering property of UWB channels, we characterize the pairwise error probability performance of the proposed scheme in terms of cluster and ray arrival rates. It turns out that the diversity advantage does not strongly depend on the random-clustering of UWB channels, and we can achieve the same diversity gain in different channel environments. However, the system performance relies on the clustering behavior through the coding gain. Simulation results show that the proposed differential scheme achieves good performance in the short-range line-of-sight scenarios. In addition, the jointly encoded differential multiband UWB scheme is able to yield superior performance to the uncoded coherent multiband UWB system at high SNR

In this paper we propose a new cooperative protocol, which takes into consideration the partial channel state information (CSI) available at the source. With such protocol a significant improvement in the transmission rate can be achieved in decode-and-forward cooperative transmission, while guaranteeing full diversity order. We derive closed-form expressions for the transmission rate and the symbol error rate (SER) for the M-PSK and the M-QAM signalling. Moreover, we consider two optimization metrics in the protocol design to enhance the system performance; the first is based on minimizing the SER only, while the second is based on minimizing a joint function of both the SER and the transmission rate. Finally, the obtained analytical results are verified through computer simulations

In this paper, a cluster-based cooperative communication scheme is introduced in which each cluster acts as a virtual node with multiple antennas. The presented model considers the effects of random deployment of nodes and hence their random distribution across the network. Taking these effects into account, a pairwise-error probability analysis for a generic space-time code structure is provided. The analysis reveals that the diversity and coding gain of the system depends on both the code structure as well as the nodes' distribution, and hence new code design criteria are developed. Simulation results are presented to confirm the theoretical analysis.

We provide symbol-error-rate (SER) performance analysis for a multi-node wireless network employing a decode-and-forward cooperation strategy. An approximate expression for the SER of an N relay network with M-ary phase-shift-keying (M-PSK) signalling is derived at high enough signal-to-noise ratio (SNR). The approximation hinges on ignoring terms in the SER which are of order higher than (N+1) in the SNR. The validity of the derived approximate SER is justified through computer simulations for networks with different numbers of relays. The simulation results show that the approximation is tight at high SNR and that the cooperation protocol can achieve full diversity order equal to the number of cooperating terminals.

In this paper, we propose a novel stochastic framework for unsupervised manifold learning. The latent variables are introduced, and the latent processes are assumed to characterize the pairwise relations of points over a high dimensional and a low dimensional space. The elements in the embedding space are obtained by minimizing the divergence between the latent processes over the two spaces. Different priors of the latent variables, such as Gaussian and multinominal, are examined. The Kullback-Leibler divergence and the Bhattachartyya distance are investigated. The latent process model incorporates some existing embedding methods and gives a clear view on the properties of each method. The embedding ability of this latent process model is illustrated on a collection of bitmaps of handwritten digits and on a set of synthetic data

In this paper, we propose a differential encoding and decoding scheme for MIMO-OFDM systems under frequency-selective fading channels. We differentially encode the signal within each OFDM symbol period. This scheme not only reduces encoding and decoding delay, but also relaxes the restriction on channel assumptions. The successful differential decoding of the proposed scheme depends on the assumption that fading channels keep constant over two OFDM symbol periods rather than multiple periods as required in previous schemes. We provide the pairwise error probability formulation, and quantify the performance criteria in terms of diversity and coding advantages. Our design criteria reveal that the existing diagonal cyclic codes can be applied to achieve full diversity with high coding gain. Performance simulations in various channel conditions show that our proposed scheme yields superior performance to the previously proposed differential schemes.

In this paper, we propose a general framework to analyze the performance of multiband UWB-MIMO systems regardless of specific coding schemes. A combination of space-time-frequency (STF) coding and hopping multiband OFDM modulation is also proposed to fully exploit all of the available spatial and frequency diversities, richly inherent in UWB environments. We quantify the performance merits of multiband UWB-MIMO systems in case of Nakagami-m frequency-selective fading channels. We show that the maximum achievable diversity of the proposed system is the product of the number of transmit and receive antennas, the number of multipath components, and the number of jointly encoded OFDM blocks. Interestingly, theoretical result shows that the diversity gain does not severely depend on the fading parameter m.. Finally, simulation results are presented to support the theoretical analysis.

We derive a closed-form symbol-error-rate (SER) formulation for the cooperation system with PSK and QAM signals. Moreover, two SER upper bounds are established to show the asymptotic performance of the cooperation protocol, in which one of them is tight at a high signal-to-noise ratio. Based on the SER performance analysis, we also determine the optimum power allocation for the cooperation systems. It turns out that an equal power strategy is, in general, not optimum in cooperation communications, and the optimum power allocation depends on the channel link quality. An interesting result is that, in the case that all channel links are available, the optimum power allocation does not depend on the direct link between source and destination, it depends only on the channel links related to the relay. Extensive simulations are performed to validate the theoretical results.

We consider the design of matrix rotation based space-time signals based on the design criterion of minimizing the union bound on block error probability. We further propose to design the signal parameters via noninteger searching to get better signals. Superior performance of our improved design over the previous design are demonstrated through numerical calculations and performance simulations. With our proposed design for two transmit antennas and one or two receive antennas, we achieve the coding gain of about 1 dB over that of the previous design.

We propose a differential amplify-and-forward (AF) transmission scheme for a two-user cooperative communications system. By efficiently combining signals from both direct and relay links, the proposed scheme provides superior performance compared to those of direct transmissions with either differential detection or coherent detection. While the exact bit-error-rate (BER) formulation of the proposed scheme is not available currently, we provide, as a performance benchmark, an exact BER formulation and its simple bounds for a case of optimum-combining cooperation system with differential M-ary phase-shift keying (DMPSK) signals. The optimum power allocation is also determined based on the provided BER formulations. We show that the proposed differential cooperative transmission scheme together with the optimum power allocation yields comparable performance to the optimum-combining scheme. Simulation results show that the proposed differential scheme with optimum power allocation yields significant performance improvement over that with an equal power allocation scheme.

In this letter, a differential space-frequency modulation (DSFM) scheme is proposed for multiple input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) systems in broadband wireless communications. We assume that the fading channels keep constant only within each OFDM block, and may change independently from one OFDM block to another. The differential schemes proposed for MIMO-OFDM systems in the literature cannot successfully decode with such a rapidly fading channel, since the successful decoding of the previously existing schemes relies on the assumption that the fading channel keeps constant within a period of several OFDM blocks, and it changes slowly from a period of several OFDM blocks to another. In our proposed DSFM scheme, the transmitted signals are differentially encoded in the frequency domain within each OFDM block. Thus, the differential decoding can be performed over subcarriers within each single OFDM block. Furthermore, if a statistical channel power-delay profile (PDP) is known at the transmitter, we propose to create a smooth logical channel to improve the performance of the DSFM scheme. We obtain the smooth logical channel by sorting the channel frequency responses over subcarriers from a statistical point of view. If the logical channel is not smooth enough, we further consider a pruning process in which we use only the "good" part of the channel and get rid of the "bad" part of the channel. Simulation results show that the proposed DSFM scheme over a smooth logical channel (with pruning, if necessary) performs well for various channel PDPs.

A general space-frequency (SF) block code structure is proposed that can guarantee full-rate (one channel symbol per subcarrier) and full-diversity transmission in multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems. The proposed method can be used to construct SF codes for an arbitrary number of transmit antennas, any memoryless modulation and arbitrary power-delay profiles. Moreover, assuming that the power-delay profile is known at the transmitter, we devise an interleaving method to maximize the overall performance of the code. We show that the diversity product can be decomposed as the product of the "intrinsic" diversity product, which depends only on the used signal constellation and the code design, and the "extrinsic" diversity product, which depends only on the applied interleaving method and the power delay profile of the channel. Based on this decomposition, we propose an interleaving strategy to maximize the "extrinsic" diversity product. Extensive simulation results show that the proposed SF codes outperform the previously existing codes by about 3-5 dB, and that the proposed interleaving method results in about 1-3-dB performance improvement compared to random interleaving.

We introduce a new method for disambiguating word senses that exploits a nonlinear Kernel Principal Component Analysis (KPCA) technique to achieve accuracy superior to the best published individual models. We present empirical results demonstrating significantly better accuracy compared to the state-of-the-art achieved by either naïve Bayes or maximum entropy models, on Senseval-2 data. We also contrast against another type of kernel method, the support vector machine (SVM) model, and show that our KPCA-based model outperforms the SVM-based model. It is hoped that these highly encouraging first results on KPCA for natural language processing tasks will inspire further development of these directions.

In this paper, the maximum achievable diversity order is determined for MIMO-OFDM systems with arbitrary spatial correlation. We show that the maximum achievable diversity order is the rank of the spatial correlation matrix of the channel including all delay paths. We also show that any space-frequency code designed to achieve full diversity in spatially independent MIMO-OFDM systems can be used to achieve full diversity in spatially correlated scenarios. Extensive simulation results are provided to support the theoretical analysis.

In this paper, a differential space-frequency modulation (DSFM) scheme is proposed for MIMO-OFDM systems with assumptions that the channel keeps constant only within each OFDM block, and it may change independently from one OFDM block to another. The transmitted/received signals are differentially en/decoded in the frequency dimension within each OFDM block. The performance of the proposed scheme depends on the channel power delay profile. Moreover, if the statistics of the power delay profile are known at the transmitter, we further propose to permutate the channel frequency responses over subcarriers; by using Dijkstra's algorithm to increase the performance of the DSFM scheme. Simulation results show that the DSFM scheme with permutations performs very well for various channel profiles.

In order to make the multi-antenna OFDM systems an attractive choice for practical applications, implementation issues such as decoding complexity must be addressed successfully. In this paper, we propose a computationally efficient decoding algorithm for space-frequency block codes. The central part of the algorithm is a complex-domain, modulation-independent sphere decoding framework. The simulation results demonstrate that the proposed algorithm can significantly reduce the decoding complexity. For the 64 QAM modulation, we observe up to about 73% reduction in the required FLOP count per code block compared to the previously proposed methods without noticeable performance degradation.

In this paper, we consider the design of matrix rotation based (MRB) space-time signals based on the design criterion of minimizing the union bound on pairwise block error probability (PBEP). We further propose to design the signal parameters via noninteger searching to get better signals. Superiority of our improved design over the previous design are demonstrated through numerical calculations and performance simulations. With our proposed design for two transmit antennas and one or two receive antennas, we achieve the coding gain of about 1 dB over that of the previous design.

A general space-frequency block code structure is proposed that can guarantee full-rate and full-diversity transmission in MIMO-OFDM systems. The proposed method can be used to construct space-frequency codes for an arbitrary number of transmit antennas, any memoryless modulation and arbitrary power delay profiles. Moreover, assuming that the power delay profile is known at the transmitter, we devise an interleaving method to maximize the overall performance of the code. The simulation results show that under the simulated environment, the proposed SF codes outperform the existing SF codes from orthogonal designs by about 3-5 dB, and that the proposed interleaving method results in about 1-3 dB performance improvement compared to random interleaving.

In this paper, we develop a systematic design method to generate high-rate space-time block codes (STBCs) from complex orthogonal designs for any number of transmit antennas. The resulting complex orthogonal STBCs have the best known rates, which are conjectured "optimal". Two constructions with rates 2/3 and 5/8 are further illustrated for 6 and 7 transmit antennas, respectively.

The robustness of space-time coding techniques for wireless channels that exhibit both temporal and spatial correlation is investigated. A general space-time covariance model is developed and employed to evaluate the exact pairwise error probability for several space-time codes. A significant degradation in the performance of space-time coding techniques is observed for cases where the scatterers are located in close proximity to the mobile and the spacing between transmit antennas is a fraction of a wavelength. The conditions for which the commonly used assumption of independent transmission paths is valid are investigated as a function of the scattering radius and the spacing of the transmit and receive antennas.

Most space-time codes in the literature were proposed based on two ideal channel conditions: either quasistatic or rapid fading. However, these codes may suffer performance degradation due to temporal correlation caused by the movement of the mobile terminal or imperfect interleaving. In this correspondence, we provide a novel analytical framework for the diversity analysis of space-time modulation in time-correlated fading environment. We show that the space-time signals of square size achieving full diversity in quasistatic fading channels also achieve full diversity in time-correlated fading channels, independently of the time correlation matrix. Consequently, various classes of space-time signals designed for quasistatic fading channels can also be used for full-diversity transmission over time-correlated fading channels. Moreover, we show that if the time correlation matrix is of full rank, the design criteria for time-correlated fading channels are the same as those for rapid fading channels. To illustrate the theoretical results, some simulations were also performed under various temporal fading conditions.

Space-time block codes (STBCs) from orthogonal designs proposed by Alamouti, and Tarokh-Jafarkhani-Calderbank have attracted considerable attention lately due to their fast maximum-likelihood (ML) decoding and full diversity. However, the maximum symbol transmission rate of an STBC from complex orthogonal designs for complex signals is only 3/4 for three and four transmit antennas, and it is difficult to construct complex orthogonal designs with rate higher than 1/2 for more than four transmit antennas. Recently, Jafarkhani, Tirkkonen-Boariu-Hottinen, and Papadias-Foschini proposed STBCs from quasi-orthogonal designs, where the orthogonality is relaxed to provide higher symbol transmission rates. With the quasi-orthogonal structure, the quasi-orthogonal STBCs still have a fast ML decoding, but do not have the full diversity. The performance of these codes is better than that of the codes from orthogonal designs at low signal-to-noise ratio (SNR), but worse at high SNR. This is due to the fact that the slope of the performance curve depends on the diversity. It is desired to have the quasi-orthogonal STBCs with full diversity to ensure good performance at high SNR. In this paper, we achieve this goal by properly choosing the signal constellations. Specifically, we propose that half of the symbols in a quasi-orthogonal design are chosen from a signal constellation set A and the other half of them are chosen from a rotated constellation e/sup j/spl phi// A. The resulting STBCs can guarantee both full diversity and fast ML decoding. Moreover, we obtain the optimum selections of the rotation angles /spl phi/ for some commonly used signal constellations. Simulation results show that the proposed codes outperform the codes from orthogonal designs at both low and high SNRs.

Unitary space-time codes have been used in differential space-time modulation, when neither the transmitter nor the receiver of a multiple antenna system knows the channel state information in Rayleigh fading channels. Among the codes in literature, unitary orthogonal space-time codes, constructed from Alamouti's scheme, have the advantage of fast maximum likelihood (ML) decoding but they require signal constellations to be phase-shift keying (PSK). In this paper, unitary space-time codes are constructed from Alamouti's scheme with amplitude/phase-shift keying (APSK) constellations. We show that the unitary space-time codes from Alamouti's scheme with APSK signals have larger diversity products than those with PSK signals while the complexity of their ML decoding algorithm is comparable. Our newly proposed 4 b/s/Hz code has about 2 dB gain over the same rate code with PSK signals at bit-error rate (BER) of 10/sup -3/ with one receive antenna. We also propose a noncoherent scheme of rate 5 b/s/Hz, which has the same BER performance as the 4 b/s/Hz unitary orthogonal space-time code in DSTM while having comparable decoding complexity.

The Alamouti's orthogonal space-time block codes are for QAM modulations and two transmit antennas. We have recently generalized it for the continuous phase modulation (CPM) by maintaining the orthogonality (for the fast ML decoding/demodulation) and the phase continuity of two signals from two transmit antennas denoted as OST-CPM. In this paper, we design orthogonal-like space-time coded CPM systems for three and four transmit antennas based on the existing orthogonal and quasi-orthogonal space-time codes in the literature. Although the signals from the transmit antennas in the proposed orthogonal-like space-time coded CPM systems are not orthogonal, the fast decoding/demodulation is maintained as in the two transmit antenna case. Simulation results show that the performance of the proposed orthogonal-like space-time coded CPM systems for four transmit antennas is much better than that of the OST-CPM systems for two transmit antennas.

The existing construction of space-time codes or modulation has mainly focused on two ideal situations: either quasi-static or rapid fading channels. In this paper, we consider the design of robust space-time modulation for time-correlated Rayleigh fading channels. We show that the space-time signals of square size achieving full diversity in quasi-static fading channels can also achieve the full diversity in time-correlated fading channels irrespectively of the time correlation matrix. Moreover, we propose a space-time signal construction method by combining orthogonal designs with sphere packings. The simulation results show that our scheme outperforms the previously existing methods. For example, we observe a coding gain of about 1.5 dB over the conventional orthogonal design, about 2 dB over the parametric code, and about 4 dB over the cyclic code under certain fading conditions.

Space-time block codes from orthogonal designs have two advantages, namely, fast maximum-likelihood (ML) decoding and full diversity. Rate 1 real (pulse amplitude modulation-PAM) space-time codes (real orthogonal designs) for multiple transmit antennas have been constructed from the real Hurwitz-Radon families, which also provides the rate 1/2 complex (quadrature amplitude modulation-QAM) space-time codes (complex orthogonal designs) for any number of transmit antennas. Rate 3/4 complex orthogonal designs (space-time codes) for three and four transmit antennas have existed in the literature but no high rate (>1/2) complex orthogonal designs for other numbers of transmit antennas exist. We present rate 7/11 and rate 3/5 generalized complex orthogonal designs for five and six transmit antennas, respectively.

Space-time block codes from orthogonal designs proposed by Alamouti (1998), and Tarokh-Jafarkhani-Calderbank (199) have attracted much attention lately due to their fast maximum-likelihood (ML) decoding and full diversity. However, the maximum symbol transmission rate of a space-time block code from complex orthogonal designs for complex constellations is only 3/4 for three and four transmit antennas. Jafarkhani (see IEEE Trans. Commun., vol.49, no.1, p.1-4, 2001), and Tirkkonen-Boariu-Hottinen (see ISSSTA 2000, pp.429-432, September 2000) proposed space-time block codes from quasi-orthogonal designs, where the orthogonality is relaxed to provide higher symbol transmission rates. With the quasi-orthogonal structure, these codes still have a fast ML decoding, but do not have the full diversity. In this paper, we design quasi-orthogonal space-time block codes with full diversity by properly choosing the signal constellations. In particular, we propose that half symbols in a quasi-orthogonal design are from a signal constellation A and another half of them are optimal selections from the rotated constellation e/sup j/spl phi// A. The optimal rotation angles /spl phi/ are obtained for some commonly used signal constellations. The resulting codes have both full diversity and fast ML decoding.

The WWW is increasingly being used as a source of information. The volume of this information is accessed by users using direct manipulation tools. The paper describes a module that sifts through a large number of texts retrieved by the user. We describe a system that learns a model of the user's preferences, filters the information, and notifies the user when relevant information becomes available. The user's model is represented as a vector in the vector space of classifiable sememes. The document is also represented as a vector. The relevance of the text to the user's interest can be measured by using the cosine angle between the two vectors. Experiments are given to demonstrate it to be a good idea.