Graph neural networks (GNNs) have been proven to have increasingly widespread applications in the real world. In the mainstream mini-batch training mode, multiple cache-based GNN training acceleration systems have been proposed because of the possibility of selecting the same vertex multiple times during the sampling process. However, on ultra-large scale graphs, especially those exhibiting power-law characteristics, these systems are difficult to fully utilize the distribution characteristics of cached data, which limits training performance. To this end, we propose TAC, a GNN training acceleration system that fully exploits the distribution characteristics of cached data to optimize both data transmission and computational efficiency. Specifically, we have designed a data affinity optimization algorithm that significantly enhances the locality of cache access. Secondly, an adaptive sparse matrix operator for sparsity perception is proposed, which dynamically selects the optimal computing mode based on the location of data. Finally, we have constructed a fine-grained training pipeline that maximizes system parallelism by hiding the sampling and computation. The experimental results show that TAC significantly outperforms existing state-of-the-art cache acceleration systems on multiple benchmark datasets, demonstrating higher training efficiency.

Abstract With the increasing number of computationally intensive applications, heterogeneous systems have become an important solution for improving computing performance. In order to effectively develop and optimize parallel programs running on these systems, performance prediction has become an indispensable part. This article aims to comprehensively review the methods and tools for predicting parallel program performance in heterogeneous systems, analyze the characteristics of existing technologies, explore their development trends, and provide valuable references and guidance for researchers and developers. This article adopts a systematic review method, first sorting out the research process of parallel program performance prediction in heterogeneous systems, and then classifying and summarizing the current mainstream performance prediction methods, including analysis model-based prediction, simulation-based prediction, and machine learning based prediction. This article also summarizes the tools and platforms used to predict parallel program performance in heterogeneous systems. Through review, it was found that various performance prediction methods and tools have their own advantages in feature richness, availability, and accuracy, but they have all improved the efficiency and accuracy of parallel program performance prediction to a certain extent. The review of this article indicates that despite various methods and tools available for performance prediction, there are still many challenges and unresolved issues. Future research should further explore more accurate, efficient and intelligent prediction methods to better support the development and optimization of parallel programs in heterogeneous systems.

As a prominent research topic, multi-view multi-label classification (MvMlC) aims to assign multiple labels to samples by integrating information from various perspectives. However, in real-world scenarios, MvMlC frequently faces the learning challenge of data with missing views and labels, typically resulting from sensor malfunctions, or the costly and time-consuming process of manual annotation. In addition, learning robust representations that are both consistent across views and specific to individual views remains a challenge. To address these issues, we propose a novel double incomplete multi-view multi-label classification framework based on Disentangling Consistent and Specific Information (DCSI). Specifically, we employ a dual-channel encoder with identical architecture but distinct objectives to extract cross-view consistent information and view-specific unique information from all views, respectively. Meanwhile, a view discriminator is constructed to decouple these two types of information, facilitating the extraction of pure consistent and specific information. Moreover, we meticulously design fusion strategies tailored to each representation type. Regarding consistent representations, we propose a dynamic-confidence-aware fusion mechanism that assesses the reliability of each view's representations in relation to the classification task, enabling the model to prioritize information from trustworthy representations. For specific representations, in light of their complementary rather than redundant property, we suggest treating such representations from each view equally to ensure fairness. Through experimental validation on five datasets, the results demonstrate that our method outperforms existing state-of-the-art methods.

Multi-access Edge Computing (MEC) can effectively process Internet of Things (IoT) data by transferring computing intensive tasks to edge servers, and has become an effective mechanism to meet the growing demand for computing. The flexible Unmanned Aerial Vehicle (UAV) and High-Altitude Platform (HAP) with powerful resources working together can significantly improve the efficiency of edge computing system. This paper investigates the resource allocation and trajectory optimization problems in HAP-UAV-MEC system with a Non-Orthogonal Multiple Access (NOMA) communication scenario. By utilizing Wireless Power Transfer (WPT) technology to provide energy support for UAV, we jointly optimize UAV trajectories, resource allocation, and offloading decisions to minimize the energy cost of IoT devices and the energy cost of UAV. This problem is described as a multi-stage Mixed Integer Nonlinear Program ming (MINLP) problem. A Deep Deterministic Policy Gradient (DDPG)-Attention-based Resource Allocation and Trajectory Optimization (DART) algorithm combining Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques is proposed to address this issue. DART algorithm utilizes the Lyapunov technique to transform the multi-stage MINLP problem into a deterministic optimization problem, and decomposes the original problem into four parallel subproblems. Through DDPG-attention algorithm based on reinforcement learning and deep learning attention mechanisms, we solve the problems of trajectory optimization and offloading decision. Meanwhile, for remaining subproblems related to resource allocation, convex optimization is used to solve them. The experimental results verify that the DART algorithm can significantly reduce the total cost while ensuring system stability and performance.

The ultra-dense low earth orbit (LEO) satellite networks are becoming indispensable infrastructures for future sixth-generation (6 G) architectures by providing low-latency and high-speed communication. A LEO satellite network can be viewed as an autonomous system (AS), which necessitates seamless integration with terrestrial ASes using Border Gateway Protocol (BGP). However, traditional BGP and existing optimization have not sufficiently considered the significant overhead of BGP and its adaptability to high dynamics. In this paper, we propose a Proactive Hierarchical Border Gateway Protocol (PH-BGP) routing scheme and system tailored for ultra-dense LEO networks. Specifically, we develop an inter-domain routing scheme leveraging hierarchical convergence and proactive updating to balance routing overhead and network stability, which accelerates route convergence. Based on previous work, we develop a modular routing system, which utilizes a prediction module, decision module, hierarchical convergence mechanism module, and proactive routing update mechanism module to collaborate. Our simulation results on real-world typical Walkerdelta type LEO constellations demonstrate that PH-BGP is effective and superior compared to other routing schemes.

Our work proposes a comprehensive solution for predicting Metaverse network traffic, addressing the growing demand for intelligent resource management in eXtended Reality (XR) services. We first introduce a state-of-the-art testbed capturing a real-world dataset of virtual reality (VR), augmented reality (AR), and mixed reality (MR) traffic, made openly available for further research. To enhance prediction accuracy, we then propose a novel view-frame (VF) algorithm that accurately identifies video frames from traffic while ensuring privacy compliance, and we develop a Transformer-based progressive error-learning algorithm, referred to as ResLearn for Metaverse traffic prediction. ResLearn significantly improves time-series predictions by using fully connected neural networks to reduce errors, particularly during peak traffic, outperforming prior work by 99%. Our contributions offer Internet service providers (ISPs) robust tools for real-time network management to satisfy Quality of Service (QoS) and enhance user experience in the Metaverse.

In this paper, we design an exclusive Metaverse network traffic classifier, named Discern-XR, to help Internet service providers (ISP) and router manufacturers enhance the quality of Metaverse services. Leveraging segmented learning, the Frame Vector Representation (FVR) algorithm and Frame Identification Algorithm (FIA) are proposed to extract critical frame-related statistics from raw network data having only four application-level features. A novel Augmentation, Aggregation, and Retention Online Training (A2R-OT) algorithm is proposed to find an accurate classification model through online training methodology. In addition, we contribute to the real-world Metaverse dataset comprising virtual reality (VR) games, VR video, VR chat, augmented reality (AR), and mixed reality (MR) traffic, providing a comprehensive benchmark. Discern-XR outperforms state-of-the-art classifiers by 7 % while improving training efficiency and reducing false-negative rates. Our work advances Metaverse network traffic classification by standing as the state-of-the-art solution.

We propose QLook, a quantum-driven predictive framework to improve viewport prediction accuracy in immersive virtual reality (VR) environments. The framework utilizes quantum neural networks (QNNs) to model the user movement data, which has multiple interdependent dimensions and is collected in six-degree-of-freedom (6DoF) VR settings. QNN leverages superposition and entanglement to encode and process complex correlations among high-dimensional user positional data. The proposed solution features a cascaded hybrid architecture that integrates classical neural networks with variational quantum circuits (VQCs)-enhanced quantum long short-term memory (QLSTM) networks. We utilize identity block initialization to mitigate training challenges commonly associated with VQCs, particularly those encountered as barren plateaus. Empirical evaluation of QLook demonstrates a 37.4% reduction in mean squared error (MSE) compared to state-of-the-art (SoTA), showcasing superior viewport prediction.

Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, often resulting from data collection oversights and uncertainties in manual annotation. Furthermore, the task of learning robust multi-view representations that are both view-consistent and view-specific from diverse views still a challenge problem in MvMLC. To address these issues, we propose a novel framework for incomplete multi-view multi-label classification (iMvMLC). Our method factorizes multi-view representations into two independent sets of factors: view-consistent and view-specific, and we correspondingly design a graph disentangling loss to fully reduce redundancy between these representations. Additionally, our framework innovatively decomposes consistent representation learning into three key sub-objectives: (i) how to extract view-shared information across different views, (ii) how to eliminate intra-view redundancy in consistent representations, and (iii) how to preserve task-relevant information. To this end, we design a robust task-relevant consistency learning module that collaboratively learns high-quality consistent representations, leveraging a masked cross-view prediction (MCP) strategy and information theory. Notably, all modules in our framework are developed to function effectively under conditions of incomplete views and labels, making our method adaptable to various multi-view and multi-label datasets. Extensive experiments on five datasets demonstrate that our method outperforms other leading approaches.

Developing an effective evaluation metric is crucial for accurately and swiftly measuring LiDAR perception performance. One major issue is the lack of metrics that can simultaneously generate fast and accurate evaluations based on either object detection or point cloud data. In this study, we propose a novel LiDAR perception entropy metric based on the probability of vehicle grid occupancy. This metric reflects the influence of point cloud distribution on vehicle detection performance. Based on this, we develop a LiDAR deployment optimization model, which is solved using a differential evolution-based particle swarm optimization algorithm. A comparative experiment demonstrated that the proposed PE-VGOP offers a correlation of more than 0.98 with the vehicle detection results in evaluating LiDAR perception performance. Furthermore, compared to base deployments, field experiments indicate that the proposed optimization model can significantly enhance the perception performance of various types of LiDARs, including RS-16, RS-32, and RS-80. Notably, it achieves a 25% increase in detection Recall for the RS-32 LiDAR. Additionally, sensitivity analysis under varying traffic densities further verifies the robustness of the proposed model. This study provides a practical and generalizable framework for enhancing roadside LiDAR deployment in diverse traffic environments.

We propose a novel two-staged segmented learning framework to enhance network traffic classification (NTC) for 5G and beyond (B5G)-driven enhanced mobile broadband (eMBB) applications, including Metaverse traffic. The first stage improves classification speed and accuracy for eMBB traffic, and the second stage extends its capability to classify the more complex and dynamic Metaverse network traffic. We introduce Essential Vector Representation (EVR) and Frame Vector Representation (FVR) feature engineering methods. These methods reduce inference time and preserve privacy by leveraging application-level features such as transmission time, packet length, direction, and inter-arrival time. The outputs from EVR and FVR are classified using our Augmentation, Aggregation, and Retention-Online Training (A2R-OT) algorithm, which enhances adaptive online learning, improving accuracy and efficiency. Additionally, we construct a comprehensive real-world Metaverse network traffic dataset to address the lack of publicly available Metaverse traffic data. To our knowledge, this is the first framework to integrate eMBB and Metaverse traffic classification. Our approach achieves a 6% improvement over state-of-the-art solutions, advancing network traffic management (NTM) for B5G networks.

An integrated network combining satellite, aerial, and terrestrial components has generated interest in offering wireless communication services because of its high flexibility, adaptable deployment, and widespread connectivity. Moreover, mobile edge computing (MEC) has positioned itself as one of the promising techniques for enabling next generation mobile networks. Besides, unmanned aerial vehicle (UAV)-assisted MEC systems have evolved the edge computing strategy in the air. This work takes into account a multi-UAV satellite-aerial-terrestrial network where a satellite station and multiple UAVs jointly serve terrestrial mobile users with computing services. By simultaneously optimizing splitting and offloading of a task, remote server selection, transmit power, UAV path control, and CPU computation resource distribution, the goal is to maximize the network’s queue-aware efficiency to compute. A block descent method-based alternating iterative strategy is suggested to address the formulated complex mixed integer problem. To reduce computation time, the proposed solution breaks the whole UAV flight trajectory into shorter periods using a segment-by-segment methodology. The reported simulation results demonstrate that the suggested strategy outperforms many advanced methods.

Detecting partial deepfake speech is essential due to its potential for subtle misinformation. However, existing methods depend on costly frame-level annotations during training, limiting real-world scalability. Also, they focus on detecting transition artifacts between bonafide and deepfake segments. As deepfake generation techniques increasingly smooth these transitions, detection has become more challenging. To address this, our work introduces a new perspective by analyzing frame-level temporal differences and reveals that deepfake speech exhibits erratic directional changes and unnatural local transitions compared to bonafide speech. Based on this finding, we propose a Temporal Difference Attention Module (TDAM) that redefines partial deepfake detection as identifying unnatural temporal variations, without relying on explicit boundary annotations. A dual-level hierarchical difference representation captures temporal irregularities at both fine and coarse scales, while adaptive average pooling preserves essential patterns across variable-length inputs to minimize information loss. Our TDAM-AvgPool model achieves state-of-the-art performance, with an EER of 0.59% on the PartialSpoof dataset and 0.03% on the HAD dataset, which significantly outperforms the existing methods without requiring frame-level supervision.

Covert communication is a significant scenario towards 6G, where the transmission between transmitter and receiver should be covert without being detected. In covert communication, the transmitter called Alice transmits signal to the receiver called Bob, and a detecter called Willie continuously observes its received signal. Alice should control the transmit power under a certain value to mislead Willie judging the received signal containing only White Gaussian Noise. Several works have been carried out focusing on the covert communication performance from the viewpoint of timeliness, throughput, etc. However, the covert relay communication, especially the UAV based relay in transparent forwarding manner (TFM) is scarcely considered. In this paper, we study a novel scenario for strict and deteriorative covert communication with the UAV based relay: 1) The transmission of both Alice and the UAV relay cannot be detected by Willie. 2) The UAV must be on the sight of Willie. The three-dimensional placement of the UAV, the transmit power of Alice, and the amplifier gain of the UAV with TFM are jointly optimized by geometric programming, where several Lemmas are also derived. Finally, the performance of the proposed algorithm is verified by extensive simulations.

Recently, the topic of multi-view multi-label classification has aroused significant attention from scholars. Plenty of methods adopt an average weighting scheme to merge the features obtained from multiple views, which commonly ignore the quality difference of information provided by multiple views and thus limit the credibility of the fusion feature for the overall task. Besides, most of these methods assume the views and labels are complete while neglecting both views and labels may be incomplete. To solve these problems, we propose a quality-aware representation fusion network for partial multi-view incomplete multi-label classification, named QARF-net. Since assigning equal fusion weights for each view may be not in line with the actual contributions of individual views, a view quality-aware module is proposed to learn suitable weights for different views dynamically based on the quality of each view’s information, which provides a reliable guide for fusing the information of multiple views. In addition, considering the consistency characteristics of multi-view data, we impose a sample-level dual constraint to preserve the consistency property of the feature in multi-view space and constrain the sample structure in the fused feature space, respectively. Last but not least, QARF-net can not only deal with complete multi-view multi-label classification tasks but also tackle partial multi-view incomplete multi-label classification tasks. Experimental results on five real-world datasets indicate that our proposed method outperforms state-of-the-art methods.

Sea surface temperature (SST) is critically important for understanding ocean dynamics and supporting various marine activities, making accurate short-term SST forecasting highly significant. However, accurately modeling the multi-scale variability of SST remains challenging for existing deep learning (DL) models. This study introduces the Coupled Transformer-CNN Network (CoTCN), a hybrid architecture designed to leverage the multi-scale variability of SST. The CoTCN combines the strengths of Transformers and convolutional neural networks (CNNs), significantly enhancing SST forecasts’ spatial continuity and predictive accuracy. Compared to five state-of-the-art DL models based on Transformer or CNN that include ConvLSTM, ConvGRU, AFNO, PredRNN, and SwinLSTM, CoTCN demonstrates superior performance in global and local areas of SST forecasting. At 1-day lead time, CoTCN reduces the global average root mean square error (RMSE) by over 15%, with forecast errors ranging from 0.20°C to 0.53°C across 1–10 day lead times. Moreover, the CoTCN effectively mitigates the checkerboard artifacts inherent to the Vision Transformer architecture. These findings highlight the effectiveness of CoTCN in capturing SST’s multi-scale features and underscore the promising potential of hybrid architectures for future DL models.

The Gassmann fluid term, an important attribute for characterizing reservoir fluid variations, is widely used in seismic inversion for reservoir prediction and fluid identification. However, most existing inversion methods rely on first-order linear approximations of the reflection coefficient equation, ignoring nonlinear responses in complex geological settings, thereby limiting the accuracy of inversion results. To address this limitation, this study derives a quadratic reflection coefficient approximation equation that explicitly incorporates the Gassmann fluid term, by combining the Russell approximation with the quadratic PP-wave reflection coefficient equation. Based on this formulation, an inversion framework is developed using the quadratic approximation. The proposed method first uses the arctangent penalty function as a sparsity constraint, which enhances the overall convexity of the objective function. The Hadamard operator is then used to decompose the variables of the quadratic terms and reduce optimization complexity. Finally, the alternating direction method of multipliers (ADMM) algorithm is introduced to decompose the nonlinear optimization problem into multiple single-variable sub-problems, which are solved through alternating iterations. Model tests and field data applications show that the proposed approach enhances the accuracy of reservoir fluid identification and validates the effectiveness of the quadratic approximation strategy.

Acoustic impedance (AI) inversion method based on the assumption of spatially invariant seismic wavelets has been widely applied in reservoir prediction. In reality, seismic wavelets are relatively stable but also exhibit subtle spatial variations, particularly when inconsistencies arise between seismic data and reservoir heterogeneity. To address this issue, we propose an alternate iterative inversion method of AI and wavelet based on sparse constraints. This method assumes that both the wavelet matrix and the reflection coefficients are sparse. Based on the Lp norm, we establish objective functions for wavelet estimation and AI inversion with sparse regularization constraints of different forms, ensuring the stability of the inversion results during the iterative process. In the inversion process, the statistical wavelet is used as the initial wavelet input to obtain the AI; then, the AI results are used to update the wavelet, and the two parameters are iteratively updated in this way. Through this alternating iteration, spatial variations of the wavelet are effectively captured, eliminating discrepancies in waveform and frequency characteristics of seismic data in the horizontal direction due to nonimpedance variations. Finally, the effectiveness of the proposed method is validated through model testing and application to actual data. The proposed method can invert impedance results that are more consistent with logging data, and the estimated wavelets synthesized seismogram show a better match with the well bypass. In particular, the proposed method is more applicable to the case where it is difficult to use a single wavelet to calibrate all wells simultaneously during the well-seismic calibration in the work area with multiple well data.

Mobile Edge Computing (MEC), as a highly promising technology, effectively processes computation-intensive tasks by offloading them to edge servers. Utilizing the advantages of Unmanned Aerial Vehicles (UAVs) in deployment flexibility and broad coverage, UAV-assisted edge computing can significantly enhance system efficiency. This paper studies a scenario where a UAV-MEC system serves multiple Mobile Users (MUs) with random task arrivals and movements. We minimize the energy consumption of MUs by jointly optimizing UAV trajectory and resource allocation for MUs subjected to the UAV energy limit. The problem is formulated as a multi-stage Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we propose an algorithm called JTORA integrated Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques. Specifically, we initially transform the multi-stage MINLP problem into a deterministic optimization problem utilizing Lyapunov techniques and decompose the original problem into two sub-problems in parallel. Through DRL, we solve the first sub-problem of trajectory and communication resources optimization. For the second sub-problem involving computing resource allocation, convex optimization is employed to get the optimal solution. Theoretical analysis and experimental results demonstrate that the JTORA algorithm can effectively reduce the energy consumption of MUs while ensuring UAV endurance.

The booming renaissance and rapid development of ultra-dense low earth orbit (LEO) satellite networks (UD-LSNs) are envisioned to realize a giant leap forward for the future sixth generation (6G) coverage expansion, bridging digital divide for remote areas and providing continuous services for user terminals worldwide. However, the inherent dual mobility, massive access scenarios and highly overlapped coverage may trigger frequent, vast and ping-pong handovers, especially with the existing limited and fixed deployment of terrestrial mobility functional entity. To this end, by exploiting the unique opportunity of UD-LSNs, we devise a medium Earth orbit (MEO) assisted distributed hierarchical mobility management architecture (HDMMA) with flexible function configuration to adapt the high dynamic and large scale network. Subsequently, the lightweight handover procedures (LHPs) are proposed for two scenarios under the HDMMA to ensure service continuity, that is on-orbit handover and off-orbit handover. Considering the user mobility attributes and satellite available resources, the on-orbit handover introduces user aggregate to share signaling overhead, while the off-orbit handover is further classified into intra-cluster, inter-cluster and inter-group handover based on the clustering and grouping. Furthermore, we conduct theoretical analysis model on the proposed LHP in terms of signaling overhead and handover latency. Simulation results verify the handover characteristics in UD-LSNs, illustrate the superiority of our HDMMA and demonstrate the handover performance improvement of the proposed LHP.

It is agreed that portfolio selection is of great importance for the financial market. Numerous outstanding exact and heuristic algorithms have been proposed in the past decades. However, their development always demands meticulous human ideas and could be time-consuming. Moreover, most of them tend to suffer from performance degradation when exposed to new portfolio selection models and different investment environments. Learning-enabled approaches have recently yielded impressive results, but these methods still grapple with challenges in model design and training. In this paper, we explore the mutual facilitation of large language models (LLMs) and huristic approaches in portfolio selection, and propose a novel LLM-based multi-objective evolutionary algorithm (MOEA) named IlmPC-NSGA-II. In this algorithm, the LLM with carefully-designed well-structured prompts serves as a straightforward yet effective engine for generating new solutions, non-dominated sorting and crowding distance calculation are adopted to enable the LLM and the evolutionary process to mutually guide toward the optimal region of the solution space. Experimental results on various scales of constrained multi-objective portfolio selection models and four benchmark problems demonstrate that our proposed approach can achieve a more competitive performance compared to widely-used MOEAs and the LLM-only method.

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic Engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility.

A medium access control (MAC) protocol design is proposed in this paper for next-generation industrial Internet of Things (IIoT) networks. Considering a nonfully connected network with multiple access points (APs), we aim to connect a massive number of IIoT devices densely populating the network and minimize the delay in channel access without packet collisions. To achieve this objective, we propose a device location-based medium access control design, which integrates scheduled access and carrier sensing. In our design, devices are assigned to time slots based on their locations, and the assignments are coordinated among APs to eliminate collisions while maximizing channel utilization. To analyze the performance of the proposed design, we derive the average delay each device experiences with the proposed scheduling scheme and verify our analysis via simulations of an IIoT network with 19 APs and over 17000 devices. The results show the effectiveness of the proposed design in supporting massive connections while at the same time achieving low delay.

Ocean general circulation models (OGCMs) are indispensable for studying the multi-scale oceanic processes and climate change. High-resolution ocean simulations require immense computational power and thus become a challenge in climate science. We present LICOMK++, a performance-portable OGCM using Kokkos, to facilitate global kilometer-scale ocean simulations. The breakthroughs include: (1) we enhance cuttingedge Kokkos with the Sunway architecture, enabling LICOMK++ to become the first performance-portable OGCM on diversified architectures, i.e., Sunway processors, CUDA/HIP-based GPUs, and ARM CPUs. (2) LICOMK++ overcomes the one simulated-years-per-day (SYPD) performance challenge for global realistic OGCM at $1-\mathrm{km}$ resolution. It records $\mathbf{1. 0 5}$ and 1.70 SYPD with a parallel efficiency of 54.8% and 55.6% scaling on almost the entire new Sunway supercomputer and two-thirds of the ORISE supercomputer. (3) LICOMK++ is the first global 1-km-resolution realistic OGCM to generate scientific results. It successfully reproduces mesoscale and submesoscale structures that have considerable climate effects.

This paper introduces a real-time collaborative sensing scheme for wireless sensor networks in time-varying environments. The objective is to maximize the sensors' performance by effectively allocating communication resources for data sharing. Specifically, we utilize digital twins (DTs) to characterize dynamic collaborative sensing demands for each sensor through data-driven methods. Building on the DT design, we propose a resource allocation scheme to optimize the communication resources allocated at each stage of collaborative sensing and determine the most effective collaborative sensing policy. By profiling sensors using DTs, the network controller can effectively coordinate the sensors without exhaustively exploring all collaborative sensing policies. Numerical results demonstrate the effectiveness of our proposed scheme in optimizing the sensing performance for all sensors.

High speed train (HST) communications with multi-user multiple-input multiple-output (MU-MIMO) techniques have shown great potential in system performance improvements. However, the challenges caused by higher pilot overhead and the belated channel state information (CSI) feedback in such high mobility scenarios still need to be further addressed. To this end, fully-decoupled radio access network (FD-RAN) with novel location-based feedback-free transmission scheme and cooperative transmission/reception in separated downlink/uplink networks is regarded as a promising solution. In this paper, we study the uplink precoding design in FD-RAN for HST communications. To capture the inherent relation between location and precoding, the line-of-sight (LoS) channel is derived from location and then fed into a proposed precoding design neural network (PDNN) which learns to jointly optimize the precoding scheme for all the multi-antenna mobile relays (MRs) in uplink MU-MIMO. We adopt a custom loss function to optimize the spectrum efficiency (SE). Moreover, a joint signal reception method is given, also based solely on the LoS channel derived from location, so as to avoid frequent pilot transmission. Simulation results show the advantages of FD-RAN against other architectures and demonstrate that our proposed PDNN achieves better performance than traditional precoding scheme in high mobility scenarios.

This article introduces an on-demand collaborative sensing scheme for industrial Internet of Things (IIoT) sensors in time-varying sensing environments, aiming to optimize the sensing performance by effectively allocating communication resources for sensory data sharing. Particularly, we propose a novel digital twins (DTs)-empowered resource allocation solution to facilitate scalable and flexible collaborative sensing. First, DTs create mathematical models using real-time network data to characterize the dynamic resource demands in collaborative sensing. Second, the performance of mathematical models in DTs is evaluated through data-driven methods. Building on our DT design, we propose a joint collaborative sensing and DT management scheme to optimize the resource allocation for sensory data sharing and DT operation. Furthermore, we develop a DT evaluation method featuring a variational autoencoder to evaluate the accuracy of DTs and enable closed-loop DT-based resource allocation. Numerical results demonstrate the effectiveness of our proposed collaborative sensing scheme in optimizing the sensing performance for all sensors.

Conventional wireless networks are half-duplex and most of them use contention-based protocols. These protocols usually adopt a principle of contention with collision avoidance and infer a collision occurrence very late from the absence of an acknowledgment after data transmission, causing low network performance. Wireless full-duplex (FD) enables simultaneous transmission (TX) and reception (RX) on the same channel. Exploiting this functionality, this article proposes the first design that enables contention with collision detection (CCD) to improve the network performance. We call the proposed design FD-CCD. With FD-CCD, in contention, a node exploits the TX antenna to transmit a signal for channel contention, while exploiting the RX antenna to sense if other nodes are transmitting too. By checking the status of the TX and RX antennas, the node can detect the contention collision before data transmission and, hence, obtain an opportunity to avoid the data collision effectively. FD-CCD also supports priority-based contentions, is of very low contention overhead, and is compatible with conventional 802.11 networks. This article then develops a theoretical model to analyze the system performance and optimize protocol parameter settings. Extensive simulations verify the effectiveness of our design and the accuracy of our model. This study is very helpful in designing efficient FD protocols.

The positioning accuracy determines the scope of the application of an unmanned aerial vehicle (UAV). In view of the existing UAV cooperative localization methods that normally require prior information and the assistance of external systems, such as the global positioning system (GPS), this study aims to adopt range radios to measure the time-of-arrival (TOA) information among UAVs and then perform clock synchronization and cooperative localization based on ranging measurements. We propose a framework to jointly estimate the clock error and relative distance, adjust the onboard clock, and perform relative positioning. To achieve autonomous clock synchronization and ranging, a practical approach based on peer-to-peer pseudorange measurements is proposed in this study. We modeled the synchronous two-way ranging (STWR) process using a discretetime state-space model, according to which a linear parameter estimation method and clock steering method are presented. Finally, a closed loop consisting of STWR, parameter estimation, and clock tuning is constructed to improve the ranging accuracy, which leads to improved localization accuracy. Simulation results show that the proposed approach outperforms existing methods and can achieve sub-nanosecond-level time synchronization and meter-level cooperative localization.

Mobile Edge Computing (MEC) has envisioned to be a promising technology to provide more efficient services for computation-intensive but delay-sensitive onboard mobile services. In this paper, the Non-Orthogonal Multiple Access (NOMA) technology is applied in a vehicular edge computing network, in which vehicular users (VUs) can offload partial computation tasks to MEC servers over wireless channels for remote execution. In this network, an optimization problem for the long-term energy consumption of the system is presented and aims to minimize it by jointly optimizing the Successive Interference Cancellation (SIC) ordering of NOMA, the VUs’ transmit power for computation offloading, and computation resource allocation of the MEC server. To deal with the intractable long-term optimization problem, we first transform it into an equivalent instantaneous form based on the Lyapunov optimization theory. Since the transformed problem is still highly non-convex, we further decompose it into the interactive resource allocation and SIC ordering sub-problems. For the resource allocation sub-problem, we exploit its convexity through the transformation and reparameterization, and derive the optimal solution in accordance with the Karush-Kuhn-Tucker (KKT) conditions and the gradient descent algorithm. After that, we propose a low-complexity algorithm by leveraging the Tabu search to obtain the sub-optimal SIC ordering. Simulation results validate the effectiveness of the proposed algorithm and the superiority of NOMA compared to Frequency Division Multiple Access (FDMA).

In many remote areas lacking ground communication infrastructure support, such as wilderness, desert, ocean, etc., an integrated edge computing network in the air with edge computing nodes is an effective solution. It can provide over-the-air computing services for ground devices (GDs) with limited computing resources and battery life. In this paper, we study task offloading and resource allocation in the aerial-based mobile edge computing (MEC) system supported by a high altitude platform (HAP) and unmanned aerial vehicles (UAVs), with the goal of minimizing the GD's energy consumption. Considering that the task arrival of GDs and wireless communication quality are both stochastic and dynamic, we apply stochastic optimization techniques to transform this task offloading and resource allocation problem into two subproblems, i.e., 1) a subproblem for local computation resource allocation, and 2) a subproblem for offloading resource allocation. For the first subproblem, we use convex optimization methods to address it. For the second subproblem, we use game theory to formulate the competition of offloading resources among GDs and propose the Distributed Game-theoretical Multi-server Selection (DGMS) algorithm and the Transmission Power Allocation (TPA) algorithm. Finally, we propose a Distributed Online Task Offloading and Resource Allocation (DOTORA) algorithm and give the theoretical performance analysis of the algorithm. We perform extensive experiments, including the comparison experiments with the UAV-Only and HAP-Only framework, and the comparison experiments with other algorithms under our HAP-UAV framework. The experimental results validate our proposed framework and the DOTORA algorithm.

Downloading a large amount of data from a low Earth orbit satellite to a ground station can be challenging due to the limited contact window, dynamic channel quality, solar energy supply, and thermal management without an atmosphere. Considering such dynamics, this paper proposes a joint design of in-orbit computation and communication for download time minimization. We combine the non-convex thermal constraints and energy constraints into unified energy budget constraints with upper bound approximation, and computational efficiency is achieved by decomposing the resulting large-scale problem into a non-convex communication sub-problem, a convex computation sub-problem solvable with interior point method and a master problem that optimizes the energy budget allocation between computation and communication. The communication sub-problem is solved with a generalized-benders-decomposition-based algorithm that decouples downlink scheduling and power allocation based on a closed-form solution of optimal dual variables in the power allocation primal problem. And the master problem is solved with ternary search by proving the minimal download time is quasi-convex with respect to the energy budget allocation between computation and communication. Simulation results demonstrate that the proposed solution effectively reduces the download time, especially under strict energy constraints and severe channel variations.

Ultra reliable low latency communication (URLLC) in vehicular networks is crucial for safety-related vehicular applications. Mini-slot with a short packet that carries only a few symbols is used to reduce the transmission time interval and enable quick scheduling for URLLC that requires extremely low latency. However, a single air interface transmission of URLLC packets may fail due to the high mobility of vehicles. Leveraging multi-connectivity technologies, the real-time reliability of URLLC can be greatly enhanced without relying on packet retransmission. In this paper, we propose a multi-connectivity URLLC downlink transmission scheme for vehicular networks, where the URLLC packet is duplicated and transmitted over multiple independent wireless links to improve packet reliability. Specifically, we design a multi-agent cooperative deep reinforcement learning algorithm, called transformer associated proximal policy optimization (TAPPO), to achieve real-time robust power allocation for multi-connectivity URLLC with imperfect channel state information (CSI). The transformer neural network architecture is employed to share the information among multiple links serving the same URLLC user and choose appropriate transmit powers, enabling cooperation to ensure reliability while minimizing inter-cell interference and energy consumption. Extensive simulation results validate the effectiveness of multi-connectivity packet duplication for URLLC and proposed TAPPO for power allocation.

Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper; therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers.

In recent years, the Internet of Things (IoT) and mobile communication technologies have developed rapidly. Meanwhile, many delay-sensitive and computation-intensive IoT services have been widely applied. Because of the limited computing resources, storage, and battery capacity of IoT devices, mobile edge computing (MEC) is emerging as a promising paradigm to help process the tasks of IoT devices. Furthermore, non-orthogonal multiple access (NOMA) has evolved as a practical approach to meeting the requirement of massive connectivity. In this paper, we study the NOMA-aided dynamic task offloading problem for the IoT, which combines task scheduling and computing resource allocation decisions. We model and formulate the problem as a stochastic optimization problem, and our goal is to minimize the system energy consumption while satisfying performance requirements. We transform the original problem into a deterministic optimization problem through stochastic optimization technology. Then, we decompose it into four sub-problems and propose the energy efficient task offloading (EETO) algorithm to solve these four sub-problems. Our proposed EETO algorithm does not rely on prior statistical knowledge related to task arrival or wireless channel conditions. Through theoretical analysis and experiment results, we demonstrate that our EETO algorithm can make a flexible trade-off between system energy consumption and performance. Additionally, the EETO algorithm can effectively decrease the system energy consumption while ensuring system performance.

Ultra-dense unmanned aerial vehicle (UAV) plays an important role in the field of communications due to its flexibility and low-cost feature. Ultra-dense unnamed aerial vehicle base station (UAV-BS) can improve communication quality by providing temporary and cost-effective wireless communication services for hotspots. In this paper, a multiple UAV-BSs assisted downlink network is investigated to maximize the system throughput. It is still a challenging problem to jointly optimize the power allocation and the 3D deployment of multiple UAV-BSs. Therefore, in this paper, for effective interference management, the power allocation problem is first formulated as a non-cooperative game with a pricing mechanism to imitate the interactions among users served by UAV-BSs. Then, based on the combination of deep reinforcement learning (DRL) and the game theory, the power allocation and the 3D deployment of UAV-BSs are transformed into a Markov decision problem. Finally, a novel price-based proximal policy optimization (3PO) algorithm is proposed to explore the optimal policy to maximize the system throughput. Simulation results reveal that the proposed 3PO algorithm can significantly improve system throughput and energy efficiency compared to other baselines by jointly optimizing power allocation and 3D deployment for UAV-BSs.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy where a UAV equipped with an MEC server is deployed to serve terminal devices. This paper considers a multi-UAV assisted network in which multiple UAVs and a terrestrial base station (BS) are deployed to provide MEC services to mobile users. The objective is to minimize an energy and latency-based cost function by jointly optimizing task offloading and MEC server selection decision, transmission power, UAV trajectory, and CPU frequency allocation. An alternating iterative approach based on the block descent method is proposed to solve this problem. In the first layer, task offloading and server selection decision subproblem is solved using a game theoretic approach. The second layer handles offloading and downloading transmission power allocations by utilizing a simplistic geometric waterfilling (GWF) technique, and the UAV trajectory by successive convex approximation (SCA). Whereas, the third layer solves the computation resource subproblem by performing CPU frequency allocation using a gradient descent method. The proposed method uses a segment-by-segment approach, which divides the entire UAV flight trajectory into shorter timeframe segments to reduce the computation time. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

Nowadays, the ultra-dense low earth orbit (LEO) satellite network has become an attractive solution for providing global Internet coverage and services. With the ever-increasing demand for higher transmission performance, multipath also attracts much attention due to its great potential. In this paper, we consider the multipath cooperative routing in the ultra-dense LEO satellite network. To cope with the high dynamics of the network environment, a deep reinforcement learning (DRL) empowered intelligent routing algorithm is proposed, where each satellite only observes the local network state and independently makes the next-hop forwarding decision. Meanwhile, the perceived conditions of each path are recorded hop by hop in a format-specific packet. In this way, multiple available paths can be found for cooperative transmission. To balance multipath load, an adaptive traffic scheduling scheme is further developed on the sender to adjust traffic distribution according to the varying path conditions, so that multiple sub-flows can be efficiently maintained. Simulation results show the superiority and the effectiveness of the proposed multipath cooperative routing scheme compared with other baseline schemes.

As one of the most promising architecture in the evolving sixth-generation (6G) systems, ultra-dense low Earth orbit (LEO) satellite network (UD-LSN) is drawing increasing attention due to its global coverage and ubiquitous access. To ensure service continuity, mobility management with provision of seamless handover is crucial in the process of satellite and user movement. However, massive service requests and overlapped satellite coverage will result in frequent handovers and diversified options in the UD-LSN. Meanwhile, existing mobility management methods based on the terrestrial networks are difficult to make timely and effective decisions due to the limited deployments of ground stations. In light of this, we propose a two-layer grouping and clustering based mobility management architecture (GCMMA) for the UD-LSN to reduce the management complexity with supporting the flexible function configurations. Under the GCMMA, we design lightweight handover procedures for different scenarios according to the established handover model, which considers user aggregation and combines with the regularity of satellite motion. Simulation results validate the effectiveness of the proposed mechanism, which has a better performance in handover delays and signaling overheads.

The densely deployment of satellites enables the realization of direct-to-satellite Internet-of-things system with tremendous terminals through multi-satellite cooperation. Multi-user detection (MUD) based on cooperative satellite network can dramatically increase the detection performance. However, it is chained by the limited number of inter-satellite link (ISL) bandwidth resources. To cope with the stringent constraints on ISLs, we propose a novel auxiliary node (AN)-aided factor graph and the corresponding multi-user detection (MUD) algorithm named auxiliary node-cooperative message passing algorithm (AN-CMPA). Simulation results show that our proposed algorithm achieves only 0.5dB loss with 75% decreased information cost under effectively designed information filter criterion.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is a prominent strategy, where a UAV equipped with a MEC server is deployed to serve on ground terminal devices. This paper considers a multi-UAV-assisted network in which multiple UAVs are deployed to provide MEC services to terrestrial users. The objective is to maximize the queue-aware computation efficiency of an aerial-terrestrial network by jointly optimizing task splitting, task offloading and MEC server selection, UAV trajectory, and CPU frequency allocation. The work utilizes Dinkelbach’s method and Lyapunov optimization to reformulate the defined problem. Moreover, an alternating iterative approach based on the block descent method is proposed to solve this mixed-integer problem. Simulation results are presented to show that the proposed approach outperforms various benchmark schemes.

The reliability of low Earth orbit (LEO) mega constellation networks (MCNs) under large-scale geographical failure of satellites remains unrevealed. In this paper, we propose an algorithm to assess the connectivity, average latency and hop count by considering topology changes resulting from geographical failure, under different topology management. Numerical simulations are conducted based on the traffics source from end users distributed among the 100 most populous cities. The results show that the MCNs are generally reliable against geographical failure, as a geographical failure with a radius of 3000 km can at most disconnect 8% of the end users, while increasing the average hop count and latency of the remaining users by less than 10%. Also, topology reconfiguration after failure have a greater impact on the hop count than latency.

In the rapidly evolving landscape of electric vehicles (EVs), blockchain technology emerges as a transformative force, empowering the industry with enhanced security, transparency, and efficiency in transactions and energy management. However, the successful integration of blockchain technology in the context of EV is still in its infancy. Thus, this work aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the integration process of blockchain technology in the EV domain. Then we explore the potential applications of blockchain technology in enhancing the efficiency, reliability, and sustainability of EV infrastructure and discuss some of the promising research in each category. Finally, we provide a typical application scenario and its specific embodiment in the EV infrastructure and strive to shed light on all-inclusive future research directions, which may facilitate the integration of blockchain technology in the EV ecosystem from theory to practice.

The Ultra-Dense LEO Satellite Networks (UDLSN) has immense potential to provide low-latency and high-reliability services in future communication networks, owing to its global coverage, high capacity and reliable connectivity. However, the LEO networks usually suffer relatively high and variable transmission errors due to multipath, shadowing and handover. For delay-constrained video transmission, existing packet protection mechanisms frequently violate the constraint and degrade quality in such environments. In this paper, we propose a QUIC-Enabled Reliable Video Transmission Scheme (QRVTS) with adaptive Forward Error Correction (FEC) to reduce loss recovery time and enhance transmission performance especially for delay-constrained videos. Specifically, QRVTS incorporates an adaptive mechanism to dynamically adjust FEC redundancy based on the prevailing channel loss conditions and frame types. We evaluate our mechanism under multiple satellite scenarios with different network characteristics. The simulation shows significant gains in overview completion time and frame-level delivery delay for delay-constrained video transmission in LEO satellite scenarios.

With the explosive growth of content request services in the vehicle network, there is an urgent need to speed up the response process of content requests and reduce the backhaul burden on base stations (BSs). However, most traditional content caching strategies only consider the content popularity or cluster-based caching strategies individually, and the access paths are fixed. This article proposes a collaborative caching strategy for reinforcement learning (RL)-based content downloading. Specifically, the vehicles are first clustered by the$K$-means algorithm, and the content transmission distance is reduced by caching the contents with high popularity in the cluster head (CH). Then, according to the historical content request information, the long short-term memory is used to predict the popularity of content. The contents with high popularity will be collaboratively cached in the BS and CHs. Finally, the content downloading problem can be described as a Markov decision process, using a deep RL algorithm, deep$Q$network (DQN), to solve the target problem which is to minimize the weighted cost, including the downloading delay and failure cost. With the DQN algorithm, the CH can make the access decision for the content request. The proposed collaborative caching strategy for the RL-based content downloading algorithm can greatly reduce the response process and the burden at the BS. The simulation results show that the proposed RL-based method achieved outstanding performance to improve the access hit ratio and reduce the content downloading delay.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS consider time-division duplexing (TDD) systems, in which the same phase shifts can be applied to both uplink and downlink due to the channel reciprocity. However, for frequency-division duplexing (FDD) systems, using the same phase shifts will result in beam misalignment, thereby leading to performance degradation. To address this issue, in this article, we study the practical RIS design and beamforming optimization for FDD systems. By representing the phase shifts of RIS with the equivalent circuit model which includes the resistance, inductances, and tunable capacitance, we propose a methodology to design the circuit parameters (i.e., inductances and capacitance) to meet the desired reflection requirements (i.e., phase tuning range, reflectivity, and zero phase slope) of both the uplink and downlink transmissions in FDD systems. Given the designed inductances, a practical binary RIS reflection model corresponding to two reflection states is then proposed. Furthermore, based on the proposed reflection model, a problem is formulated to jointly optimize the active and passive beamforming such that the minimum array response gain of the uplink and the downlink is maximized. An efficient iterative algorithm is proposed to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing either uplink or downlink.

Two-tier computing paradigm that takes full advantage of both the end-user and the cloud computation capabilities has emerged as a promising way to deal with computationally-intensive tasks in the next generation wireless networks. For promoting the integration of the two-tier computing, federated learning (FL) provides an effective framework to enable the collaboration between the end-user and the cloud. However, the key performance metric, i.e., FL training latency, will be severely affected by the worst wireless link quality in both uplink and downlink. In this paper, aiming at accelerating the FL enabled end-cloud two-tier computing over the wireless networks, we introduce the uplink and downlink fully-decoupled radio access network (FD-RAN) architecture to enhance the minimum wireless link rate via multiple base stations (BSs) access collaboration and power management solution. First, the Lagrange dual decomposition and the binary variable relaxation methods are leveraged to obtain an optimal multiple BS access scheme for the enhancement of minimum uplink and downlink SINR. Subsequently, we exploit the successive convex approximation (SCA) algorithm to deal with the uplink power control and downlink power allocation with a proved data rate lower bound. Furthermore, considering the dynamic channel realizations, a stochastic optimization technique with a convex surrogate function is utilized to find the best end-cloud two-tier computing scheme for FL applications. Simulation results have demonstrated the effectiveness of our proposed joint multiple access collaboration and power management solution over FD-RAN for achieving a faster FL enabled two-tier computing task.

Traditional seismic inversion has the problems of low lateral resolution and poor anti-noise performance. To address these challenges, the advantages of inversion methods in time and frequency domains are combined; the Lp norm that can preserve more sparsity information is introduced into seismic inversion. Meanwhile, multi-trace simultaneous inversion has become the mainstream of inversion methods, which can effectively improve the quality of pre-stack seismic inversion. However, traditional multi-trace simultaneous inversion methods have high computational costs and are difficult to obtain the 3D result. Ensuring spatial continuity of inversion results is a challenging task. To address this issue, a 3D global pre-stack inversion method in the time-frequency mixed domain is proposed. The proposed method adds a frequency domain fidelity constraint the objective function of traditional time domain inversion to improve the resolution of the inversion results. To improve the computational efficiency of multi-trace simultaneous inversion, the anisotropic total variation regularization method under Lp norm constraint is employed. In addition, the 2D Sylvester equation is extended to 3D space to enable 3D global inversion. The method not only improves computational efficiency, but also ensures spatial continuity of the results. The stability and feasibility of the proposed method have been demonstrated through its application on both the 3D overthrust model and field pre-stack seismic data.

The joint problem of task offloading, collaborative computing, and resource allocation for multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the problem, we propose a deep reinforcement learning (DRL)-based bilevel optimization framework. The task offloading decision, computing collaboration decision, and power and subcarriers allocation subproblems are solved at the upper level, whereas the computing resource allocation subproblem is solved at the lower level. We combine dueling-DQN and double-DQN and add adaptive parameter space noise to improve DRL performance in MEC. Simulation results demonstrate that the proposed algorithm achieves near-optimal performance in energy efficiency and task completion rate compared with other DRL-based approaches and other benchmark schemes under various network parameter settings.

Reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) communications has been envisioned as a prominent technology for future wireless networks, since it is capable of simultaneously providing abundant spectrum resources and favorable propagation environments. The small wavelength at mmWave bands also enables the widespread use of large antenna arrays, of which the hybrid beamforming structure has emerged as a cost-effective solution. In this paper, we aim to minimize the sum-mean-square-error (sum-MSE) in the RIS-assisted mmWave multiuser multiple input multiple output (MU-MIMO) system by jointly optimizing the hybrid analog-digital precoders and the RIS reflection matrix. We demonstrate that the role of RIS in assisting mmWave communications can be completely replaced by a large-scale Kronecker-structured hybrid array. Moreover, an accelerated Riemannian gradient algorithm using majorization minimization technique is proposed to tackle the unit-modulus constrained analog precoder/RIS design. Under the assumption of perfect channel state information (CSI), we firstly consider the single-user MIMO (SU-MIMO) setup and propose an effective alternating minimization (AM) procedure to characterize the system performance limit. Moreover, a two-stage scheme is developed for low-complexity implementation. This AM procedure is then extended to the general MU-MIMO scenario. In addition, we develop a novel enhanced regularized zero-forcing (ERZF) scheme for simultaneously combating strong noise in the low-SNR regime and mitigating multi-user interference (MUI) in the high-SNR regime. The optimality of our proposed algorithms is validated for some simplified practical scenarios. Numerical results illustrate that the proposed algorithms outperform existing benchmark schemes in terms of the actual complexity and performance.

With the rapid expansion of the scale of deployed low earth orbit (LEO) satellites, the ultra-dense LEO satellite-terrestrial integrated network (LTIN) is envisioned as a promising architecture in the sixth-generation (6G) system to implement seamless connectivity and high-speed data rate service. Especially for ultra-remote real-time services with long transmission distance and high delay requirements, the integrated network can guarantee its end-to-end service continuity. However, many challenges have been posed to the efficient resource orchestration for the service delivery, owing to the large scale, heterogeneity and high mobility of the integrated network. For each service, its data needs to go through a series of on-board processing, before being downloaded to the terrestrial network for further applications. To this end, service function chain (SFC), an ordered concatenation of network functions (NFs), is introduced to support service provision. By allocating the constituent NFs over the LTIN, we propose an efficient multiple service delivery scheme to minimize the overall delivery completion latency, while taking into account resource sharing and competition among multiple SFCs. First, we formulate the multiple SFC embedding problem as a noncooperative game that is further proved as the weighted potential game with at least one Nash equilibrium (NE). With the help of the proposed global coordination mechanism, we design two algorithms to obtain the NE. One is the best response (BR) algorithm with faster convergence, while the other is adaptive play (AP) algorithm with more capacity for best solutions. Then, the stochastic learning (SL) algorithm is proposed to adapt to network dynamics and reduce global information exchange. Finally, extensive simulations validate the convergence and effectiveness of the proposed algorithms.

In this paper, we investigate the joint problem of task offloading, Unmanned Aerial Vehicle (UAV) trajectory design, and resource allocation for UAV-enabled edge computing, considering and highlighting the dependency among different tasks. The corresponding optimization problem, which is a mixed-integer problem, is formulated. To solve this problem, we propose an iterative method based on Block Coordinate Descent (BCD) to decompose the original problem into two subproblems. Given the offloading decision and resource allocation, the subproblem of UAV trajectory optimization is solved by convex optimization methods. Then, given the UAV trajectory, the subproblem of task offloading decision and the corresponding resource allocation is solved by dynamic programming and convex optimization methods. Simulation results show that our proposed method can significantly reduce energy consumption compared to the benchmark schemes.

Due to the wireless broadcasting and broad coverage in satellite-supported Internet of things (IoT) networks, the IoT nodes are susceptible to eavesdropping threats. Considering the distance difference between satellite and nearby destinations is negligible, the main and wiretapping channels between satellite and IoT node are similar, it poses great challenges to reach physical layer security in satellite-assisted IoT networks. In this paper, to guarantee secure transmissions for satellite-assisted IoT downlink communications, the multi-domain resource multiplexing based secure approach is proposed. Particularly, the self-induced co-channel interference between adjacent nodes is leveraged to increase the difference of signal transmission quality over both main and wiretapping channels. By comprehensively optimizing multi-domain resources, i.e., frequency, power, and spatial domains, secure transmissions from satellite to IoT nodes are reached. Specifically, the problem to maximize the sum secrecy rate of IoT nodes is formulated with a constraint of common communication rate of IoT nodes. To solve this non-convex problem, an alternating optimization (AO) algorithm with two inner successive convex approximation (SCA) algorithms are executed to solve the power allocation, spectral multiplexing, and precoding. In addition, simulation results are carried out to evaluate the secrecy rate performance and verify the efficiency of our proposed approach.

In this paper, we propose a general framework on the hybrid analog-digital transceiver design for multi-hop communications. For the inclusive purpose, a transceiver model unifying both linear and nonlinear transceivers has been taken into account. Various performance metrics, including the most representative capacity and weighted mean-squared error (MSE), have been investigated in a unified manner. In particular, to meet practical needs for the quality of services (QoS), a general eigenvalue power constraint model is introduced, which contains a sum power constraint and box eigenvalue constraints as special cases. Specifically, by carefully designing the auxiliary analog and digital beamformers, the multi-hop transceiver optimization is decomposed into a series of independent sub-problems, where the analog beamformers for different hops are completely decoupled. Based on that, this framework establishes a majorization-minimization (MM) based analog beamformer design algorithm, which is able to handle the complicated weighted unit-modulus matrix optimizations by finding their semi-closed-form solutions. Furthermore, an efficient waterfilling algorithm is proposed for the digital beamformer designs to deal with the difficulties of optimizations subject to the multiple eigenvalue power constraints. The numerical results are provided to demonstrate the performance advantages of the proposed framework.

Reconfigurable intelligent surface (RIS) has recently emerged as a promising technology for wireless communications, which intelligently controls the phase shift of each unit cell to form desired beams. Most prior works on RIS focus on a single frequency band, and thus for time-division duplexing (TDD) systems, the same phase shifts can be applied to both uplink and downlink. However, for the frequency-division duplexing (FDD) mode, if the same phase shifts are applied in both uplink and downlink, the directions of the uplink RIS beams will not be aligned with those of the downlink, which will in turn cause performance degradation. To address this issue, in this paper, we investigate the practical RIS design and optimization for FDD systems. By representing the reflection coefficients of RIS with the equivalent circuit model which includes the resistor, inductor and tunable capacitor, we first provide the guidelines on the circuit design to realize 2π phase control over the two frequency bands of the FDD system. In addition, we propose a low-resolution RIS configuration scheme with two tunable modes corresponding to two capacitances, and we formulate a max-min signal-to-noise ratio (SNR) problem to maximize the minimum SNR of uplink and downlink. To solve the non-convex problem, we propose an alternating optimization algorithm to obtain a suboptimal solution. Simulation results show that our proposed RIS design outperforms those benchmarks which design the circuits by only optimizing uplink or downlink.

In this article, we consider the design of dynamic transmission scheduling policies for the industrial network systems sharing scarce communication resources. Only a few subsystems can obtain channel access for information updates to close their control loops at each time step, which highlights the necessity of designing optimal transmission scheduling schemes to achieve a minimum average linear quadratic cost of the industrial network systems. We first propose a greedy state-error-dependent scheduling (SES) policy based on the one-step expected profit and discuss its stability employing the Lyapunov function method. After formulating the scheduling optimization as a Markov decision process problem and relaxing with a soft constraint, we develop a heuristic near-optimal solution that guarantees the optimality of certainty equivalent controllers, namely, Whittle’s index-inspired error-dependent scheduling (WIES). A stochastic stability condition of WIES is further given based on f-ergodicity. Due to low computational complexity and ease of implementation, the proposed schemes are suitable for large-scale heterogeneous industrial network systems. Finally, simulation results show that the proposed policies outperform the existing round-robin, holding-time-prioritized, and error-aware scheduling schemes.

Cloud–edge heterogeneous network is an emerging technique built on edge infrastructure, which is based on the core of cloud computing technology and edge computing capabilities. The joint problem of computation offloading, cache decision, and resource allocation for cloud–edge heterogeneous network system is a challenging issue. In this article, we investigate the joint problem of computation offloading, cache decision, transmission power allocation, and CPU frequency allocation for cloud–edge heterogeneous network system with multiple independent tasks. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the transmission power and CPU frequency constraint of the tasks. The constraint of computing resource and cache capacity of each access point (AP) are considered as well. The formulated problem is a mixed-integer nonlinear optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on reinforcement learning (RL) and sequential quadratic programming (SQP). In the upper level, given the allocated transmission power and CPU frequency, the task offloading decision and cache decision problem is solved using the deep$Q$-network method. In the lower level, the optimal transmission power and CPU frequency allocation with the offloading decision and cache decision is obtained by using the SQP technique. Simulation results demonstrate that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

The rapid development of self-driving cars and breakthroughs in key technologies have made the truck platoon possible. In addition to reducing truck fuel consumption and air pollution by reducing air resistance, effective platoon strategies can also maximize highway throughput while improving driving safety. However, the truck platoon strategy’s current resource allocation model is still in the preliminary research stage. Therefore, inspired by the successful experience of deep reinforcement learning (DRL) in solving resource allocation problems, this article proposes a dynamic resource allocation model for the truck platoon based on the semi-Markov decision process (SMDP) and DRL, which is used to maximize system revenue when considering the resource cost and income balance of the transportation system. Precisely, the proposed method first models the process of controlling the dynamic in and out of the truck platoon as SMDP. The action value in a specific state obtained by the planning algorithm is used as a DRL sample for model training. Finally, the SMDP is optimized through the trained model to obtain a truck platoon resource that approximates the optimal strategy distribution plan. The experimental results show that compared with the traditional greedy algorithm, value iteration, and${Q}$-learning scheme concerning solving the dynamic resource allocation model of the truck platoon, the Deep${Q}$-Network (DQN) used in this article can reduce the probability of request processing delay while causing the system to obtain higher rewards.

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

The hybrid nonlinear transceiver optimization problem of reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MU-MIMO) downlink is investigated. Specifically, the Tomlinson-Harashima precoder (THP) and the hybrid transmit precoder (TPC) of the base station are jointly optimized with the linear digital receivers of mobile users. The triangular feedback matrix of the THP is optimized and the optimal solution is derived in closed form based on a matrix inequality. Moreover, in order to tackle the nonconvexity of the constant-modulus constraints imposed on the analog TPC, the Majorization-Minimization (MM) based reconfigurable optimization framework is proposed, which strikes a trade-off between the implementation complexity and system performance in a reconfigurable manner. Explicitly, our MM-based reconfigurable optimization framework is capable of optimizing the analog TPC in a dynamically reconfigurable manner on an element-by-element, column-by-column, row-by-row or block-by-block basis. Moreover, an MM-based reconfigurable algorithm is proposed for the optimization of the phase shifting matrix at RIS, which also suffers from constant-modulus constraints. In the proposed MM-based reconfigurable algorithm, the RIS can be partitioned into a series of subarrays for striking different performance vs. complexity tradeoffs. Finally, our numerical results demonstrate the performance advantages of the proposed nonlinear hybrid transceiver optimization techniques.

In this paper, the content service provision of information-centric vehicular networks (ICVNs) is investigated from the aspect of mobile edge caching, considering the dynamic driving-related context information. To provide up-to-date information with low latency, two schemes are designed for cache update and content delivery at the roadside units (RSUs). The roadside unit centric (RSUC) scheme decouples cache update and content delivery through bandwidth splitting, where the cached content items are updated regularly in a round-robin manner. The request adaptive (ReA) scheme updates the cached content items upon user requests with certain probabilities. The performance of both proposed schemes are analyzed, whereby the average age of information (AoI) and service latency are derived in closed forms. Surprisingly, the AoI-latency trade-off does not always exist, and frequent cache update can degrade both performances. Thus, the RSUC and ReA schemes are further optimized to balance the AoI and latency. Extensive simulations are conducted on SUMO and OMNeT++ simulators, and the results show that the proposed schemes can reduce service latency by up to 80 percent while guaranteeing content freshness in heavily loaded ICVNs.

In this article, we investigate the routing problem of packet networks through multiagent reinforcement learning (RL), which is a very challenging topic in distributed and autonomous networked systems. In specific, the routing problem is modeled as a networked multiagent partially observable Markov decision process (MDP). Since the MDP of a network node is not only affected by its neighboring nodes' policies but also the network traffic demand, it becomes a multitask learning problem. Inspired by recent success of RL and metalearning, we propose two novel model-free multiagent RL algorithms, named multiagent proximal policy optimization (MAPPO) and multiagent metaproximal policy optimization (meta-MAPPO), to optimize the network performances under fixed and time-varying traffic demand, respectively. A practicable distributed implementation framework is designed based on the separability of exploration and exploitation in training MAPPO. Compared with the existing routing optimization policies, our simulation results demonstrate the excellent performances of the proposed algorithms.

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.

Internet-of-Things (IoT) is being widely embraced with the number of connected devices growing rapidly. Moreover, IoT applications are emerging in diverse verticals such as connected cars, connected factories, and smart agriculture. For new business models, in order to meet key network performance indicators, connectivity must be flexible and agile. An integrated satellite-aerial-terrestrial network (I-SAT) has recently stimulated interest in providing wireless communication due to its high maneuverability, versatile deployment, and pervasive connectivity. The resource planning, task distribution, and action management of an I-SAT can be accomplished through effective acquisition, coordination, transmission, and aggregation of diverse information. This paper considers an I-SAT network, in which multiple unmanned aerial vehicles (UAVs) with aerial stations and a terrestrial base station (BS), in a cognitive setting, in the presence of satellite-receiver communication, are deployed to support smart vehicles on the ground. By taking into account different limitations and Quality of Service (QoS) constraints, the goal is to maximize the average throughput among users by jointly optimizing user association, BS/UAV transmission power, and UAV trajectory. The formulated problem is a non-convex optimization problem with a complicated expression that is hard to solve. To tackle this problem, an alternating iterative algorithm based on the block descent method is proposed. Precisely, the problem is subdivided into three subproblems, transmitter-vehicle association optimization, BS/UAV power allocation optimization, and UAV trajectory control. Then, in an iterative process, these subproblems are solved sequentially. The proposed solution uses a segment-by-segment technique, which breaks the complete UAV flight trajectory into smaller time segments to reduce computation time when the network service period is considerable. As a result, each time segment’s optimization can be solved more quickly. Furthermore, the paper presents the results of network simulations carried out to assess the efficiency of the proposed solution. The findings show that the presented scheme outperforms different benchmark schemes in terms of the average user throughput when observing multiple different scenarios.

Mobile edge computing (MEC) is an emergent architecture, which brings computation and storage resources to the edge of mobile network and provides rich services and applications near the end users. The joint problem of task offloading and resource allocation in the multi-user collaborative mobile edge computing network (C-MEC) based on Orthogonal Frequency-Division Multiple Access (OFDMA) is a challenging issue. In this paper, we investigate the offloading decision, collaboration decision, computing resource allocation and communication resource allocation problem in C-MEC. The delay-sensitive tasks of users can be computed locally, offloaded to collaborative devices or MEC servers. The goal is to minimize the total energy consumption of all mobile users under the delay constraint. The problem is formulated as a mixed-integer nonlinear programming (MINLP), which involves the joint optimization of task offloading decision, collaboration decision, subcarrier and power allocation, and computing resource allocation. A two-level alternation method framework is proposed to solve the formulated MINLP problem. In the upper level, a heuristic algorithm is used to handle the collaboration decision and offloading decisions under the initial setting; and in the lower level, the allocation of power, subcarrier, and computing resources is updated through deep reinforcement learning based on the current offloading decision. Simulation results show that the proposed algorithm achieves excellent performance in energy efficient and task completion rate (CR) for different network parameter settings.

Click-through rate (CTR) prediction is crucial for online sponsored search advertising. Several successful CTR models have been adopted in the industry, including the regularized logistic regression (LR). Nonetheless, the learning process suffers from two limitations: 1) Feature crosses for high-order information may generate trillions of features, which are sparse for online learning examples; 2) Rapid changing of data distribution brings challenges to the accurate learning since the model has to perform a fast adaptation on the new data. Moreover, existing adaptive optimizers are ineffective in handling the sparsity issue for high-dimensional features.

Reinforcement learning is a machine learning technique that focuses on exploring an uncharted territory exploiting of current knowledge. This paper proposes a Mobility Aware Channel Allocation (MACA) algorithm for 5G Vehicular Networks using a combination of Multi-Agent Reinforcement Learning (MARL) and Semi-Markov Decision Process (SMDP). In this work, we use multiple autonomous agents operating in a common environment to address the sequential decision-making problem to optimize the long-term rewards. In MACA, first we predict the mobility of vehicles using Teammate-Learning model as it allows the vehicles to cooperate and collaborate with each other without prior coordination. Secondly, during SMDP resource allocation phase, MARL inputs are applied to the Action Selection model for each vehicle based on their priorities. This is done at Road-Side Units (RSUs). Through numerical results and evaluations, we verify that the proposed method demonstrates efficient channel allocation and high packet delivery ratio as compared in the scenario of vehicles with multiple (high, medium, and low) priorities to existing conventional SMDP and Greedy algorithms.

The ability to find optimal egress routing is critical for safe and effective firefighting processes. This article presents a novel smoke emulator that can be used to provide this ability in firefighting processes. It has two fundamental components: 1) a sensor network based on the Internet of Things (IoT) and 2) a simplified computational fluid dynamics (CFD) smoke simulator based on Navier-Stokes equations. Supported by IoT, real-time events, such as door opening and window breaking, can be detected, and the relevant information can be used to update the variables in CFD to ensure high accuracy and relevancy. A long short-term memory (LSTM) is employed to evaluate the values of any sensors temporarily malfunctioning. A two-level of A* routing algorithm (Nosrati et al., 2012) has also been developed to optimize the egress routing for evacuees with an aim to minimize the time of smoke exposure. Simulation studies demonstrated that this smoke emulator helps significantly improve the firefighting process's safety and effectiveness.

Resource allocation is a significant task in the emerging area of Internet of Things (IoT). IoT devices are usually low-cost devices with limited computational power and capabilities for long term communication. In this article, the network function virtualization (NFV) technique is used to access resources of the network and a reinforcement learning (RL) algorithm is used to solve the problem of resource allocation in IoT networks. The traffic of the IoT network uses the substrate network which is available through NFV for its data transmission. The data transmission needs of the IoT network are translated to virtual requests and service function chain (SFC) are mapped to the substrate network to serve the requests. The problem of SFC placement while meeting the system constraints of the IoT network is a nonconvex problem. In the proposed deep RL (DRL)-based resource allocation, the virtual layer acts as a common repository of the network resources. The optimization problem of SFC placement under the system constraints of IoT networks can be formulated as a Markovian decision process (MDP). The MDP problem is solved through a multiagent DRL algorithm where each agent serves an SFC. Two Q-networks are considered, where one Q-network solves the SFC placement problem while the other updates weights of the Q-network through keeping track of long-term policy changes. The virtual agents serving SFCs interact with the environment, receive reward collectively and update the policy by using the learned experiences. We show that the proposed scheme can solve the optimization problem of SFC placement through adequate reward design, state, and action space formulation. Simulation results demonstrate that the multiagent DRL scheme outperforms the reference schemes in terms of utility gained as measured through different network parameters.

Symbiotic radio (SR) is a promising technology for energy- and spectrum-efficient wireless communication, which exploits passive communication for Internet-of-Things (IoT) transmission and achieves a mutualistic spectrum sharing between the passive and active transmissions. In this paper, we study an reconfigurable intelligent surface (RIS) empowered symbiotic radio over a broadcasting system, i.e., a base station (BS) broadcasts signals to multiple primary receivers (PRs) under the assistance of an RIS, while the RIS also transmits information to an IoT receiver (IR) by riding over the broadcasting signals. We formulate a problem to minimize the BS’s transmit power by jointly optimizing the BS’s active precoding and the RIS’s passive beamforming, under the signal-to-noise-ratio constraints of the primary and IoT transmissions. However, the problem is challenging to be solved optimally, since the variables are coupled and the constraints are non-convex. An iterative algorithm based on block coordinated descent (BCD) and semidefinite relaxation (SDR) techniques is first proposed, and its convergence together with complexity are analyzed. Then, to tackle the problem of high computational complexity caused by SDR technique, we further propose an alternative algorithm based on generalized power method (GPM) technique. Simulation results validate that the proposed system outperforms the traditional broadcasting system without RIS. The GPM-based algorithm achieves nearly the same transmit power performance as SDR-based algorithm, with a significantly reduced computational complexity.

Nowadays, driven by the rapid development of smart mobile equipments and 5G network technologies, the application scenarios of Internet of Things (IoT) technology are becoming increasingly widespread. The integration of IoT and industrial manufacturing systems forms the industrial IoT (IIoT). Because of the limitation of resources, such as the computation unit and battery capacity in the IIoT equipments (IIEs), computation-intensive tasks need to be executed in the mobile edge computing (MEC) server. However, the dynamics and continuity of task generation lead to a severe challenge to the management of limited resources in IIoT. In this article, we investigate the dynamic resource management problem of joint power control and computing resource allocation for MEC in IIoT. In order to minimize the long-term average delay of the tasks, the original problem is transformed into a Markov decision process (MDP). Considering the dynamics and continuity of task generation, we propose a deep reinforcement learning-based dynamic resource management (DDRM) algorithm to solve the formulated MDP problem. Our DDRM algorithm exploits the deep deterministic policy gradient and can deal with the high-dimensional continuity of the action and state spaces. Extensive simulation results demonstrate that the DDRM can reduce the long-term average delay of the tasks effectively.

In this paper, we design dynamic probabilistic caching for the scenario when the instantaneous content popularity may vary with time while it is possible to predict the average content popularity over a time window. Based on the average content popularity, optimal content caching probabilities can be found, e.g., from solving optimization problems, and existing results in the literature can implement the optimal caching probabilities via static content placement. The objective of this work is to design dynamic probabilistic caching that: i) converge (in distribution) to the optimal content caching probabilities under time-invariant content popularity, and ii) adapt to the time-varying instantaneous content popularity under time-varying content popularity. Achieving the above objective requires a novel design of dynamic content replacement because static caching cannot adapt to varying content popularity while classic dynamic replacement policies, such as LRU, cannot converge to target caching probabilities (as they do not exploit any content popularity information). We model the design of dynamic probabilistic replacement policy as the problem of finding the state transition probability matrix of a Markov chain and propose a method to generate and refine the transition probability matrix. Extensive numerical results are provided to validate the effectiveness of the proposed design.

Terrestrial-satellite networks (TSNs) are envisioned to play a significant role in the sixth-generation (6G) wireless networks. In such networks, hot air balloons are useful as they can relay the signals between satellites and ground stations. Most existing works assume that the hot air balloons are deployed at the same height with the same minimum elevation angle to the satellites, which may not be practical due to possible route conflict with airplanes and other flight equipment. In this paper, we consider a TSN containing hot air balloons at different heights and with different minimum elevation angles, which creates the challenge of non-uniform available serving time for the communication between the hot air balloons and the satellites. Jointly considering the caching, computing, and communication (3C) resource management for both the ground-balloon-satellite links and inter-satellite laser links, our objective is to maximize the network energy efficiency. Firstly, by proposing a tapped water-filling algorithm, we schedule the traffic to relay among satellites according to the available serving time of satellites. Then, we generate a series of configuration matrices, based on which we formulate the relation between relay time and the power consumption involved in the relay among satellites. Finally, the collaborative resource allocation problem for TSN is modeled and solved by geometric programming with Taylor series approximation. Simulation results demonstrate the effectiveness of our proposed scheme.

In recent decades, much attention has been paid to timely and guaranteed delivery in industrial automation networks. Toward this aim, the IEEE 802.1 Time-Sensitive Networking (TSN) task group has developed a series of standards. IEEE 802.1AS Timing and Synchronization protocol is the basis for TSN flow control mechanisms. As a rather new protocol, modeling and security analysis is a highly attractive candidate for developing IEEE 802.1AS. In this paper, we model the IEEE 802.1AS using Hierarchical Colored Petri Nets (HCPNs) and verify the proposed model by state space analysis and synchronization performance analysis. On the basis of our model, the security of the protocol is analyzed, including attack and defense against IEEE 802.1AS. Simulation results verify the validity and practicability of the model.

This paper studies reconfigurable intelligent surface (RIS) empowered symbiotic radio over broadcasting signals, i.e., a base station (BS) broadcasts to multiple primary receivers (PRs) under the assistance of a RIS, while the RIS also transmits information to an Internet-of-Things receiver (IR) by modulating the incident broadcasting signals. We formulate a problem to minimize the BS's transmit power by jointly optimizing the BS's active beamforming and the RIS's phase shifts (i.e., passive beamforming), under the signal-to-noise-ratio constraints of the primary and IoT transmission as well as the RIS's phase-shift constraints. However, the problem is challenging to be solved optimally, since the optimization variables are coupled and the constraints are non-convex. An efficient iterative algorithm based on the block coordinated descent and modified semidefinite relaxation techniques is proposed to solve this problem for both discrete and continuous phase shift scenarios. The convergency of the algorithm is proved and the complexity of the algorithm is analyzed. Numerical results validate that the proposed system outperforms the benchmark of traditional broadcasting system without RIS.

Mobile edge computing (MEC) has been recognized as a promising technology to support various emerging services in vehicular networks. With MEC, vehicle users can offload their computation-intensive applications (e.g., intelligent path planning and safety applications) to edge computing servers located at roadside units. In this paper, an efficient computing offloading and server collaboration approach is proposed to reduce computing service delay and improve service reliability for vehicle users. Task partition is adopted, whereby the computation load offloaded by a vehicle can be divided and distributed to multiple edge servers. By the proposed approach, the computation delay can be reduced by parallel computing, and the failure in computing results delivery can also be alleviated via cooperation among edges. The offloading and computing decision-making is formulated as a long-term planning problem, and a deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to achieve the optimal solution of the complex stochastic nonlinear integer optimization problem. Simulation results show that our collaborative computing approach can adapt to different service environments and outperform the greedy offloading approach.

3GPP has actively been working on vehicular communication standards for LTE and 5G New Radio (NR), making Cellular Vehicle-to-Everything (C-V2X) an emerging significant enabler for autonomous and connected intelligent transportation. Though 5G NR V2X is expected to offer the required low latency and high data rate to provision autonomous driving, scarce radio resources remain an issue for service providers. In this paper, we apply the cross-layer optimization for efficient resource allocation in a 5G NR based V2X network, which takes into account the application layer and the radio link layer of the protocol stack. The optimization aims at maximizing the perceived quality of the vehicular streamers that are either served by a V2N or a V2I link. Simulation results confirm that the proposed scheme provides improved user-perceived quality by observing average utility and video playout discontinuity.

Programmable logic controller (PLC) is an industrial digital computer that has been ruggedized and adapted for the control of manufacturing processes, such as automobile manufacture, or gas pipelines, or power generation. Due to closed source and vendor-specific proprietary firmware, it is difficult to develop a scalable high-interaction honeypot for PLCs. In this paper, we present and discuss a new scalable high-interaction PLC honeypot framework based on physical devices. This framework aims to solve the problems of existing physical honeypots while providing the advantages of virtual honeypots. Specially, we first introduce the main gap existing in virtual PLC honeypots. Then, we present a cheap, flexible, and large-scale-deployment solution for physical PLC honeypots according to the concrete problems. Finally, we evaluated our framework based on Siemens S7-300 PLCs. Our experiment shows that physical PLC honeypots have the absolute advantage in interaction capability and it is entirely feasible to extend the deployment scope with low response delay.

The Internet of Things (IoT) for smart ocean is a promising paradigm that will support emerging applications in the areas of maritime transport, emergency search and rescue, security and border surveillance, environmental protection, etc. There has been a surging amount of data acquired from different maritime terminals, such as vessels, buoys, and offshore platforms. As a result, the demand for high-speed, ultrareliable, and low-latency maritime communications and data processing is proliferating. In this context, transmission and processing of maritime data have become a research hotspot. IoT technologies are expected to dramatically enhance the capacity, safety, and efficiency of connected vessels and other maritime terminals. Meanwhile, the unique characteristics of smart ocean applications create heterogeneous challenges in achieving viable, reliable, and secure communications and data processing. Addressing the challenges calls for novel approaches and consideration for the deployment of next-generation maritime communication networks. Therefore, it is essential to pursue research on new theories, architecture, and technologies to fully exploit the capability that is delivered by IoT for smart ocean to form efficient and intelligent maritime communication systems. This special issue aims to create a platform for researchers from both academia and industry to disseminate state-of-the-art results and to advance the applications of IoT for the smart ocean.

Neighbor discovery is a prerequisite procedure in communication among energy-limited Internet-of-Things (IoT) devices. Discovering neighbors should be achieved in an energy-efficient way. Communication between heterogeneous IoT devices is a common phenomenon for achieving extensive connections among IoT devices. Generally, existing asymmetric neighbor discovery methods are evolved from the symmetric methods and lack specialized designs for heterogeneous IoT devices which led to relatively poor energy-efficient performance. In this article, we use a generalized slot model using pure sending (PS) slot and pure listening (PL) slot, called PSPL, to convert existing neighbor discovery algorithms (symmetric and asymmetric) to highly energy-efficient asymmetric ones. The core idea of PSPL is that to achieve highly energy-efficient one-way discovery, PS slot and PL slot are only used in devices with smaller and larger energy budgets, respectively, and the length of PL slot is much larger than that of PS slot. In this way, the energy efficiency of asymmetric neighbor discovery can be substantially improved. After implementing one-way discovery, two-way discovery can be easily achieved with mutual assistance. Two examples of how to convert existing algorithms into highly efficient asymmetric ones are presented, the one is to convert Disco to Disco+PSPL and the other one is to convert Griassdi to Griassdi+PSPL. The theoretical analysis results indicate that with the same energy budget, Disco+PSPL and Griassdi+PSPL reduce the worst case latency bounds by up to 82.28% and 87.69%, respectively, compared with the best known asymmetric solutions. The simulation evaluation verifies the effectiveness of our designs.

Plug-in Electric Vehicles (PEVs) keep on penetrating the automobile market. However, uncoordinated PEV charging can impair the reliability of power grid. In this paper, an interesting problem of PEV charging power allocation is investigated, in which both power distribution and transportation constraints are considered. A novel approach for PEV charging management based on optimal power flow (OPF) analysis is proposed to optimize PEV charging energy in a power distribution system. Firstly, spatial and temporal PEV demand scheduling is introduced to maximize PEV charging service capacity while considering the maximum traveling distance of PEVs. Secondly, to ensure the scalability of the OPF analysis, a distributed optimization technique, i.e., proximal Jacobian alternating direction multiplier method, is applied to attain the optimal power allocation in a decentralized manner. The resulting PEV charging service capacity in the power distribution system is improved without violating power distribution and transportation constraints. Furthermore, kernel density estimation method is adopted to identify the PEV range anxiety constraint without the PEV battery information. Simulation results are presented to validate the effectiveness of our approach with high PEV penetration.

Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN's universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

In this paper, we investigate service offloading in an integrated terrestrial-satellite (T-S) system. We consider the terrestrial base station (TBS) and satellites to be service providers, all user equipment (UE) to be service requesters, and the service can be content delivery, computation, etc. While offloading services to the satellites can prevent the TBS from being overloaded, the quality of service (QoS), e.g., content delivery latency, may degrade, necessitating a balance between the user preference and the utilities of the TBS and satellites. From the perspective of network management, we propose an abstract model that incorporates the utilities of the TBS, the satellites, and the UE, as well as the service capacity, service load, and service cost at the TBS and the satellites. While finding the optimal offloading decision, a problem of integer programming, is NP-hard, we develop two algorithms with low complexity for finding sub-optimal solutions of the offloading decision problem in the scenarios of one satellite and multiple satellites, respectively. Moreover, we prove that the solution found by the first algorithm is guaranteed to be optimal under the condition that the tasks for service from all UE have an identical size. Numerical results demonstrate the performance of the proposed algorithms compared to that of the optimal offloading by exhaustive search and the offloading by the greedy algorithm.

In this paper, we focus on the energy efficiency aware architecture of caching the necessary production messages and the transaction process to support a readable and tamper proof internet of things (IoT). To achieve this, blockchain can provide a reliable distributed storage of the messages, because any changes of the cached messages will break the structure of the blockchain. Specifically, we assume that the access points belonging to different telecom operators collect the messages in the IoT network, wherein multiple servers used for either caching or computing are placed at each access point. We define that the caching servers can be divided into the data loading caches for caching the received wireless IoT data and the data transmission caches for transmitting the IoT data into the blockchain based cloud caching servers. The blocks generated in the data loading caches at each access point will be written into the blockchain based on both the proof-of-work and the capacity of the data loading caches at each access point. Then, we formulate the optimization problem maximizing the system energy efficiency by optimizing the allocation of cache, computation and communication resources by a geometric programming model. By the CVX tool in matlab software, the geometric programming model can be solved effectively. We study the impact of different parameters involved in the blockchain on the system performance, and verify the effectiveness of our proposed energy efficiency aware optimization mechanism in the blockchain based IoT.

As the number of Plug-in Electric Vehicles (PEVs) is increasing in recent years, there has been a growing interest in terms of improving the charging service for on-the-move PEVs. In this paper, a task time allocation and reward scheme for advertising PEV charging station information is proposed. PEVs passing by a charging station are assigned a period of time to spread the charging station information within an interest area. To stimulate PEVs behaving cooperatively, two incentive policies provided by the charging station are studied in the task time allocation: the pre-determined reward policy and the optimal reward policy. For the former one, a fairness task time allocation scheme is developed to maximize the utility of the recruited PEVs. For the latter one, a Stackelberg game based optimization problem is formulated to obtain the optimal reward according to the utility of the charging station. An optimization tool, Geometric Water-filling, is utilized to analyze both problems efficiently. Simulation results are provided to validate the optimality of proposed schemes.

Device‐to‐device (D2D) communication can effectively use cognitive radio network approach to coexist with cellular users. For such a cognitive D2D system, two approaches (underlay and overlay) are considered to manage the spectrum sharing among the cellular (primary) users and the D2D (secondary) users. Energy efficiency (EE) is crucial in both these cases due to limited battery capacity and quality of service requirements of the D2D users. This study effectively models the power allocation problem of such a cognitive D2D system by maximising the EE of the D2D users subject to a minimum rate requirement for both the D2D users and the cellular users. This leads to a non‐linear fractional optimisation problem which is more complicated and computationally intractable. Alternatively, geometric water‐filling approach have been utilised for power allocation to solve this optimisation problem which results in an ‘ exact ’ and ‘ low complexity ’ solution. Simulation results reveal the benefits of the proposed algorithm.

Mobile edge computing (MEC) is a promising technique to enhance computation capacity at the edge of mobile networks. The joint problem of partial offloading decision, offloading scheduling, and resource allocation for MEC systems is a challenging issue. In this paper, we investigate the joint problem of partial offloading scheduling and resource allocation for MEC systems with multiple independent tasks. A partial offloading scheduling and power allocation (POSP) problem in single-user MEC systems is formulated. The goal is to minimize the weighted sum of the execution delay and energy consumption while guaranteeing the transmission power constraint of the tasks. The execution delay of tasks running at both MEC and mobile device is considered. The energy consumption of both the task computing and task data transmission is considered as well. The formulated problem is a nonconvex mixed-integer optimization problem. In order to solve the formulated problem, we propose a two-level alternation method framework based on Lagrangian dual decomposition. The task offloading decision and offloading scheduling problem, given the allocated transmission power, is solved in the upper level using flow shop scheduling theory or greedy strategy, and the suboptimal power allocation with the partial offloading decision is obtained in the lower level using convex optimization techniques. We propose iterative algorithms for the joint problem of POSP. Numerical results demonstrate that the proposed algorithms achieve near-optimal delay performance with a large energy consumption reduction.

Neighbor discovery, meaning that a node receives other nodes' radio frequency (RF) signals to be aware of their existence, is an indispensable procedure in Internet of Things (IoT)-oriented peer-to-peer (P2P) networks with energy-limited nodes. The main objective of neighbor discovery methods is to improve the energy efficiency, since node energy is usually very limited. As the neighborship maintaining time is very short in the mobile networks, neighbor discovery should be achieved in a very energy-efficient manner. The existing neighbor discovery methods only consider the received RF signals from other nodes (or neighbor table information derived from the received RF signals) as the basis of neighborship evaluation. The neighborship evaluation is inaccurate when the single source of neighbor information is employed. Inaccurate neighborship causes incorrect active slot scheduling and low energy efficiency of neighbor discovery. This paper proposes a generalized and probabilistic neighborship evaluation model to unify a variety of neighborship information in IoT-oriented P2P networks into neighborship probability values. Based on the model, we propose an energy-efficient neighbor discovery middle-ware algorithm by carefully replanning active slots of nodes according to the neighborship probability. The simulation evaluation results show that our proposed method decreases the average discovery delay by up to 11.46%, approximately, compared with other methods at the same energy budget.

Multi-carrier based non-orthogonal multiple access (NOMA) is an effective method to meet the ever-increasing demands of both user throughput and energy efficiency by multiplexing multiple users on the same carrier. Since interference from users with a poorer channel gain can be canceled at a user with a strong channel gain by successive interference cancellation, NOMA can enhance the system performance. To improve the downlink system performance, it is crucial to appropriately determine users scheduled on each carrier and power allocation at the base station. However, the existing works are generally either heuristic or local optimal due to the mixed optimization problem. In this paper, we focus on the global optimal solutions to maximize user throughput and energy efficiency in NOMA, respectively. In particular, we first formulate the mixed integer optimization problem which are intractable to be solved. Fortunately, by the provided analytical results, the optimization models can be largely simplified. Then, we propose the architectures of joint user scheduling and power allocation in NOMA, as well as the corresponding optimal algorithms. Simulation results demonstrate that our proposed algorithms indeed outperform existing works in terms of the user throughput and energy efficiency, respectively.

This two-part paper investigates cache replacement schemes with the objective of developing a general model to unify the analysis of various replacement schemes and illustrate their features. To achieve this goal, we study the dynamic process of caching in the vector space and introduce the concept of state transition field (STF) to model and characterize replacement schemes. In the first part of this work, we consider the case of time-invariant content popularity based on the independent reference model (IRM). In such case, we demonstrate that the resulting STFs are static, and each replacement scheme leads to a unique STF. The STF determines the expected trace of the dynamic change in the cache state distribution, as a result of content requests and replacements, from any initial point. Moreover, given the replacement scheme, the STF is only determined by the content popularity. Using four example schemes including random replacement (RR) and least recently used (LRU), we show that the STF can be used to analyze replacement schemes such as finding their steady states, highlighting their differences, and revealing insights regarding the impact of knowledge of content popularity. Based on the above results, STF is shown to be useful for characterizing and illustrating replacement schemes. Extensive numeric results are presented to demonstrate analytical STFs and STFs from simulations for the considered example replacement schemes.

In the second part of this two-part paper, we extend the study of dynamic caching via state transition field (STF) to the case of time-varying content popularity. The objective of this part is to investigate the impact of time-varying content popularity on the STF and how such impact accumulates to affect the performance of a replacement scheme. Unlike the case in the first part, the STF is no longer static over time, and we introduce instantaneous STF to model it. Moreover, we demonstrate that many metrics, such as instantaneous state caching probability and average cache hit probability over an arbitrary sequence of requests, can be found using the instantaneous STF. As a steady state may not exist under time-varying content popularity, we characterize the performance of replacement schemes based on how the instantaneous STF of a replacement scheme after a content request impacts on its cache hit probability at the next request. From this characterization, insights regarding the relations between the pattern of change in the content popularity, the knowledge of content popularity exploited by the replacement schemes, and the effectiveness of these schemes under time-varying popularity are revealed. In the simulations, different patterns of time-varying popularity, including the shot noise model, are experimented. The effectiveness of example replacement schemes under time-varying popularity is demonstrated, and the numerical results support the observations from the analytic results.

Heterogeneous computing powered by remote clouds and local fogs is a promising technology to improve the performance of user terminals in the Internet of Things. In this paper, two semi-Markov decision process (SMDP)-based coordinated virtual machine (VM) allocation methods are proposed to balance the tradeoff between the high cost of providing services by the remote cloud and the limited computing capacity of the local fog. We first present a model-based planning method in which it is necessary to train the state transition probabilities and the expected time intervals between adjacent decision epochs. To facilitate training them, the SMDP is degraded into a continuous-time Markov decision process (CTMDP) in which the service requests and ongoing service completions follow a continuous-time Markov chain. The relative value iterative algorithm for the CTMDP is used to find an asymptotically optimal VM allocation policy. In addition, we also propose a model-free reinforcement learning (RL) method, where an optimal coordinated VM allocation policy is approximated by learning from the states and rewards of feedback. The simulation results show that the performance of the model-free RL method can converge to a level similar to that of the model-based planning method and outperform the greedy VM allocation method.

With files proactively stored at base stations (BSs), mobile edge caching enables direct content delivery without remote file fetching, which can reduce the end-to-end delay while relieving backhaul pressure. To effectively utilize the limited cache size in practice, cooperative caching can be leveraged to exploit caching diversity, by allowing users served by multiple base stations under the emerging user-centric network architecture. This paper explores delay-optimal cooperative edge caching in large-scale user-centric mobile networks, where the content placement and cluster size are optimized based on the stochastic information of network topology, traffic distribution, channel quality, and file popularity. Specifically, a greedy content placement algorithm is proposed based on the optimal bandwidth allocation, which can achieve (1 - 1/e)-optimality with linear computational complexity. In addition, the optimal user-centric cluster size is studied, and a condition constraining the maximal cluster size is presented in explicit form, which reflects the tradeoff between caching diversity and spectrum efficiency. Extensive simulations are conducted for analysis validation and performance evaluation. Numerical results demonstrate that the proposed greedy content placement algorithm can reduce the average file transmission delay up to 45 percent compared with the non-cooperative and hit-ratio-maximal schemes. Furthermore, the optimal clustering is also discussed considering the influences of different system parameters.

In this paper, we investigate the power allocation in a multi-user wireless system to maximize the energy efficiency, while meeting the power constrains of each individual user and the whole system. Specifically, a geometric ceiled-water-filling algorithm is proposed to solve this non-linear fractional optimization problem, which can compute exact solutions with a low degree of polynomial computational complexity. Optimality of the proposed algorithm is strictly proved with mathematical analysis. In addition, the proposed algorithm is further extended to the general case with the minimum system-level throughput constraint, considering the quality of service requirement. To the best of our knowledge, no prior algorithm in the open literature offered such optimal solutions to the target problems, with the merit of exactness and the efficiency. Simulation results demonstrate that the proposed power allocation algorithms can improve the energy efficiency by nearly 50%, compared with the conventional Dinkelbach's method with the same amount of computations.

Mobile edge caching has the potential to reduce file transmission delay as well as core network load, by utilizing the cache of base stations to store content with high hit rates. However, in practice, the performance of mobile edge caching can be constrained by BS cache size. Traffic steering can enable end users to obtain requested file directly from the cache of a non-homing BS without remote file fetching, and thus enlarge the set of cached contents by exploiting the content diversity in space. On the other hand, traffic steering can also degrade spectrum efficiency, due to the higher path loss of steered users. In this paper, we investigate the performance of traffic steering on mobile edge caching, taking into account the tradeoff between content diversity and spectrum efficiency. The average file transmission delay is derived by applying stochastic geometry, under constraints of cache size and radio resources. Specifically, a greedy content placement algorithm is proposed, which can achieve near- optimal delay performance with low polynomial computational complexity. Simulation results demonstrate that the average file transmission delay can be reduced up to 55% when 10% contents can be stored in cache, by introducing traffic steering in mobile edge caching.

Energy harvesting (EH) becomes more desirable to save the world's energy consumption. This paper investigates energy resource allocation problem for EH networks. We propose a resource allocation framework based on a Semi-Markov Decision Process (SMDP). The objective of the framework is to provide a solution for a throughput maximization problem in EH networks by maximizing the total long-term expected reward of the EH system. The system reward is derived by considering both the income and the cost of the EH wireless communications. The numerical results illustrate a significant expected reward performance gain over a Greedy scheme. Moreover, simulations illustrate that the proposed approach is efficient and provides important guidelines for network deployment and resource management in a green radio network with EH technology.

Incentives for distributed optimization are investigated in two types of scenarios in which network users have private valuations (objective functions). A network center aims at maximizing the sum of users' valuations in the first scenario or the sum of its own valuations in the second scenario. It is shown that nontrivial strategies can be found by a user so that it can improve its own utility by providing false information to the center without leading a distributed algorithm to diverge. It demonstrates that a dual variable based pricing mechanism in distributed optimization cannot guarantee truthful reporting. While truthful reporting can be realized by using the classic Groves mechanism in the first scenario, the possibility of incentivizing truthful reporting in the second scenario depending on whether the center is willing to consider the valuations of the users in addition to those of its own.

Due to the mobility and flexibility of the Electrical Vehicles (EV), the power allocation of EV loads conducts as a critical part of demand side management. Since the EV charging stations provide the essential access for mass EV loads into the power grid, we introduce an efficient and decentralized real-time EV power allocation scheme among the neighbouring charging stations. In this paper, firstly, EV load power is managed in parallel according to current base load power inside the bus, and the power fluctuation is minimized for the whole system via Proximal Jacobian Alternating Direction Method of Multipliers (ADMM) technique. Then, the EV units access the network by the random charging scheme which is defined by the power analysis results and the EV's characteristics. Facilitated by the proposed approach, the stability and efficiency of the whole system can be improved by the local optimization process with decentralized manner.

IP geolocation determines geographic location by the IP address of Internet hosts. The physical location of Internet hosts is critical for many location-aware applications. Most geolocation methods are based on linear assumption of correlation between network latency and geographic distance on a large scale. In this paper, a lightweight geolocation approach is proposed to accurately determine the location of Internet hosts. This approach takes advantage of relative delay measurement and common routers. We studied localized delay- distance correlation in small region. We proposed an approach of discovering the accurate positions of common routers and converted common routers as secondary landmarks on a small scale and evaluated the efficiency of our method in the city level. The evaluation results show that the proposed algorithm improves the accuracy of IP geolocation by about 9.5% compared to Street-level Geolocation (SLG), one of the latest methods.

To enhance the reliability of the power grid, further processing of the power demand to achieve load balancing is regarded as a critical step in the context of smart grids with Internet of Things technology. In this paper, dynamic offline and online scheduling algorithms are proposed to minimize the power fluctuations by applying a geometric water-filling approach. For the offline approach, full information in the power demand is available, possibly by predicting from the power utilities. We present an exact approach in order to allocate the elastic loads based on the inelastic load's information considering the group-and node-power upper constraints. For the online approach, the reference level is computed dynamically using historical demand data to minimize the fluctuation in the grid, and the elastic loads can only be scheduled in the future time slots. Two dynamic algorithms are investigated to achieve load balancing in the power grid without influencing user experience by real-time reference level adjustment. Facilitated by the proposed methodologies, the power utilities can significantly reduce the cost of improving the power capacity, and the consumers are able to enjoy more stable electrical power.

Multiple-input multiple-output (MIMO) technology equips the transmitters with the multiple antennas. It can combine with energy harvesting (EH) to lift the spectrum efficiency and make use of a greener energy resource. A power grid is added to serve as a supplementary source to regulate the not-so-stable harvested energy supply of the system. Besides the MIMO technology being used, the power allocated to the user provided by both EH and the power grid is subject to the epoch power upper bound constraints. The background of these constraints comes from field requirements, such as avoiding the saturation of power allocated to the user(s), avoiding system-level out-of-band power leakage, and reducing interference with other transmitter(s) due to the non-linearity generated via the transmitting mechanisms to the user(s). The epoch power upper bound constraints make this problem more challenging, with the controllable power grid energy budget and its allocation. This paper applies our recently proposed geometric water-filling with group upper bounded power constraints and recursion machinery to form the proposed algorithm for solving the proposed throughput maximization problem. Our algorithm is precisely defined, and further provides the exact solution via the lower degree polynomial complexity. This point is very suitable for the massive MIMO system. To the best of our knowledge, no prior algorithm has been reported in the open literature to solve the targeted problem in this paper.

Cooperative network localization plays an important role in wireless sensor network (WSN), wherein neighboring sensor nodes will help each other to calibrate their locations. However, due to the dynamic wireless propagation environment and different surroundings, the measurement accuracy at different network nodes is different and varies overtime. In this paper, the uncertainties in both measurement accuracy and reference node locations are considered to account for the impact of different surrounding environments and the initial node location errors on the cooperative network localization. A mean-field variational inference-based positioning (VIP) algorithm is proposed for cooperative network localization. The mechanism of the proposed VIP algorithm, the convergence properties, implementation complexity, and the parallel implementation structure are presented to show that the VIP algorithm provides an effective mechanism to incorporate and share the localization information among all network nodes for an improved localization performance. Finally, a concise Cramer-Rao lower bound (CRLB) is derived to reveal the principle of localization error propagation. It is disclosed that the localization error propagation principle is similar to the Ohm's Law in circuit theory, which provides a new insight into the impact of the measurement accuracy, the reference node location errors and the number of reference nodes on the cooperative network localization performance.

With development of outsourcing computation, it is possible for clients with limited computing resources to outsource heavy computational tasks to the cloud server and thus relieve huge burden of the client. As continuous attention of delegation in recent years, requirements of security and efficiency are badly concerned undoubtedly, especially for matrix multiplication. Considering wide applications of matrix multiplication, e.g. graph processing and large date processing, in this paper, we present an identity-based publicly verifiable delegation scheme in amortized model which meets the need of security and efficiency both. Moreover, by using an secure encryption algorithm and a verification certification, the security analysis of the proposed scheme demonstrates the privacy of matrixes involved and the correctness. To demonstrate efficient properties, we compared our scheme with some existing works in terms of functionality as well as computation, storage and communication overhead.

Data-driven solutions to Electric Vehicle (EV) range estimation is attracting attention recently due to the prevalence of Internet of Things (IoT). However, there raise the Big Data problems with the increased volume and number of sensory sources of unstructured data collected from the EV equipped with In-Vehicle Networks. This means that traditional statistical analysis and Machine Learning tools are not suitable to be directly applied to analyse and interpret data. Hence, we aim to develop a Hybrid Machine Learning Model to predict the power consumption of EV trips practically considering multivariate high- dimensional data and meanwhile extract knowledge from the historical trip features for further applications. The proposed Hybrid Model is a modified Self-Organizing Maps (SOM) integrating Regression Trees (RT) to predict the power consumption of EV trips. The experimental results, including both cross-validation and mathematical accuracy measuring criteria, demonstrate that our Hybrid Model could not only provide a better power consumption estimation of EV trips but also reveal the inherent of the EV Big Data.

With the growing popularity of outsourcing data and services to the cloud, performing queries on encrypted data becomes a promising technique. Searchable encryption (SE) allows encryption while still enabling search for a variety of data. However, most of the existing arts focus on rectangular range query on common database. Query on encrypted spatial database has not been well studied. Moreover, as a vital type of geometric query on spatial data, the circular range search (CRS) is widely utilized in Location-Based Services (LBSs) and computational geometry. A recently proposed CRS scheme achieved security and privacy requirements. However, it exhibits low performance in terms of encryption and search efficiency. In this paper, we propose an Efficient Privacy-preserving CRS scheme (EP-CRS) on outsourced spatial data. Specifically, our scheme achieves CRS by leveraging an R-tree based SE scheme and adding a trusted-third party (TTP) to system. Security analysis indicates that EP-CRS can preserve data and query privacy. In addition, we conduct real experiments and compare EP-CRS with the existing one to show that the proposal is more efficient in terms of data encryption, token generation and search.

Cognitive radio (CR) lifts efficiency of information resource. As one way of utilizing the renewable energy resources, energy harvesting makes use of energy from the environment. Due to intermitted feature of the renewable energy, the power grid needs to be integrated to regulate the harvested energy supply of the system. Thus, the transmit power of the second user (SU), including the power from both the renewable energy and the power grid is often subject to a peak power constraint to control the interference level of the SU to the primary user (PU). The combination of these three types of emerging communication machineries renders great challenge to provide exact optimal power allocation solution with rapid computation. To the best knowledge of the authors, no such kind of solutions were reported in the open literature. In this paper, our recently proposed geometric water-filling with peak power constraints (GWFPP) and recursion machinery are applied and exploited to solve the throughput maximization problems, making the power grid smart. The proposed algorithms are precisely defined. They provide the exact optimal solution with efficient finite computation. Their optimality is strictly proved. Numerical examples and computational complexity analysis are presented to illustrate the procedures and demonstrate the efficiency of the proposed algorithms.

Cognitive radio (CR) is a promising wireless paradigm that provides efficient spectral usage. Orthogonal frequency division multiplexing (OFDM) is a potential technology providing many advanced functionalities in terms of power and rate control for cognitive radio networks (CRNs). Power allocation for CRNs is a crucial task for better interference management. In this paper, a subcarrier assignment scheme and a novel power allocation algorithm using geometric water filling is presented for OFDM based CRNs. This algorithm is optimized such a way to maximize the sum rate of secondary users by allocating power more efficiently, while constraining the 1) total transmit power, 2) individual subchannel transmit power as well as 3) individual subcarrier peak power of secondary users, for a given interference level to the primary users. Numerical results show that this algorithm provides better utilization of power resources thus maximizes the sum rate than the existing algorithms.

Based on the efficient generalized water-filling with group peak power constraints (GWFGP), this paper proposes an iterative algorithm to compute the optimal solutions to system throughput (sum-rate) maximization problems. This class of problems is equipped with the multiuser multiple input multiple output multiple access channels (MU-MIMO MAC) in the general communication systems. The proposed iterative GWFGP algorithm (IGWFGP) has two levels of loops. The inner loop aims at computing the solution to each member in the family, while the outer loop aims at computing the solution to the target problem based on the results obtained by the inner loop. Both GWFGP and the convergence theory of an algorithm are used in the inner loop and the outer loop respectively. Furthermore, by exploiting the concept of variable weighting factor for covariance update, IGWFGP owns fast convergence and provides optimal solutions to the sum rate maximization problems. The usage of the convergence theory in IGWFGP and the algorithm of GWFGP are efficient and novel. To the best of the authors' knowledge, no prior algorithm has been reported in the open literature to solve the targeted problem in this paper. In addition, the proposed algorithm does not require to choose the initial value for computation. This feature is a significant advantage of the algorithm, especially for large and complicated systems.

Cognitive radio (CR) can be combined with energy harvesting and multiple antenna mechanics to lift the spectrum efficiency and make use of green energy. The allocated power for the secondary user (SU), equipped with multiple antennas, needs to have peak power constraints to restrict the interference with the primary user (PU). On the other side, the energy harvesting property of the nodes leads to the causality feature when allocating the harvested energy. In this paper, we apply our recently proposed geometric water-filling with group upper bounded power constraints (GWFGUP) and recursion machinery to form the proposed algorithm for solving the target throughput maximization problem. This proposed CR multiple input multiple output energy harvesting power allocation algorithm (CRMPA) is precisely defined. It provides the exact optimal solution via efficient finite computation. Significant throughput gain of our proposed algorithm can be observed over the existing optimization methods, e.g., the well-known primal- dual interior point method (PD-IPM), although the used PD-IPM is based on our proposed equivalent real problem.

Cognitive radio (CR) can be combined with energy harvesting to lift the spectrum efficiency and make use of green energy. The allocated power for the secondary user (SU) needs to have peak power constraints to restrict the interference with the primary user (PU). On the other side, the energy harvesting property of the nodes leads to the causality feature when allocating the harvested energy. In this paper, we apply our recently proposed geometric water-filling with peak power constraints (GWFPP) and recursion machinery to solve the target throughput maximization problem. The proposed algorithm is precisely defined. It provides the exact optimal solution via efficient finite computation. Optimality of the proposed algorithm is strictly proved. Numerical results are presented to illustrate steps and effectiveness of the proposed algorithm, and the exact optimal solutions to the problem. Significant throughput gain can be observed over the well-known primal-dual interior point method (PD-IPM), which only guarantee to generate an ∈ solution to the problem.

Radio resource management (RRM) plays an important role in wireless communication systems, especially in more advanced systems with more constraint conditions. In this paper, we first propose a generalized water-filling approach to solve the power allocation problem of minimizing sum power while meeting the target sum rate constraint with weights. Based on this sum power objective function, we extend the proposed method to more complicated RRM problems with more stringent constraints. The proposed algorithms with this generalized approach possess several distinguished features. They provide exact optimal solutions based on non-derivative methods, as the implementation of the proposed algorithms invokes neither the derivative nor the gradient. With geometric interpretation, the proposed algorithms provide more insights into and intuitions of the problems and could be used to efficiently solve a family of the sum power minimization problems. Optimality of the proposed algorithms is strictly proved. Numerical results that illustrate the steps and demonstrate efficiency of the proposed algorithms are presented.

In this paper, the performance of multichannel transmission in cognitive radio is studied. Both QoS constraints and interference limitations are considered. The activities of the primary user (PU) are initially detected by cognitive user (CU) who performs sensing process over multiple channels. The CU transmits over a single channel at variable power and rates depending on the channel sensing decision and the fading environment. The cognitive operation is modeled as a state transition model in which all possible scenarios are studied. The QoS constraint of the cognitive user is investigated through statistical analysis. Analytical form for the effective capacity of the cognitive radio channel is found. Optimal power allocation and optimal channel selection criterion are obtained. Impact of several parameters on the transmission performance, as channel sensing parameters, number of available channels, fading and others, is demonstrated through numerical example.

Traditionally, the frequency spectrum is licensed to users who have the exclusive right to access the allocated band. However, an unlicensed (cognitive) user may share a frequency band with a licensed (primary) owner as long as the interference is below a certain threshold. This makes capacity analysis a critical important issue in these networks. Lots of research on cognitive radio (CR) networking have now focused on the satisfaction of quality-of-service (QoS) demands for cognitive users (CU). In this paper, we study how the delay QoS requirements affect the dynamic spectrum access (DSA) strategy on network performance. We treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity concept, focusing on one of the dominant DSA schemes: overlay. Optimal power allocation scheme is obtained. This scheme considers the transmit-power/interference-power constraints and the primary user activity. Performance analysis and numerical evaluations demonstrate the proposed effective capacity optimization on the DSA overlay scheme. The impact of delay QoS requirements and other related parameters are evaluated as well.

Well-established fact shows that fixed spectrum allocation policy conveys to the low spectrum utilization. The cognitive radio technique promises to improve the low efficiency. This paper proposes an optimized access strategy combining overlay scheme and underlay scheme for the cognitive radio. We model the service state of the system as a continuous-time Markov model. Based on the service state, the overlay manner or the underlay manner is used by the secondary users. When the primary user is not transmitting and only one secondary user has the requirement to transmit, the secondary system adopts the overlay scheme. When the primary user is transmitting and the secondary users want to transmit simultaneously, an underlay scheme with an access probability is adopted. We obtain the optimal access probability which maximizes the overall system throughput.

In this paper, a simple and elegant geometric water-filling (GWF) approach is proposed to solve the unweighted and weighted radio resource allocation problems. Unlike the conventional water-filling (CWF) algorithm, we eliminate the step to find the water level through solving a non-linear system from the Karush-Kuhn-Tucker conditions of the target problem. The proposed GWF requires less computation than the CWF algorithm, under the same memory requirement and sorted parameters. Furthermore, the proposed GWF avoids complicated derivation, such as derivative or gradient operations in conventional optimization methods, while provides insights to the problems and the exact solutions to the target problems. Most importantly, the GWF can be extended to solve a generalized form of radio resource allocation problem with more stringent constraints: (weighted) optimization problem with individual peak power constraints (GWFPP), and to include (weighted) group bounded power constraints (GWFGBP). On the other side, the CWF cannot solve these two general forms of the RRA problems, due to the difficulty to solve the non-linear system with multiple non-linear equations and inequalities in multiple dual variables. Optimality of the proposed water-filling solution is strictly proved for each of the proposed algorithms. Furthermore, numerical results show that the proposed approach is effective, efficient, easy to follow and insight-seeing.

Due to vehicles high mobility, there have been many clustering-based MAC protocols proposed to control Vehicular Ad hoc Network topology more effectively. Cluster head (CH) selection and cluster formation is of paramount importance in a highly dynamic environment such as VANETs. In this paper, we propose a novel cluster head selection criteria where cluster heads are selected based on their relative speed and distance from vehicles within their neighborhood. The maintenance phase in the proposed algorithm is adaptable to drivers' behavior on the road and has a learning mechanism for predicting the future speed and position of all cluster members using fuzzy logic inference system. The simulation results show that the proposed algorithm has a high average cluster head lifetime and more stable cluster topology with less communication and coordination between cluster members compared to other schemes.

The IEEE 802.11p or Wireless Access in Vehicular Environment (WAVE) has been adopted as a main technology for vehicular ad hoc networks (VANETs). Its Medium Access Control (MAC) protocol is based on the Distributed Coordination Function (DCF) of the IEEE 802.11 which has low performance and high collision rate especially when using a single channel. Therefore, many clustering-based multi-channel MAC protocols have been proposed to limit channel contention, provide fair channel access within the cluster, increase the network capacity by the spatial reuse of network resources and control the network topology more effectively. Most of these protocols did not study the optimized cluster parameters such as average cluster size, communication range within the cluster and between cluster heads, and the life time of a path. In this paper, we analyze the reliability and connectivity of a typical cluster-based MAC protocol in terms of these parameters.

Bilingual Named Entity (NE) pairs are valuable resources for many NLP applications. Since comparable corpora are more accessible, abundant and up-to-date, recent researches have concentrated on mining bilingual lexicons using comparable corpora. Leveraging comparable corpora, this research presents a novel approach to mining English-Chinese NE translations by combining multi-dimension features from various information sources for every possible NE pair, which include the transliteration model, English-Chinese matching, Chinese-English matching, translation model, length, and context vector. These features are integrated into one model with linear combination and minimum sample risk (MSR) algorithm. As for the high type-dependence of NE translation, we integrate different features according to different NE types. We experiment with the above individual feature or integrated features to mine person NE (PN) pairs, location NE (LN) pairs and organization NE (ON) pairs. When using transliteration and length to mine PN pairs, we achieve the best performance of 84.9% ( F -score). The LN pairs can be mined with the features of transliteration model, length, translation model, English-Chinese matching and Chinese-English matching. And the best performance is 83.4% ( F -score). The ON pairs can be mined with the features of English-Chinese matching and Chinese-English matching. It reaches the best performance with 84.1% ( F -score).

This paper first identifies a flaw in the proof of the convergence theorem of Algorithm 2 of the paper by Jindal[“Sum power iterative water-filling for multi-antenna Gaussian broadcast channels,” IEEE Trans. Inf. Theory, vol. 54, no. 4, pp. 1570–1580, Apr. 2005] and then presents a corrected convergence proof for that algorithm.

Since IEEE 802.11p has been adopted as Vehicular Ad hoc Networks (VANET) main technology, the research and development of vehicular safety applications has gained momentum. Because broadcasting is the predominant traffic type in VANETs, their safety applications will face a challenge in managing the channel capacity to insure good performance in terms of throughput, delay, fairness and broadcast coverage. In this paper we analyze the performance of the broadcast scheme of IEEE 802.11p standard analytically and verify the model by simulation. We then derive the optimal values of its parameters considering the probability of packets successful reception, throughput, delay and collision probability in a harsh vehicular environment.

Vehicular Ad hoc Networks (VANETs) differ from the predominant models of Mobile Ad hoc Networks (MANET) due to their high speed, mobility constraints and drivers' behaviour. Most researches on analyzing the performance of VANETs' applications done without taking into account the vehicles' high dynamics assuming stationary distribution of vehicles on the road. They assume that all vehicles within the range of the transmitter will receive the transmitted packets successfully. While vehicles near the outer edge of the transmitter's range are more vulnerable to cross the boundary before they receive the packet completely. They also used Most Forward Within Range (MFR) to forward the message from one hop to the next. In this paper, we develop a new mobility model to derive the number of vehicles on the road and the probability of receiving the broadcasted packets successfully from all vehicles within the range of the transmitter. We also derive the probability of multi hop connectivity taking into account the location of relay vehicles and prove that MFR is not a valid scheme in VANETs.

Most Vehicular Ad Hoc Networks' (VANET) applications use broadcasting as a main block for safety messages' dissemination. The broadcast range is a critical parameter in the success of any VANET's safety application. Moreover broadcasting in its normal way may lead to a broadcast storm problem. In this paper we present a geometric model to predict the recommended maximum range of a one hop broadcast message. We also introduce a new scheme to alleviate the impact of the broadcast storm problem taking into account the network topology and traffic parameters. Our simulation results show that the proposed scheme provides higher reception rates and lower message travel time compared to the existing solutions.

For a class of the important problems that seek to maximize the sum rate of the multiple input multiple output relay communication system (MIMO RCS) and compute the optimal transmission distributions, we present their mathematical models, and then develop an efficient algorithm for solving this class of the problems. The fixed point theory is used to prove convergence of the proposed algorithm. The performance result of the proposed new algorithm indicates that the proposed algorithm overcomes some limitations of other algorithms. First it utilizes the machinery of parallel computation, a limitation of the previously known iterative water-filling algorithms. Second it is robust to the large number of users K, as the previously known iterative water-filling algorithms are not. Not only does the proposed algorithm sufficiently utilize the machinery of parallel computation, but it also shows a strong robustness for the number of the users K. At the same time, the proposed algorithm shows fast convergence.

One of the challenging problems of cognitive radio networks is the interference which occurs when a cognitive radio accesses a licensed band but fails to notice the presence of the licensed user. To allow the cognitive radio to access the same spectrum band where the primary user is operating creates a problem, the cognitive radio may interfere with the primary system, and hence degrading the quality of service for the primary receiver. This paper presents an adaptive power control scheme for cognitive radio. The proposed scheme estimates the distance between the primary user and the cognitive radio, using the SNR as proxy for distance. On the basis of this information the cognitive radio adaptively changes its maximal transmit power to prevent the primary user from harmful interference. Numerical results are presented to demonstrate the effectiveness of the proposed algorithm.

In our previous works, we have pointed out that when the iterative water-filling algorithms are applied to find the sum capacity of the multi-input multi-output broadcast channel (MIMO BC), it has some limitations. One of the major limitations is that the greater the number of the users becomes, the slower convergence of the iterative water-filling algorithms appear to be. To address this limitation, this paper first presents a new iterative water-filling algorithm for the MIMO BC. When compared with previous research, as the first advantage, the performance of this proposed algorithm has a strong robustness for the number of the users K; as the second advantage, parallel processing can be utilized to benefit the speed of computation during the implementation of this proposed algorithm. In addition, the proposed new algorithm is effective for finding the optimal transmission policy due to its simplicity and fast convergence.

For a class of the important problems that seek to maximize the sum rate of the multi-user multiple input multiple output multiple access channel (MIMO MAC) and compute the corresponding optimal input distribution, we developed a more efficient algorithm for solving this class of the problems compared with currently known algorithms. The performance result of this new algorithm indicates that the proposed algorithm overcomes some of the weaknesses of other algorithms. One of the key weaknesses that it overcomes is that the well-known iterative water-filling algorithms cannot utilize the machinery of parallel computation, owning to their inherent structure defects. Not only does the proposed algorithm sufficiently utilizes the machinery of parallel computation, it also shows faster convergence compared with previous research results. Numerical results show that the same properties of the proposed algorithm are also effective for finding the optimal input policy due to its simplicity and fast convergence.

This paper describes an effective method for vehicle positioning estimation for range-based wireless network. The problem of locating a mobile terminal has received significant attention in the field of wireless communications. Time of arrival (TOA), received signal strength (RSS), time difference of arrival (TDOA) and angle-of-arrival (AOA) are commonly used measurements for estimating the position of the vehicles. In this paper, Constrained weighted least squares (CWLS) for vehicle position tracking approach with TDOA technique describes the optimized ranging measurement for the vehicles. Kalman filter is used for smoothing range data and mitigating the NLOS errors. In proposed algorithm positioning problem is formulated in a state-space framework and the constraints on system states are considered explicitly. The paper presents a simple recursive model by using time difference of arrival based position measurement and incorporating state equality constraints in the Kalman filter. From the process of Kalman filtering, the standard deviation of the observed range data can be calculated and then used in NLOS/LOS hypothesis testing. The proposed recursive positioning algorithm, compared with a Kalman tracking algorithm that estimates the target track directly from the TDOA measurements, will be comparatively more robust to measurement errors because it updates the technique that feeds the position corrections back to the Kalman Filter. It compensates for the measured geometrical position and decreases random error influence to the position precision. Simulation results show that the proposed tracking algorithm can improve the accuracy significantly.

Inferential sensors are used to infer the critical control variables that are otherwise difficult, if not impossible, to measure in broad range of engineering fields. All inferential sensors are based on an inferential modelling module that represents the dynamics between the inputs and the outputs. Two commonly used artificial intelligence based approaches for the development of the inferential modelling modules are: (1) Neural Networks and (2) Adaptive Neuro-Fuzzy Inference Systems. This paper is presenting the estimation of average air temperature in the built environment by using Integer Neural Network and Adaptive Neuro-Fuzzy Inference System based inferential sensor models. By comparing the results of these models with one another, advantages and disadvantages of each are discussed.

Diversity is an effective way to combat the impacts of channel fading by obtaining diversity gains and therefore, is attractive in the design of wireless sensor networks (WSNs) where energy efficiency is important. In this paper, we consider a cluster-based WSN in which the base station (BS) is located far from the sensor nodes and cluster heads cooperatively transmit information to the BS using decode and forward approach in a code-division multiple-access (CDMA) system. The cluster heads serve as both information sources and relays at the same time. The energy efficiency improved by the proposed approach is presented in this paper. How the system parameters such as bit rate, quality of service (QoS) and distance from the BS influence the scheme selection is given in this paper as well. Simulation results show that the proposed scheme saves energy significantly when BS is far from WSN and the QoS requirement is stringent.

The benefits of adaptive joint power control and rate allocation for uplink transmission in a wideband code division multiple access cellular system are investigated. Closed-loop power control (CLPC), to adaptively adjust the transmit power, has the effect of maintaining a target signal-to-interference ratio and bit error rate (BER) performance. On the other hand, rate adaptation requires less transmit power, although the BER performance may be poorer. The authors differentiate the power update interval from the data rate update interval, analyse and evaluate the performance of two joint rate/power adaptation algorithms in a fading environment: optimal spreading factor-power control (OSF-PC) and greedy rate packing-power control (GRP-PC). Numerical results show that GRP-PC exhibits superior throughput performance compared with other three adaptation schemes. CLPC alone exhibits throughput and BER performances comparable to those of the OSF-PC scheme, but consumes a significantly higher amount of transmit power. Rate adaptation only is not efficient in enhancing throughput, but its power consumption is minimal.

In this paper, efficient power allocation strategy for user cooperative transmission using amplify-and-forward (AF) approach is investigated. Power allocation is solved by finding the desirable ratio of power used for cooperative- information transmission to total power in an attempt to minimize bit error rate (BER) with a constraint of fixed total transmit power for each user. User fairness is also considered in the analysis. Simulation results show that with appropriate power allocation, BER performance of cooperative schemes can achieve a significant gain over non-cooperative systems.

As an accompany paper [1], efficient power allocation strategies for cooperative transmission applying decode-and- forward (DF) approach are further investigated in this paper. The cooperative ratio, defined as the ratio of the power used for cooperative-information transmission to the total power, is investigated in an attempt to minimize bit error rate (BER) with a constraint of fixed total transmit power for each user. Our results show that efficient power allocation greatly depends on the cooperative method. The preferred cooperative ratio changes from low to high by using DF with no parity check, amplify-and-forward, DF with parity check. Simulation results show that with appropriate power allocation, BER performance of all cooperative schemes can achieve a significant gain over non-cooperative systems.

In this paper, least squares (LS) approximation approach is used to solve the approximation problem of a sum of lognormal random variables. It is shown that Least Squares Quadratic (LSQ) approximation exhibits an excellent match with the simulation results in a wide range of the distributions of the summands. Using the coefficients obtained from the LSQ method, closedform expressions for the cumulative distribution function (CDF) and the probability density function (pdf) for the sum random variable and its logarithm, are presented.

A unified approach for power distribution and allocation in a multirate wideband CDMA system is investigated. It is shown that the traffic demand and the background disturbance fully govern the feasibility of the system and the optimal power distribution solutions, where the traffic demand is specified by the user QoS requirement, data rate, and spread spectrum bandwidth; the background disturbance includes the background noise and the intercell interference. Closed form expressions of the optimal power allocation, subject to power constraints in the practical system design, are derived. Convergent conditions are applied to evaluate the capacity region of the system. Numerical examples are provided to illustrate the applications of the obtained theoretical results.

We propose an adaptive prioritizing soft handoff algorithm for concurrent handoff requests aiming at a same cell. A predicted set, an adaptive priority profile jointly exploiting the impact of required handoff power and call holding time have been developed to realize the proposed algorithm. A link-layer scheduler residing in each base station to ensure the desired operation of the prioritizing procedure is also designed. Numerical results are acquired through comparing the proposed algorithm with no-prioritizing scenario and performance gain is obtained in terms of handoff dropping probability, average guard power utilization, and average guard power efficiency, by no less than 24%, 5%, respectively for the first two criteria, and some amount for the last.

The variability of the wireless channel requires adaptive techniques for efficient, radio resource service The throughput gains for ideal rate adaptation over nonadaptation in a wideband CDMA communication system is analyzed. The result is obtained by using the derived probability density functions of service ratio. We further investigate how performance degrades with adaptation restrictions, which include adaptation interval, Doppler effect, delays associated with channel state information feedback, and finite number of spreading factors. Our results show that rate adaptation with restrictions does not always bring gains over nonadaptation.

Power distribution for a multirate wideband CDMA system is investigated. The target power vector is expressed in terms of the spread bandwidth, user data rates, and user QoS specifications. The power distribution problem is solved via a decomposition of the traffic demand matrix, subject to a power constraint. Based on the power distribution law and the corresponding convergence conditions, simple yet efficient strategies for call admission and capacity evaluation are proposed.

Based on the zero crossing rate of the slope (first derivative) of the underlying fading process, a mobile speed-estimation scheme, constructed by counting the average number of sampling steps in a positive-going (and/or negative-going) fade envelope slope, is proposed. The proposed speed-estimation approach requires neither knowledge of the average fade power nor a variable temporal observation window. The computational complexity and the required memory storage are negligibly small. Simulation results show that the proposed speed estimator yields good estimation accuracy, with relatively small estimation error.

The use of rate-compatible punctured turbo and rate-compatible punctured convolutional (RCPT/RCPC) codes as channel codes in a direct-sequence code-division multiple-access system where the system bandwidth expansion is fixed is investigated. The best RCPC and RCPT code rate in terms of maximizing the system spectral efficiency and minimizing the optimal power allocation where the receiver is either a matched filter (MF) or a minimum mean-square error (MMSE) device is assessed. It is shown that for the MF receiver, the coding-spreading tradeoff favors a code-rate reduction. In the case of the MMSE receiver, when the E/sub b//N/sub 0/ value and the system load are increased, the best code rate also increases. By examining the slope of the performance curves, it is deduced that, under similar operating conditions, the best code rate of the RCPT codes is lower than that of the RCPC codes. Also, the best code rate for a Rayleigh fading channel is lower than that for an additive white Gaussian noise channel.

A closed-loop power control strategy, which includes both power control and power allocation functions, for a code-division multiple-access system is proposed. The target power level for a minimum mean squared error (MMSE) or a matched filter (MF) linear receiver is iteratively computed, and the power control command (PCC) is generated by comparing the received power with the generated target power. The PCC history and the channel fade slope information, which contains the Doppler effect, are used to generate variable stepsizes for regulating the transmit power level. Closed-loop power control is based on a criterion that minimizes the average transmit power and the standard deviation of the received power/signal-to-interference ratio. The power control strategy also tends to reduce the bit error rate. Simulation results demonstrate the effectiveness of the proposed power control algorithm. The results also indicate that the tracking ability of the MMSE and MF receiver is essentially similar, except that the average transmit power is lower with the MMSE receiver but is more complex to implement.

The variability of the wireless channel requires adaptive error control. Conventional approaches use either link adaptation (LA), which adjusts the coding scheme based on the estimated channel condition, or incremental redundancy (IR), which adjusts the code rate by incrementally transmitting redundancy until decoding is successful. IR usually achieves a higher effective throughput than that of LA, but a larger delay compared to LA. A protocol which combines the advantages of LA and IR to enhance data transmission is proposed. In contrast to the conventional IR with a highest starting code rate, the proposed approach adaptively adjusts the starting code rate based on the current channel condition. Three schemes are proposed to accomplish adaptation. Simulation results show that the average number of transmissions is significantly reduced, with a small sacrifice in the average throughput compared with those of IR.

The best tradeoff between coding and spreading in a single-cell direct-sequence code division multiple access (DS-CDMA) system is investigated. The best code rate in terms of the system spectral efficiency for a single-class system and the optimal power allocation for a multi-class system is analyzed by applying both a matched filter (MF) receiver and a minimum mean square error (MMSE) receiver. It is shown that for the MF receiver, the coding-spreading tradeoff favors a code rate reduction. In the case of the MMSE receiver, the spectral efficiency vs. code rate curve is convex, so there is a best code rate corresponding to a given E/sub b//N/sub 0/ specification. Numerical results show that the best code rate is a function of the system load, the required bit error rate, and the steepness of the required SIR vs. the code rate curve, i.e., the error correction capability of the applied coding codes. The best code rate to maximize the spectral efficiency is further related to the system design parameter E/sub b//N/sub 0/.

In this paper, highly reliable data transmission using concatenated Reed-Solomon (RS) /convolutional coding for a direct-sequence code division multiple access system is studied. The study is based on the analysis of the tradeoff between coding and spreading, and on the compromise between RS outer code and convolutional inner code under the constraint of a fixed bandwidth expansion for each service class. Some insightful observations about the mechanism of the concatenated coding scheme and their explanations are presented. Numerical results show that a best outer/inner code rate pair exists. By applying this best code rate pair over a slow Rayleigh fading channel, depending on the system load and the constraint length of the inner code, a gain of 2dB to more than 9dB in received power reduction for each user can be achieved, compared to the conventionally used code rate pair (outer/inner code rate = 0.8/0.5).