Precision medicine requires cuffless blood pressure (BP) estimation technologies to achieve high accuracy levels suitable for healthcare monitoring. Recent advances, notably in personalized BP estimation methods utilizing transfer learning, have partially met these requirements. However, traditional personalized fine-tuning pipelines often ignore key physiological factors across age groups, thus limiting their effectiveness. This study identifies an important fine-tuning principle: target subjects benefit most when fine-tuned using a source model pretrained on an age-matched group. We introduce a novel two-level ensemble learning method, ATTE, which integrates multiple BP models for cuffless BP estimation based on pulse waves. It first performs feature-level ensemble learning (FEL) to combine age-group-specific fine-tuned models and enrich representations, then applies an optimized Bayesian model averaging (BMA) method that adaptively weights model outputs to boost accuracy and reduce model-selection uncertainty. Using only 50 transfer samples, ATTE improved systolic and diastolic BP estimation accuracy by 6.55% and 5.58% on a public dataset, compared to the traditional fine-tuning paradigm. It also achieved improvements of 5.10% and 8.30% on a self-collected dataset, which has been preliminarily validated in an elderly care scenario. Ablation and interpretability analyses validated the age-matched fine-tuning principle, the effectiveness of ATTE, and its model-agnostic generalizability. This work represents a pioneering step in optimizing personalized BP estimation and underscores its significant potential for real-world healthcare applications.

Augmented reality is projected to be a primary mode of information consumption on the go, seamlessly integrating virtual content into the physical world. However, the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety, and the implications of these demands are not well understood. Here, we investigate the impact of virtual path guidance and augmentation density (visual clutter) on search performance and memory. Participants walked along a predefined path, searching for physical or virtual items. They experienced two levels of augmentation density, and either walked freely or with enforced speed and path guidance. Augmentation density impacted behavior and reduced awareness of uncommon objects in the environment. Analysis of search task performance and post-experiment item recall revealed differing attention to physical and virtual objects. On the basis of these findings we outline considerations for AR apps designed for use on the go.

Low Earth Orbit (LEO) observation satellite systems play a critical role in a variety of applications, including environmental monitoring, urban planning, and national security. However, transmitting the data collected by numerous observation satellites to Earth remains a significant challenge. One promising approach to improve data transmission efficiency is to utilize LEO communication satellites as data relays. Existing researches in this area primarily focus on the inter-satellite communication scheduling, often neglecting the importance of satellite observation scheduling, which limits the potential performance gain. In this work, we investigate the joint optimization of observation and transmission scheduling in a satellite network with resource-constrained LEO satellites, taking into account the heterogeneity of observation missions and the dynamics of network topology. Specifically, we first introduce a Time-Expanded Graph (TEG) model to effectively represent dynamic network topology and satellite resource constraints. Based on this model, we formulate a network flow problem that incorporates both mission-specific characteristics and satellite energy costs. To reduce the solution complexity in large-scale networks, we propose a novel low-complexity Augmented Lagrangian-based Distributed Parallel Splitting (ALDPS) algorithm. Simulation results show that our proposed algorithm can improve the network utility by 8.3% to 28.1% compared to baseline methods.

Accurate identification of key anatomy structures in airway intubation, the primary step in general anesthesia, is crucial for surgical success and patient safety. Achieving both object detection and segmentation in this context using deep learning technologies is challenging due to limited labeled data, especially for difficult intubation conditions with anatomical abnormalities, trauma, or tumors. This study proposes an efficient improvement of Segment Anything Model (SAM) through Mask R-CNN prompt and an adaption technology to achieve a competent performance on airway anatomy instance segmentation tasks. We first constructed a labelled dataset of 1000 samples from difficult intubation conditions. Compared to U-Net, Mask R-CNN, and DeepLab, our model improved the Intersection over Union (IoU) by 4.4%, 6.5%, and 6.6%, and Dice coefficient by 4.4%, 5.5%, and 6.1%, respectively. Using Parameter-Efficient Fine-Tuning (PEFT) with adapter modules, our model demonstrates significant enhancement in identification performance of airway anatomies, achieving a Dice coefficient of 97.3% and improving the IoU up to 90.3%. Notably, our model outperformed others when using fewer segmentation mask labels, with improvement more pronounced as the number of labels decreases. Fine-tuning on similar medical images from public datasets of different scenarios resulted an IoU of up to 86.0% and a Dice coefficient of up to 91.9%, comparable to results from fine-tuning on 200 airway samples. These findings demonstrate that our proposed Mask R-CNN prompt Adaptation-SAM approach can effectively enhance performance while reducing computational resources demands, making it well-suited for complex clinical applications such as intubation. This study also offers a promising framework for future medical instance segmentation tasks.

Federated Learning (FL) is an innovative learning paradigm that allows multiple devices to collaboratively train a shared model without uploading the raw data to the cloud, thereby enhancing privacy and security. Leveraging Mobile Edge Computing (MEC), Hierarchical Federated Learning (HFL) can further reduce the communication overhead, thereby increasing the efficiency and scalability of FL systems by enabling model aggregation at the network edge. However, this framework often encounters security challenges, such as single points of failure and the risk of malicious model tampering. To address these challenges, researches have employed blockchain technology to enhance the security of FL systems, but most of these solutions incur significant resource burdens due to the intensive computation demands of blockchain consensus mechanisms, such as Proof-of-Work (PoW). In this work, we aim to explore a lightweight blockchain-enabled federated learning (LBFL) framework that utilizes the Delegated Proof-of-Stake (DPoS) consensus mechanism, which employs a simple voting process to elect a small number of candidate block producers (known as delegates) to aggregate the FL model and produce blocks. This framework significantly reduces the number of consensus nodes, thereby minimizing resource consumption during the consensus process. We study the joint optimization of mobile device association, bandwidth allocation, computing frequency management, and block producer selection, aiming to minimize the overall delay and energy consumption. To address the challenges posed by discrete and continuous decision variables, we decompose the problem into three sequential subproblems and solve them iteratively. Simulation results show that compared with the existing benchmarks, the proposed scheme can reduce overall delay and energy consumption by 15% to 22%.

Blockchain is a decentralized and secure digital ledger system that ensures data integrity through immutable records and cryptographic consensus mechanisms. However, in mobile blockchain networks, the computation-intensive proof-of-work (PoW) mining process often imposes a significant burden on mobile users (MUs) who serve as miners, particularly given their limited computing resources. Mobile edge computing (MEC) offers a promising solution to alleviate the burden on MUs, by enabling them to offload their mining tasks to nearby edge servers. While existing studies have explored MEC-assisted blockchain networks in both single-server and multi-server scenarios, they often overlook crucial aspects of blockchain networks, such as the transmission and computation delays inherent in the mining process. In this work, we investigate a more realistic MEC-assisted mobile blockchain network, where mining tasks are explicitly modeled with delay constraints to better capture real-world performance challenges. To analyze the strategic interactions between MUs and edge computing service providers (ECPs), we formulate a two-stage multi-leader and multi-follower Stackelberg game, which consists of an ECP Resource Pricing (ERP) game at Stage I, and an MU Resource Competition (MRC) game at Stage II. Specifically, in the ERP game at Stage I, ECPs, acting as leaders, set the resource prices for MUs; and in the MRC game at Stage II, MUs, acting as followers, determine their computing resource demands based on the prices of ECPs. We first prove the existence of Nash equilibrium (NE) for both games, and then derive the closed-form conditions for the NE of the MRC game at Stage II. Based on the above, we further propose a sub-gradient-based resource pricing algorithm that can converge to the NE of the ERP game at Stage I. Simulation results show that, when compared to the centralized cooperative solution, our proposed non-cooperative game approach can significantly reduce the computational complexity, while incurring only a modest performance degradation, e.g., the social welfare loss ranges from 6.64% to 9.96%.

Individual-specific calibration is a major bottleneck in motor imagery (MI) electroencephalogram (EEG) decoding, limiting real-world neural-feedback rehabilitation. Transfer learning, particularly Test-Time Adaptation (TTA), offers a promising solution for direct online cross-subject adaptation, handling sequentially arriving unlabeled MI-EEG data. However, existing TTA methods, primarily designed for domains such as computer vision, face challenges when applied to MI-EEG data due to its scarcity and non-stationary nature. To address the challenges in direct online MI-EEG decoding, this paper proposes MI-IASW, a novel framework combining Information-Aggregation (IA) and Source-Guided Pseudo-Label Weighting (SW). IA leverages Mixed and Adaptive Batch Normalization (MABN) to ensure effective aggregation of statistical and gradient information. Additionally, IA adopts a Weight Aggregation (WA) strategy to improve generalization under limited data. Meanwhile, SW first evaluates the overconfident pseudo-labels with the guidance of source centers and then employs Class-Aware Weighting (CAW) to adjust sample contributions to the loss function. Experimental evaluations on two public MI-EEG datasets demonstrate that our proposed framework outperforms various competitive baselines, achieving an average performance gain of 3.17% over the baseline TTA methods and 6.80% over the source model. By eliminating the need for individual-specific offline calibration, MI-IASW enables practical deployment in real-world rehabilitation and improves cross-subject decoding.

Beam tracking is a promising technology in mmWave-enabled cellular-connected unmanned aerial vehicle (UAV) communications. However, conventional beam tracking schemes always incur a large training overhead, and it is difficult to determine the time duration of a training cycle due to the high mobility of UAVs, which is essential for improving the effective achievable rate (EAR). To address this issue, we first adopt angular velocity estimation to obtain the beam coherence time, which serves as the time duration of the training cycles. To further reduce the training overhead in each training cycle, we then design an adaptive beam tracking algorithm based on bandit learning, where the actions are taken based on the accuracy of the angular velocity estimation. If the estimation is not accurate, more beams will be swept in the next cycle. In this way, the beam misalignment incurred by estimation inaccuracy will largely alleviate. Thus, the EAR can be effectively improved with smaller training overhead. The simulation results demonstrate the superior performance of the proposed algorithm in terms of the training overhead and the EAR.

In this paper, we investigate the adoption of frequency domain differential modulation to support mini-slot-assisted URLLC, which can eliminate the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Furthermore, we derive the block error rate (BLER) for frequency domain differential OFDM (FDDi-OFDM) using non-asymptotic information-theoretic bounds. Simulation results verify the accuracy of the analysis and show that, for a high Doppler environment, FDDi-OFDM can yield a much better result than the pilot-assisted coherent scheme.

Multi-UAV cooperative search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a multilayered aerial computing network (MACN) scenario, which consists of a low-altitude platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a high-altitude platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of search probability map (SPM), and meanwhile maximizing the number of target discovery and coverage rate. The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a multiagent deep reinforcement learning (MADRL) approach based on the parameter sharing and action mask (PSAM), called PSAMMA, where the state-action-reward-state-action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that 1) the proposed PSAMMA algorithm outperforms existing algorithms in the literature, and can increase the average utility by 9.89%–31.15% and 2) we evaluate the search performance by analyzing the average uncertainty, target rate, and coverage rate under different parameter settings.

BACKGROUND: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid demand for intelligent systems that can recommend comprehensive long-term care plans for older adults with disabilities who have stable clinical conditions. OBJECTIVE: This study aims to utilize deep learning methods to recommend comprehensive care plans for the older adults with disabilities. METHODS: We model the care data of older adults with disabilities using a bipartite graph. Additionally, we employ a prediction-based graph self-supervised learning (SSL) method to mine deep representations of graph nodes. Furthermore, we propose a novel graph Transformer architecture that incorporates eigenvector centrality to augment node features and uses graph structural information as references for the self-attention mechanism. Ultimately, we present the Bipartite Graph Transformer (BiT) model to provide personalized long-term care plan recommendation. RESULTS: We constructed a bipartite graph comprising of 1917 nodes and 195 240 edges derived from real-world care data. The proposed model demonstrates outstanding performance, achieving an overall F1 score of 0.905 for care plan recommendations. Each care service item reached an average F1 score of 0.897, indicating that the BiT model is capable of accurately selecting services and effectively balancing the trade-off between incorrect and missed selections. DISCUSSION: The BiT model proposed in this paper demonstrates strong potential for improving long-term care plan recommendations by leveraging bipartite graph modeling and graph SSL. This approach addresses the challenges of manual care planning, such as inefficiency, bias, and errors, by offering personalized and data-driven recommendations. While the model excels in common care items, its performance on rare or complex services could be enhanced with further refinement. These findings highlight the model's ability to provide scalable, AI-driven solutions to optimize care planning, though future research should explore its applicability across diverse healthcare settings and service types. CONCLUSIONS: Compared to previous research, the novel model proposed in this article effectively learns latent topology in bipartite graphs and achieves superior recommendation performance. Our study demonstrates the applicability of SSL and graph transformers in recommending long-term care plans for older adults with disabilities.

One of the primary challenges in ultra-reliable and low-latency communications (URLLC) is to achieve accurate channel estimation and data detection while minimizing latency. Given the small packet size in URLLC, relying solely on pilot-assisted (PA) coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore both frequency domain differential modulation (FDDM) and time domain differential modulation (TDDM), enabling non-coherent short packet URLLC with mini-slot structures. The minimum achievable block error rate and the maximum achievable rate for all three modes (i.e., FDDM, TDDM and PA modes) are derived using non-asymptotic information-theoretic bounds. Furthermore, we show that FDDM can more than compensate for the training overhead inadequacy and performance degradation of PA mode in medium-to-high-mobility scenarios, thereby improving the performance of short packet transmission with mini-slot by dynamically activating FDDM. Simulation results validate the feasibility and effectiveness of the proposed low overhead FDDM mini-slot transmission scheme.

On-board distributed computing can provide more powerful computation capabilities for future low-earth-orbit (LEO) satellite constellations, serving intelligent information sensing and spatial large model through multi-satellite cooperation. On-board distributed computing depends on the efficient exchanging data flows between satellites termed coflow. The application of laser inter-satellite links (LISLs) will drastically improve the transmission capacity among the satellite distributed computing network (SDCN). However, due to the temporary interruptions of LISLs and the characteristics of optical circuit switching (OCS), the flow interruptions and conflicts significantly affect the coflow completion time (CCT). In this paper, we propose a conflict-aware coflow scheduling scheme to reduce the CCT in the OCS-based SDCN. Firstly, the time-varying LISLs and OCS-based coflow transmission are modeled and the problem of minimizing CCT is formulated. After that, we characterize the routing paths of coflow as the conflict graph and transform the coflow concurrent matching problem into the maximum independent set (MIS) problem in conflict graph. Based on this, we design the coflow polling greedy scheduling (CPGS) algorithm, which not only considers the sequence of coflow scheduling, but more importantly maximizes concurrent flows by MIS search. We deploy three different simulation scenarios to evaluate the algorithm performance. Simulation results show that our algorithm can significantly reduce the CCT by about 28.9% to 42.1% compared with existing works.

This paper investigates the problem of caching placement and wireless backhaul resource allocation for enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) coexistence. Different from existing works which focus on the resource allocation for eMBB and URLLC in the access but ignore the backhaul traffic, this paper considers that the backhaul traffic can be alleviated by caching eMBB contents at the edge, such that more transmission resources can be released for scheduling URLLC traffic in wireless backhaul networks. We first propose an upper confidence bound (UCB)-based caching algorithm to reduce eMBB traffic in the backhaul, resulting in more backhaul resources that can be punctured by URLLC traffic. To minimize the URLLC delay while ensuring eMBB throughput, a greedy resource allocation algorithm is then proposed to allow URLLC traffic to puncture resources of eMBB traffic in the backhaul as many as possible. Simulation results demonstrate the superior performance of the proposed algorithms in terms of URLLC delay.

Accurate medical image segmentation especially for echocardiographic images with unmissable noise requires elaborate network design. Compared with manual design, Neural Architecture Search (NAS) realizes better segmentation results due to larger search space and automatic optimization, but most of the existing methods are weak in layer-wise feature aggregation and adopt a "strong encoder, weak decoder" structure, insufficient to handle global relationships and local details. To resolve these issues, we propose a novel semi-supervised hybrid NAS network for accurate medical image segmentation termed SSHNN. In SSHNN, we creatively use convolution operation in layer-wise feature fusion instead of normalized scalars to avoid losing details, making NAS a stronger encoder. Moreover, Transformers are introduced for the compensation of global context and U-shaped decoder is designed to efficiently connect global context with local features. Specifically, we implement a semi-supervised algorithm Mean-Teacher to overcome the limited volume problem of labeled medical image dataset. Extensive experiments on CAMUS echocardiography dataset demonstrate that SSHNN outperforms state-of-the-art approaches and realizes accurate segmentation. Code will be made publicly available.

Multi-UAV Cooperative Search (MCS) can significantly enhance the efficiency and effectiveness of search by enabling multiple unmanned aerial vehicles (UAVs) to collaborate in conducting search missions. Thus, it has played a vital role in various applications, such as surveillance, target detection, and information gathering. While existing works in this field mainly focused on a single UAV layer, in this work we consider a Multi-layered Aerial Computing Network (MACN) scenario, which consists of a Low-Altitude Platform (LAP) layer with multiple high-flexibility and low-capacity UAVs (called LUAVs) and a High-Altitude Platform (HAP) layer with one low-flexibility and high-capacity UAV (called HUAV). In such a scenario, We focus on the joint optimization of flying trajectories, computation offloading, and resource allocation, aiming at minimizing the uncertainty of Search Probability Map (SPM). The problem is challenging due to the co-existence of discrete and continuous decision variables, as well as the fast and randomly changing of wireless environment. To solve the problem in an online and distributed manner, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) approach based on Parameter Sharing and Action Mask (PSAM), called PSAMMA, where the State-Action-Reward-State-Action (SARSA) method is leveraged to determine the discrete flying and offloading decisions. Experiment results show that the proposed PSAMMA algorithm outperforms existing methods in terms of the average SPM uncertainty, the target discovery rate, and the coverage rate.

Unmanned Aerial Vehicles (UAVs), with their high levels of flexibility and maneuverability, can greatly enhance the capabilities of Mobile Edge Computing (MEC) by acting as edge computing servers. In practice, however, it is often challenging to jointly optimize the flying trajectories of UAVs and the offloading decisions of tasks, due to the fast and randomly changing of physical environments. In this work, we investigate an UAV-enable MEC network with the assistance of Digital Twin (DT), where a DT layer is introduced to simulate the Physical Entity (PE) layer, generate different strategies, and evaluate their performances. Specifically, we formulate a joint flying trajectories, task offloading, and resource allocation problem on the DT layer, aiming at minimizing both task delay and energy consumption, under the maximum tolerated delay and resource constraints. To solve the problem in an online distributed manner and implement the derived strategies on the real PE layer, we propose a hierarchical learning approach, which consists of a Deep Reinforcement Learning (DRL) module and a Constrained Optimization (CO) module. First, the DRL module determines the UAVs' flying trajectories. Then, the CO module determines the MDs' task offloading decisions and the associated resource allocations, given the UAV s' flying decisions. Finally, the outputs of both modules are combined together to train the DRL module by using the Deep Deterministic Policy Gradient (DDPG) method. Experiment results show that our proposed DT-assisted scheme outperforms existing benchmark schemes in terms of both task delay and energy cost.

Acquiring accurate channel state information (CSI) in finite blocklength (FBL) ultra-reliable and low-latency communications (URLLC) with millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) necessitates a significant pilot overhead. To overcome this challenge, differential modulation (DM) is a promising solution for eliminating the costly pilot overheads in FBL and avoiding pilot contamination in mmWave massive MIMO systems. In this paper, we propose a combination of DM and hierarchical codebook-based beam training schemes as a feasible way to enable FBL URLLC with mmWave massive MIMO. Additionally, we derive the block error rate (BLER) of the joint DM and hierarchical codebook-based beam training scheme in the FBL regime by employing non-asymptotic information-theoretic bounds. The simulation results verify the accuracy of the analysis and show that DM does offer an advantage over the pilot-assisted coherent schemes under FBL with mmWave massive MIMO.

Multi-agent reinforcement learning (RL) has recently been adopted to solve massive ultra-reliable and low-latency communications (mURLLC) energy efficiency (EE) optimization problem in a single-cell cellular network under random access. Bursty traffic is an important characteristic of mURLLC users (UEs). This characteristic and its impact on the RL scheme are generally ignored in many RL-based studies related to the optimization of EE for uplink mURLLC. Moreover, in a smart factory with multiple cells, inter-cell interference and shadow fading further complicate EE optimization. To address these issues, we propose a novel cooperative multi-agent scheme to maximize the long-term EE in a multi-cell cellular network with mURLLC bursty traffic and a K-repetition scheme by optimizing the repetition value and transmission power. A UE clustering algorithm and an intermittent learning mode are adopted to reduce the computational complexity and mitigate the impact of bursty traffic on the RL scheme. A proper reward function is designed to address both long-term EE maximization and the number of successfully served UEs under high reliability requirement. The simulation results show that our proposed cooperative multi-agent reinforcement learning scheme greatly outperforms other existing schemes in terms of long-term accumulated EE and the number of successfully served UEs.

Pulse wave analysis, a non-invasive and cuff-less approach, holds promise for blood pressure (BP) measurement in precision medicine. In recent years, pulse wave learning for BP estimation has undergone extensive scrutiny. However, prevailing methods still encounter challenges in grasping comprehensive features from pulse waves and generalizing these insights for precise BP estimation. In this study, we propose a general pulse wave deep learning (PWDL) approach for BP estimation, introduc-ing the OVAR-BPnet model to powerfully capture intricate pulse wave features and showcasing its effectiveness on multiple types of pulse waves. The approach involves constructing population pulse waves and employing a model comprising an omni-scale convolution subnet, a Vision Transformer subnet, and a multilayer perceptron subnet. This design enables the learning of both single-period and multi-period waveform features from multiple subjects. Additionally, the approach employs a data augmentation strategy to enhance the morphological features of pulse waves and devise a label sequence regularization strategy to strengthen the intrinsic relationship of the subnets' output. Notably, this is the first study to validate the performance of the deep learning approach of BP estimation on three types of pulse waves: photoplethysmography, forehead imaging photoplethysmography, and radial artery pulse pressure waveform. Experiments show that the OVAR-BPnet model has achieved advanced levels in both evaluation indicators and international evaluation criteria, demonstrating its excellent competitiveness and generalizability. The PWDL approach has the potential for widespread application in convenient and continuous BP monitoring systems.

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments.

Extracting 3D environmental insights from endoscopy images holds immense value for minimally invasive surgical procedures. The Self-supervised Monocular Depth Estimation (SMDE) framework is promising for achieving this objective. However, existing methods struggle with low-texture and drastic illumination fluctuations in endoscopic images. To tackle this, we incorporate Photometric Aliagnment method based on pixel-wise Color Offset, and propose a carefully designed Color Offset penalty based on Reconstruction Confidence. We furthur apply an Auto-Mask mechanism and a cutting-edge backbone to enhance the performance. Our experiments employ a dataset of airway intubation images captured with a low-resolution and near-field electronic bronchoscope. The experimental results unequivocally highlight the exceptional performance of our approach, achieving root mean square error of 1.64 ± 0.22 mm after point cloud registration, which decreases by 16% than the current SoTA. Furthermore, we introduce an innovative evaluation metric rooted in Photometric Alignment Symmetry, and conduct ablation experiments on this metric. The ablation experiments furthur validate the effectiveness of proposed modules. This study underscores the efficacy of the Reconstruction Confidence-based Color Offset penalty and symmetric alignment evaluations in extracting 3D information from low-resolution and near-field endoscopy images.

Federated Learning (FL) is a distributed machine learning approach that enables multiple individual devices (clients) to collaboratively train a global machine learning model without directly sharing their raw data with each other. By keeping the raw data on local devices, FL can effectively preserve data privacy and security. However, the performance of FL system is highly dependent on the amount and quality of client data, as well as the relevance of data from different clients. In this article, we investigate the client and data selection problem in FL system from both system and individual perspectives, while considering the impacts of data quality and data relevance. Specifically, from the system perspective, we establish a centralized optimization problem that aims to optimize social welfare in a centralized manner. That is, a central controller decides on the amount of each client's data to be utilized for the FL system, aiming at maximizing the overall social welfare. From the individual perspective, we formulate a two-stage Stackelberg game for incentivizing clients to contribute their data and resources to the FL system in a decentralized manner. In the first stage, the FL server acts as the game leader and specifies a reward mechanism. In the second stage, each client acts as a game follower and competes for the reward by deciding on the amount of data to contribute to the FL system, aiming at maximizing its individual payoff. We analyze the centralized optimization problem and the Stackelberg game systematically for both low and high data relevance scenarios. In particular, we derive the closed-form optimal solution and game equilibrium for the low data relevance scenario, and propose iterative algorithms that effectively converge to a suboptimal solution and subgame equilibrium for the high data relevance scenario. Simulation results verify that data relevance has a significant negative impact on the system performance. That is, the social welfare achieved through centralized optimization and distributed Stackelberg game approaches in the high data relevance scenario is only 19.7% and 24.0%, respectively, of those achieved in the low data relevance scenario.

Mobile edge computing (MEC) is a promising technology for improving the efficiency and security of mobile blockchain networks, by allowing miners with limited computing resources to offload the computation-intensive mining tasks to edge computing servers that are proximate to them. Collaborative block mining can further improve the mining efficiency and increase the miner profit, by enabling multiple miners to pool their computation resources and transaction data together to mine new blocks collaboratively. Thus, an MEC-assisted collaborative blockchain network can leverage the advantages of both technologies, offering superior efficiency, security, and scalability for blockchains. While existing research in this area mainly focused on the single-coalition collaboration mode where each miner can only join one collaborative coalition, this work explores a more comprehensive multi-coalition collaboration mode, which allows each miner to join multiple collaborative coalitions. To analyze the miner behavior in such a scenario, we formulate a novel two-layer sequential game, consisting of a coalition formation game as the first-layer and an edge resource competition game (among the formed coalitions) as the second layer. Specifically, in the first layer, each miner acts as a game player and selects multiple coalitions to join, leading to an overlapping coalition formation (OCF) game among miners. In the second layer, each established coalition acts as a game player and decides the amount of edge computing resource to invest, leading to an edge resource competition (ERC) game among coalitions. We derive the closed-form Nash equilibrium for the ERC game, and propose an iterative algorithm that converges to a stable coalition structure for the OCF game. Simulation results show that the proposed multi-coalition collaboration mode can improve the system efficiency by 34.1% ~ 54.3%, compared to the single-coalition collaboration mode.

Due to the advantages of high flexibility, large workspace, and good human-body compatibility, flexible tendon-driven surgical continuum robots have attracted a lot of attention in robot-assisted minimally invasive surgery. However, due to the coupling of the position and angle of the continuum robot, and the easy deformation of the external force, its inverse kinematics solution has always been a challenge. This paper proposes a fast inverse kinematics solver for surgical continuum robots with a variable curvature model. Firstly, the deformation of the continuum robot is analyzed, and a representation method of the variable curvature model is proposed. Next, to solve the inverse kinematics problem when the continuum robot deforms under load, FABRIKv is proposed by improving the Forward And Backward Reaching Inverse Kinematics (FABRIK). During the inverse kinematics solution, the algorithm preserves the real-time nature of FABRIK and corrects for deformation effects caused by the load. Finally, the experiment verifies the rationality and effectiveness of the variable curvature model representation method, as well as the fastness and accuracy of the FARIKv solver.

With the highly increasing demands of vehicular applications, the cloud intelligence is pushed towards the edge by placing services next to vehicle users. Due to the limited resources of edge nodes and the high mobility of vehicle users, it is challenging to place vehicular services effectively at the edge to serve vehicle users with high quality of experience (QoE). In this paper, we investigate a vehicular service placement problem with unknown demands. Different from previous works, we consider that vehicle users have various service demands, which are related to their contexts. To enable on-demand service placement, we propose a context-aware vehicular service placement algorithm based on the estimated service demands. For better demand estimation, a fine-grained partition method is developed to divide the context space. The simulation results show that the proposed algorithm has superior performance in terms of cumulative system rental utility.

Federated Learning (FL) is a novel distributed learning framework to train the global model locally without collecting the raw data of clients. However, the performance of FL is greatly restricted by the limited network communication capacity between the cloud and clients. MEC-assisted Hierarchical Federated Learning (HFL) can effectively relieve the network pressure in FL, by transmitting and aggregating model parameters at the network edge based on the idea of Mobile Edge Computing (MEC). The existing researches on HFL often adopt the traditional multiple access techniques (e.g., OFDMA) for the model transmission between clients and edge servers, which may be inefficient. In this work, we consider a novel Over-the-Air Computing (AirComp) based HFL framework, where clients send model parameters to edge servers simultaneously, and edge servers can directly complete the aggregation of model in the air by exploiting the superposition property of wireless channels. In such a scenario, we study the joint client association, transmission, and computation optimization problem, aiming at minimizing the overall energy consumption and latency. The problem is challenging due to the multi-level coupling between edge servers and clients. We decouple it into a client association subproblem and a resource optimization subproblem. We first show that the second subproblem is convex and can be easily solved by a coordinate descent algorithm. We then show that the first subproblem is a combinational optimization, and propose a near-optimal solution where each client is associated with the nearest edge server. Simulation results show that the AirComp-based HFL scheme outperforms the existing OFDMA-based schemes in terms of both energy consumption and latency.

Palpation is necessary in Traditional Chinese Medicine (TCM). In TCM, doctors need to touch patient’s wrist and exert pressure on it to obtain physiological signal. Locating the pulse position is an important step in palpation. Nowadays, researchers have proposed numerous methods to locate the pulse position accurately. In this paper, we propose an accurate and effective framework to locate the pulse position. Our framework is based on 3-dimensional convolutional neural network (3DCNN), and we propose a novel temporal attention module to further improve the performance. Our framework achieves superior results, the accuracy of locating the pulse position on a video with a resolution of 2048 × 1088 within 100 pixels is 97%. More and more cross-domain applications of computer science and medical science provides more ways for doctors to treat patients, such as telemedicine, computed tomography image analysis, and physiological signal analysis. Our research presents the great potential to be applied to palpation automation and intelligent medical treatment.

Photometric stereo aims at recovering the surface and shape of an object from a set of observations under different light conditions. Deep learning based methods have made substantial contributions to the surface normal estimation under complex surface reflections, various materials, near-light settings, etc. These deep learning based methods learn the mapping from observed images to the surface normal of an object directly via training deep neural networks on large labeled datasets. However, the shapes, materials, and reflectance properties of objects in these datasets are limited, leading to abridged performances in realistic environments. In this paper, we introduce a work-piece dataset for near-light photometric stereo under industrial application scenarios, which consists of observed images taken under at most 40 light conditions, and the ground truth surface normals. Based on this datasets, we propose the Hybrid Near-light Uncalibrated Photometric Stereo (HNUPS) for both unsupervised light calibration and surface normal estimation. Experimental results on the work-piece dataset demonstrate that HNUPS can obtain the least mean angular error when compared to recent photometric stereo methods, which have verified the effectiveness of the proposed HNUPS.

Pulse localization is the basic task of the pulse diagnosis with the robot. Using neural network for localization can not only reduce the contact between the machine and the subject, relieve the discomfort of the process, but also reduce the preparation. Since the networks with the coordinate regression directly have large parameters and are not suited for input images with different size. In this paper, we propose a novel method, spoon fully convolutional networks (SFCN) with the landmark fitting method for pulse localization. SFCN includes the fully convolutional networks which are like the spoon while the landmark fitting method finds the pulse in the sub-pixels. The experiments show that our proposed method can locate the pulse with high accuracy and few parameters, which is suitable for application on robots.

In this paper, we consider the dynamic content caching issue in the cache-enabled Internet of Things (IoT) systems. For real-time applications in cache-enabled IoT systems, it is imperative to design dynamic content caching schemes to reduce the energy consumption of sensors and improve the freshness of information at users. We first design a dynamic content caching procedure for a cache-enabled IoT system with limited cache capacity and express the evolution of the Age of Information (AoI) at both the edge caching node and each user. Then, we formulate the dynamic content caching problem as a Markov Decision Process to minimize the expectation of a long-term accumulative cost, which jointly considers the average AoI of users and the energy consumption of sensors. To solve this problem, we propose an actor-critic based caching algorithm without prior knowledge of users’ content demands. The numerical results show that the proposed algorithm can achieve lower average AoI and energy consumption than other baselines.

Cellular networks are promising for unmanned aerial vehicle (UAV) communications. In this paper, we contribute to this emerging area by focusing on delay measurements of drones connecting to commercial LTE and 5G networks in real-world scenarios, which are rarely reported in prior works. Measurements are carried out in two typical scenarios, i.e., suburban environment and urban environment. Different delay behaviors are observed in these two scenarios. By analyzing the impact of different parameters on delay performance, we find that the average delay is highly related to SINR, while the delay variance (delay fluctuation) shows a strong relation to handover frequency in both scenarios. The gained insights could provide some guidelines for integrating aerial users to cellular networks.

Grant-free (GF) access is expected to support low-latency services in fifth-generation (5G) systems, while non-orthogonal multiple access (NOMA) has been proposed to enable massive connectivity in cellular networks. However, the performance analysis for the GF access mode based on NOMA is not trivial, especially for large-scale multi-cell networks due to the inherent random near-far phenomenon. In this paper, we exploit tools from stochastic geometry to develop a tractable framework for analysing uplink performance in large-scale multi-cell networks under GF NOMA and short packet transmission. To make the framework tractable, we further transform the intra- and inter-cell interference to an equivalent interference model. The URLLC performance of GF NOMA networks is derived under the assumption of perfect successive interference cancellation (SIC) and short packet transmission. Numerical results obtained from theoretical calculations and Monte Carlo simulations verify the correctness of our analysis.

In mobile Internet ecosystem, mobile users (MUs) purchase wireless data services from Internet service provider (ISP) to access to Internet and acquire the interested content services (e.g., online game) from Content Provider (CP). The popularity of intelligent functions (e.g., AI and 3D modeling) increases the computation-intensity of the content services, leading to a growing computation pressure for the MUs’ resource-limited devices. To this end,edge computing serviceis emerging as a promising approach to alleviate the MUs’ computation pressure while keeping their quality-of-service, via offloading some computation tasks of MUs to edge (computing) servers deployed at the local network edge. Thus, edge service provider (ESP), who deploys the edge servers and offers the edge computing service, becomes an upcoming new stakeholder in the ecosystem. In this work, we study the economic interactions of MUs, ISP, CP, and ESP in the new ecosystem with edge computing service, where MUs can acquire the computation-intensive content services (offered by CP) and offload some computation tasks, together with the necessary raw input data, to edge servers (deployed by ESP) through ISP. We first study the MU's Joint Content Acquisition and Task Offloading (J-CATO) problem, which aims to maximize his long-term payoff. We derive theoff-linesolution with crucial insights, based on which we design anonlinestrategy with provable performance. Then, we study the ESP's edge service monetization problem. We propose a pricing policy that can achieve aconstant fractionof the ex post optimal revenue with an extraconstant lossfor the ESP. Numerical results show that the edge computing service can stimulate the MUs’ content acquisition and improve the payoffs of MUs, ISP, and CP.

Mobile Edge Computing (MEC) is a promising solution to reduce the task execution delay by placing the computation resource at the network edge close to the end-users, and has received an extensive attention in the 5G era. In this work, we study a multi-layer task offloading framework for MEC, where each task generated by a mobile device can be offloaded to other mobile devices via D2D links, or edge servers with cellular links, or remote cloud server via Internet. We consider a generic task model, where each task can be divided into a set of dependent subtasks and each subtask can be offloaded to different locations. In such multi-layer offloading framework with dependence-aware tasks, we are interested in the optimal subtask offloading problem for mobile devices, that is, how to optimally offload the subtasks of all devices. To study this, we formulate an Energy Consumption Minimization problem for mobile devices, which decides when and where each subtask will be scheduled, aiming at minimizing the total energy consumption of mobile devices. The problem is challenging due to the non-convex constraints. We propose some mathematical operations to relax the nonlinear constraints into linear constraints, and hence transform the original non-convex problem into a linear programming, which can be solved efficiently. Simulation results show that our proposed solution outperforms the existing solutions in terms of energy consumption and task success rate. For example, it can reduce the mobile devices’ energy consumption by up to 40%.

For a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). Doppler shift, however, leads to a loss of subcarrier orthogonality, resulting in inter-carrier interference (ICI), especially in a high speed environment. In this paper, we solve the resource allocation problem by considering ICI caused by Doppler spread and imperfect channel state information (CSI) caused by estimation errors, quantization errors and feedback delay. However, the resultant resource allocation algorithm is so complicated that it may not be applicable to the wireless communications environment under high mobility since it may change rapidly and therefore needs real-time computation. As such we propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time while achieving very good prediction accuracy. Simulation results verify the influence of Doppler shift on the SE performance and the effectiveness of DNNs in terms of computing time.

To alleviate the ever-increasing data demands, edge caching plays a crucial role in improving the performance of system, especially in data-intensive applications. Previous works mainly focus the caching policy over reliable channels. For unreliable channel scenarios, the system performance is jointly affected by the user preference and the channel reliability, whereas both the user preference and the reliability are unknown commonly. A high retrieval cost may be incurred on unreliable channels even when the requested content is in the nearby cache. To solve the issues mentioned above, we jointly optimize the service scheduling policy and the content caching policy in this paper. We propose a maximal reward priority (MRP) policy to serve user requests, and a collaborative multi-agent actor critic (CMA-AC) policy to update the local cache. Simulation results show that the proposed MRP policy outperforms the shortest distance priority (SDP) policy [4]. And the proposed CMA-AC policy obtains a better performance compared with a distributed multi-agent deep Q-network (DMA-DQN) policy, especially when the number of contents and the capacity of local cache are large. Furthermore, the proposed CMA-AC policy is robust.

Mobile edge caching is a promising approach for enhancing content delivery efficiency and alleviating backbone network burden, via caching popular contents at network edge devices (e.g., base stations or WiFi access points). The successful commercial deployment relies on a comprehensive understanding of the economic interactions among different stakeholders involved. In this paper, we study an edge caching system consisting of a Content Provider (CP), an Internet Service Provider (ISP) who provides the backbone network service, a wireless Access Provider (AP) who provides the wireless access service, and a set of mobile End-Users (EUs), where the CP provides contents for EUs either via the remote server (on the Internet) or via the edge cache (purchased from the AP). We formulate their interactions as a three-stage Stackelberg game. In Stage I, the CP decides the edge cache space to purchase from the AP and cache access fee to charge EUs. In Stage II, the ISP and AP determine the backbone and wireless access service prices, respectively. In Stage III, EUs decide whether to subscribe to the CP' s edge cache service, taking the cache hit probability, cache access fee, backbone and wireless access prices into consideration. We analyze the subgame perfect equilibrium of the dynamic game systematically under two different network pricing scenarios: cooperative pricing and competitive pricing, depending on whether ISP and AP cooperate or compete with each other to make their pricing decisions. Our analysis and simulation results show that all profits of the CP, ISP, AP, and utilities of EUs can be increased by adopting edge cache, compared with the case without edge cache.

Mobile Edge Computing (MEC) is a promising solution to tackle the upcoming computing tsunami in 5G era, by effectively utilizing the idle resource at the mobile edge. In this work, we study such an MEC scenario, where mobile devices at edge share their heterogeneous resources with each other, hence forming a multi-dimensional resource crowdsourcing (sharing) framework. We are interested in the problem of how to optimally offload tasks to mobile devices under this framework, aiming at minimizing the total energy cost and maximizing the overall task completion. To study the problem, we first propose a general task model, where each task is divided into multiple sequential subtasks according to their functionalities as well as resource requirements. Then, based on the task model, we propose a Joint Energy Consumption and Task Failure Probability Minimization Problem, which decides when and where each subtask will be offloaded to. The problem is challenging to solve, mainly due to the inherent constraints between the scheduling of different subtasks. Therefore, we propose several linearization methods to relax the constraints, and convert the original problem into an integer linear programming (ILP), which can be solved by many classic methods effectively. We further perform simulations, which show that our proposed solution outperforms the existing solutions (with indivisible tasks or without resource sharing) in terms of both the total cost and the task failure probability. Precisely, our proposed solution can reduce the total cost by 25%~85% and the task failure probability by 10%~35%.

Mobile edge computing (MEC) has shown its potential in serving computation intensive tasks via offloading. However, the heterogeneity of MEC systems and the dynamic nature of wireless environment pose a great challenge to the design of offloading policies. In this paper, we investigate this computation offloading problem, where the heterogeneities of computational resource, channel state, task type and input data size are considered. We first propose a greedy algorithm, in which each arrival task is greedily offloaded to the edge server with minimal utility, based on a global information of network states. While this greedy algorithm performs well in terms of system utility, the overhead incurred to collect the global information is large, especially in dense MEC scenarios and time-varying channel scenarios. Inspired by this observation, we then propose a model-free offloading algorithm based on reinforcement learning, which does not rely on such kind of information and can make offloading decisions based on learning experience. By so doing, the communication overhead can be largely reduced. Extensive simulations show that the two proposed algorithms have similar performance in terms of system utility and can decrease the system utility by up to 50% compared with two widely used algorithms. The robustness of the two proposed algorithms is further verified.

Pulse localization is the basic task of the pulse diagnosis with robot. More accurate location can reduce the misdiagnosis caused by different types of pulse. Traditional works usually use a collection surface with a certain area for contact detection, and move the collection surface to collect changes of power for pulse localization. These methods often require the subjects place their wrist in a given position. In this paper, we propose a novel pulse localization method which uses the infrared camera as the input sensor, and locates the pulse on wrist with the neural network. This method can not only reduce the contact between the machine and the subject, reduce the discomfort of the process, but also reduce the preparation time for the test, which can improve the detection efficiency. The experiments show that our proposed method can locate the pulse with high accuracy. And we have applied this method to pulse diagnosis robot for pulse data collection.

Considering a multi-cell OFDM downlink network, a basic problem is to perform resource allocation to maximize the spectral efficiency (SE). In this paper, we divide it into a user scheduling subproblem and a power allocation subprolem, and then adopt a resource allocation algorithm based on imperfect channel state information (CSI). Universal frequency reuse is considered, and the cochannel interference is dealt with via the cooperation of multiple base stations (BSs) sharing CSI but not user data. Since the wireless communication environment may change rapidly and need real-time computation, we then propose a deep neural network (DNN) approach to approximate the resource allocation algorithm, which greatly reduces the computation time and is capable of ”on-the-fly” adaptation to a time-varying environment. Simulation results verify the effectiveness of the DNN implementation, especially when the number of cells and subcarriers is large.

Cooperative caching between multiple small base stations (SBSs) plays a critical role for easing the traffic congestion of the backhaul link. Previous works assume that cooperative caching policies can achieve good performance when each user in the region is mainly served by its nearest SBS, which may not be the case in small cell networks with heterogeneous channel qualities. In this paper, we study cooperative caching problem in a small cell network with heterogeneous channel qualities when content popularity profile is unknown. These two features impose new challenges in optimizing content placement in multiple SBSs. To address this problem, we first propose a bayes-based learning algorithm that learn the popularity profile by sampling from a Beta distribution at each time period. Based on the estimated popularity profile, we then optimize the content placement at each time period by caching contents with higher popularity/size ratio in SBSs with better channel qualities. Numerical results show that the proposed algorithms outperforms three baselines in terms of average transmission delay and cache hit rate.

In this paper, we jointly explore random caching and cooperative transmission in heterogenous networks (HetNets) with random discontinuous transmission (DTX). We consider a realistic scenario where joint transmission is not always available and assume two cases depending on whether joint transmission is available. With the help of stochastic geometry, a tractable expression for the average successful transmission probability (STP) is obtained. We then formulate the STP optimization problem to find the optimal caching policy. In addition, we analyze the STP under random DTX. Compared with several existing caching policies in the previous works, we show that the optimal caching policy indeed achieves a significant performance gain.

Mobile crowdsourced edge caching is emerging as a promising caching paradigm by crowdsourcing the storage resources of massive edge devices (EDs) for content caching. The successful technology adoption and commercial deployment rely on a comprehensive understanding of the economic interactions among different network entities involved in such a system. In this paper, we focus on the economic interactions between one content provider (CP) and a large number of EDs, where the CP shares a certain revenue with EDs as the incentive of caching contents, and EDs decide whether to cache contents and share the cached contents with others. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue shared with EDs, aiming at maximizing its own profit. In Stage II, each ED chooses to be an agent who caches contents and shares the cached contents with other EDs, or a requester who does not cache but requests contents from agents. e first analyze the existence and uniqueness of the Stage II subgame equilibrium by using the evolutionary game theory. Then, we identify the piece-wise structure of the CP's profit function, and derive the optimal revenue sharing ratio for the CP in Stage I. Simulation results show that a higher revenue sharing ratio for EDs or a larger serving capacity of EDs can drive more EDs to choose to be agents and meanwhile achieve a higher total welfare for EDs at the equilibrium. Moreover, a larger content price of the CP will lead to a larger welfare loss for EDs.

In this paper, we jointly explore random caching and cooperative transmission based on cooperative radius in HetNets. Using tools from stochastic geometry, we obtain the tractable expression of the average successful transmission probability (STP) under our proposed scheme. By maximizing the average STP, we formulate the optimization problem to find the optimal policy. Given the existence of popularity prediction errors in practice, we then examined two types of errors: errors in file popularity prediction and errors in file library size estimation. By comparing with several existing caching policies in the literature (e.g., the most popular caching and the uniform caching), we demonstrated the robustness of our proposed scheme in the presence of errors.

Traditional loss-based congestion control protocols interpret packet loss as network congestion. Recently, Google proposed BBR, which is a congestion-based congestion control protocol. It employs delivery rate as the knob for congestion control, which achieves higher throughput and lower latency. Interestingly, BBR is found to have a preference for longer round-trip time (RTT) flows, which enjoy higher bandwidth ratio compared to flows with shorter RTT. To address this fairness issue, we proposed Adaptive-BBR, which creatively uses adaptive pacing gain to adjust the sending rate. The objective is that, via the proposed fine-grained adaptive mechanism, flows with different RTTs share similar portion of bottleneck bandwidth. Simulation results show that Adaptive-BBR can improve fairness by at least 47.8 %, and reduce average queuing delay by up to 93.3%, compared with that of BBR.

Optical technologies based on Wavelength Division Multiplexing (WDM) are gaining popularity for Data Center Networks (DCNs) due to their technological strengths such as low communication latency, low power consumption, and high link bandwidth. Observe that the BCube networking topology has been widely applied to modular DCNs due to its high scalability and cost effectiveness. Therefore, it is worth investigating optical techniques into BCube DCNs. Routing and Wavelength Assignment (RWA) is a critical problem in optical networks, which can be formulated as an integer programming problem. To gain better insights into RWA solutions, researchers proposed two concepts: the forwarding and optical indices. Consider the all-to-all traffic in an all-optical network, where every host sets up a connection with every other host. The optical index is defined as the minimum number of wavelengths, required to support simultaneous all-to-all communication, under the restriction that each connection is assigned a fixed wavelength. The forwarding index is measured to be the minimum of maximum link loads over all possible all-to-all routings, where we define the maximum link load as the maximum number of paths passing through any link, and define an all-to-all routing as a set of paths specified for all host pairs. In this paper, we study the forwarding and optical indices of an all-optical BCube DCN. First, we compute the forwarding index, which is also a natural lower bound of the optical index. Second, we propose an oblivious RWA scheme, which is further used to derive an upper bound of the optical index. Finally, we derive a tighter upper bound of the optical index by means of the chromatic numbers in Graph Theory.

In this paper, we aim to effectively utilize in-network buffering to schedule and route packets with low communication overhead, small delay and throughput optimality in fat-tree networks. While nearly zero in-network queueing can be guaranteed by performing precise time allocation and path assignment at network endpoints as in Fastpass, there is a high communication overhead, and buffer occupancy at the endpoints can become bottlenecks. By spreading scheduling functionalities to different network layers in a fat-tree network, the complexity can be decreased significantly at the cost of moderate buffer occupancy at intermediate switches. Inspired by this observation, we propose a simple dynamic pod scheduling (DPS) scheme, which performs scheduling at the granularity of pod units, each of which is paired to at most one other pod unit to transmit packets at each slot. By doing so, less information is required to arrange the packet transfers and inter-pod traffic will experience less downlink contentions. Through extensive evaluations, we find that DPS outperforms Fastpass in terms of delay while still guaranteeing throughput-optimality.

The fat-tree networking topology have gained prominence in various parallel and distributed systems such as high-performance computing clusters and data centers. To support high throughput and low latency applications, effective load-balancing schemes are in great demand. However, the commonly deployed scheme, equal-cost multipath, suffers from severe hash collisions that lead to poor performance. Recently, another realization of randomized load balancing, DRILL, has been proposed, which adopts the two-choice algorithm to achieve in-network local-to-switch load balancing. Although DRILL can well balance uplink traffic, it shows some limitations on alleviating downlink contentions. Motivated by this observation, we propose a thresholded two-choice (TTC) scheme, which modifies the two-choice algorithm such that it can balance both uplink and downlink traffic. To balance downlink traffic, TTC sets a default path for every source-destination pair using the D-mod-k scheme. The rationale for this is that D-mod-k minimizes the level of path collision over downlinks for any permutation in a fat-tree network. To balance uplink traffic, TTC makes dynamic path decisions using an algorithm that is based on the two-choice idea. To better leverage default paths in downlink load balancing, it is desirable that TTC routes the majority of traffic onto the default paths. To this end, we introduce a new thresholding mechanism to the two-choice algorithm, which contributes to a better overall performance. Our analysis shows that the introduced thresholding mechanism does not significantly affect the uplink load balancing performance. Moreover, our numerical study indicates that TTC can achieve a better system-wide performance than DRILL.

Recently, performance analysis for large-scale content-centric mobile ad hoc networks (MANETs) has received intense attention. In content-centric MANETs, content delivery consists of two operations, i.e., content placement and content retrieval, which may involve different network costs. However, the existing performance studies in content-centric MANETs mainly focus on content retrieval, and hence may not reflect the impact of content placement. In this paper, we investigate the asymptotic throughput and delay performance by considering the two operations of possibly different network costs. In particular, we introduce a general weighted sum delay cost of content placement and content retrieval as the delay performance metric. We consider an arbitrary content popularity distribution and study two mobility models in different time scales, i.e., fast and slow mobility. For each mobility model, we characterize the impacts of the network parameters on the network performance. By optimizing the content placement and retrieval for contents of different popularities, we design a general near-optimal scheme, the parameters of which reflect the delay weights of the two phases. We show that the network performance improves as the number of cached replicas increases until the number reaches a threshold. Finally, we show that our results are general and can incorporate some existing results as special cases.

In this paper, we study the capacity and delay scaling laws of cognitive radio networks (CRN) with static primary nodes (PNs) and mobile secondary nodes (SNs). The primary network consists of randomly distributed primary nodes of density n, which have a higher priority to access the spectrum. The secondary network consists of randomly distributed secondary nodes of density m = nβ, where β represents the density relationship in CRN. Secondary nodes move according to hybrid random walk models with parameter α (0 ≤ α-2α). Motivated by observation that the performance of CRN can benefit from the cooperation among primary nodes and secondary nodes, we propose a novel cooperative scheduling mechanism to fully utilize the mobility and geographic information of secondary nodes to enhance the performance of the primary network. For both networks, the delay performance varies with α. We show that the delay performance of primary network can be significantly improved from O(n/log n) [16] to Θ(nβ/3log n) when β <; 3 for an optimal value of α, while a near-optimal throughput of Θ(1/log n) is obtained. Furthermore, the secondary network can still achieve the same throughput and delay scaling laws as a stand-alone network simultaneously.

With the advent of smart portable devices and location-based applications, user's mobility pattern is found to be highly dependent on varying locations. In this paper, we analyze asymptotic throughput-delay performance of mobile ad hoc networks (MANETs) under a location popularity based scenario, where users are more likely to visit popular locations. This work provides a complementary perspective compared with previous studies on fundamental scaling laws for MANETs, mostly assuming that nodes move uniformly in the network. Specifically, we consider a cell-partitioned network model with cells of known popularity, which follows a Zipf's law distribution with popularity exponent α. We first conduct the analysis under traditional store-carry-forward paradigm, and find that location heterogeneity affects the network performance negatively, which is due to the waste of potential transmission opportunities in popular cells. Motivated by this observation, we further propose a novel store-carry-accelerate-forward paradigm to enhance the network communication, exploiting these potential transmissions. Theoretical results demonstrate that our proposed scheme outperforms all delay-capacity results obtained in conventional scheme for any α. In particular, when α = 1, it can achieve a constant capacity with an average delay of Θ(√n) (except for a polylogarithmic factor), while the delay is Θ(n) in conventional scheme. And by letting α = 0, our results can cover Neely's scaling laws. Moreover, we show that the delay-capacity tradeoff ratio satisfies ≥Θ(√n), revealing that exploiting location popularity can effectively improve the performance in MANETs.

In the asymptotic analysis of large scale mobile networks, most previous works assume that nodes move in all the cells identically. We put forward this line of research by considering location popularity, which is verified by recent experimental studies. Nodes tend to visit some popular locations and go to other locations less frequently. We first analyze its multicast capacity and delay under the traditional 2-hop store-carry and forward paradigm. With different location popularity distributions, network capacity and delay will vary according to the distribution exponent. As the popularity becomes more diverse, less concurrent transmissions are tolerated in the network, which brings down the performance. Observing that transmission opportunity is not fully utilized in popular cells for 2-hop paradigm, we put forward a 3-hop scheme, which is called store-carry-accelerate-forward scheme. In this 3-hop scheme, each packet is first sent to an initial relay, who carries the packets into the popular cells and then broadcasts to multiple nodes to accelerate the delivery. By so doing, we showed that the 3-hop scheme outperforms the 2-hop scheme for all popularity distributions. Furthermore, we study the delay-capacity tradeoffs for multicast under both schemes and the buffer needed for stability requirement. Our results reveals the joint impact of multicast and location heterogeneity on the design of transmission schemes, and may shed new insights for future studies.

Previous works on multicast capacity mainly focus on deriving asymptotic order results in large-scale wireless networks, which can explore the general scaling laws of throughput capacity but cannot predict the exact achievable throughput. In this paper, we investigate the non-asymptotic capacity of multihop wireless networks for multicast applications wherein for each source node, k nodes are randomly selected as receivers. Since multicast routing has a dynamic nature, it is challenging for the exact performance analysis. To tackle the problem, we propose an explicit analytical model which describes multicast transmissions, considers networks of arbitrary size, takes data burst into account, and also covers the notion of time scales for transient analysis. By developing a practical multicast scheme, stochastic network calculus is employed for the exact analysis. With the analytical model, we derive lower and upper bounds on multicast capacity, which are non-asymptotic functions of the above variables, and also recover the scaling laws from an asymptotic point of view. Simulations further verify the accuracy of the analytical bounds.

In static hybrid wireless networks (SHWNs), it requires a large deployment cost in order to achieve a throughput of Θ(1)1. Motivated by this shortcoming, mobile hybrid wireless networks (MHWNs) are proposed in the most recent work, which can boost throughput to Θ(1) at a smaller cost under the same cell routing policy. However, such an improvement comes at the expense of larger delay. In this paper, we aim at reducing the delay while maintaining the optimal throughput in MHWNs. Hence, a novel scheme with K length routing policy is proposed, which combines multi-hop relay, mobile relay and infrastructure relay for the first time. Our main results demonstrate that a significant gain in delay is realized by the proposed scheme compared with the existing work of MHWNs while guaranteeing a throughput of Θ(1).