Urban Air Mobility (UAM) offers a promising solution to urban congestion by utilizing low-altitude airspace, effectively alleviating pressure on ground transportation. The integration of air-taxi services with existing ground transport infrastructure enables streamlined, efficient door-to-door travel. However, current research on air-taxi systems often overlooks the role of passenger decision-making in selecting the optimal boarding vertiport, a factor that can greatly impact overall system efficiency and user satisfaction. To address this problem, we propose a Unified Air-Ground Mobility Coordination (UAGMC) framework. This framework, powered by deep reinforcement learning (RL) and Vehicle-to-Everything (V2X) communication, optimizes vertiport selection and dynamically plans air-taxi routes based on real-time air and ground traffic conditions. Experimental results show that our approach reduces average travel time by 32 % compared to traditional allocation methods using proportional distribution. This framework advances overall travel efficiency and provides novel insights for integrating multimodal transportation systems.

Urban Air Mobility (UAM) systems are emerging as promising solutions to alleviate urban congestion, with path planning becoming a key focus area. Unlike ground transportation, UAM trajectory planning has to prioritize communication quality for accurate location tracking in constantly changing environments to ensure safety. Meanwhile, the UAM system, serving as an air taxi, requires adaptive planning to respond to real-time passenger requests, especially in ride-sharing scenarios. However, conventional trajectory planning strategies based on predefined routes lack the flexibility to meet unpredictable passenger ride demands. To address these challenges, this work first proposes constructing a radio map to evaluate communication quality. Building on this, we introduce a novel Multi-Source Hybrid Attention Reinforcement Learning (MSHA-RL) framework that integrates diverse data sources, balancing global and local insights for responsive, realtime path planning. Experimental results demonstrate that our approach enables communication-compliant trajectory planning, reducing travel time and enhancing operational efficiency.

Traffic signal control (TSC) is a core challenge in urban mobility, where real-time decisions must balance efficiency and safety. Existing methods—ranging from rule-based heuristics to reinforcement learning (RL)—often struggle to generalize to complex, dynamic, and safety-critical scenarios. We introduce \textbf{VLMLight}, a novel TSC framework that integrates vision-language meta-control with dual-branch reasoning. At the core of VLMLight is the first image-based traffic simulator that enables multi-view visual perception at intersections, allowing policies to reason over rich cues such as vehicle type, motion, and spatial density. A large language model (LLM) serves as a safety-prioritized meta-controller, selecting between a fast RL policy for routine traffic and a structured reasoning branch for critical cases. In the latter, multiple LLM agents collaborate to assess traffic phases, prioritize emergency vehicles, and verify rule compliance. Experiments show that VLMLight reduces waiting times for emergency vehicles by up to 65% over RL-only systems, while preserving real-time performance in standard conditions with less than 1% degradation. VLMLight offers a scalable, interpretable, and safety-aware solution for next-generation traffic signal control.

Multimodal remote sensing data, acquired from diverse sensors, offer a comprehensive and integrated perspective of the Earth’s surface. Leveraging multimodal fusion techniques, semantic segmentation enables detailed and accurate analysis of geographic scenes, surpassing single-modality approaches. Building on advancements in vision foundation models, particularly the Segment Anything Model (SAM), this study proposes a unified framework incorporating a novel Multimodal Fine-tuning Network (MFNet) for remote sensing semantic segmentation. The proposed framework is designed to seamlessly integrate with various fine-tuning mechanisms, demonstrated through the inclusion of Adapter and Low-Rank Adaptation (LoRA) as representative examples. This extensibility ensures the framework’s adaptability to other emerging fine-tuning strategies, allowing models to retain SAM’s general knowledge while effectively leveraging multimodal data. Additionally, a pyramid-based Deep Fusion Module (DFM) is introduced to integrate high-level geographic features across multiple scales, enhancing feature representation prior to decoding. This work also highlights SAM’s robust generalization capabilities with Digital Surface Model (DSM) data, a novel application. Extensive experiments on three benchmark multimodal remote sensing datasets, ISPRS Vaihingen, ISPRS Potsdam and MMHunan, demonstrate that the proposed MFNet significantly outperforms existing methods in multimodal semantic segmentation, setting a new standard in the field while offering a versatile foundation for future research and applications. The source code for this work is accessible at https://github.com/sstary/SSRS.

Local Climate Zones (LCZs) offer a standardized urban classification system critical for studying climate variations and the urban heat island effect. Large-scale LCZ mapping facilitates cross-city climate comparisons. Remote sensing (RS), particularly supervised learning, has become the primary LCZ classification method due to satellite imagery’s broad coverage and high resolution. However, current RS approaches face challenges, including high labeling demands and limited transferability. In this work, we aim to leverage limited labeled data from a source city (i.e., source domain) to improve LCZ classification performance for multiple unlabeled target cities (i.e., target domains). To address these challenges, this work proposes a novel Source-free Multi-target Unsupervised Domain Adaptation (SFMT-UDA) framework for cross-city LCZ classification. Our approach operates in two stages: first performing single-target adaptation between source and target domains using a self-supervised pseudo-labeling module enhanced by an LCZ class similarity matrix to reduce label noise, then conducting multi-target adaptation among target domains through a Multi-head Multi-target Domain Adaptation (MH-MTDA) network with an integrated style-transfer module for consistent self-training. Extensive experiments on the newly developed VHRLCZ dataset demonstrate that the proposed SFMT-UDA framework achieves superior performance compared to state-of-the-art methods, showing significant improvements in overall accuracy across multiple target domains. The related code and data are available at https://github.com/ctrlovefly/SFMT-UDA.

This work develops real-time three-dimensional indoor positioning techniques for mobile platforms such as smartphones by exploiting human activity recognition (HAR). Taking into account practical constraints on mobile platforms including limited computing power and noisy built-in sensors, we develop a deep neural network (DNN)-based approach to perform HAR by focusing on the pedestrian activity transitions, rather than recognizing each individual activity class. Furthermore, we propose to first extract key location information from indoor floor-plans before combining the information with improved Pedestrian Dead Reckoning (PDR) technology to achieve real-time estimation and correction of pedestrian locations. Extensive computer simulation and field experiments confirm that our proposed system can achieve three-dimensional indoor positioning of impressive positioning accuracy.

Cloud Removal (CR) utilizing Deep Learning (DL) has been widely employed to enhance the downstream applications of Remote Sensing (RS) satellite imagery affected by cloud coverage. Segmenting thin and thick cloud images into separate training sets and utilizing the CR model with a targeted learning strategy will result in improved performance. In this work, we propose a one-stop automatic cloud processing scheme for CR, including cloud classification and effective CR. To address the varying visibility of thin and thick cloudy images, we train a cloud classification network to distinguish between these two types of cloudy images, subsequently feeding them into two distinct CR networks. Furthermore, we propose a Semantic Distortion-Aware Network (SDAN) designed for similar cloud images after classification. Within SDAN, the Distortion Swin Transformer Block (DSTB) enhances the capability to extract contextual semantic information by incorporating global feature extraction and expression. This enhancement allows for targeted learning for thin or thick cloud images. Experiments conducted on the CR dataset named RICE demonstrate the enhanced performance of our model compared to various existing CR methods.

Rapid urban development necessitates advanced methodologies for efficiently acquiring and analyzing detailed building information. This study proposes an automated framework, named Urban Street Buildings Scanner (USBS), which combines geospatial data and deep learning techniques to analyze street-level imagery, thereby providing valuable insights into building features. Specifically, we leverage the OSMnx library for geospatial data retrieval before exploiting coordinate sorting and interpolation techniques to extract information about buildings on both roadsides from street images. After that, Segment Anything Model (SAM) and GPT are utilized to segment each building in street-view images for analysis. YOLOv7 is used to further identify the windows of individual buildings and to analyze the buildings in a more detailed way. Meanwhile, GPT can also estimate the numbers of floors and the heights of the buildings, providing a more comprehensive street view analysis. The experimental results demonstrate the efficacy of the proposed framework in effectively extracting and analyzing building characteristics from street-level images. Visual representations and statistical data illustrate the successful application of the framework, providing valuable information for urban planning and development.

Recent advancements in remote sensing (RS)-based methods have shown remarkable effectiveness in large-scale local climate zone classification. However, conventional convolutional neural network (CNN)-based methods encounter limitations in effectively incorporating ground object priors. Additionally, commonly used medium-scale data sources, such as Sentinel-2, face challenges in capturing detailed ground object information. In light of these obstacles, we propose a data fusion method that integrates ground object priors extracted from high-resolution Google imagery with Sentinel-2 multi-spectral imagery. The proposed method introduces a novel dual-stream fusion framework (DF4LCZ-Net), which fully leverages instance-based location features from Google imagery and combines them with the scene-level spatial-spectral features extracted from Sentinel-2 images. For effective feature extraction of Google imagery, a SAM-based Graph Convolutional Network (GCN) branch is designed in the framework. We conducted experiments on a newly created multi-source remote sensing image dataset, and the final classification results show the superiority of our method.

Semantic segmentation of remote sensing images is a fundamental task in geoscience research. However, convolutional neural networks (CNNs) and transformers have some significant shortcomings. The former are limited by insufficient long-range modeling capabilities, while the latter are hampered by computational complexity. Recently, a novel visual state space (VSS) model represented by Mamba has emerged, capable of modeling long-range relationships with linear computability. In this research, we propose a novel dual-branch network named remote sensing image semantic segmentation Mamba (RS3Mamba) designed specifically for remote sensing tasks. RS3Mamba uses VSS blocks to construct an auxiliary branch, providing additional global information to a convolution-based main branch. Moreover, considering the distinct characteristics of the two branches, we introduce a collaborative completion module (CCM) to refine and fuse features from the dual-encoder using a novel adaptive mechanism. Through experiments on two widely used datasets, the proposed RS3Mamba was found to outperform the state-of-the-art methods in terms of mIoU with 0.66% on ISPRS Vaihingen and 1.70% on LoveDA Urban, demonstrating its effectiveness and potential. The source code is available athttps://github.com/sstary/SSRS.

Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise information for diverse downstream applications. Recent development of the segment anything model (SAM), an advanced general-purpose segmentation model, has revolutionized this field, presenting new avenues for accurate and efficient segmentation. However, SAM is limited to generating segmentation results without class information. Meanwhile, the segmentation map predicted by current methods generally exhibits excessive fragmentation and inaccuracy of boundary. This article introduces a streamlined framework designed to leverage the raw output of SAM by exploiting two novel concepts called SAM-generated object (SGO) and SAM-generated boundary (SGB). More specifically, we propose a novel object consistency loss and further introduce a boundary preservation loss in this work. Considering the content characteristics of SGO, we introduce the concept of object consistency to leverage segmented regions lacking semantic information. By imposing constraints on the consistency of predicted values within objects, the object consistency loss aims to enhance semantic segmentation performance. Furthermore, the boundary preservation loss capitalizes on the distinctive features of SGB by directing the model’s attention to the boundary information of the object. Experimental results on two well-known datasets, ISPRS Vaihingen and LoveDA Urban, demonstrate the effectiveness and broad applicability of the proposed method. The source code for this work is accessible athttps://github.com/sstary/SSRS.

Unsupervised domain adaptation (UDA) techniques are vital for semantic segmentation in geosciences, effectively utilizing remote sensing imagery across diverse domains. However, most existing UDA methods, which focus on domain alignment at the high-level feature space, struggle to simultaneously retain local spatial details and global contextual semantics. To overcome these challenges, a novel decomposition scheme is proposed to guide domain-invariant representation learning. Specifically, multiscale high/low-frequency decomposition (HLFD) modules are proposed to decompose feature maps into high- and low-frequency components across different subspaces. This decomposition is integrated into a fully global-local generative adversarial network (GLGAN) that incorporates global-local transformer blocks (GLTBs) to enhance the alignment of decomposed features. By integrating the HLFD scheme and the GLGAN, a novel decomposition-based UDA framework called De-GLGAN is developed to improve the cross-domain transferability and generalization capability of semantic segmentation models. Extensive experiments on two UDA benchmarks, namely ISPRS Potsdam and Vaihingen, and LoveDA Rural and Urban, demonstrate the effectiveness and superiority of the proposed approach over existing state-of-the-art UDA methods. The source code for this work is accessible athttps://github.com/sstary/SSRS.

Accurate semantic segmentation of remote sensing data plays a crucial role in the success of geoscience research and applications. Recently, multimodal fusion-based segmentation models have attracted much attention due to their outstanding performance as compared to conventional single-modal techniques. However, most of these models perform their fusion operation using convolutional neural networks (CNN) or the vision transformer (Vit), resulting in insufficient local-global contextual modeling and representative capabilities. In this work, a multilevel multimodal fusion scheme called FTransUNet is proposed to provide a robust and effective multimodal fusion backbone for semantic segmentation by integrating both CNN and Vit into one unified fusion framework. Firstly, the shallow-level features are first extracted and fused through convolutional layers and shallow-level feature fusion (SFF) modules. After that, deep-level features characterizing semantic information and spatial relationships are extracted and fused by a well-designed Fusion Vit (FVit). It applies Adaptively Mutually Boosted Attention (Ada-MBA) layers and Self-Attention (SA) layers alternately in a three-stage scheme to learn cross-modality representations of high inter-class separability and low intra-class variations. Specifically, the proposed Ada-MBA computes SA and Cross-Attention (CA) in parallel to enhance intra- and cross-modality contextual information simultaneously while steering attention distribution towards semantic-aware regions. As a result, FTransUNet can fuse shallow-level and deep-level features in a multilevel manner, taking full advantage of CNN and transformer to accurately characterize local details and global semantics, respectively. Extensive experiments confirm the superior performance of the proposed FTransUNet compared with other multimodal fusion approaches on two fine-resolution remote sensing datasets, namely ISPRS Vaihingen and Potsdam. The source code in this work is available at https://github.com/sstary/SSRS.

Satellite images are highly susceptible to forgery due to various editing techniques. Traditional forgery detection methods, designed for natural images, often fail when applied to satellite images because of differences in sensing technology and processing protocols. The rise of generative models, such as diffusion models, has further complicated the detection of forgeries in satellite images. This study tackles these challenges from both methodological and data perspectives. We introduce a multitask forgery localization and detection collaborative framework (FLDCF), comprising a multiview forgery localization network (M-FLnet) and a forgery detection network. The M-FLnet, leveraging a content-based prior, generates forgery masks that serve as auxiliary information to improve the detection network’s accuracy. Conversely, the detection network refines these masks, reducing noise for authentic images. Furthermore, two novel forgery datasets, namely, Fake-Vaihingen and Fake-LoveDA, are derived from the Vaihingen and LoveDA satellite image sets, respectively, by exploiting the latest generative models. These datasets represent the first open-source datasets for forgery localization and detection in remote sensing. Extensive experimental results on Fake-Vaihingen and Fake-LoveDA demonstrate that the proposed FLDCF can effectively detect sophisticated forgeries in satellite imagery. The source code and datasets in this work are available athttps://github.com/littlebeen/Forgery-localization-for-remote-sensing.

The presence of cloud layers severely compromises the quality and effectiveness of optical remote sensing (RS) images. However, existing deep-learning (DL)-based cloud removal (CR) techniques, which usually take the fidelity-driven losses as constraints, e.g.,$L_{1}$or$L_{2}$losses, tend to generate smooth results, often failing to reconstruct visually pleasing results and cause semantic loss. To tackle this challenge, this work proposes to encompass enhancements at the data and methodology fronts. On the data side, an ultra-resolution benchmark named CUHK cloud removal (CUHK-CR) of 0.5 m spatial resolution is established. This benchmark incorporates rich detailed textures and diverse cloud coverage, serving as a robust foundation for designing and assessing CR models. From the methodology perspective, a novel diffusion-based framework for CR named diffusion enhancement (DE) is introduced. This framework aims to gradually recover texture details, leveraging a reference visual prior providing foundational structure of the images to enhance inference accuracy. Additionally, a weight allocation (WA) network is developed to dynamically adjust the weights for feature fusion, thereby further improving performance, particularly in the context of ultra-resolution image generation. Furthermore, a coarse-to-fine training strategy is applied to effectively expedite training convergence while reducing the computational complexity required to handle ultra-resolution images. Extensive experiments on the newly established CUHK-CR and existing datasets such as RICE confirm that the proposed DE framework outperforms existing DL-based methods in terms of both perceptual quality and signal fidelity.

Deep learning (DL)-based methods have recently shown great promise in bitemporal change detection (CD). Existing discriminative methods based on convolutional neural networks (CNNs) and Transformers rely on discriminative representation learning for change recognition while struggling with exploring local and long-range contextual dependencies. As a result, it is still challenging to obtain fine-grained and robust CD maps in diverse ground scenes. To cope with this challenge, this work proposes a generative CD model called GCD-DDPM to directly generate CD maps by exploiting the denoising diffusion probabilistic model (DDPM), instead of classifying each pixel into changed or unchanged categories. Furthermore, the difference conditional encoder (DCE), is designed to guide the generation of CD maps by exploiting multilevel difference features. Leveraging the variational inference (VI) procedure, GCD-DDPM can adaptively recalibrate the CD results through an iterative inference process, while accurately distinguishing subtle and irregular changes in diverse scenes. Finally, a noise suppression-based semantic enhancer (NSSE) is specifically designed to mitigate noise in the current step’s change-aware feature representations from the CD Encoder. This refinement, serving as an attention map, can guide subsequent iterations while enhancing CD accuracy. Extensive experiments on four high-resolution CD datasets (CDD) confirm the superior performance of the proposed GCD-DDPM. The code for this work will be available athttps://github.com/udrs/GCD.

Recent advances in remote sensing technologies have highlighted their capability for accurate classification of local climate zones (LCZs). However, traditional methods using convolutional neural networks (CNNs) often fall short of effectively incorporating prior knowledge of ground objects. In addition, data sources such as Sentinel-2 struggle with capturing detailed information on ground objects. To address these issues, we introduce a novel data fusion approach that combines high-resolution Google imagery, which provides ground object priors, with Sentinel-2 multispectral imagery. Our method, the Dual-stream Fusion framework for LCZ classification (DF4LCZ), merges instance-based location features from Google imagery and spatial-spectral features from Sentinel-2. This framework is enhanced by a graph convolutional network (GCN) module, powered by the segment anything model (SAM), to improve feature extraction from Google imagery. Concurrently, a 3D-CNN architecture is utilized to process the spectral-spatial features of Sentinel-2 imagery. The effectiveness of DF4LCZ is demonstrated through experiments conducted on a specialized multisource remote sensing image dataset for LCZ classification. The related code and dataset are available athttps://github.com/ctrlovefly/DF4LCZ.

Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.

Vehicle-to-everything (V2X) is communication between a vehicle and any entity that may affect, or may be affected by the vehicle. It is useful for road safety, traffic efficiency, energy savings and mass surveillance, etc. Intelligent traffic signal control (TSC) is a critical building block for V2X. In contrast to most existing TSC designs that assume real-time traffic flow information is available, this work proposes a reinforcement learning (RL)-based TSC system by explicitly taking into account the delay in the TSC input. To cope with the input delay, the proposed RL-based system extracts features to characterize the traffic flow by exploiting multiple delayed historical observations using either the convolutional neural networks (CNN) or the recurrent neural networks (RNN). As a result, the proposed TSC system can make more appropriate actions on choosing traffic signal plans that better match with the traffic flow. Extensive computer experiments performed on the Simulation of Urban MObility (SUMO) platform confirm that the proposed RL-based TSC system can achieve up to 76.0% reduction in the average vehicle waiting time as compared to the conventional RL-based TSC system for observations with large feedback delays.

Cloud removal has attracted significant research attention in various remote sensing applications, such as object detection and semantic segmentation. In this study, the classical cycle-consistent generative adversarial network (CycleGAN) is adopted for suppressing clouds from a feature enhancement perspective by capitalizing on the transformer architecture. Specifically, a transformer-based feature enhancement (TFE) module is proposed to extract high-level cloud-clear features by leveraging the Swin transformer’s capability of building long-range dependencies. As a result, the proposed TFE module can eliminate clouds in remote sensing images while retaining cloud-free regions unchanged. Extensive simulation experiments on the RICE dataset are conducted to substantiate the impressive performance of the proposed TFE model as compared to several existing cloud-removal methods.

In this work, a practical Machine Learning (ML) approach is proposed to produce Landslide Susceptibility Mapping (LSM) by exploiting multiple data sources. In contrast to conventional ML-based methods that consider a large number of factors, the proposed method fuses only three carefully selected factors, namely inventory Landslide and Debris Flow (LDF) data, rainfall data and forest change information to model and predict landslides and debris flow. More specifically, a Global Deforestation Detection Algorithm (GDDA) based on Synthetic Aperture Radar (SAR) and Convolutional Neural Network (CNN) are first developed to generate a hazard map by identifying areas of substantial forest changes. Capitalizing on rainfall forecast data derived from the Doppler Weather Radar (DWR) over the areas of severe deforestation detected by GDDA, an SVM-based classifier is established to predict the likelihood of the occurrence of landslide and debris flow. Using two real-world landslide and debris flows in 2021, we demonstrate that the proposed LSM system was able to provide early warning in advance.

The synergy of the transformer and the convolutional neural network (CNN) has been well regarded as a promising technique for single image super-resolution (SISR) based on low-quality satellite remote sensing images. In this work, a Dual Transformer Residual Network (DTRN) consisting of one transformer branch and one CNN-based residual branch is proposed. More specifically, the transformer branch is designed to capture the global relationships of feature maps by exploiting three pairs of token embedding blocks and convolutional transformer blocks (CTB). Furthermore, the residual branch employs several residual blocks (resblocks) to effectively learn hierarchical features through global feature fusion. Extensive experiments on a large-scale remote sensing dataset called OLI2MSI confirm the superior performance of the proposed DTRN as compared to the existing SISR methods.

This work proposes an end-to-end Monocular Depth-Assisted Fusion Network (MDAFNet) for semantic segmentation of complex urban remote sensing data. The proposed MDAFNet consists of a Monocular Depth Estimation Network (MDENet) and a Crossmodal Fusion Network (CFNet). More specifically, the MDENet first generates the earth surface depth information while the CFNet fuses the generated depth information and RGB images to address the segmentation task. In particular, the MDENet is capable of effectively extracting features of the ground surface while overcoming artifacts such as building shadows. Furthermore, the CFNet is designed to perform segmentation by extracting and fusing semantic information from generated depth information and Red-Green-Blue (RGB) images. Extensive experiments performed on a large-scale fine-resolution remote sensing dataset named the ISPRS Vaihingen confirm that the proposed MDAFNet outperforms conventional crossmodal models equipped with Digital Surface Model information.

This work investigates unsupervised domain adaptation (UDA)-based semantic segmentation of very high-resolution (VHR) remote sensing (RS) images from different domains. Most existing UDA methods resort to generative adversarial networks (GANs) to cope with the domain shift problem caused by the discrepancies across different domains. However, these GAN-based UDA methods directly align two domains in the appearance, latent, or output space based on convolutional neural networks (CNNs), making them ineffective in exploiting long-range dependencies across the high-level feature maps derived from different domains. Unfortunately, such high-level features play an essential role in characterizing RS images with complex content. To circumvent this obstacle, a mutually boosted attention transformer (MBATrans) is proposed to capture cross-domain dependencies of semantic feature representations in this work. Compared with conventional UDA methods, MBATrans can significantly reduce domain discrepancies by capturing transferable features using global attention. More specifically, MBATrans utilizes a novel mutually boosted attention (MBA) module to align cross-domain feature maps while enhancing domain-general features. Furthermore, a novel GAN-based network with improved discriminative capability is devised by integrating an additional discriminator to learn domain-specific features. Extensive experiments on two large-scale VHR RS datasets, namely, International Society for Photogrammetry and Remote Sensing (ISPRS) Potsdam and Vaihingen, confirm the superior performance of the proposed MBATrans-augmented GAN (MBATA-GAN) architecture. The source code in this work is available athttps://github.com/sstary/SSRS.

Deep learning has shown promising results on dense three-dimensional (3D) model reconstruction from RGB images in recent years. However, the reconstruction of large-scale 3D models required for digital city remains very challenging even for such deep learning based methods. In this paper, we propose a convolutional neural network (CNN)-based Multi-View-Stereo (MVS) method that uses a double U-Net approach searching for image features. The proposed network first utilizes a double U-Net to extract the image features of a coarser resolution for the sake of reduced memory requirements. After that, the cost volume is built via the differentiable homography warping. The cascade structure is designed to extract the information in a small-scale cost volume before a large-scale cost volume carries out fusion and finer depth map estimation. As a result, the proposed network can efficiently produce highly accurate 3D point clouds using a fraction of the GPU memory and runtime required by conventional methods. Extensive experiments on the DTU benchmarks as well as the Tanks and Temples benchmarks confirm that the proposed network can achieve outstanding reconstruction accuracy and model completeness.

Accurate three-dimensional (3D) city models are critical for applications, such as digital city, urban planning and geo-graphic surveying and mapping. The synergy of deep learning and LiDAR-based Simultaneous Localization and Mapping (SLAM) has achieved some success in obtaining dense 3D color maps for large-scale outdoor scenes. In such a frame-work, this paper proposes a sensor-fusion system composed of a solid-state LiDAR, an inertial measurement unit (IMU) and a monocular camera by introducing image-guided depth completion into LiDAR SLAM. In the proposed system, the LiDAR-IMU odometry constructs the geometry structure of 3D maps, and subsequently, the images are used to render the texture of 3D color maps and guide the depth completion. In particular, the proposed system generates denser point clouds by exploiting the solid-state LiDAR before further increasing the density of point clouds via the depth completion. Our ex-perimental results show that the proposed system can achieve impressive 3D color models for large-scale outdoor scenes through faster scan and reconstruction process.

This work proposes a Multi-Stage Fusion Network (MSFNet) for semantic segmentation of fine-resolution remote sensing data by exploiting a multi -stage transformer architec-ture. The proposed MSFNet fuses information of different scales and modalities using a multi-stage scheme based on cross-attention mechanism. More specifically, the proposed MSFNet is composed of two Multi-Level Transformers (ML-Trans), one Crossmodal Fusion Transformer (CFTrans) and one Global-Context Augmented Transformer (GCATrans). DMLTrans and CFTrans are designed to fuse features in dif-ferent levels in each modality and high-level crossmodal ab-stract features, respectively, whereas GCATrans enhances the fusion feature of the main modal. Capitalizing on MSFNet, this work demonstrates the fusion of red-green-blue (RGB) remote sensing images and digital surface model (DSM) data. Extensive experiments on large-scale fine-resolution remote sensing data sets, namely the ISPRS Vaihingen, confirm the excellent performance of the proposed architecture as compared to conventional multimodal methods.

Despite its impressive performance on semantic segmentation of remote sensing imagery, deep learning requires a large amount of labeled data for model training, which is both laborious and timeconsuming for an individual institution. To cope with this obstacle, federated Learning (FL) has been proposed to enable multiple institutions to train a global model collaboratively without violating privacy rules. However, the performance of FL is poor in the presence of heterogeneous training data, i.e. the data is not independently and identically distributed (non-i.i.d) among participating clients, especially for remote sensing images with high spatial and spectral heterogeneity. In this paper, we propose an FL algorithm combined with prototype-based hierarchical clustering (Fed-PHC). Instead of updating a single global model to capture the shared knowledge of all clients, we utilize a mixture of multiple global models to handle the heterogeneity between various clients using hierarchical clustering (HC) based on the prototypical representations of clients' datasets. As a result, FedPHC can reduce the domain discrepancy within each group and obtain more representative models for heteroge-neous datasets. Extensive experiments on the Inria Aerial Image Dataset confirm the effectiveness of FedPHC.

Cloud layer contamination is a common problem in optical remote sensing (RS) images. Deep-learning-based cloud removal from RS imagery has attracted increasing attention in recent years. However, it remains challenging to exploit useful multiscale cloud-aware representations from cloud imagery due to the lack of effective modeling of cloud distortion effects and the weak feature representation capabilities of networks. To circumvent these challenges, we propose a multiscale distortion-aware cloud removal (MSDA-CR) network consisting of multiple cloud-distortion-aware representation learning (CDARL) modules combined in a multiscale grid architecture. Specifically, cloud distortion control functions (CDCFs) are defined and incorporated into the CDARL modules to adaptively model the distortion effects induced by cloud interference in the imaging process, with learnable parameters for the exploitation of distortion-restored representations. These representations are further distilled across different scales in the MSDA-CR network and integrated based on an attention mechanism to restore cloud-free images while retaining the spatial structures of ground objects. Extensive experiments on visible and multispectral RS datasets confirm the effectiveness of the proposed MSDA-CR network.

Deep learning (DL) approaches based on convolutional encoder–decoder networks have shown promising results in bitemporal change detection. However, their performance is limited by insufficient contextual information aggregation because they cannot fully capture the implicit contextual dependency relationships among feature maps at different levels. Moreover, harvesting long-range contextual information typically incurs high computational complexity. To circumvent these challenges, we propose multilevel deformable attention-aggregated networks (MLDANets) to effectively learn long-range dependencies across multiple levels of bitemporal convolutional features for multiscale context aggregation. Specifically, a multilevel change-aware deformable attention (MCDA) module consisting of linear projections with learnable parameters is built based on multihead self-attention (SA) with a deformable sampling strategy. It is applied in the skip connections of an encoder–decoder network taking a bitemporal deep feature hypersequence (BDFH) as input. MCDA can progressively address a set of informative sampling locations in multilevel feature maps for each query element in the BDFH. Simultaneously, MCDA learns to characterize beneficial information from different spatial and feature subspaces of BDFH using multiple attention heads for change perception. As a result, contextual dependencies across multiple levels of bitemporal feature maps can be adaptively aggregated via attention weights to generate multilevel discriminative change-aware representations. Experiments on very-high-resolution (VHR) datasets verify that MLDANets outperform state-of-the-art change detection approaches with dramatically faster training convergence and high computational efficiency.

Abstract Large-scale high-resolution three-dimensional (3D) maps play a vital role in the development of smart cities. In this work, a novel deep learning-based multi-view-stereo method is proposed for reconstructing the 3D maps in large-scale urban environments by exploiting a monocular camera. Compared with other existing works, the proposed method can perform 3D depth estimation more efficiently in terms of computational complexity and graphics processing unit memory usage. As a result, the proposed method can practically perform depth estimation for each pixel before generating 3D maps for even large-scale scenes. Extensive experiments on the well-known DTU dataset and real-life data collected on our campus confirm the good performance of the proposed method.

In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that either equally divides the transmission time slots among users or maximizing some ratios without practical physical interpretations, we propose to use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness. Since it is difficult to directly optimize 5TUDR, we first cast the problem into the stochastic game framework and subsequently propose a multi-agent reinforcement learning (MARL)-based algorithm to perform distributed optimization on the resource block group (RBG) allocation. Furthermore, each MARL agent is designed to take information measured by network counters from multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the input states while the RBG allocation as action with a carefully designed reward function developed to maximize 5TUDR. Extensive simulation is performed to show that the proposed MARL-based scheduler can achieve fair scheduling while maintaining good average network throughput as compared to conventional schedulers.

The 3D indoor positioning and indoor navigation (IPIN) system is of great significance for promoting and expanding indoor intelligent services and applications. The rapid development of unmanned aerial vehicles (UAVs) has provided new opportunities in this field. However, in contrast to their outdoor applications, IPIN for UAVs is more challenging since the Global Positioning System (GPS) is in general inaccessible in indoor environments. In this work, we propose a UAV-enabled 3D IPIN system based on visible light communication (VLC). Firstly, a novel VLC-based indoor positioning scheme is developed using a fusion algorithm based on the dynamic time warping (DTW) method with visible light intensity sequence (VLIS) and inertial measurement unit (IMU) data. To reduce the workload of fingerprint measurements, we propose to modularize a floor site using a standard symmetric structure for VLC positioning. In this manner, the navigation can be achieved by recognizing the edge of each module. Furthermore, since the sampling frequency of IMU is much higher than that of VLIS, discrete Kalman filter (KF) is introduced to correct the location measured by IMU when VLIS is unavailable. A proof-of-concept IPIN prototype is constructed. Field experiments confirm the effectiveness of our proposed IPIN system.

In this work, an inverse domain adaptation (IDA) method is proposed to cope with the distributional mismatch between the training images in the source domain and the test images in the target domain in remote sensing. More specifically, a cycleGAN structure using the Wasserstein distance is developed to learn the distribution of the remote sensing images in the source domain before the images in the target domain are transformed into similar distribution while preserving the image details and semantic consistency of the target images via style transfer. Extensive experiments using the GF1 data are performed to confirm the effectiveness of the proposed IDA method.

This work develops robust semi -supervised classifiers to tackle three most challenging problems in land-use classification using remote sensing data, namely mixed pixels, weak labels and imbalanced data. Specifically, this work proposes to first divide the pixels in remote sensing images into two groups, namely the pixels with accurate labels and pixels with weak labels. To cope with the imbalanced data problem in the pixels with accurate labels, an improved cross entropy-based cost function is proposed to weigh the contributions from data of different classes based on its importance by exploiting the term frequency-inverse document frequency (TF-IDF) algorithm. Furthermore, for the pixels with weak labels, a nuclear norm-based cost function is developed to direct the training process without requiring data labels. To cope with the interference due to weakly labeled data with unrepresentative spatial features, an artificial class called “Unknown” is proposed. Extensive experiments validate the effectiveness of the proposed semi-supervised classifier.

Cloud layer contamination is a common problem in optical remote sensing images. Cloud removal from remote sensing images has attracted increasing attention in recent years. To this end, we propose a multi-scale distortion-aware network for cloud removal from remote sensing images. A novel Cloud Aware and Feature Extraction (CAFE) module is developed by incorporating the physical model of cloud distortion considering atmosphere light, cloud reflectance light and cloud transmission. These distortion factors in CAFE module are encoded into trainabile parameters for feature extraction from contaminated images. The network is trained in an end-to-end manner with cloud contaminated images and ground truth data. Finally, experimentl results on the Remote sensing Image Cloud rEmoving (RICE) dataset demonstrate the effectiveness of the proposed approach.

Deep learning has shown promising results on change detection (CD) from bi-temporal remote sensing imagery in recent years. However, it still remains challenging to cope with the pseudo-changes caused by seasonal differences and style variations of bi-temporal images. In this paper, an object-level boundary-preserving generative adversarial network (BPGAN) is developed for style transformation-based CD of bi-temporal images. To achieve this purpose, image objects derived in the spectral domain are incorporated into the image translation to generate object-level target-style-like images. In particular, constraints on object boundary consistency and object homogeneity are established in the adversarial learning to maintain the style and content consistency while regularizing the network training. Furthermore, the Superpixel-Based Fast Fuzzy c-Means (SF-FCM) algorithm is utilized for efficient CD from the object-level style-transformed images. Extensive experiments on SPOT5 and GF1 data confirm the effectiveness of the proposed approach.

How to predict the wireless network level performance such as the network capacity, the average user data rate, and the 5%-tile user data rate is a million-dollar question. In the literature, some pioneering works have been proposed by exploiting either the information theoretic techniques on the physical layer (PHY) information or the Markov chain techniques on the multiple access control (MAC) layer information. However, since these mathematical model-driven approaches usually focus on a small part of the network structure, they cannot characterize the whole network performance. In this paper, we propose to utilize a data-driven machine learning approach to tackle this problem. More specifically, both PHY and MAC information is fed into a deep neural network (DNN) specifically designed for network-level performance prediction. Simulation results show that the network level performance can be accurately predicted at the cost of higher computational complexity.

This work investigates geomagnetism-based indoor localization by exploiting the magnetometers built-in smartphones. The main challenge arises from the fact that the localization accuracy is handicapped by the limited dimensionality of the magnetometer data. To cope with this problem, the magnetic field strength (MFS) sequence has been proposed to improve the localization accuracy. However, it remains an open challenge to derive the exact location through the MFS. In this work, a novel multi-level link-node model containing geometry and topology information is first proposed to construct the MFS sequence fingerprint database which can be easily constructed by crowdsourced technology. By exploiting this database, a hybrid approach combining dynamic time warping (DTW), pedestrian dead reckoning (PDR) and k-nearest neighbor (kNN) is developed to achieve efficient MFS sequence matching and accurate indoor localization. The proposed method provides a convenient and pervasive indoor localization solution that uses only the built-in sensors of smartphones. Without knowing the initial position, the user's location can be quickly determined. Extensive experimental results confirm that the proposed approach can achieve accurate and efficient MFS sequence matching results while providing accurate initial and end position estimation in the indoor environments.

Hybrid analog and digital precoding have recently been proposed for massive multiple-input multiple-output (MIMO) systems. However, it is challenging to jointly optimize the design of precoding and power allocation as the optimization problem is highly non-convex. In this work, the non-convex problem is cast into the D.C. (difference of two convex functions) programming framework. To cope with the high dimensionality of the problem, an iterative rank-constrained D.C. programming technique is developed. As a result, the proposed algorithm is capable of directly maximizing the weighted sum-rate (WSR) of all users while taking into account the quality of service (QoS) requirement of each user. Furthermore, the monotonic convergence behavior of the proposed iterative algorithm is proved. Finally, simulation results confirm the effectiveness of the proposed iterative algorithm.

Indoor pathway models have recently emerged as promising indoor positioning techniques for indoor location-based Internet of Things (IoT) applications. However, the indoor pathway models reported in the literature are commonly extracted from indoor maps that are both coverage-limited and expensive. Furthermore, the extraction process is also time-consuming and error-prone. In this work, a novel method is developed to automatically construct semantic-rich indoor pathway models from the crowdsourced trajectory data collected by smartphone sensors without requiring maps and additional devices. More specifically, the pedestrian trajectories are first obtained using the built-in inertial sensors of the smartphone. After that, indoor pathway models are constructed by exploiting the pedestrian activity information derived from human activity recognition (HAR) and structural nodes (e.g. doors and elevators) extracted from the trajectory. Furthermore, a short-range trajectory clustering method is proposed to improve the accuracy of the indoor pathway model. In addition to indoor positioning, the resulting model can provide structural information of an indoor environment as well as semantic information about pedestrians and the environment, which is particularly useful for advanced IoT applications and services. Extensive field measurements demonstrate that the resulting indoor pathway models are of about one-meter accuracy in most experiments.

This paper reports our recent work on creating the world's first super-resolution 3D digital maps for indoor and outdoor wireless channel modeling. By exploiting the recent technological breakthroughs in unmanned aerial vehicle (UAV), Light Detection and Ranging (LiDAR) and the Simultaneous Localization and Mapping (SLAM) technology, this work develops a surveying system prototype to produce super-resolution 3D digital maps of centimeter-level resolution for large areas covering both indoor and outdoor environments. In addition, the resulting maps are designed to precisely capture building shapes even for skyscrapers with wall material information. It is believed that these new maps can potentially revolutionize the network planning practice commonly performed in the telecommunication industry by providing highly accurate indoor and outdoor channel models. By exploiting these new channel models, wireless service operators will be able to better optimize their wireless networks with reduced necessity of labor-intensive drive tests.

• A large-scale JND-based coded video quality dataset is presented. • The VideoSet contains 220 5-s sequences in four resolutions coded by H.264/AVC. • The subjective test procedure, JND data cleaning and properties are described. • The significance and implications of the VideoSet are discussed. • This work points out a clear path to data-driven perceptual coding. A new methodology to measure coded image/video quality using the just-noticeable-difference (JND) idea was proposed in Lin et al. (2015). Several small JND-based image/video quality datasets were released by the Media Communications Lab at the University of Southern California in Jin et al. (2016) and Wang et al. (2016) [3]. In this work, we present an effort to build a large-scale JND-based coded video quality dataset. The dataset consists of 220 5-s sequences in four resolutions (i.e., 1920 × 1080 , 1280 × 720 , 960 × 540 and 640 × 360 ). For each of the 880 video clips, we encode it using the H.264/AVC codec with QP = 1 , … , 51 and measure the first three JND points with 30 + subjects. The dataset is called the “VideoSet”, which is an acronym for “Video Subject Evaluation Test (SET)”. This work describes the subjective test procedure, detection and removal of outlying measured data, and the properties of collected JND data. Finally, the significance and implications of the VideoSet to future video coding research and standardization efforts are pointed out. All source/coded video clips as well as measured JND data included in the VideoSet are available to the public in the IEEE DataPort (Wang et al., 2016 [4]).

This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.

We develop a recursive least-squares (RLS) algorithm which employs L1-Lqregularized sparse regressions to estimate a sparse channel matrix in frequency-and-time selective fading for multi-input multi-output (MIMO) wireless communications. We propose an improved sparse RLS by using an order extension technique for rapid fading channels. Simulation results demonstrate that the proposed sparse RLS algorithm offers a significant improvement over the conventional RLS algorithm.

In this paper, we propose a distributed interference alignment which employs physical-layer network coding and superposition coding for successive-cancelling multiuser detection (MUD) receivers in K-pair bidirectional relaying networks. The proposed scheme enables the transmitter to align only partial interference while strong interference is cancelled by MUD, and the transmitter can have more degrees of freedom in controlling the filter designs. Simulation results demonstrate that our proposed scheme significantly improves sum-rate performance in multiuser bidirectional relaying systems.

In this work, we propose a super-resolution blind channel modeling algorithm to characterize wide-band channels comprised of disjoint frequency subbands. Since sounding signals are not available over the frequency guard bands separating adjacent subbands, conventional channel modeling methods suffer from poor performance in modeling the channel frequency response over the guard bands. To circumvent this obstacle, a three-step super-resolution blind algorithm is developed. First, the path delays are estimated by exploiting super-resolution algorithms such as MUSIC or ESPRIT. After that, the proposed algorithm performs blind channel estimation over the guard bands and subsequently derive the frequency response over the whole wideband channel. Finally, estimates derived from different subbands are combined via a soft combining technique. It is shown by computer simulations that the proposed superresolution blind algorithm can achieve a significant performance gain over conventional methods.

In a multiple-input multiple-output (MIMO) system, cross-polarized antenna selection yields significant reduction in cost and hardware size. However, actual benefits of the technique are dependent on the propagation characteristics including channel polarization. To accurately characterize the target 2 GHz-band MIMO channels, the authors conduct 2 GHz cross-polarized channel measurement campaigns. Based on the measured data, novel channel models specifically for the 2 GHz bands are established. In addition, we evaluate the performance improvement obtained with cross-polarized antenna selection using the channel models. Simulation results reveal that antenna selection is particularly useful in the low SNR regime, and that the system capacity at cell edges can be increased up to 13%.

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals,${\bf R}$, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal,${\bf R}_{0}$, is assumed to be equal to$\lambda{\bf R}$, i.e.,${\bf R}_{0}=\lambda{\bf R}$. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor$\lambda$between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

In this work, the sum-rate performance of joint opportunistic scheduling and receiver design (JOSRD) is analyzed for multiuser multiple-input-multiple output (MIMO) space-division multiple access (SDMA) downlink systems. In particular, we study linear rake receivers with selective combining, maximum ratio combining and optimal combining in which signals received from all antennas of each mobile terminal (MT) are linearly combined to improve the effective signal-to-interference-plus-noise ratios (SINRs). By exploiting limited feedback on the effective SINRs, the base station (BS) schedules simultaneous data transmission on multiple beams to the MTs with the largest effective SINRs. Using extreme value theory, the average sum-rates and their scaling laws for JOSRD are derived. In particular, it is shown that the limiting distribution of the effective signal-to-interference (SIR) is of the Frechet-type whereas that of the effective SINR converges to the Gumbel-type. Furthermore, the SIR-based sum-rate scaling laws are found to follow ε log K with 0<;ε<;1, which stands in contrast to the SINR-based scaling laws governed by the conventional log log K form. Both analytical and simulation results confirm that significant performance improvement can be achieved by incorporating low-complexity linear combining techniques into the design of scheduling schemes in MIMO-SDMA downlink systems.

This work proposes a low-complexity space-time adaptive processing (STAP) algorithm for sensing applications built on a moving platform in the presence of strong clutters. The proposed algorithm achieves low-complexity computation via two steps. First, it utilizes improved fast approximated power iteration methods to compress the data into a much smaller subspace. To further reduce the computational complexity, a progressive singular value decomposition (SVD) approach is employed to update the inverse of the covariance matrix of the compressed data. As a result, the proposed low-complexity STAP algorithm can achieve order-of-magnitude computational complexity reduction as compared to conventional STAP algorithms. Simulation results are shown to confirm the validity of the proposed algorithm.

QR decomposition (QRD)-based precoded MIMO-OFDM systems with reduced feedback are proposed to convert the MIMO-OFDM channel into layered subchannels. QRD-M is further combined with either singular value (SVD) or geometric mean decomposition (GMD) of the time-domain channel impulse response matrix. As a result, the receiver in the proposed systems only needs to feed back information describing one precoding matrix for all carriers. Simulation results confirm the bit-error-rate (BER) and throughput performance superiority of the proposed systems compared to conventional SVD per-carrier precoding schemes.

Joint carrier frequency offset (CFO) and channel estimation for uplink MIMO-OFDMA systems over time-varying channels is investigated. To cope with the prohibitive computational complexity involved in estimating multiple CFOs and channels, pilot-assisted and semi-blind schemes comprised of parallel Schmidt Extended Kalman filters (SEKFs) and Schmidt-Kalman Approximate Particle Filters (SK-APF) are proposed. In the SK-APF, a Rao-Blackwellized particle filter (RBPF) is developed to first estimate the nonlinear state variable, i.e. the desired user's CFO, through the sampling-importance-resampling (SIRS) technique. The individual user channel responses are then updated via a bank of Kalman filters conditioned on the CFO sample trajectories. Simulation results indicate that the proposed schemes can achieve highly accurate CFO/channel estimates, and that the particle filtering approach in the SK-APF outperforms the more conventional Schmidt Extended Kalman Filter.

Distributed opportunistic scheduling (DOS) is studied for wireless ad hoc networks in which many links contend for a channel using random access before data transmission. Simply put, DOS involves a process of joint channel probing and distributed scheduling for ad hoc (peer-to-peer) communications. Since, in practice, link conditions are estimated with noisy observations, the transmission rate must be backed off from the estimated rate in order to avoid transmission outages. Then, a natural question to ask is whether or not it is worthwhile for the link with successful contention to perform further channel probing to mitigate estimation errors, at the cost of additional probing. Thus motivated, this work investigates DOS with two-level channel probing by optimizing the tradeoff between the throughput gain from more accurate rate estimation and the resulting additional delay. By capitalizing on optimal stopping theory with incomplete information, it is shown that the optimal scheduling policy is threshold-based and is characterized by either one or two thresholds, depending on network settings. Necessary and sufficient conditions for both cases are rigorously established. In particular, this analysis reveals that performing second-level channel probing is optimal when the first-level estimated channel condition falls in between the two thresholds. Numerical results are provided to illustrate the effectiveness of the proposed DOS with two-level channel probing. This study is also extended to the case with limited feedback, in which the feedback from the receiver to its transmitter takes the form of$(0,1,e)$.

Optimized opportunistic multicast scheduling (OMS) is studied for cellular networks, where the problem of efficiently transmitting a common set of fountain-encoded data from a single base station to multiple users over quasi-static fading channels is examined. The proposed OMS scheme better balances the tradeoff between multiuser diversity and multicast gain by transmitting to a subset of users in each time slot using the maximal data rate that ensures successful decoding by these users. We first analyze the system delay in homogeneous networks by capitalizing on extreme value theory and derive the optimal selection ratio (i.e., the portion of users that are selected in each time slot) that minimizes the delay. Then, we extend results to heterogeneous networks where users are subject to different channel statistics. By partitioning users into multiple approximately homogeneous rings, we turn a heterogeneous network into a composite of smaller homogeneous networks and derive the optimal selection ratio for the heterogeneous network. Computer simulations confirm theoretical results and illustrate that the proposed OMS can achieve significant performance gains in both homogeneous and heterogeneous networks as compared with the conventional unicast and broadcast scheduling.

Optimized opportunistic multicast scheduling (OMS) has been studied previously by the authors for homogeneous cellular networks, where the problem of efficiently transmitting a common set of data from a single base station to multiple users that have identical channel statistics was examined. It has been demonstrated that OMS can achieve significant performance improvement by exploiting the optimal tradeoff between multiuser diversity and multicast gain. In this work, we extend our studies to heterogeneous networks with users subject to different channel statistics. Specifically, we consider a single cell wireless network with users uniformly distributed in a circular region around the base station. Since users with low SNR are the ones that hinder system throughput, we argue that system performance may be predicted by the behavior of users in the outmost ring of the cell, which are approximately homogeneous. Using extreme value theory and results obtained from the homogeneous case, we determine the optimal user selection ratio for a homogeneous ring of users near the edge of the cell and then use it to derive the optimal selection ratio over the entire heterogeneous network. Simulations confirm theoretical results and illustrate the effectiveness of the proposed scheme.

Distributed opportunistic scheduling (DOS) is studied for wireless ad-hoc networks in which many links contend for the channel using random access before data transmissions. Simply put, DOS involves a process of joint channel probing and distributed scheduling for ad-hoc (peer-to-peer) communications. Since, in practice, link conditions are estimated with noisy observations, the transmission rate has to be backed off from the estimated rate to avoid transmission outages. Then, a natural question to ask is whether it is worthwhile for the link with successful contention to perform further channel probing to mitigate estimation errors, at the cost of additional probing. Thus motivated, this work investigates DOS with two-level channel probing by optimizing the tradeoff between the throughput gain from more accurate rate estimation and the resulting additional delay. Capitalizing on optimal stopping theory with incomplete information, we show that the optimal scheduling policy is threshold-based and is characterized by either one or two thresholds, depending on network settings. Necessary and sufficient conditions for both cases are rigorously established. In particular, our analysis reveals that performing second-level channel probing is optimal when the first-level estimated channel condition falls in between the two thresholds. Finally, numerical results are provided to illustrate the effectiveness of the proposed DOS with two-level channel probing.

An energy-efficient opportunistic collaborative beamformer with one-bit feedback is proposed for ad hoc sensor networks transmitting a common message over independent Rayleigh fading channels to a relatively distant destination node. In contrast to conventional collaborative beamforming schemes in which each relay node uses channel state information (CSI) to pre-compensate for its channel phase and local carrier offset, the relay nodes in the proposed beamforming scheme do not perform any phase precompensation. Instead, the destination node broadcasts a relay node selection vector to the pool of available relay nodes to opportunistically select a subset of relay nodes whose transmitted signals combine in a quasicoherent manner at the destination. Since the selection vector only indicates which relay nodes are to participate in the collaborative beamformer and does not convey any CSI, only one bit of feedback is required per relay node. Theoretical analysis shows that the received signal power obtained with the proposed opportunistic collaborative beamforming scheme scales linearly with the number of available relay nodes under a fixed total power constraint. Since computation of the optimal selection vector is exponentially complex in the number of available relays, three low-complexity sub-optimal relay node selection rules are also proposed. Simulation results confirm the effectiveness of opportunistic collaborative beamforming with the low-complexity relay node selection rules.

The design of optimal opportunistic multicast scheduling (OMS) that maximizes the throughput of cellular networks is investigated. In a cellular network, the base station (BS) transmits the same information to multiple users within a cell. Recently, Gopala et al. proposed a median-user static OMS scheme in which a BS selects and broadcasts to the best 50% users in each transmission until all users are served. This scheduling scheme has been shown to exploit both the multiuser diversity and the multicasting gain simultaneously. In this work, we examine the optimal user selection ratio in each transmission. Based on extreme value theory, we first derive the limiting distribution of the maximum decodable rate of the M-th best user and the corresponding average number of transmissions required to serve all users. Then, the optimal selection ratio is established by maximizing the average network throughput. Furthermore, a dynamic selection algorithm is proposed to adjust the user selection ratio adaptively in each transmission. It is shown by simulation results that the OMS scheme with optimized static and dynamic selected ratios outperform the conventional unicast, multicast and the median-user static OMS schemes.

A QRD-based preceded MIMO-OFDM system with reduced feedback is proposed. Unlike the conventional preceding schemes in which the receiver has to feed back information about the channel frequency response of each carrier to the transmitter, the proposed system converts the MIMO-OFDM channel into layered channels by effectively exploiting the QR decomposition of the time-domain channel impulse response matrix. As a result, the receiver in the proposed system only needs to feed back information about one preceding matrix, regardless of the total number of carriers. Furthermore, a computationally efficient implementation scheme is devised for the proposed system. Analytical and simulation results show that the proposed scheme can achieve impressive BER performance compared to the conventional schemes, yet with considerably reduced feedback.

Distributed opportunistic scheduling (DOS) protocols are proposed for multiple-input multiple-output (MIMO) ad-hoc networks with contention-based medium access. The proposed scheduling protocols distinguish themselves from other existing works by their explicit design for system throughput improvement through exploiting spatial multiplexing and diversity in a distributed manner. As a result, multiple links can be scheduled to simultaneously transmit over the spatial channels formed by transmit/receiver antennas. Taking into account the tradeoff between feedback requirements and system throughput, we propose and compare protocols with different levels of feedback information. Furthermore, in contrast to the conventional random access protocols that ignore the physical channel conditions of contending links, the proposed protocols implement a pure threshold policy derived from optimal stopping theory, i.e. only links with threshold-exceeding channel conditions are allowed for data transmission. Simulation results confirm that the proposed protocols can achieve impressive throughput performance by exploiting spatial multiplexing and diversity.

Opportunistic scheduling and beamforming schemes with reduced feedback are proposed for MIMO-OFDMA downlink systems. Unlike the conventional beamforming schemes in which beamforming is implemented solely by the base station (BS) in a per-subcarrier fashion, the proposed schemes take advantages of a novel channel decomposition technique to perform beamforming jointly by the BS and the mobile terminal (MT). The resulting beamforming schemes allow the BS to employ only one beamforming matrix (BFM) to form beams for all subcarriers while each MT completes the beamforming task for each subcarrier locally. Consequently, for a MIMO-OFDMA system with Q subcarriers, the proposed opportunistic scheduling and beamforming schemes require only one BFM index and Q supportable throughputs to be returned from each MT to the BS, in contrast to Q BFM indices and Q supportable throughputs required by the conventional schemes. The advantage of the proposed schemes becomes more evident when a further feedback reduction is achieved by grouping adjacent subcarriers into exclusive clusters and returning only cluster information from each MT. Theoretical analysis and computer simulation confirm the effectiveness of the proposed reduced-feedback schemes.

Opportunistic scheduling (OS) schemes have been proposed previously by the authors for multiuser MIMO-SDMA downlink systems with linear combining. In particular, it has been demonstrated that significant performance improvement can be achieved by incorporating low-complexity linear combining techniques into the design of OS schemes for MIMO-SDMA. However, this previous analysis was performed based on the effective signal-to-interference ratio (SIR), assuming an interference- limited scenario, which is typically a valid assumption in SDMA-based systems. It was shown that the limiting distribution of the effective SIR is of the Frechet type. Surprisingly, the corresponding scaling laws were found to follow isin log K with 0 < isin < 1, rather than the conventional log log K form. Inspired by this difference between the scaling law forms, in this paper a systematic approach is developed to derive asymptotic throughput and scaling laws based on signal-to- interference-noise ratio (SINR) by utilizing extreme value theory. The convergence of the limiting distribution of the effective SINR to the Gumbel type is established. The resulting scaling law is found to be governed by the conventional log log K form. These novel results are validated by simulation results. The comparison of SIR and SINR-based analysis suggests that the SIR-based analysis is more computationally efficient for SDMA-based systems and it captures the asymptotic system performance with higher fidelity.

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation, where the transmission rate needs to be backed off from the estimated rate to reduce the outage. It is shown that the optimal scheduling policy remains to be threshold-based, and that the rate threshold turns out to be a function of the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, a suboptimal linear backoff scheme that backs off the estimated signal-to-noise ratio (SNR) and hence the rate is proposed. The corresponding optimal backoff ratio and rate threshold can be obtained via an iterative algorithm. Finally, simulation results are provided to illustrate the tradeoff caused by increasing training time to improve channel estimation at the cost of probing efficiency.

Computing the continuous-time peak-to-average- power ratio (PAPR) of OFDM signals is computationally challenging. The pioneering work by Tellambura applies only to OFDM signals using real-valued modulation schemes. In this paper, a practical technique for evaluating the continuous-time PAPR of OFDM signals using complex modulation is presented. Using the proposed scheme, it is confirmed that the four-time oversampled discrete-time PAPR is a good approximation of the continuous-time PAPR even for complex OFDM signals. Furthermore, the proposed scheme is employed to verify some existing analytical bounds on continuous PAPR in the literature.

Distributed opportunistic scheduling is studied for wireless ad-hoc networks, where many links contend for one channel using random access. In such networks, distributed opportunistic scheduling (DOS) involves a process of joint channel probing and distributed scheduling. It has been shown that under perfect channel estimation, the optimal DOS for maximizing the network throughput is a pure threshold policy. In this paper, this formalism is generalized to explore DOS under noisy channel estimation. In such cases, the transmission rate needs to be backed off from the estimated rate to reduce outages. It is shown that the optimal scheduling policy remains threshold-based, and that the rate threshold turns out to hinge on the variance of the estimation error and be a functional of the backoff rate function. Since the optimal backoff rate is intractable, we devise suboptimal linear backoff schemes that back off the estimated signal-to-noise ratio (SNR) and hence the rate. The corresponding optimal backoff ratios and rate thresholds can be obtained via iterative algorithms. Finally, simulation results are provided to illustrate the tradeoff between increased training time to improve channel estimation and probing efficiency.

Joint estimation of the carrier frequency offset (CFO) and channel response of each active user in the uplink of an OFDMA system over time-varying channels is investigated in this work. To cope with the enormous computational complexity involved in tracking the time variations of CFOs and channels, we propose to use the parallel Schmidt Kaiman Filter (PSKF) to break down the complicated optimization problem into multiple parallel but smaller optimization problems. This results in an estimation scheme whose complexity only grows linearly with the number of users. Simulations indicate that the proposed scheme can achieve high estimation accuracy.

Opportunistic scheduling and beamforming schemes are proposed for multiuser MIMO-SDMA downlink systems with linear combining in this work. Signals received from all antennas of each mobile terminal (MT) are linearly combined to improve theeffectivesignal-to-noise-interference ratios (SINRs). By exploiting limited feedback on the effective SINRs, the base station (BS) schedules simultaneous data transmission on multiple beams to the MTs with the largest effective SINRs. Utilizing the extreme value theory, we derive the asymptotic system throughputs and scaling laws for the proposed scheduling and beamforming schemes with different linear combining techniques. Computer simulations confirm that the proposed schemes can substantially improve the system throughput.

Orthogonal frequency division multiple access (OFDMA) has recently attracted vast research attention from both academia and industry and has become part of new emerging standards for broadband wireless access. Even though the OFDMA concept is simple in its basic principle, the design of a practical OFDMA system is far from being a trivial task. Synchronization represents one of the most challenging issues and plays a major role in the physical layer design. The goal of this paper is to provide a comprehensive survey of the latest results in the field of synchronization for OFDMA systems, with tutorial objectives foremost. After quantifying the effects of synchronization errors on the system performance, we review some common methods to achieve timing and frequency alignment in a downlink transmission. We then consider the uplink case, where synchronization is made particularly difficult by the fact that each user's signal is characterized by different timing and frequency errors, and the base station has thus to estimate a relatively large number of unknown parameters. A second difficulty is related to how the estimated parameters must be employed to correct the uplink timing and frequency errors. The paper concludes with a comparison of the reviewed synchronization schemes in an OFDMA scenario inspired by the IEEE 802.16 standard for wireless metropolitan area networks.

The problem of frequency synchronization, channel estimation, and data detection for all active users in the uplink of an OFDMA system is investigated in this work. Since the exact maximum likelihood (ML) solution to this problem turns out to be too complex for practical purposes, we derive an alternative scheme that operates in an iterative fashion. At each step, the superimposed signals arriving at the base station (BS) are separated by means of the space-alternating generalized expectation-maximization (SAGE) algorithm. Each separated signal is then passed to an expectation-conditional maximization (ECM)-based processor that updates frequency estimates and performs channel estimation and data detection for each user. The resulting architecture is reminiscent of the parallel interference cancellation (PIC) receiver, where interference is generated and removed from the received signal to improve the system performance. Simulations indicate that the proposed scheme outperforms other benchmark solutions at the price of increased computational complexity

Low-complexity low-rate super-orthogonal turbo codes (SOTC) are proposed in this work to replace the implicit repetition code (RC) in ultra-wide band impulse radio (UWB-IR) systems to improve the transmission range and system throughput. Various receivers, including the matched-filter and the RAKE receivers, are examined for data detection in the additive white Gaussian noise (AWGN) channel and the indoor IEEE 802.15.3a channels. The performance of SOTC-coded UWB with perfect and imperfect timing and channel information is analyzed and corroborated by computer simulation. It is demonstrated that the SOTC-based UWB-IR system can achieve significant performance improvement over the conventional direct-sequence UWB (DS-UWB) system encoded by RC over both ISI and ISI-free channels.

Low-complexity low-rate super-orthogonal turbo codes (SOTC) are proposed in this work to replace the implicit repetition code (RC) in an ultra-wide band impulse radio (UWB-IR) system to improve BER, transmission range and system throughput. Various receivers including matched-filter and RAKE receivers are adopted for data detection in the additive white Gaussian noise (AWGN) channel and the indoor IEEE 802.15.3a channel. The performance of SOTC-encoded UWB is analyzed and validated by computer simulation. It is shown that the SOTC-based UWB-IR system can achieve significant performance improvement over the conventional DS- UWB system encoded by RC.

A frequency-domain pre-equalization scheme for single-carrier space-division multiple-access (SC-SDMA) downlink transmissions is proposed in this work. In the system of our interest, both the base station (BS) and mobile terminals (MTs) are equipped with multiple antennas. It operates in a time-division-duplex (TDD) mode and the channel reciprocity between alternative uplink and downlink transmissions is exploited to feed the channel state information (CSI) back to the transmit side. Pre-filtering coefficients are designed so as to minimize the sum of mean-squared-errors at all MTs under an overall transmit power constraint. Several strategies are investigated for linearly combining the received signals at each MT in the frequency domain. Simulation results are provided to demonstrate the effectiveness of the proposed scheme.

For the uplink transmission of a coded OFDMA system, we present an iterative receiver that performs joint frequency offset acquisition, channel estimation and maximum a posteriori (MAP)-based decoding for each active user in this work. The proposed receiver attempts to separate users' combined signals by resorting to the space-alternating generalized expectation-maximization (EM) algorithm. Each separated user's signal is then passed to an expectation-conditional maximization (ECM)-based processor that jointly performs frequency acquisition, channel estimation and decoding at each iteration. As compared with conventional OFDMA systems that employ hard- decision equalization and decoding, the proposed receiver can exploit the soft-decision feedback derived from the MAP decoder to provide more reliable synchronization, channel estimation and interference suppression. Simulations indicate that the proposed scheme provides accurate decoding for unsynchronized OFDMA uplink transmissions over frequency-selective fading channels.

Maximum-likelihood estimation of the carrier frequency offset (CFO), timing error, and channel response of each active user in the uplink of an orthogonal frequency-division multiple-access system is investigated in this study, assuming that a training sequence is available. The exact solution to this problem turns out to be too complex for practical purposes as it involves a search over a multidimensional domain. However, making use of the alternating projection method, we replace the above search with a sequence of mono-dimensional searches. This results in an estimation algorithm of a reasonable complexity which is suitable for practical applications. As compared with other existing semi-blind methods, the proposed algorithm requires increased overhead but has more flexibility as it can be used with any subcarrier assignment scheme. Simulations indicate that the accuracy of the CFO estimates asymptotically achieves the Cramer-Rao bound.

In this work we consider the uplink of an OFDMA system and present a novel receiver that performs joint frequency offset acquisition, channel estimation and data detection for each active user. The proposed receiver can be used with any subcarrier assignment scheme, and it operates in an iterative fashion. Users' separation is accomplished at the base station through space-alternating generalized-expectation (SAGE) techniques. Each separated user's signal is then passed to an expectation-conditional maximization (ECM)-based processor that jointly performs frequency acquisition, channel estimation and data detection at each iteration. Compared to conventional OFDMA systems, the proposed receiver allows significant reduction of the synchronization overhead as it dispenses from the need of returning estimated frequency offsets back to active users for frequency adjustment. Simulations indicate that the proposed scheme provides accurate data detection for unsynchronized OFDMA uplink transmissions over doubly-selective fading channels.

We consider the uplink of an OFDMA system and address the problem of estimating the carrier frequency offset, timing error and channel response of each active user. In doing so we follow a maximum likelihood (ML) approach and assume that a training sequence is available. Unfortunately, joint ML estimation of all the above parameters involves a multi-dimensional grid-search that is difficult to implement in practical systems. Therefore we resort to the alternating-projection algorithm and replace the multidimensional search with a sequence of one-dimensional searches. Compared to other existing methods, the proposed estimator has more flexibility since it can be used with any subcarrier assignment scheme. Simulations indicate that the accuracy of the frequency estimates asymptotically achieve the relevant Cramer-Rao bound (CRB).

A maximum likelihood estimator (MLE) that jointly estimates the carrier frequency offset (CFO) and the channel response of each user in uplink OFDMA systems is investigated in this research. The proposed MLE distinguishes itself from existing methods by its applicability to more flexible carrier assignment schemes. It achieves high computational efficiency by transforming a multidimensional optimization problem into a one-dimensional optimization problem. A suboptimal method is developed to further reduce the computational complexity. It is demonstrated by simulation results that the proposed MLE can provide accurate CFO and channel estimation in both SISO and SIMO environments.

Most previously reported carrier frequency off-set (CFO) estimation algorithms for OFDM systems rely on the assumption of sufficient cyclic prefix (CP) - i.e. the channel length is less than the length of CP. In practice, this can be violated leading to significant performance degradation characterized by an irreducible error floor due to model mismatch. This paper focusses on CFO estimation for uncompensated ISI channels - it introduces a modified signal model and by exploiting the special structure of the filtering matrix due to CP and virtual carriers, a novel subspace CFO estimator is proposed. The method is attractive for its low complexity by avoiding SVD computation and potential to achieve high channel utilization by decreasing the length of CP in ISI channels. Preliminary computer simulations illustrate the effectiveness of the proposed algorithm.