Integrated sensing and communication (ISAC) is an emerging paradigm designed to support next-generation wireless services and applications. However, ISAC systems with limited computation capabilities are unable to handle computation-intensive and latency-sensitive tasks. This paper proposes a novel integrated sensing, communication, and computation (ISCC) network empowered by a reconfigurable intelligent surface (RIS) to mitigate the performance degradation caused by interference between radar sensing and uplink offloading. To effectively coordinate the cross-layer resource allocation among communication, sensing, and computation, we propose a resource scheduling problem. Specifically, we maximize the total computation rate while satisfying the sensing signal-to-noise ratio (SNR) requirement by jointly optimizing the energy allocation for local computing and offloading, the transmit and receive beamforming at the base station (BS), and the RIS reflective beamforming. To address this complex non-convex problem, we develop an efficient scheduling algorithm based on the block coordinate descent (BCD) framework. The iterative algorithm employs the fractional programming algorithm based on Lagrangian dual transform and quadratic transform, the generalized eigenvector methods, the convex relaxation techniques, and the successive convex approximation (SCA) algorithms to solve each subproblem separately. Experimental results demonstrate that the proposed scheme outperforms several baseline methods, confirming that RIS technology can effectively enhance system performance. In addition, we reveal the impact of various parameters on system performance.

Integrated sensing and communication (ISAC) is a key enabling technology of 6G networks, allowing networks to jointly perform data transmission and environmental sensing. However, ISAC devices often have limited computing resources, making it difficult to process complicated tasks and meet stringent low-latency requirements. To address this, this paper introduces a novel integrated sensing, communication, and computation (ISCC) network. We aim to minimize the maximum overall latency for communication and computation among the users under radar sensing performance constraints by jointly optimizing the transmit power of users, the allocation of computing resources at the edge server (ES), and the transmit and receive beamforming of the base station (BS). To solve this non-convex optimization problem, we design an overall iterative algorithm based on an alternating optimization (AO) framework. The algorithm decomposes the original problem into three non-convex subproblems, and solves them using the convex relaxation techniques based on the mean inequality, Taylor expansion, and semi-definite relaxation (SDR), respectively. The final solutions are obtained by alternatively optimizing the subproblems until convergence. Simulation results show that the proposed algorithm outperforms several baseline schemes in terms of latency reduction, user fairness and sensing quality. In addition, it reveals the impact of relevant parameters on system performance.

Integrated sensing, communication, and computing (ISCC) technology significantly improves spectrum efficiency and reduces hardware costs by unifying the functionalities of sensing, communication, and computation. However, the degradation of wireless link quality caused by obstacles may lead to severe offloading latency. This paper investigates the application of reconfigurable intelligent surface (RIS) technology in ISCC network to enhance the reliability of wireless links and improve the efficiency of computation offloading. In the proposed network, integrated sensing and communication (ISAC) devices employ the same hardware and signaling mechanisms to perform both sensing and communication tasks, while mobile edge computing (MEC) technology is leveraged to process sensing data. To address the cross-layer resource management problem, we formulate a total latency minimization problem for both communication and computation under sensing accuracy constraints, by jointly optimizing the waveform precoding design matrix of ISAC devices, the beam pattern scaling factor, the RIS’s reflective beamforming, and the computing frequency of edge server (ES). Since this problem is highly non-convex, we propose an iterative algorithm based on block coordinate descent (BCD) framework, which leverages semi-definite relaxation (SDR) method, the Charnes-Cooper transform (CCT) method, and closed-form solution to alternately optimize multiple variable blocks until convergence is achieved for the final solution. Extensive experiments validate the effectiveness of the proposed scheme, demonstrating that the RIS-assisted joint optimization scheme effectively reduces the total system latency. Moreover, we reveal the trade-off between sensing accuracy and system latency.

Coded caching under various heterogeneous settings has been intensively studied. Specifically, in a shared-link model, a coded caching scheme based on a popularity-first caching strategy has been proved to be order-optimal under arbitrary popularity distributions. It is of great interest to investigate whether a similar conclusion holds in a device-to-device (D2D) network. Unlike shared-link models, since the server does not participate in the delivery phase in D2D networks, the caching scheme must ensure that all files can be recovered from the union of users’ caches. In this paper, we consider a D2D network constrained to uncoded prefetching and propose a new achievable scheme that caches popular files preferentially while allocating the minimal but necessary cache space to unpopular files. Then we derive new lower bounds on the average transmission rate under arbitrary popularity distributions. The proposed lower bounds are established via a novel decoupling method that isolates the rate analysis of popular files from the cache space allocated to unpopular ones, which could be of independent interest for future work. Finally, we prove that our proposed scheme achieves order-optimality among uncoded schemes under arbitrary popularity distributions. Numerical results validate the effectiveness of the proposed scheme and the tightness of the new lower bounds.

Network performance monitoring and troubleshooting are crucial yet challenging tasks in datacenter management. Despite the numerous solutions that have been proposed in recent years, their efforts are often hindered by high costs and unreliable failure localization, making it difficult to deploy them in real-world environments. In this paper, we present LMon, a highly reliable and efficient system for monitoring and troubleshooting in datacenter networks. LMon utilizes the characteristic of ECMP hashing linearity to control probe packet routing, enabling the monitoring of targeted paths without any modification of underlying protocols and devices. Additionally, LMon leverages a lightweight probing technique to reduce monitoring overhead, as well as integrates the improved LASSO regression and hypothesis testing for higher accuracy and faster processing in link failure localization. We evaluate the performance of LMon in our testing environment. Compared to the monitoring system Pingmesh, LMon generates only one-third probes while maintaining 99% accuracy and 1% false negatives.

Deep reinforcement learning (DRL) has been used in congestion control algorithms (CCAs) for its ability to adapt to different network environments. However, its effectiveness is often hindered by the limited availability of training data and constrained training scales. While it has been proved that combining rule-based (expert) CCAs as a guide for DRL (namely hybrid CCAs) can address this limitation, we show through experimental measurements that rule-based CCAs potentially restrict action exploration of DRL models and may cause the DRL models to overly rely on them for higher reward gains. To address this gap, this paper proposes Marten, a framework that improves the effectiveness of rule-based CCAs for DRL. Marten’s key innovations include an entropy-based dynamic exploration scheme that expands the exploration of DRL, and a reward adjustment scheme to prevent the DRL models’ over-reliance on experts in hybrid CCAs. We have implemented Marten in both simulation platform OpenAI Gym and deployment platform QUIC. Experimental results in both emulated and production networks demonstrate Marten can improve throughput by 0.31% and reduce latency by 12.69% on average compared to the state-of-the-art hybrid CCAs. Compared to BBR, Marten achieves a 2.79% increase in throughput and an 11.73% reduction in latency on average.

Integrating Frame Replication and Elimination for Reliability (FRER) with Cyclic Queuing and Forwarding (CQF) in Time-Sensitive Networks (TSN) encounters redundancy failures and resource reservation inefficiencies due to length disparities across redundant paths. To address these challenges, we propose ReCQF, a Reliability-Enhanced CQF scheduling framework built on Multi-Instance CQF. ReCQF adaptively assigns redundant flows to multiple CQF queue pairs with specific cycles, effectively aligning transmission delays across redundant paths to ensure low delay and inter-path delay differences while significantly reducing resource reservations.

Uploading videos from low-cost cameras to the cloud for retrospective analysis presents challenges in privacy, network, and computation. To address these issues and achieve low latency, we propose READY, a novel client-cloud collaborative system. READY aims to enhance the quality of uploaded frames by selectively uploading only the frames relevant to queries. To achieve this, READY establishes an index during video capture, recording object categories and probabilities for each frame. READY adopts an innovative semi-supervised approach for frame indexing, wherein frames are indexed through a continuously updated feature distribution space constructed by k-nearest neighbors (KNN). This enables resource-constrained low-cost cameras to independently establish long-term frame indexes. Additionally, READY utilizes progressively improving operators (lightweight classification models) dispatched by the cloud to optimize the upload order of frames, prioritizing positive frames. By sharing the backbone of low-performance operators, high-performance operators can be efficiently executed, significantly enhancing the camera's frame processing capability. The established frame index also enables efficient multiple consecutive queries on different classes. Over 110 h of diverse queries across 11 videos, READY outperformed competing alternative designs by achieving an average response time of 67.8% and reducing the proportion of uploaded videos by an average of 79.8% (compared to CloudOnly).

Communication overhead is a major bottleneck in distributed AI training, particularly in multi-tenant environments, limiting GPU utilization. Existing job-level scheduling methods fail to address the varying urgency of individual communication operations. We propose Flux, a novel fine-grained scheduler that prioritizes communication operations based on their Urgency Score and job intensity. Our evaluation shows Flux improves GPU utilization by up to 10 % compared to state-of-the-art job-level algorithms. This demonstrates the significant advantage of fine-grained communication scheduling in multitenant AI clusters.

Synchronization-induced congestion is a critical performance bottleneck in modern distributed machine learning (ML) training, where simultaneous gradient exchanges create bursty traffic patterns. Existing solutions, both reactive and proactive, struggle to balance throughput and latency in the presence of synchronized flows. We propose DeSync, a proactive traffic shaping scheme that introduces structured random delay to de-synchronize communication rounds. Evaluations with DCQCN, HPCC, DCTCP, and TIMELY demonstrate that DeSync significantly improves FCT, job completion times, and congestion metrics, enhancing existing CC mechanisms without specialized hardware.

Userspace packet processing applications need to balance high-performance with strong memory safety, making Rust a suitable choice for implementing such applications. But Rust has not been a mainstream choice for implementing userspace packet processing applications, due to the lack of a well-designed Packet Processing Library (PPL) that can efficiently handle non-contiguous memory buffers and achieve highperformance without breaking Rust's memory safety guarantees. This paper presents the design of rpkt, a new Rust PPL that is designed to systematically address the problems faced by existing Rust PPLs. At the center of rpkt lies a novel packet buffer abstraction which is compatible with different memory buffer types. rpkt provides safe packet processing APIs and customizes efficient packet buffer implementations for different memory buffer types. In this way, rpkt establishes an efficient and safe way to process packets, regardless of whether the underlying memory buffer is contiguous or not. We compare rpkt with state-of-the-art Rust PPLs. The results show that rpkt achieves better packet processing performance and can leverage various Network Interface Card (NIC) offloading features that require non-contiguous memory buffers.

Time-sensitive networking (TSN), essential in industrial networks for its promise of reliable and deterministic data transmission, faces deployment challenges due to the limitations of existing time-aware shaper (TAS)-based scheduling algorithms. Specifically, the size of the generated gate control lists (GCLs) is usually too large to be deployed in actual devices. To bridge the gap between theory and practice, we propose FlexTAS, a flexible and practical solution for TSN. The key insight behind FlexTAS is that relaxing gating does not introduce uncertainty, as long as nonoverlap reserved time slots are guaranteed. FlexTAS is comprised of two main components: first, a novel gating model deviates from the conventional TAS model by incorporating selective relaxation of gating at certain nodes; and second, a deep reinforcement learning-based engine to rapidly generate valid schedules. We build a real testbed and validate the effectiveness of our proposed solution. Our evaluation demonstrates that FlexTAS effectively controls the number of gate entries within the GCL capacity of devices, while simultaneously meeting the Quality of Service(QoS) requirements of time-triggered streams. It significantly reduces the number of GCL entries by 60% to 80%, and facilitates deployment in heterogeneous networks, thus offering a practical solution for TSN.

The crux of graph classification lies in the effective representation learning for the entire graph. Typical graph neural networks focus on modeling the local dependencies when aggregating features of neighboring nodes, and obtain the representation for the entire graph by aggregating node features. Such methods have two potential limitations: 1) the global node saliency w.r.t. graph classification is not explicitly modeled, which is crucial since different nodes may have different semantic relevance to graph classification; 2) the graph representation directly aggregated from node features may have limited effectiveness to reflect graph-level information. In this work, we propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification, which consists of two core modules: 1) a traditional graph neural network serving as the backbone for learning node features and 2) the Graph Neural Memory designed to distill a compact graph representation from node features of the backbone. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone, which facilitates the message passing of features for salient nodes and suppresses the less relevant nodes. Thus, our model can learn more effective graph representation. We demonstrate the merits of SAR-GNN by extensive experiments on seven datasets across various types of graph data.

In this paper, we consider a point-to-point dual-polarized fluid antenna system (FAS), where the transmitter is equipped with two separated orthogonally-polarized antennas and the receiver is equipped with aN-ports dual-polarized fluid antenna. The dual-polarized FAS channel model, which takes into account the cross-polar discrimination (XPD) and cross-polar correlations (XPC) as polarization characteristics, has been considered. Then, the outage probability under the exact correlated channel in the multi-integration expression is given, and based on this, the closed-form outage probability under the approximate correlated channel is derived. The simulation results demonstrate the influence of XPC and XPD on outage probability.

In the domain of Time-Sensitive Networking (TSN), the quest for ultra-reliable low-latency communication is paramount. Current scheduling strategies, which hinge on strict isolation to ensure low latency and jitter, confront the challenges of high overhead in worst-case latency evaluation and consequent limitations in network flow capacity. This paper introduces an innovative framework that transcends traditional isolation constraints, thereby expanding the solution space and augmenting network schedulability. At the heart of this framework lies a novel latency jitter analysis method that assesses the viability of non-isolation scenarios with constant time complexity. This method underpins a heuristic scheduling algorithm that not only boasts the smallest time complexity among existing heuristics but also significantly increases the number of scheduled flows. Complementing this, we integrate a discrete time reference approach to hasten time-intensive scheduling operations, achieving an optimal balance between schedulability and runtime efficiency. The framework further incorporates a workload-shifting technique to enhance online scheduling responsiveness. It adeptly manages the variability in scheduling times caused by disharmonious flow periods, further bolstering the framework’s robustness. Experimental validations demonstrate that our framework can increase the scheduled flows up to 269%. It reduces scheduling runtime by up to 98.44% for medium-scale networks while maintaining a flat runtime growth curve, ensuring predictable performance in online scheduling scenarios.

Dual-function radar-communication (DFRC) system has been recognized as a potential technology to address the issues of radio frequency spectrum congestion. Despite the advan-tages of the existing orthogonal frequency-division multiplexing (OFDM) chirp waveform, such as its high range resolution and low peak-to-average ratio, it is still plagued by issues related to ghost targets in complex communication environments with multiple targets. In this paper, a novel waveform, leveraging trapezoidal frequency modulation OFDM, is introduced to address the challenge of multi-target detection scenarios. Addition-ally, a power allocation and subcarrier assignment algorithm has been developed to maximize communication performance while adhering to the radar performance threshold, thereby achieving overall system optimization while enhancing multi-target detection capabilities. Finally, the effectiveness of the proposed algorithm is demonstrated through simulation experiments, showcasing its ability to handle multi-target detection scenarios and achieve superior system performance while maintaining a delicate equilibrium between communication and radar considerations.

Industrial networks require deterministic and reliable communication, which can be achieved by Time-Sensitive Networking (TSN), a set of standards that enable precise timing and synchronization of data transmission. However, TSN is susceptible to unexpected delays caused by device malfunction, interference or cyber attacks, which can have a domino effect and disrupt multiple data flows. To address this challenge, we propose RobustTSN, a framework that protects TSN against the domino effect of delayed frames and tolerates harmless accident frames using Per-Stream Filtering and Policing (PSFP) mechanism. We develop algorithms to calculate ingress filtering schedules based on local-safe delay and global-safe interval concepts, which decide whether to accept or discard out-of-schedule frames. We use a finite state machine to model the interaction between frames and evaluate frame safety. We build a software-defined networking based system to dynamically monitor network states and reconfigure device filtering after out-of-schedule transmission occurs. We conduct experiments on practical scenario topologies and large groups of random flows to demonstrate the effectiveness and efficiency of our framework.

The booming edge computing market that is supported by the edge cloud (EC) infrastructure has brought huge operating costs, mainly the energy cost, to edge service providers. The energy cost in form of electricity bills usually consists of energy charge and demand charge, and the demand charge based on peak power may account for a large proportion of the energy cost given a significant fluctuating power curve. In this work, we investigate the backup battery characteristics and electricity charge tariffs at ECs and explore the corresponding cost-saving potential. Specifically, we transform the backup battery group into distributed battery energy storage system (BESS) and strategically schedule the BESS to minimize the energy cost of service providers. We then propose a deep reinforcement learning (DRL) based approach to BESS charging/discharging in coping with the dynamic power demand and BESS state at each EC. To enable better decision-making and speed up agent training, we further design the customized invalid action masking (IAM) method and apply the prioritized experience replay (PER) scheme. The experiment results based on real-world EC power traces show that the proposed approach can reduce the demand charge and overall electricity bill by up to 27% and 13%, respectively.

Mobile edge computing (MEC) and digital twin (DT) technologies have been recognized as key enabling factors for the next generation of industrial Internet of Things (IoT) applications. In existing works, DT-assisted edge network resource optimization solutions mostly focus on short-term performance optimization, and long-term resource optimization has not been well studied. Thus, this paper introduces a digital twin-assisted heterogeneous edge network (DTHEN), aiming to minimize long-term energy consumption by jointly optimizing transmit power and computing resource. To solve the stochastic optimization problem, we propose a long-term queue-aware energy minimization (LQEM) scheme for joint communication and computing resource management. The proposed scheme uses Lyapunov optimization to transform the original problem with long-term time constraints into a deterministic upper bound problem for each time slot, decouples it into three independent sub-problems, and solves each sub-problem separately. We then theoretically prove the asymptotic optimality of the LQEM scheme and the tradeoff between system energy consumption and task queue backlog. Finally, experimental results verify the performance analysis of the LQEM scheme, demonstrating its superiority over several benchmark schemes, and reveal the impact of various parameters on the system.