Federated learning (FL) has provided a new methodology for coordinating a group of clients to train a machine learning model collaboratively, bringing an efficient paradigm in edge intelligence. Despite its promise, FL faces critical challenges in Internet of Things (IoT) networks, particularly the combined impact of data heterogeneity and communication delays. This paper examines the theoretical and empirical impact of these factors in Asynchronous Federated Learning (AFL). Initially, we categorize existing memoryless asynchronous strategies as Asynchronous Updates with Delayed Gradients (AUDG). Our theoretical analysis of AUDG reveals the coupling effect of delays exacerbate the adverse impact of data heterogeneity, causing the global model to drift towards frequently active clients. To address this, we propose a gradient reusing mechanism, termed Pseudo-Synchronous Updates by Reusing Delayed Gradients (PSURDG). By leveraging storage to reuse historical gradients, PSURDG effectively decouples the correlation between delay and data heterogeneity. Crucially, we conducted a comprehensive convergence analysis covering both convex and non-convex settings, confirming the algorithm’s effectiveness in diverse optimization landscapes. Finally, both schemes are validated through rigorous analysis and extensive simulations on multiple datasets. The results demonstrate a clear trade-off that AUDG remains efficient under low data heterogeneity, while PSURDG improves convergence in high-heterogeneity scenarios with moderate delays, thereby providing a theoretical guideline for aggregation strategy selection in different edge intelligence scenarios.

As a prevalent field of study in machine learning, intelligent agents can perceive surroundings and make informed decisions. In many research areas such as autopilot systems, undersea explorations, and distributed robotics, researchers have traditionally employed unidirectional systems, which usually rely on perceptions from sensors to controllers, or end-to-end models, which generate actions directly from raw data. Nonetheless, in unidirectional systems, controller efficiency is intrinsically linked to sensor accuracy, which makes unidirectional systems lack a self-improving capability. Meanwhile, compared with functionally separated frameworks, end-to-end methods may have their own limitations in scalability, generality, interoperability, training costs, and so on. To fulfill this gap, we propose a new Coordinated Optimization Framework for Intelligent Agents (COIA). We introduce an inverted optimization channel from controllers to sensors in traditional functionally separated frameworks through communication between devices, enabling closed-loop online evolutive learning. To the best of our knowledge, this paper first presents a universal coordinated optimization framework among supervised learning and RL models, without human labels or intervention. Our method allows heterogeneous agents to autonomously adapt to some special situations in open environments, which forms a basis of networked Artificial General Intelligence (AGI). We design an experimental paradigm of COIA with concrete cases, which shows a significantly large performance margin over unidirectional and end-to-end models. The performance margin grows with task complexity.

Unmanned aerial vehicle (UAV) swarms are extensively used in emergency communications, area monitoring, and disaster relief. Their operations are coordinated by control centers, making them well-suited for federated learning (FL) frameworks. However, current UAV FL methods ignore the rich information contained in UAV images and the potential of using a single dataset to accomplish multiple tasks. For instance, in disaster relief scenarios, images acquired by UAVs can support tasks like crowd detection, road passability analysis, and disaster impact assessment. These tasks exhibit time-varying demands and may have potential correlations. To meet these requirements, this paper introduces two core mechanisms: a dynamic task attention mechanism to evaluate task importance for efficient resource allocation, and a task affinity (TA) metric to capture inter-task correlations for knowledge sharing. Building on these innovations, we propose FedDya, a novel UAV swarm-based one-dataset multi-task FL framework, where ground emergency vehicles (EVs) collaborate with UAVs to accomplish multiple tasks leveraging a single dataset. To optimize resource allocation, we formulate a two-layer optimization problem to jointly optimize UAV transmission power, computation frequency, bandwidth allocation, and UAV-EV associations. For the inner problem, we derive closed-form solutions for transmission power, computation frequency, and bandwidth allocation and apply the block coordinate descent method for optimization. For the outer problem, a novel two-stage algorithm is designed to determine optimal UAV-EV associations. Furthermore, theoretical analysis reveals a trade-off between UAV energy consumption violation and multi-task performance, characterized by anO( √V, 1/V) relationship. Extensive simulation results further validate the effectiveness of the proposed scheme.

The rapid development of Unmanned aerial vehicles (UAVs) technology has spawned a wide variety of applications, such as emergency communications, regional surveillance, and disaster relief. Due to their limited battery capacity and processing power, multiple UAVs are often required for complex tasks. In such cases, a control center is crucial for coordinating their activities, which fits well with the federated learning (FL) framework. However, conventional FL approaches often focus on a single task, ignoring the potential of training multiple related tasks simultaneously. In this paper, we propose a UAV-assisted multi-task federated learning scheme, in which data collected by multiple UAVs can be used to train multiple related tasks concurrently. The scheme facilitates the training process by sharing feature extractors across related tasks and introduces a task attention mechanism to balance task performance and encourage knowledge sharing. To provide an analytical description of training performance, the convergence analysis of the proposed scheme is performed. Additionally, the optimal bandwidth allocation for UAVs under limited bandwidth conditions is derived to minimize communication time. Meanwhile, a UAV-EV association strategy based on coalition formation game is proposed. Simulation results validate the effectiveness of the proposed scheme in enhancing multi-task performance and training speed.

Cellular-connected UAV systems have enabled a wide range of low-altitude aerial services. However, these systems still face many challenges, such as frequent handovers and the inefficiency of traditional transport protocols. To better study these issues, we develop a modular and scalable simulation platform specifically designed for UAVs communication leveraging the research ecology in wireless communication of MATLAB. The platform supports flexible 5G NR node deployment, customizable UAVs mobility models, and multi-network-interface extensions. It also supports multiple transport protocols including TCP, UDP, QUIC, etc., allowing to investigate how different transport protocols affect UAVs communication performance. In addition, the platform includes a handover management module, enabling the evaluation of both traditional and learning-based handover strategies. Our platform can serve as a testbed for the development and evaluation of advanced transmission strategies in cellular-connected UAV systems.

Event cameras offer unique advantages in scenarios involving high speed, low light, and high dynamic range, yet their asynchronous and sparse nature poses significant challenges to efficient spatiotemporal representation learning. Specifically, despite notable progress in the field, effectively modeling the full spatiotemporal context, selectively attending to salient dynamic regions, and robustly adapting to the variable density and dynamic nature of event data remain key challenges. Motivated by these challenges, this paper proposes EventMG, a lightweight, efficient, multilevel Mamba-Graph architecture designed for learning high-quality spatiotemporal event representations. EventMG employs a multilevel approach, jointly modeling information at the micro (single event) and macro (event cluster) levels to comprehensively capture the multi-scale characteristics of event data. At the micro-level, it focuses on spatiotemporal details, employing State Space Model (SSM) based Mamba, to precisely capture long-range dependencies among numerous event nodes. Concurrently, at the macro-level, Component Graphs are introduced to efficiently encode the local semantics and global topology of dense event regions. Furthermore, to better accommodate the dynamic and sparse characteristics of data, we propose the Spatiotemporal-aware Event Scanning Technology (SEST), integrating the Adaptive Perturbation Network (APN) and Multidirectional Scanning Module (MSM), which substantially enhances the model's ability to perceive and focus on key spatiotemporal patterns. By employing this novel collaborative paradigm, EventMG demonstrates the ability to effectively capture multi-level spatiotemporal characteristics of event data while maintaining a low parameter count and linear computational complexity, suggesting a promising direction for event representation learning.

This paper proposes an autoencoder (AE) framework with transformer encoder and extended multilinear mixing model (EMLM) embedded decoder for nonlinear hyperspectral anomaly detection. Specifically, the proposed AE frame-work adopts the transformer as the encoder so that not only the local spatial information, but also the transitive global spatial information can be considered, and the EMLM is embedded into the decoder to accurately characterize the high-order nonlinear mixing phenomenon. By using this AE framework, the background of HSIs can be reconstructed accurately. Finally, the anomalous level of pixel is computed by the reconstruction error. The experimental results on two real hyperspectral datasets demonstrate that the proposed method outperforms the current state-of-the-art (SOTA) anomaly detectors.

Lithium-ion batteries, which are vital for powering mobile devices, experience performance degradation over time due to capacity fading and other aging phenomena, thereby presenting safety risks. This work introduces the DeTransformer-Physics model, a predictive model for battery capacity. This model synergistically integrates physical knowledge and a de-noising autoencoder to enhance predictive accuracy. Firstly, it employs a denoising autoencoder as a preprocessing step to reconstruct denoised input data, thereby facilitating the extraction of more effective input data. Subsequently, a model describing the procedure for lithium-ion battery capacity degradation is derived and incorporated into a neural network, with the model training constrained by physical principles. Finally, using the lithium-ion battery degradation dataset from NASA, we demonstrate that incorporating the denoising autoencoder and embedding physical knowledge substantially improves the predictive accuracy of the model.

The continuous increase in carbon dioxide (CO2) concentrations has become a primary driver of global warming. Tracking CO2levels in high-emitting enterprises is crucial for achieving carbon neutrality. Although high-precision ground-based sensors provide real-time monitoring, their widespread deployment is limited by cost, leading to data gaps in certain areas. To tackle this issue, we design an aerial-ground cooperative sensing system that enables three-dimensional CO2observation of high-emitting enterprises. The system comprises a long-term monitoring module employing ground-based sensors and a periodic monitoring module using Unmanned Aerial Vehicles (UAVs) with portable sensors. Based on the collected data, we develop a Kriging model to estimate the CO2concentrations at unobserved locations and construct a fine-grained three-dimensional CO2distribution map. The experimental results demonstrate that our proposed scheme can achieve large-scale CO2assessment using limited sensing resources, thereby serving as a valuable reference for effectively regulating corporate CO2emissions. Our implementation has been deployed around a high-emitting enterprise in Nanjing since February 2024.

The beamforming technique has been widely used to improve the link budget in wireless communications. Compared with the digital beamformer, the analog beamformer has much lower hardware complexity and is more suitable for low-cost mobile applications. In this paper, we consider element-level phase and gain control of the analog beamformer using two phase shifters only. By setting the phase shifts properly, simultaneous 360◦phase and 6-dB gain control (SPGC) can be achieved to form the beam pattern. We first propose a low-complexity SPGC method tailored for massive multiple-input multiple-output (MIMO). Based on the conventional and proposed SPGC methods, we then design the full-featured accelerator (FFA) and the hardware-efficient accelerator (HEA) to accelerate the computing process. These two accelerators are implemented in 28 nm technology. FFA integrates 137 kilogate equivalents (kGE) in a core area of 0.0693 mm2and dissipates 104.4 mw at 2.0 GHz with 16 degrees of parallelism, while HEA can reduce the core area by 44.6% and power consumption by 42.7% without significant performance loss.

Event cameras provide exceptionally high temporal resolution in dynamic vision systems due to their unique event-driven mechanism. However, the sparse and asynchronous nature of event data makes frame-based visual processing methods inappropriate. This study proposes a novel framework, Event-based Graph Spatiotemporal Sensitive Transformer (EGSST), for the exploitation of spatial and temporal properties of event data. Firstly, a well-designed graph structure is employed to model event data, which not only preserves the original temporal data but also captures spatial details. Furthermore, inspired by the phenomenon that human eyes pay more attention to objects that produce significant dynamic changes, we design a Spatiotemporal Sensitivity Module (SSM) and an adaptive Temporal Activation Controller (TAC). Through these two modules, our framework can mimic the response of the human eyes in dynamic environments by selectively activating the temporal attention mechanism based on the relative dynamics of event data, thereby effectively conserving computational resources. In addition, the integration of a lightweight, multi-scale Linear Vision Transformer (LViT) markedly enhances processing efficiency. Our research proposes a fully event-driven approach, effectively exploiting the temporal precision of event data and optimising the allocation of computational resources by intelligently distinguishing the dynamics within the event data. The framework provides a lightweight, fast, accurate, and fully event-based solution for object detection tasks in complex dynamic environments, demonstrating significant practicality and potential for application.

Interactive behavior-aware planning benefits from the hierarchical learning process when adapting to dense traffic. However, the difficulty in the Intelligent Transportation System (ITS) is that the autonomous vehicle fails to execute real-time response due to hardly perceiving dynamic objects beyond the visual range. This problem can be tackled by vehicle-road-cloud cooperation that synchronously collects global perception information and makes strategic policy for deployment. Here we propose a hierarchical edge-decision framework, which addresses real-time motion skill that distills from analogical reasoning of spatial-temporal events. The first step is establishing the goal-conditioned motion library from the centralized edge-cloud perception, to compose the belief-based best response with collision avoidance. In addition, a novel perspective of latent space is presented to promote motion rehearsal in the cloud, which could generate prior credible trajectories based on the policy distillation procedure of extracting informative action from thoroughly exploring changing events. Moreover, the two-stage hierarchy decision is developed to boost the efficiency of advanced policy modification, through evaluating the hierarchical judgment matrices considering conditional criteria, thereby constituting an optimum auto-driving motion with vehicle-road-cloud collaborative system. Extensive validation on challenging autonomous driving scenarios outperforms, demonstrating that our edge-decision method significantly promotes adaption to the complex time-varying environment in ITS system in a smooth and sustainable manner.

Federated learning has provided a new approach to coordinating a group of clients to train a machine learning model collaboratively, which can be easily embedded into Unmanned Aerial Vehicle (UAV) swarms. Compared with the terrestrial wireless networks, the UAV swarm faces more precarious communication conditions, rendering synchronous aggregation no longer tenable. Additionally, the data collected from UAVs tend to be heterogeneous due to different deployment regions or requirements. To overcome these restrictions, this paper has proposed a novel two-stage Asynchronous Federated Learning scheme for the UAV swarm. Initially, the convergence property of both convex and non-convex models trained by the proposed scheme is analyzed. In the pre-training stage, we modeled the learning process as a cooperative game with demonstrated monotonicity and submodularity. Furthermore, the Shapley Value is imported to quantify data values of UAVs, and the upper bound of its estimation error rate is derived. In the training stage, a new concept named Network Age of Updates (AoU) is proposed to address the fairness issue, quantifying the model’s generalization capability with data value consideration, and a sequential UAV selection scheduling is performed through the AoU minimization by Whittle Index method. Finally, the system performance is validated through both theoretical analysis and simulations.

Nowadays, the Unmanned Aerial Vehicle (UAV) has emerged as a powerful platform for diversified application development. In traffic areas specifically for the much-anticipated Intelligent Transportation System (ITS), UAVs can coordinate with the ground internet of things infrastructure, such as the Road Side Unit (RSU), to perform 3D data collection and processing, improving traffic safety and transportation efficiency. In ITS, decision-making is crucial and relies heavily on proper and timely processing and transmission of the massive amounts of data generated by ubiquitous sensors, especially in emergency disposal scenarios. In this paper, the latency incurred from RSU-aided data offloading and UAV-aided data relay plays a decisive role, characterized by both the average and risk performance metric, Conditional Value at Risk (CVaR), for quantifying the risk incurred from the latency violation over a certain threshold in probability. Specifically, the peak Age of Information (AoI) with its distribution is adopted to underlying the CVaR analysis in relay latency. Besides, the matching algorithm to coordinate the mutual transmission between UAVs and RSUs is proposed, achieving low computation complexity and minimum risk for both sides. The system performance is validated through analysis, simulation, and field experiments.

Sampling and interpolation of continuous graph signals have been extensively studied, in order to reconstruct or estimate the entire graph signal from the signal values on a subset of vertices. Whereas in a lot of real-world scenarios, only the signs of signals are available. For example, a rating system may only provide simple options such as "like" or "dislike". We are interested in whether it is possible to recover the original signal from such coarse information. In this paper, the reconstruction of bandlimited graph signals based on sign measurements is discussed and a greedy sampling strategy is proposed. The simulation experiments are presented, and the greedy sampling algorithm is compared with the random sampling algorithm, which verifies the feasibility of the proposed approach.

Capturing complex high-order interactions among data is an important task in many scenarios. A common way to model high-order interactions is to use hypergraphs whose topology can be mathematically represented by tensors. Existing methods use a fixed-order tensor to describe the topology of the whole hypergraph, which ignores the divergence of different-order interactions. In this work, we take this divergence into consideration, and propose a multi-order hypergraph Laplacian and the corresponding total variation. Taking this total variation as a regularization term, we can utilize the topology information contained by it to smooth the hypergraph signal. This can help distinguish different-order interactions and represent high-order interactions accurately.

The problem of anomaly detection among multiple processes is considered within the framework of sequential design of experiments. The objective is an active inference strategy consisting of a selection rule governing which process to probe at each time, a stopping rule on when to terminate the detection, and a decision rule on the final detection outcome. The performance measure is the Bayes risk that takes into account not only sample complexity and detection errors, but also costs associated with switching across processes. While the problem is a partially observable Markov decision process to which optimal solutions are generally intractable, a low-complexity deterministic policy is shown to be asymptotically optimal and offer significant performance improvement over existing methods in the finite regime.

This paper investigates the active sampling for estimation of approximately bandlimited graph signals. With the assistance of a graph filter, an approximately bandlimited graph signal can be formulated by a Gaussian random field over the graph. In contrast to offline sampling set design methods which usually rely on accurate prior knowledge about the model, unknown parameters in signal and noise distribution are allowed in the proposed active sampling algorithm. The active sampling process is divided into two alternating stages: unknown parameters are first estimated by Expectation Maximization (EM), with which the next node to sample is selected based on historical observations according to predictive uncertainty. Validated by simulations compared with related approaches, the proposed algorithm can reduce the sample size to reach a certain estimation accuracy.

Compared with single image based crowd counting, video provides the spatial-temporal information of the crowd that would help improve the robustness of crowd counting. But translation, rotation and scaling of people lead to the change of density map of heads between neighbouring frames. Meanwhile, people walking in/out or being occluded in dynamic scenes leads to the change of head counts. To alleviate these issues in video crowd counting, a Locality-constrained Spatial Transformer Network (LSTN) is proposed. Specifically, we first leverage a Convolutional Neural Networks to estimate the density map for each frame. Then to relate the density maps between neighbouring frames, a Locality-constrained Spatial Transformer (LST) module is introduced to estimate the density map of next frame with that of current frame. To facilitate the performance evaluation, a large-scale video crowd counting dataset is collected, which contains 15K frames with about 394K annotated heads captured from 13 different scenes. As far as we know, it is the largest video crowd counting dataset. Extensive experiments on our dataset and other crowd counting datasets validate the effectiveness of our LSTN for crowd counting. All our dataset are released in https://github.com/sweetyy83/Lstn_fdst_dataset.

Hyperspectral image classification with limited training samples has become a hot research topic recently. Though deep convolution neural network shows powerful ability for feature extraction, its good performance often relies on sufficient training data. In this paper, we propose a multitask learning framework based on siamese network and auto-encoder to fully exploit limited labeled samples’ information and obtain discriminative features for classification of hyperspectral images. A low intraclass and high interclass variability of features can be learned by metric learning using our framework. And superpixel-based 3D sample preprocessing is applied to improve the classification accuracy on the hyperspectral images’ boundaries. The experimental results demonstrate that our framework can achieve competitive results compared with the state-of-the-art methods.

The imbalance between high dimensionality and limited labeled samples has been a great challenge for classification task of hyperspectral images (HSIs). In this paper, a novel semi-supervised classification method for HSIs is proposed. This method contains two major parts: representation using spatial-spectral graph model and graph convolutional networks (GCN) using edge-conditioned convolution. For the proposed method, spatial-spectral information is considered simultaneously during the process of graph construction, and then GCN is used to extract the feature from input data and learn their topology relationships with edge label involved. Experimental results on multiple hyperspectral datasets with various contexts and resolutions demonstrate that the proposed classifier outperforms several graph-based methods.

Wavelength dependent nonlinearity is an essential issue in hyperspectral unmixing, which was overlooked in the past. In this paper, a band-wise nonlinear unmixing method is presented. An extended multilinear mixing model is adopted for interpreting different degrees of nonlinear contributions per band. Moreover, regularizers including abundances' sparsity and nonlinear parameters' smoothness are exploited to formulate the optimization problem and obtain better unmixing results. Finally, unmixing is implemented in the scheme of alternating direction method of multipliers. Experimental results on both simulated and real hyperspectral data validate that the proposed method can improve the unmixing accuracy and reveal the change of nonlinearity at each band as well.

Wireless communication system incorporating unmanned aerial vehicles (UAVs) has gained much popularity recently, especially in video transmission application. This paper investigates the base station (BS) selection scheme for cellular-connected UAVs that possess the function of video collection and streaming to BS for online decision in remote processing center. We aim to minimize the expected access latency while the throughput requirement is satisfied. To this end, a sequential BS selection scheme is proposed by designing an optimal transmission rate threshold for each candidate BS. Due to the mission-driven nature, the access rate varies as the UAV moves, so an effective average transmission rate rather than instantaneous transmission rate is considered. A recursive algorithm is proposed to obtain the rate thresholds which can be used to guide whether UAV should stop or continue measuring the links of the remaining candidate BSs. The proof of the optimality of this algorithm is given. Simulation results validate the effectiveness of the proposed scheme on access latency performance compared with conventional throughput-oriented scheme.

We consider a device-to-device (D2D) underlaid cellular network, where each cellular channel can be shared by several D2D pairs and only one channel can be allocated to each D2D pair. We try to maximize the sum rate of D2D pairs while limiting the interference to cellular links. Due to the lack of global information in large scale networks, resource allocation is hard to be implemented in a centralized way. Therefore, we design a novel distributed resource allocation scheme which is based on local information and requires little coordination and communication between D2D pairs. Specifically, we decompose the original problem into two cascaded subproblems, namely channel allocation and power control. The cascaded structure of our scheme enables us to cope with them respectively. Then a two-stage algorithm is proposed. In the first stage, we model the channel allocation problem as a many-to-one matching with externalities and try to find a strongly swap-stable matching. In the second stage, we adopt a pricing mechanism and develop an iterative two-step algorithm to solve the power control problem.

In device-to-device (D2D) underlaid cellular networks, several D2D pairs can share one channel to improve the system throughput. Most existing works allow D2D pairs to reuse all the channels, which may incur complicated interference management. Therefore, each D2D pair is limited to reuse at most one channel to reduce overhead. We aim to maximize the throughput of D2D pairs while suppressing the interference to cellular links. However, the optimization problem is an intractable mixed integer non-linear programming (MINLP) problem. Meanwhile, as network size increases, acquiring the global channel state information (CSI) is expensive even impossible in practice. Therefore, we propose a novel local CSI-based distributed channel-power allocation scheme. The base station (BS) broadcasts a control signal to indicate its received interference from D2D pairs. Upon this signal, each D2D pair executes channel selection and power update independently and iteratively. Specifically, the channel allocation problem is formulated as a many-to-one matching game with externalities. The power control problem is modeled as a Stackelberg game. We prove the existence of two-sided swap-stable matching and show that the outcomes of the Stackelberg game are locally optimal under some mild conditions. Simulation results show that our scheme is efficient with low overhead.

Caching at the small base station (SBS) is a promising architecture to alleviate the highly-loaded wireless video networks. SBS can cache popular video files, thus serves mobile users without going through backhaul connection to the core network and provides content- level offloading. This paper proposes a novel method for the content caching problem that optimizes cache performance. Our proposed algorithm learns the content popularity profile by predicting the probability of files to be requested, and then refreshes the cache based on the learned content popularity. Popularity learning method runs in an online fashion and has no assumption of the file requests, thus it can be used for predicting either fixed or time-varying popularity. Our simulation results show that our proposed algorithm has similar performance compared to the traditional algorithms when the content popularity profile is fixed, and performs better than other algorithms when the content popularity profile is time-varying, which is more realistic.

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as starvation probability and buffering time interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on the real traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the views can be categorized into two classes, short and long views with separated time scales. We then map the measured traffic pattern to bandwidth shared cellular networks and propose an analytical framework to compute the closed-form starvation probability on the basis of ordinary differential equations (ODEs). Our framework can be naturally extended to investigate practical issues including the progressive downloading and the finite video duration. Extensive trace-driven simulations validate the accuracy of our models. Our study reveals that the starvation metrics of the short and long views possess different sensitivities to the scheduling priority at base station (BS). Hence, a better QoE tradeoff between the short and long views has a potential to be leveraged by offering them different scheduling weights. The flow differentiation involves tremendous technical and non-technical challenges because video content is owned by content providers but not the network operators and the viewing time of each session is unknown beforehand. To overcome these difficulties, we propose an online Bayesian approach to infer the viewing time of each incoming flow with the “least” information from content providers.

Distributed filtering in network is a fundamental problem in the field of network signal processing. Each node estimates or tracks some unknown state relying on the private observation and the fusion information from the network. Network fusion is generally a way of interaction over network, by which nodes can learn from each other and make decision mutually. Unlike conventional methods, we construct a distributed filter using Bayesian network game as a fusion tool, where all the nodes exchange their best strategies instead of exchanging local estimators. The proposed algorithm is a coalition of signal processing and game theory in network, which can be extended to more general signal processing and decision making models.

The explosion of mobile video traffic imposes tremendous challenges on present cellular networks. To alleviate the pressure on backhaul links and to enhance the quality of experience (QoE) of video streaming service, small cell base stations (SBS) with caching ability are introduced to assist the content delivery. In this paper, we present the first study on the optimal caching strategy of scalable video coding (SVC) streaming in small cell networks with the consideration of channel diversity and video scalability. We formulate an integer programming problem to maximize the average subjective quality of SVC streaming under the constraint of cache size at each SBS. By establishing connections between subjective quality and caching state of each video, we simplify the proactive caching of SVC as a multiple-choice knapsack problem (MCKP), and propose a low-complexity algorithm using dynamic programming. Our proactive caching strategy reveals the structural properties of cache allocation to each video based on their popularity profiles. Simulation results manifest that the SBSs with caching ability can greatly improve the average quality of SVC streaming, and that our proposed caching strategy acquires significant performance gain compared with other conventional caching policies.

The device-to-device (D2D) communication theoretically provides both the cellular traffic offloading and convenient content delivery directly among proximity users. However, in practice, no matter in underlay or overlay mode, the employment of D2D may impair the performance of the cellular links. Therefore, it is important to design a spectrum sharing scheme, under which the performance of both links can be improved simultaneously. In this paper, we consider the cell-edge user (CEU) scenario, where both sides have the demand to improve the quality of experience or service. Therefore, CEUs and D2D users both have intentions to form pairs, namely, CEU-D2D pairs, to cooperate mutually. Different from the conventional equilibrium point evaluation, the stable matching between D2D users and CEUs are formulated under matching theory framework instead. For each CEU-D2D pair, a two-stage pricing-based Stackelberg game is modeled to describe the willingness to cooperate, where the win-win goal is reached finally.

In this paper, we consider the scenario of the mobile network operator (MNO) incentivizing femtocell access points (FAPs) to form a shared network. We propose an incentive mechanism under which the licensed femtocell user (FU) of each FAP can use a portion of another FAP's spectrum resources when moving into that FAP's coverage. The FAPs are rewarded based on both the amount of provided resources and the quality of service (QoS). We formulate the problem as a Stackelberg game. The MNO acts as the leader to decide incentive price. When observing the price, each FAP decides the amount of provided resources according to its type information (resource constraint, QoS and especially its licensed FU's mobility pattern). The best response functions of FAPs are first obtained and the existence of the Nash Equilibrium (NE) is investigated. And then we investigate the optimal strategy of the MNO given the FAPs' strategies. Simulation results show that the proposed mechanism can effectively motivate FAPs to share their resources with each other. We will also mainly analyze the influence of FUs' mobility patterns on their adopted strategies.

In this paper, a low-complexity sparse channel estimation scheme is proposed for massive three dimensional multi-input multi-output (3D-MIMO) systems. In outdoor propagation environment, 3D-MIMO channels exhibit joint sparseness in both temporal and angular domains, sharing the common support in delay domain. By taking prior knowledge of the structured sparseness, the proposed heuristic channel estimation method can greatly reduce the complexity of channel estimation, and achieve a near optimal performance. Simulation results verify the effectiveness of the proposed algorithm.

This paper investigates simultaneous wireless information and power transfer (SWIPT) in K-user multiple-input multiple-output (MIMO) interference channels. In particular, the power splitting (PS) technique is leveraged at each receiver to divide the received signal into two flows, for information decoding (ID) and energy harvesting (EH), respectively. As a whole system, our objective is to minimize the total transmit power of all transmitters by jointly designing transmit beamformers, power splitters, and receive filters, subject to the signal-to-interference-plus-noise ratio (SINR) constraint for ID and the harvested power constraint for EH at each receiver. Due to the coupling nature of all variables, the formulated joint transceiver design problem is nonconvex, and has not yet been well addressed in the literature. In this paper, we first propose a semidefinite relaxation-based alternating optimization (SDRAO) solution to approach the optimal solution of the problem. Then, we semidecouple the joint optimization by the derived diversity interference alignment (DIA) technique, and obtain a solution of lower complexity. Finally, a closed-form solution is further developed relying on the transmitter-side zero-forcing (TZF), which can be implemented in a distributed manner, with the lowest computational complexity and CSI exchanging overhead.

In today WSNs, sensor nodes are able to obtain energy from ambient with energy-harvesting components. However, the energy consumption are diverse across these nodes due to functional or geographical variation, which may lead to potential energy imbalance in network. In virtue of recent wireless power transfer (WPT) technology, the imbalance can be alleviated if all sensor nodes share energy with each other. In order to achieve the maximum energetically sustainable workload, we design an energy cooperation strategy in network by WPT, named energy routing, which should be jointly optimized with data routing simultaneously. An iterative distributed algorithm is developed to achieve the optimal data routing and energy routing solutions, where all sensors only need to exchange local information with neighbors. Simulation results show that the proposed algorithm can achieve higher workload than algorithms without energy cooperation.

This paper investigates the beamforming for three- dimensional multiple input multiple output (3D-MIMO) systems with inaccurate channel state information (CSI). From the view of angle-domain, the 3D-MIMO channel is sparse on the high 3D resolution provided by planar antenna array with large number of antenna elements at the base station (BS) in 3D-MIMO systems. Prior knowledge of sparsity is not only beneficial to channel estimation in literature, but also implies more efficient beamforming with inaccurate CSI at transmitters as discussed in this paper. We prove that the optimal beamforming vector is correspondingly sparse in angle-domain with a sparse channel. Therefore, we add the ℓ1-norm penalty to the beamforming vector in optimization design in angle-domain, which can fight against the perturbation due to inaccurate CSI. Technically, the problem is reformulated as a second order cone program (SOCP) form that can be solved efficiently. Simulation results demonstrate that the proposed beamforming method can achieve considerable system sum-rate improvement with high CSI error.

Unraveling quality of experience (QoE) of video streaming is very challenging in bandwidth shared wireless networks. It is unclear how QoE metrics such as buffering time and starvation behavior interact with dynamics of streaming traffic load. In this paper, we collect view records from one of the largest streaming providers in China over two weeks and perform an in-depth measurement study on flow arrival and viewing time that shed light on realistic streaming traffic pattern. Our most important observation is that the viewing time of streaming users fits a hyper-exponential distribution quite well. This implies that all the videos can be categorized into two classes, short and long viewing time with separated time scales. We then map the traffic pattern of large-scale measurement to bandwidth sharing cellular networks. We propose two models to compute the close-form starvation probability and mean sojourn time on the basis of ordinary differential equations (ODEs). Extensive trace-driven simulations validate their accuracy. The proposed models precisely capture how the QoE metrics of video streaming in each class are influenced by the scheduling algorithms at a base station.

This paper focuses on the simultaneous wireless information and power transfer (SWIPT) in coordinated multi-cell networks. In particular, the considered system has a location-aware feature, i.e., the conventional coordinated multi-point (CoMP) information transmission is available to the cell-edge users; meanwhile, the emerging wireless power transmission is available to the cell-center users. Subject to the SINR constraints for information decoding at edge CoMP users and the received power constraints for energy storage at center users, our objective is to minimize the total transmit power of all coordinated beamformers. Although the celebrated semi-definite relaxation (SDR) technique can be applied to obtain a centralized optimal solution, we prefer to decompose the original problem and derive a decentralized closed-form solution to decrease the cost of inter- cell cooperation. With this low-complexity solution, some important insights are further presented on the benefit of the location-aware SWIPT setup and the potential user scheduling mechanism. Simulation results validate our derivation and show that the decentralized solution can asymptotically approach the centralized optimal solution.

Change detection for multitemporal hyperspectral images (HSIs) involves two major steps: change feature extraction and classification. For the first part, conventional methods mostly consider spectral features but neglect spatial patterns. Since multitemporal HSIs consist of four dimensions (one for time, one for spectral domain and two for spatial domain), we propose using 4-dimensional Higher Order Singular Value Decomposition (4D-HOSVD) based on tensor algebra to capture the details in all the dimensions simultaneously and thus producing comprehensive change features. To emphasize on the effectiveness of the change feature extraction method, this paper reduces the change classification to a simple binary problem: a pixel is either changed or unchanged. Experimental results show that 4D-HOSVD can outperform its matrix counterpart, Principal Component Analysis (PCA), as well as some other widely adopted method.

There are two great challenges for classification of hyperspectral images (HSIs): lack in prior knowledge and serious internal-class variability. To address the issues, we propose a novel semisupervised method based on affinity scoring (AS). It can harness the fuzzy state of the contributions of spectral and spatial features to classification. The method consists of three major steps: over-segmentation, semisupervised classification and modification. First, superpixels are generated to maintain local class consistency, which can balance spectral variability. Then unlabeled samples are classified by AS in an iterative manner, whereas precious labeled samples are made most use of. Finally, AS is adopted again to refine the classification map, which further exploits spatial smoothness in HSIs. Experiments show that the proposed method can largely outperform several state-of-the-art classifiers.

Sparse representation has been introduced to tackle the target detection problem in hyperspectral imagery. While using windows to build the sparse dictionary, there exists target contamination problem. In our approach, we utilize a learning method based on convex optimization to build a dictionary for sparse target detection. Through its application, prior information such as the size of windows can be spared, while considerably reducing the occurrence of contamination. To verify the efficacy of using the learned dictionary, the dictionary built through the dual-window method is used as a comparison and two sparse target detection methods are employed afterward. Experimental results show that, by using the learned dictionary, a better result is obtained compared to the methods using traditional dual-window background dictionary.

Many signal processing problems in wireless sensor networks can be solved by graph filtering techniques. Finite impulse response (FIR) graph filters (GFs) have received more attention in the literature because they enable distributed computation by the sensors. However, FIR GFs are limited in their ability to represent the global information of the network. This letter proposes a family of GFs with infinite impulse response (IIR) and provides algorithms for their distributed realization in wireless sensor networks. IIR GFs bring more flexibility to GF designers, as they can be designed and realized even when the graph spectrum is unknown. Numerical results show that IIR GFs are more accurate in approximating ideal GFs and more robust against network variation than FIR GFs.

Channel prediction is an appealing technique to mitigate the performance degradation due to the inevitable feedback delay of the channel state information (CSI) in modern wireless systems. We first propose a general MIMO-OFDM channel prediction framework, which exploits both the spatial and temporal correlations among antennas. Then we derive two predictors which select data for auto-regressive (AR) predictors in different ways based on the proposed framework. The first predictor chooses the data set via minimizing the mean square error (MSE) of prediction model. The second predictor chooses the data in a heuristic way, which aims to reduce the computational complexity. Our algorithms can be applied to improve the precoding performance in multi-user MIMO-OFDM systems. Simulation results show that the proposed methods can overcome the feedback delay effectively, even when the channel changes rapidly.

Hyperspectral unmixing is an important issue to analyze hyperspectral data. Based on the present mixing models, this paper proposes a new nonlinear unmixing framework for hyperspectral imagery. The proposed framework transforms the hyperspectral unmixing problem to a constrained nonlinear least squares problem by introducing the abundance nonnegative constraint, abundance sum-to-one constraint and the bound constraints of nonlinear parameters. Accordingly, an alternating iterative optimization algorithm is developed to solve the arising nonlinear least squares problem. The method decomposes the nonlinear unmixing problem into two sub-problems, which obtain alternately the abundance vectors and nonlinear parameters of the observation pixels. The experimental results on synthetic and real hyperspectral dataset demonstrate that the proposed algorithm can effectively overcome the inherent limitations of the linear mixing model. Meanwhile, the proposed algorithm performs well for noisy data, and can also be used as an effective technique for the nonlinear unmixing of hyperspectral imagery.

Interference is a major limiting factor for wireless communications. When interference is independent and identically distributed (i.i.d.), its statistics is invariant with time. The conventional statistical analysis can be used to mitigate the interferences. However, when the interferences are non-i.i.d., few signal processing techniques are available for mitigating the interferences. The matched filter technology is probably the best signal processing technique. In this presentation, a least cross-correlation criterion based rotation-matched filter is presented that can mitigate the interferences and achieve the optimal signal to interference-plus-noise ratio (SINR). Based on the simulation study it is better than the matched filter by 5∼10 dB.

Identifiability is a fundamental issue in blind signal processing. It is well known that there are two types of inherent ambiguities: the scalar and permutation ambiguities. However, this is obtained under the condition that no a priori information of input signals is assumed. We show that if the information of source constellation is exploited, with a general constellation, there is an inherent discrete phase ambiguity, which is uniformly spaced on the unit circle. We also illustrate our identifiability results to blind channel estimation in orthogonal frequency division multiplexing (OFDM) systems.

Blind channel estimation is a promising technique to reduce the pilot overhead. Unfortunately, most existing algorithms suffer from the scalar ambiguity problem, and hence only achieve semi-blind identification. In this paper, we show that with the information of source constellation, the phase of the ambiguous scalar can be divided into a fractional part and an integer part. Then we propose a multiple-constellation scheme enabling totally blind identification regardless of constellation type for OFDM systems. The necessary and sufficient condition for eliminating the scalar ambiguity is given. An application example shows that our scheme can help other algorithms circumvent the annoying ambiguity.

The current multiple accesses, such as CDMA, are designed for multi-user wireless communication systems in the licensed spectrum, where uncoordinated interferences can be kept to a minimum. On the other hand, the uncoordinated interferences do exist in the unlicensed spectrum, and they compromise the orthogonal properties and hence degrade the performance of the current multiple accesses. In this presentation, the Autocorrelation-Division Multiple Access (ADMA) is presented, which achieves the best SINR among all passive pre-/post-filters, by eliminating all MAI and CCI, and retaining the signal power when the channel is lossless without enhancing the noise power.

A novel algorithm is proposed to perform object tracking with multiple cameras in the Bayesian Inference framework. The key contribution is the exploitation of Bayesian network to fuse spatial-temporal position and object template feature in multiple cameras. Firstly, Bayesian network is used to model the multiple static cameras' tracking system. Then, the high-dimensional joint posterior is propagated spatiotemporally. Finally, the estimation of the target location in each camera view is achieved by using sequential Monte Carlo Approximation. The robust tracking algorithm efficiently fuses information from different views and is capable of dealing with partial and full occlusion. Besides, the distributed tracking algorithm is implemented on OMAP3530 platform. Both qualitative and quantitative experiments have demonstrated the effectiveness and robustness of the proposed algorithm.

A total rejection of interferences under any noise power is difficult, if not impossible, by current filtering technology. In this presentation, we will show that this is indeed achievable by a new filtering technique that extracts the part of the statistics from the received signals, which depend only on the interferences, not on the signals and noises. The algorithm is blind to the signal and interference channels; and hence it needs not to be updated when these channels vary with time. It is also important when the channels cannot be or is too costly to be estimated by pilot signals. The computation is based on statistics that is noise-invariant and hence its accuracy is not impacted by the noise power. Simulation confirmed what is proven that interferences are totally rejected over a wide range of noise power.

The problem of locating a mobile terminal has received significant attention in the field of wireless communications. The wireless location problem is made difficult by nonsymmetric contamination of measured time of arrival (TOA) data caused by non-line-of-sight (NLOS) propagation. In this paper, a novel robust NLOS error mitigation algorithm based on minimum entropy estimation is proposed without prior statistics knowledge of NLOS propagation error.We compare the proposed algorithm with two additional ones, the normal least-squares estimator and the Huber estimator, through MATLAB simulation in different COST 259 channel environment. Results reveal that the proposed algorithm is more robust to NLOS error than the other two, although it is not always superior to the other two on location accuracy.

Due to mathematical limitations, conventional phase-only correlation (POC) technique can only be applied to grayscale images or at most complex images. A full color image must be first converted to a grayscale one before performing the POC, during which the chrominance information has been wasted. In this paper, a novel extension of the POC technique to the quaternion field (QPOC) is proposed, which can naturally make full use of the luminance as well as the chrominance information in color images. The effectiveness of the proposed QPOC for color images is demonstrated through its applications in color template matching and subpixel color image registration.

In multihop wireless sensor networks, global clock error will accumulate as the hops grow. Sensors far away from the root are likely to suffer larger synchronization errors, which may deteriorate the accuracy of sensing data or even result in the failure of TDMA MAC protocols. A novel algorithm based on vector Kalman filter using multiple parents (KFMP) is proposed to address this problem. It's pointed out that receiving a synchronization message from one parent is equivalent to obtaining an observation of the global clock, thus more accurate result could be achieved by combining observations from multiple parents. In KFMP, vector Kalman filter is adopted to combine these observations. Both hardware implementation and system simulation are conducted to evaluate the performance of the proposed algorithm. The results show that global clock error is significantly reduced compared with FTSP, especially for sensors at the edge of the network.

In orthogonal frequency division multiplexing (OFDM) systems, spare subcarriers are available due to low signal-to-noise ratio (SNR) in some subcarriers or fragments after resource scheduling. Utilizing these spare subcarriers to carry scrambling codes, the peak-to-average-power ratio (PAPR) of the system can be largely reduced without sacrificing bandwidth, with no in-band distortion or out-of-band radiation, but only with a little increase of signal transmission power. In this letter, we derive that the PAPR reduction problem using one spare subcarrier is equivalent to the well-known minimum spanning circle problem. Based on this concept, an approach called minimum spanning circle method (MSCM) is proposed to find the optimal scrambling codes. As validated in simulations, our method is effective, efficient, and simple to realize.

We propose a complete solution to robust and accurate object tracking in face of various types of occlusions which pose many challenges to correct judgment of occlusion situation and proper update of target template. In order to tackle those challenges, we first propose a content-adaptive progressive occlusion analysis (CAPOA) algorithm. By combining the information provided by spatiotemporal context, reference target, and motion constraints together, the algorithm makes a clear distinction between the target and outliers. Accurate tracking of an occluded target is achieved by rectifying the target location using the variant-mask template matching (VMTM). In order to deal with template drift in the process of template update, we propose a drift-inhibitive masked Kalman appearance filter (DIMKAF) which accurately evaluates the influence of template drift when updating the masked template. Finally, we devise a local best match authentication (LBMA) algorithm to handle complete occlusions, so that we can achieve a much more trustworthy detection of the end of an arbitrarily long complete occlusion. Experimental results show that our proposed solution tracks targets reliably and accurately no matter when they are under: short-term, long-term, partial or complete occlusions.

In object tracking, occlusions significantly undermine the performance of tracking algorithms. Unlike the existing methods that solely depend on the observed target appearance to detect occluders, we propose an algorithm that progressively analyzes the occlusion situation by exploiting the spatiotemporal context information, which is further double checked by the reference target and motion constraints. This strategy enables our proposed algorithm to make a clearer distinction between the target and occluders than existing approaches. To further improve the tracking performance, we rectify the occlusion-interfered erroneous target location by employing a variant-mask template matching operation. As a result, correct target location can always be obtained regardless of the occlusion situation. Using these techniques, the robustness of tracking under occlusions is significantly promoted. Experimental results have confirmed the effectiveness of our proposed algorithm.

We propose a new method addressing the problem of template drift, a common phenomenon in which the target gradually shifts away from the template in object tracking. Much effort has been devoted to this problem, but the results are not satisfactory enough due to the lack of quantitative analysis of its cause. In this paper, after carefully examining where template drift stems from and how it influences template update, we derive expressions that accurately evaluate the model noises of the Kalman appearance filter employed to update the template. The appearance filter therefore achieves an optimal balance between reducing template drift and keeping track of target appearance variations. We perform experiments on a wide range of real-world video sequences containing diverse degrees of target appearance variations. All the experimental results confirm the effectiveness of our algorithm.

Clock synchronization is an essential service for various applications in wireless sensor networks (WSNs). Most of available synchronization protocols are based on CSMA MAC scheme. They are energy inefficient in dense sensor networks. Despite many power-saving TDMA MAC protocols have been proposed, few of them are qualified to flood synchronization messages. The novel one here adopts a carefully designed time division flooding scheme, which divides time into slots and assigns each node a unique one for transmitting synchronization packets. In this way, the active duration of each node can be dramatically reduced. The new slot allocation policy guarantees active periods of nodes along the forwarding paths be scheduled successively, so synchronization packets can be spread out in a short time. Besides, a special clock frequency skew compensation technique is employed to prolong the re-synchronization interval. Simulation shows that our protocol achieves high energy efficiency and fast convergence speed.

In this paper, a sampling based channel estimation as well as an iterative particle filter (PF) signal detection scheme for fast flat fading multiple-input multiple-output (MIMO) channels is proposed, which aims at achieving the required bit error rate (BER) performance under the scenario that the accurate channel estimation is difficult to attain. The channel estimation is comprised of two parts: the adaptive iterative update on the channel distribution mean and a regular update on the "adaptability" via pilot. In the detection procedure, the particle filter (PF) is employed to produce the optimal decision given the known received signal and the sequence of the channel samples, where an asymptotic optimal importance density is constructed, and in terms of the asymptotic update order, the parallel importance update (PIU) and the serial importance update (SIU) scheme are implemented respectively.

Based on a new diversity, the diversity of autocorrelation, a new multiple-access scheme, called A-CDMA, is presented here. Like WCDMA, it packs high-density information into transmitted signals. Unlike WCDMA, it can block all the ISI and MAI completely under all noise level. The simulation supports the theory and shows that the total interference due to ISI and MAI is reduced to 1% when SNR varies from 1 to 11. By comparison, it outperforms WCDMA by a factor of 9 in the reduction of ISI and MAI. Its overall performance, as measured by BER is better than WCDMA in general, and by 10-20 dB when SNR ranges from 9 dB to 11 dB.

Conventional blind signal separation algorithms do not adopt any asymmetric information of the input sources, thus the convergence point of a single output is always unpredictable. However, in most of the applications, we are usually interested in only one or two of the source signals and prior information is almost always available. In this paper, a principal independent component analysis (PICA) concept is proposed.We try to extract the objective independent component directly without separating all the signals. A cumulant-based globally convergent algorithm is presented and simulation results are given to show the hopeful applicability of the PICA ideas.