Cellular-connected UAV corridors reuse terrestrial infrastructure to provide predictable aerial connectivity, but coexistence with ground users (GUEs) creates competing demands on antenna tilt and physical resource blocks (PRBs). Prior work optimizes tilt for signal quality alone and does not address joint tilt-PRB planning under corridor rate and GUE delay constraints, nor the allocation policy needed when combined resource claims exceed the PRB budget. We address both gaps by formulating a joint antenna-tilt and PRB-allocation problem that maximizes GUE sum-log-rate subject to a minimum UAV corridor rate and an $ M/G/1$-based GUE mean-delay bound. We decompose the problem into two subproblems: antenna tilts are adapted via projected gradient ascent, while PRB allocation follows a techno-economic approach. Under resource scarcity, we cast the PRB scarcity as a bankruptcy problem and apply the weighted Talmud rule to balance GUE and UAV claims. Simulation results demonstrate that relaxing the GUE delay bound consistently reduces UAV rate dissatisfaction under the proposed allocation.
Millimeter-wave (mmWave) links can lose tens of decibels within milliseconds when a person or vehicle crosses the line of sight, and reactive handover recovers only after the outage has begun. Integrated sensing and communication (ISAC) promises to let the network see blockers coming and move users to another cell in advance. This paper asks how much such foresight is worth relative to the best reactive handover, and what sensing accuracy is needed to harvest it. We formulate cost-aware cell selection with knowledge of the future signal-to- noise ratio as a sequential decision problem, define the value of foresight through a policy-independent bound, and develop an ISAC-assisted handover framework in which a radio unit senses blockers with its own communication waveform and a simulated Open RAN xApp plans handovers. Ray-traced experiments in a 28 GHz street canyon, reported on held-out scenarios, show that, with a short look-ahead, perfect foresight closes most of the gap between service-aware (Layer-3) reactive handover and an instantaneous oracle. Harvesting this value, however, requires the user and nearby blockers to be localized to about a decimeter, well beyond what our radar tracker and meter- level user positioning provide; with realistic errors, even an uncertainty-aware planner largely defers to reactive handover.
Faris B. Mismar, Abdol Saleh, Ivan Maxmillian Putra Pasaribu, Suhelmy Syaifuddin
This paper presents an explainable AI-driven framework for predicting net churn among prepaid mobile subscribers. The framework formulates churn prediction as a machine learning task mapped onto an industry reference architecture. Moreover, it introduces a quantile-based feature densification transform that reconciles learning features of unequal length and granularity into a homogeneous design matrix, evaluates a label-aware truncation diagnostic that sets aside low-confidence subscribers, and generates automated English-language insights describing the impact of each feature on churn propensity. We implement and validate the framework on real production data from a globally leading telco with tens of millions of prepaid subscribers spanning approximately $800$ learning features and over $250$ TB of commercial, experience, and network data. As a diagnostic, setting aside roughly $3$% of the subscribers, selected using the true labels, lifts $F_1$ on the retained subscribers to the target. This is not an achievable operating performance on all subscribers. In our data and feature set, aggregated radio access network counters carried less predictive signal than commercial and experience indicators, despite their greater representation. The quality of experience index, which is derived from network measurements, ranked second in importance. Our results also suggest that churn risk rises as subscriber value declines, with high-value segments disproportionately exposed to gradual value leakage rather than outright attrition.
Mayan Sharma, Kriti Saini, Devansh Behl, Suganya R, S. Vijayaragavan
Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission control can act on it safely. We reproduce the state of the art, BG-CFQS, on the three Starlink traces its authors used and on an independent fourth dataset, and report four findings. First, its risk guarantee is reproducible but conditional on the calibration split: under a random split we recover every published quantity, including risk-pass on 3 of 3 datasets, but under a contiguous temporal split, which is what a deployed terminal faces, it holds on 1 of 3. Second, independent of any split, the method cannot serve a risk budget below 0.15, the low end of its candidate quantile set. On all four datasets the achieved rate is identical at budgets of 0.05, 0.10 and 0.15, overshooting by 1.4x to 3.7x at the tightest. Third, static conformal calibration cannot improve a ranking, only shift an operating point; we confirm this exactly on latency spike detection, where online calibration improves AUPRC by up to 51%. Fourth, group-conditional online recalibration splits into two mechanisms. The online part replicates: it tracks the budget within 1% from 0.05 to 0.35, where static calibration is 16 to 21% off. The group-conditional part does not: on three of four datasets, conditioning on any available covariate, including measured serving-satellite geometry, is worse than not conditioning, and our attempt to predict which datasets benefit failed. What is new here is empirical, not algorithmic: we claim the findings, not the methods.
Angela Sara Cacciapuoti, Claudio Pellitteri, Marcello Caleffi
Spatial diversity is a key mechanism in classical Multi-Input Multi-Output (MIMO) communications, where multiple propagation alternatives provide redundant observations that improve reliability in noisy environments. A direct translation of this principle to quantum communications is forbidden by the no-cloning theorem, since an unknown quantum state cannot be copied and transmitted through independent links. In this Perspective article, we argue that spatial path superposition provides a no-cloning-compatible analogue of the spatial-diversity principle of MIMO. Rather than duplicating the transmitted quantum state, a quantum carrier is coherently delocalized over multiple communication links, so that alternative spatial evolutions are experienced in quantum superposition. In this framework, classical redundancy is replaced by spatial coherence, and the communication link itself becomes part of the quantum dynamics. Indeed, spatial coherence can enhance the fidelity of the received quantum state, providing the quantum counterpart of the classical diversity gain, and can be converted into bipartite and multipartite entanglement during propagation. This implies that, under suitable coherent path control, noisy channel evolutions can contribute constructively to entanglement generation. This Perspective identifies spatial path superposition as a quantum-native primitive for the Quantum Internet, linking no-cloning-compatible spatial diversity, fidelity enhancement, entanglement generation, and coherent path control.
The rising pressure on DRAM availability and contract pricing reflects generative AI's massive high-performance memory requirements. This pressure is heavily compounded by hyperscale data center expansion, which now consumes a significant portion of global DRAM output. In this work, we propose a new architecture, Fiber Memory, which reimagines the role of optical fiber in a hyperscale data center, deploying it as an active, recirculating delay-line memory for immutable data, such as large language model weights. We present a hybrid data- and pipeline-parallel optical broadcast delay-line memory architecture that accounts for fiber's physical realities. By incorporating space-division multiplexed multi-core fibers, passive optical tap-and-amplify interfaces, co-packaged optics, and centralized in-ring 3R (re-amplify, reshape, retime) regeneration, our case study evaluation suggests that Fiber Memory can eliminate redundant weight storage across tens of thousands of AI accelerators and reduce weight-delivery power by over 75% compared to traditional HBM3e configurations.
The web cannot tell whether a request comes from a person or from a program. Sites have checked the network address and puzzles in its place, and both have given way: automated traffic exceeds human traffic, and puzzles are solved for pay and by agents. This paper proposes the Clear Human Web, the part of the public web that sites open only to clients a person vouches for. A person's wallet presents their person identification data (PID) to one issuer, the only party that registers as a relying party, and receives a bounded number of blind-signed tokens, proofs of personhood that carry no attribute. The browser spends one token at each site that runs the gate, and the site learns that a valid token of a trusted issuer was spent, and nothing else. No field is added to the PID, no protocol is introduced, no site registers, and the publisher of a site holds the same proof as its readers. We define the Clear Human Web through five properties and seven adversaries, and state which properties the construction satisfies against each adversary. The identities a person can hold on one site in an epoch are bounded to k. The bound holds once the wallet limits a person to one pseudonym at the issuer. In the optimum k = 1, and a zero-knowledge proof of PID possession and a pseudonym per site replace the tokens.
5G networks increasingly resemble distributed systems in their design. It then becomes crucial to move beyond traditional network-based detection methods and to explore alternative approaches. In this work, we propose a formalization of 5G network exchanges as a dynamic graph, on which we apply techniques from the field of graph-based learning to detect anomalies effectively. This representation allows for fine-grained analysis of packet-level semantic content, while simultaneously capturing sequential and topological contexts, both of which are essential for identifying sophisticated and multi-stage attacks. To assess the effectiveness of this formalism, we evaluate it with the most popular dynamic graph models, demonstrating its potential for 5G anomaly detection.
Nguyen Thi My Binh, Do Dinh Hoan, Tong Tien Manh, Vu Minh Yen, Dang Trong Hop, Ho Viet Duc Luong
Underwater wireless sensor networks (UWSNs) face critical challenges in energy imbalance, connectivity, and data collection due to harsh environments. To address these issues, this paper focuses on the problem is how to achieve energy-balanced data collection in IoT-enabled UWSNs with a mobility-aware mobile edge computing (MEC), named MECEBDC. Unlike existing approaches, the proposed scheme jointly optimizes energy-aware clustering, cluster head selection, and AUV-assisted data collection. Furthermore, we design a novel metaheuristic-based AUV routing algorithm that incorporates realistic underwater mobility dynamics, explicitly considering gravitational pressure and buoyancy forces acting on the AUV. By leveraging the computing, storage, and mobility capabilities of MEC-enabled AUVs, the proposed approach enables efficient trajectory planning and adaptive data gathering in a stable 3D underwater environment. Extensive simulations and theoretical analysis demonstrate that the proposed method significantly reduces node energy consumption, improves data collection efficiency, and prolongs network lifetime compared with existing schemes, making it well suited for long-term underwater IoT applications.
Lyutianyang Zhang, Yixin Ying, Liu Cao, Vanlin Sathya, Jinke Ren, Shuguang Cui
Channel sounding in Wi-Fi multiple-input multiple-output (MIMO) systems must balance channel state information (CSI) accuracy against CSI acquisition overhead. Device mobility causes channel aging, while autoregressive CSI prediction introduces errors that accumulate over sounding intervals. However, standardized statistical channel models cannot fully capture the environment-specific channel dynamics encountered in physical artificial intelligence (AI) applications, motivating the use of Sionna-based ray tracing to evaluate CSI prediction under device mobility and evolving propagation geometry. This paper develops a dynamic sounding framework that integrates autoregressive CSI prediction with signal-to-interference-plus-noise ratio (SINR)-constrained interval selection. A Transformer with rotary position embedding that converts device positions into CSI sequences predicts CSI across multiple subcarriers for beamforming updates between sounding exchanges. We formulate sounding-interval selection as a predicted average-throughput maximization problem under per-stream SINR lower-bound constraints, including CSI acquisition and processing overhead. A prediction-error perturbation analysis provides the SINR bounds, and cumulative-sum enumeration globally solves the finite single-variable problem. Actual SINR feasibility holds whenever the prediction errors satisfy the bounds. Ray-tracing experiments assess channel prediction, trajectory transfer, and autoregressive prediction errors. In single-STA Wi-Fi 7 link-level simulations, predicted-CSI beamforming improves mean goodput by 67.3%, 112.7%, and 119.1% over stale CSI for linear, circular, and cosine trajectories, respectively.
Starlink and other low Earth orbit (LEO) satellite broadband systems exhibit stochastic access dynamics whose effects propagate from network measurements to throughput prediction and adaptive bitrate (ABR) streaming, making reasoning over multiple plausible futures essential. Existing studies mainly characterize realized traces or individual system layers, leaving cross-layer world modeling of these evolving dynamics largely unexplored. To address this gap, we propose StarWorld, a cross-layer world model for Starlink prediction and streaming. StarWorld couples learned stochastic network dynamics with executable prediction, control, and playback processes to imagine multi-step cross-layer futures, while a calibrated Trust Gate determines whether these futures are sufficiently reliable for use. On real Starlink measurements, StarWorld achieves the lowest network-future distributional errors among the comparison methods at the principal horizons and reduces pairwise controller quality-of-experience (QoE) effect error by 76%. These results support StarWorld as a cross-layer imagination environment for studying prediction and streaming outcomes under uncertain Starlink access conditions.
Reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication is a promising enabler for beyond-fifth-generation wireless networks. However, RIS phase shifters typically rely on a tuning element (for example a variable-length stub), which can be optimally configured for only one carrier frequency at a time. This constraint complicates RIS deployment in multi-carrier systems and often necessitates additional measures, such as using multiple RIS panels or partitioning RIS elements across carriers. In this work, we propose a novel scheme in which all RIS elements simultaneously serve multiple carriers, delivering improved performance over existing approaches. Our method leverages the multiple reflections captured by physically consistent RIS models to realize the desired phase shifts for all carriers using a single stub length setting. We further develop a joint active and passive beamforming optimization framework tailored to multi-carrier, physically consistent RIS-aided systems. Simulation results confirm the effectiveness of the proposed
Rip Sohan, Eric Spada, Eric Davis, Mark Handley, Idan Burstein, Tony Hurson, Jithin Jose, Vivek Kashyap 等 39 位作者
MRC is an open, production-grade transport designed for large-scale AI/ML training over best-effort Ethernet. It extends RoCEv2 with explicit, composable primitives for per-packet multipath and sender-based congestion control, decouples packet delivery from semantic processing, adds multiple new capabilities for accelerated packet-loss recovery and adds resilience against port and path failures. This paper presents MRC and details its core capabilities and mechanisms.
Shadeeb Hossain, Elias Calboreanu, Kristopher Wilson
The rapid proliferation of automated, multi-vector malware threats poses a significant risk to heterogeneous, resource constrained cyber-physical networks. Conventional epidemiological models often treat security defenses as static parameters, failing to capture the strategic, asymmetric maneuvers between an attacker and a defender. To address the gap, this paper proposes a Game-Theory-Integrated Modified Multi- Wireless Sensor Epidemic Malware Propagation (GTI-mSEMP) framework. This paper analyzed and compared the operational trajectories of Susceptible (S) and Recovered (R) node populations across three different operational regimes: Balanced Matchup, Exploit Surge and Hardened Defense. Numerical simulation results capture the real-time transient dynamics of the network state variables, demonstrating how the epidemic curve shifts when either the defensive or offensive scaling vectors hold an efficiency advantage. The proposed mathematical and numerical framework provides a rigorous foundation that can be deployed in highly adversarial network environments to evaluate dynamic malware propagation and predict localized node population states.
Connor Clayton, Cory Nunn, Quinn Carmack, Wayne McKenzie, Anne Marie Richards, Xiaodi Wu, Bobby Bhattacharjee
Quantum network links must distribute entanglement at high rates while satisfying application-specified fidelity demands. However, wide-area deployed fiber links suffer from polarization drift which destabilizes end-to-end fidelity and forces periodic compensation. Current deployments often use active stabilization with fixed control policies, and improvements generally stem from advances in quantum hardware. Meanwhile, software control remains relatively underexplored. Here, we formulate quantum link operation as a joint control problem over tunable rate-fidelity tradeoffs and uncontrollable link drift. From this framework, we construct a link control protocol that dynamically adapts source pump power and polarization compensation to maximize entanglement distribution rate subject to a minimum fidelity constraint. We evaluate the protocol through trace-driven simulations driven by data from a 64 km deployed optical fiber. Compared with optimized static policies, our adaptive controller improves mean entanglement distribution rate by 14% over a 24 hour trace, without requiring any offline policy optimization. Our results show that software-based physical layer control can provide a practical mechanism for improving near-term quantum link performance without requiring additional quantum hardware.
Pooja Plavilayil Anil, Te-Chuan Chiu, Nguyen Van Huynh
2030 is expected to witness the first commercial 6G systems. However, traditional modular communication architectures cannot meet the latency, optimisation, and integration requirements demanded by various sub-millisecond, data-intensive emerging 6G applications. End-to-end (E2E) communication addresses these limitations by replacing traditional processing blocks with neural networks. Nevertheless, conventional artificial neural networks (ANNs) in E2E systems use dense computation. This results in substantial energy consumption and computational resource requirements, which may not be feasible in resource constrained devices. This paper addresses the aforementioned challenge by developing a spiking neural network (SNN) encoder that targets lower encoder inference energy while maintaining communication reliability. In particular, the SNN encoder combines sparse spike activity, sinusoidal temporal modulation, and weighted temporal aggregation to map one-hot encoded messages to distinguishable transmit signals. The model is evaluated across antenna configurations and SNN timesteps to investigate the bit error rate (BER)-energy trade-off. The simulation results show that the SNN encoder achieves comparable BER under suitable timestep selections, while reducing the average estimated encoder energy by 68.93%, 52.20%, and 35.69% for SNN timesteps 2, 3, and 4, respectively, across five antenna configurations. These findings highlight the potential of the proposed SNN encoder for energy-efficient E2E communication systems.
José Oliveira, Pedro Valente, Tiago Mendes, André Vasconcelos, Tiago Fernandes, Rhyan Matos, Duarte Raposo, Pedro Rito 等 10 位作者
During disasters, public mobile networks may become unavailable, congested, or degraded, requiring deployable communication systems to support emergency operations. This article proposes a mission-critical communications system supporing Mission Critical Push-to-Talk (MCPTT), Mission Critical Video (MCVideo), and Mission Critical Data (MCData) over a private 5G standalone network with Non-Terrestrial Network (NTN) connectivity. A context-aware Quality, Priority, and Pre-emption (QPP) mechanism is proposed to dynamically adapt network resource allocation according to the operational state of Mission Critical Services (MCX). Extensions to the Open5GS Policy Control Function (PCF) and Session Management Function (SMF) enable priority and pre-emption updates for existing flows without session re-establishment. The architecture supports tactical edge deployment of service and media functions while retaining centralized service control. Experimental evaluation considers three configurations: terrestrial backhaul with centralized media processing, satellite backhaul with centralized media processing, and satellite backhaul with edge media processing. Results show that tactical edge processing reduces communication delay and packet loss. Under satellite backhaul, edge processing reduced the 95th-percentile MCPTT audio transport delay by 60.03 ms and packet loss by 9.42 percentage points, while achieving a 95th-percentile MCPTT access time of 184.52 ms. These findings demonstrate the benefits of tactical edge media processing and provide an experimental foundation for context-aware priority mechanisms in public-safety networks integrating terrestrial and non-terrestrial components.
Uncrewed Aerial Vehicles (UAVs) provide on-demand wireless coverage for mission-critical Air-Ground Integrated Networks (AGINs). However, DRL-based orchestration faces challenges from multi-agent non-stationarity and the trade-off between energy efficiency (EE) and service fairness. To address these issues, we propose ORCHID (Orchestration of Resilient Coverage via Hybrid Intelligent Deployment), a stability-enhanced two-stage learning framework. First, GBS-aware topology partitioning provides a topology-informed warm start for UAV deployment. Second, a Reset-and-Finetune (R\&F) mechanism is integrated into Multi-Agent Proximal Policy Optimization (MAPPO) to support stable late-stage policy refinement through event-triggered rollback and learning-rate adaptation. To study the efficiency-fairness trade-off, ORCHID employs a Max-Min Fairness (MMF)-oriented bandwidth allocation based on a closed-form Egalitarian Bargaining Game (EBG) solution and is compared with Proportional Fairness (PF). Experiments show stronger served-user bottleneck-rate protection under MMF, together with competitive coverage and user-rate fairness and favorable energy-efficiency and UAV-load-balancing performance under zero-shot scalability and mobility tests. Overall, ORCHID provides an effective balance among user-level service protection, energy efficiency, and policy stability for resilient mission-critical UAV networks.
Maximilian Thürmer, Nicolas Scotto di Perto, Eloise Stein, Natanijel Vasic, Mikaeil Bagherli, Zhenguo Wu, Benjamin Klenk, Holger Fröning 等 9 位作者
LLM inference decode executes fine-grained collectives whose latency directly limits per-user interactivity, yet scale-up fabrics are still built from electronic packet switches designed for training. We observe that, for a given model deployment and parallelization, the communication structure of decode is fixed, and propose workload-static optical circuit switching (OCS): circuits are configured once per deployment, co-designed with model parallelism and with topology-aware collective schedules that turn spare accelerator ports into fewer communication phases or higher bandwidth. We evaluate this design with GXNET, a graph-based workload emulator coupled to analytical and flit-level network models grounded in hardware measurements. In a 576-accelerator scale-up domain serving 5T-parameter MoE models with up to 5M tokens of context, all-OCS fabrics improve interactivity by up to 65% and energy efficiency by up to 119% over 4:1 oversubscribed packet-switched fabrics.
Executing service workflows with directed acyclic graph (DAG) dependencies over wireless distributed computing networks requires joint control of function placement, routing, and scheduling, together with synchronization of inputs from the same request at merge points. We study throughput-optimal control of these networks under stochastic request arrivals, time-varying wireless links, and interference constraints. We use a valid mapping abstraction that assigns each service function to a computation node and each DAG dependency to a routing path. Using this abstraction, we characterize the network capacity region to support service DAGs, and prove that synchronizing the packets of a request at merge points does not shrink the region. We develop Dynamic Computing Network Control for service DAGs (DCNC-DAG), a dynamic control policy that selects queue-aware valid mappings and schedules service DAG execution by nearest-to-origin (NTO) while enforcing input synchronization at merge points. With exact minimum-cost valid mappings, DCNC-DAG is throughput-optimal and ensures rate stability of the physical queues under bounded arrivals. These guarantees hold for any fixed finite interval between mapping updates, even when mapping decisions rely on boundedly stale queue length information. For larger service DAGs, we develop DCNC-DAG-H, which replaces exact mapping optimization with a queue-aware heuristic. Simulations show that DCNC-DAG supports $2.7\times$-$5.5\times$ the throughput of queue-blind baselines, while DCNC-DAG-H achieves up to $2.68\times$ those of the corresponding baselines on larger workflow and AI service DAGs.
Pedro Lucas M. S. Rosa, Bruno L. Dalmazo, Leticia S. Machado, Jéferson Campos Nobre
This work proposes, implements, and evaluates a modular architecture based on software design patterns applied to latency measurement in Software-Defined Networks (SDN) using the Extended Berkeley Packet Filter (eBPF) and the Express Data Path (XDP). Unlike approaches that focus solely on measurement precision, this study examines how design patterns can structure observability systems in an extensible and maintainable way while preserving data-plane efficiency. The proposed architecture applies the Observer, Strategy, and Factory Method patterns to modularize the metric collection and analysis pipeline, allowing dynamic integration of policies and data sinks. The experimental environment was implemented with Mininet, Python agents, and eBPF/XDP programs, and was evaluated in terms of processing time, overhead, and evolution effort. The results show that the introduction of design patterns shows no clear evidence of measurable performance degradation, maintaining continuous measurements with a minimum of 5% overhead. The findings suggest that software engineering practices may be applied to the construction of eBPF-based monitoring and observability systems.
Shiri Chechik, Avi Kadria, Gur Lifshitz, Liam Roditty
Compact routing schemes route messages along short paths while every vertex stores little information. We study the trade-off between the stretch and the storage of compact routing schemes in undirected graphs, in four models: labeled and name-independent schemes, with average or worst-case local storage. We improve the best known stretch in all four models. Our main result is a labeled routing scheme with expected stretch $2k-1$ and $\widetilde O(n^{1/k})$ average local storage, in which only the routing is randomized. Under the Erdős girth conjecture, no scheme with this storage has stretch below $2k-1$, so our scheme is optimal, up to polylogarithmic factors in the storage. With deterministic routing, we obtain stretch $\approx2.47k$, the best known deterministic stretch with average storage. Worst-case local storage is the classical setting of compact routing. The leading coefficient $4$ in the stretch $4k-5$ of Thorup and Zwick~[SPAA'01] was improved only by Chechik~[PODC'13], to $\approx3.68$ in unweighted graphs. In unweighted graphs, we obtain expected stretch $(\frac83k+O(1),O(k))$, and deterministic stretch $\approx3.05k$. Name-independent routing is harder, since the source knows only the name of the destination. The best known stretch with $\widetilde O(n^{1/k})$ local storage in unweighted graphs was $96k$, by Abraham, Gavoille, and Malkhi~[DISC'04]. We improve it by more than a factor of $8$. With average storage, we obtain expected stretch $4k-5$, which matches the labeled scheme of Thorup and Zwick. With worst-case storage in unweighted graphs, we obtain expected stretch $(8k+O(1),O(k))$. All our schemes for unweighted graphs extend to weighted graphs with a small loss in the stretch, and still improve the previous unweighted bounds.
Xizhi Zhang, Zhongjie Chen, Yang Zhang, Fengyuan Ren
Modern datacenter applications demand high network bandwidth, motivating packet-level multipath transport to exploit the aggregate capacity of multiple paths. However, packet reordering under packet spraying poses challenges for hardware transports. Excessive packet reordering makes it difficult to infer packet loss, imposes substantial pressure on NIC resources, and ultimately degrades performance. Existing transports either prioritize throughput without controlling packet reordering, or mitigate reordering by restricting transmissions, which leaves available path capacity unused. In this paper, we identify two fundamental sources of packet reordering: transient inter-path queueing differences caused by control dynamics, and persistent path-delay differences due to path heterogeneity. These observations motivate a joint design of a multipath controller and a packet scheduler to address transient and persistent delay differences, respectively. We present Rondo, a packet-level multipath transport that achieves both high throughput and low packet reordering. Its multipath controller uses ACK-based per-path capacity estimation and probing to maintain stable path loads, while its packet scheduler adjusts packet transmission order to compensate for persistent delay differences. Testbed experiments and large-scale simulations show that Rondo reduces the maximum out-of-order degree by up to 97.5% while maintaining high throughput. With constrained receiver buffers, Rondo achieves up to 8.51 times higher goodput.
Yaxuan Jiao, Jiaxuan Lv, Jyoti Sahni, Wuyungerile Li, Winston K. G. Seah
The integration of unmanned aerial vehicles (UAVs), mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) provides an effective solution for computation-intensive and latency-sensitive services in infrastructure-deficient environments. However, the joint optimization of UAV trajectories, task offloading, communication and computing resources under heterogeneous task priorities results in a highly coupled mixed-integer non-convex problem. To address these challenges, this paper proposes a Hierarchical Intelligent Evolutionary Algorithm (HIEA) for priority-aware multi-UAV cooperative NOMA-MEC networks, which integrates multi-head deep Q-network (MH-DQN)-based intelligent decision generation, communication-aware initialization, adaptive evolutionary exploration and successive convex approximation (SCA)-based precision refinement. Specifically, a novel communication-aware initialization mechanism, termed Channel Quality Indicator (CQI)-guided Load-Aware Trajectory Initialization (CQI-LATI), is designed to incorporate CQI and task workload characteristics into evolutionary population construction, generating high-quality and diversity-preserving initial populations. Theoretical analysis characterizes HIEA's search-space reduction, the quality-diversity trade-off of CQI-guided initialization, and the accuracy-efficiency trade-off of SCA refinement, and extensive simulations demonstrate that HIEA outperforms benchmark schemes, with gains of up to 6.5% in throughput, 13.6% in task completion rate and 15.3% in energy efficiency.
A frontier AI model is trained in an AI data center, on tens of thousands of accelerators joined by a dedicated network, and every training step ends with all of them exchanging data at once. Between racks, that network is optical, and each of its four layers, from lasers to switches, runs a fixed or after-the-fact policy. Lasers sharing an enclosure, cooling, and power can fail together. At a fixed spare ratio, correlated failure provably caps a pool's availability however large it grows. In this paper, we present a comprehensive predictive control plane framework, called Halley, that reads each resource's next state and acts before the failure occurs. It has four components, each carrying a formal guarantee: LightReserve that sizes the laser pool to provide a coverage bound, FoldFit that schedules the switch within $11/9$ of the optimal, ThermaLead that holds a microring with one-step-ahead control, and TriageGuard that protects the highest-risk modules first. Halley gives each layer a predictive controller that defers to the deployed policy when unsure. Composed from simulations at that point, effective goodput, the share of ideal throughput kept, rises from 65.2\% to 76.5\% if a window that exhausts its spares is lost. If only the dark channels' bandwidth is lost, it reaches 67.4\%. Per megawatt, that is up to 1.03--1.17 times the training; for the same training, up to 3.3--14.7\% less data center capacity. \textsf{Halley}'s fallback circuits are formally verified and are shown to achieve close timing on a commercial FPGA, and pass a 130~nm signoff.
Eilaf M. A. Babai, Aalaa M. A. Babai, Koji Okamura
Network traffic prediction is an essential tool for automating modern network management. It is a difficult time series forecasting (TSF) problem that has been addressed by Deep Learning (DL) models due to their ability to capture complex patterns. Recent years have witnessed advances in deep forecasting models, from sophisticated transformer architectures to simple linear models, improving performance across diverse prediction tasks. However, given the diversity of these models, the variability of network traffic across environments and timescales, and the inherent challenges of network traffic prediction, it is essential to identify effective deployment choices and modeling directions. Henceforth, this study systematically identifies 12 deep forecasting models, comprising 8 advanced TSF models and 4 traditional deep learning architectures -- each with unique advantages for network traffic prediction -- against 3 statistical baselines, for a total of 15 forecasting methods and evaluate them on four real traffic datasets, across multiple time scales and horizons, assessing performance, robustness to anomalies, data gaps, external factors, data efficiency, and resource efficiency in terms of time, memory, and energy. Our results provide insights into model behaviour and guidelines for network management, highlighting performance regimes, efficiency thresholds, and promising architectures that balance accuracy, efficiency, and robustness to traffic challenges.
Ziyu Chen, Yitong Shen, Jingzhe Zhang, Yao Zheng, Yili Ren, Xuyu Wang, Shiwen Mao, Hanqing Guo
Reconfigurable Intelligent Surfaces (RIS) have emerged as a transformative technology capable of reshaping wireless environments through dynamic manipulation of electromagnetic waves. Although RISs have been widely studied for communication, sensing, and localization, their security and privacy implications are still fragmented across application-specific studies, prototypes, and testbeds. This survey provides a deployment-oriented review of RIS applications, with a focus on the security and privacy threats, vulnerabilities, and defensive strategies relevant to practical use. We analyze scenarios with two types of systems (with and without legitimate RIS) and two types of attackers (with and without malicious RIS), and clarify how RIS introduces new attack surfaces to practical systems, including eavesdropping, jamming, and spoofing attacks. In response, we review defenses against RIS-related attacks in these systems, such as protocol- and algorithm-level protections, unauthorized RIS detection, and interference/disruption-based mitigation. We also discuss scenarios in which the legitimate user applies an additional RIS to defend against attacks. To support future research, we also curate papers and available open resources at https://awesome-ris-security.github.io/. By highlighting RIS's functionality and its security/privacy challenges and opportunities, this survey aims to guide researchers and engineers toward the development of secure, resilient, and privacy-preserving RIS-enabled practical wireless systems and environments.
Shiva Kazemi Taskooh, Mehdi Monemi, Ekram Hossain, Georges Kaddoum, Mehdi Rasti
Jointly supporting communication and distributed sensing over a shared, limited pool of entanglement resources is an attractive prospect for future quantum networks. We develop a network-level quantum integrated sensing and communication (Q-ISAC) framework in which a quantum base station dynamically allocates elementary Bell pairs between communication users and distributed sensing. Sensing resources are adaptively partitioned among multiple GHZ probes with dynamically selected sizes and sensing-node assignments. We derive an effective quantum Fisher information model accounting for probabilistic fusion, transmission loss, and memory decoherence, and formulate a finite-horizon allocation problem subject to communication QoS, expected sensing-information, sensing-reliability, and hardware constraints. A hierarchical constrained proximal policy optimization (HC-PPO) framework combines learned discrete sensing allocation with analytical continuous communication allocation. Numerical results show that GHZ probing requires approximately $27$--30% fewer sensing qubits than separable probing under modest probe-size caps and 65-67% fewer at the highest considered target under the same sensing requirements. Adaptive multi-GHZ partitioning provides greater robustness than single-GHZ sensing, while adaptive communication--sensing sharing outperforms the evaluated fixed splits. The proposed HC-PPO framework satisfies the joint requirements in at least 99.9% of test instances across the considered sensing targets and fusion probabilities. Monte Carlo evaluation separately shows that realized terminal information reached its target in 96-98% of realizations, distinguishing expected-information satisfaction from realized sensing performance.
The Ethereum ecosystem offers the most widespread support for Decentralised Applications (DApps), with over 120 networks currently documented as using variants of the Ethereum Virtual Machine (EVM) for core execution logic. The ERC-4337 standard provides convenient access to DApps for users who do not possess the required cryptocurrency or prefer flexible authorisation of DApp actions. The infrastructure for ERC-4337 implementation can be deployed on any EVM network. However, to aggregate user requests and submit them to the EVM network, ERC-4337 requires operating a bundler, an additional component. Recently, the potential use of ERC-4337 was boosted by another standard, EIP-7702, which can enable ERC-4337 utility for any existing user account. However, the use of the combined EIP-7702/ERC-4337 pattern remains considerably low at less than 6% of EIP-7702 activity. We believe that this underutilisation is at least partly caused by the low number of existing bundling services and their popularity, currently fewer than 15 publicly documented. Expanding the number of bundler nodes would benefit the ecosystem but would introduce operational costs, part of which is represented by the added nodes' power consumption. In this paper, we provide a thorough view of the bundling power consumption aspect using server-grade infrastructure, evaluating both CPU and RAM consumption, as well as the overhead of container-based deployments specific to production environments.
Md Fazley Rafy, Niloy Patari, Anurag K. Srivastava
The increasing penetration of distributed energy resources (DERs) in distribution system necessitates adaptive coordination frameworks. These frameworks must remain optimal during normal operation and resilient under cyber physical disturbances. Existing coordinated control approaches are typically deployed as static architectures with limited ability to adapt when communication degrades, local instability emerges, and operating conditions become spatially heterogeneous. This work addresses the gap by proposing a DER service-independent edge autonomous holonic adaptive coordination framework. In this framework, each DER controller executes its own control actions and transitions among centralized, distributed, decentralized, and local autonomous coordination modes without always relying on static coordination commands from the grid operator. The framework preserves coordination continuity across modes by retaining local controller states while reconfiguring only the coordination topology, information exchange pattern, and fallback action associated with the active DER service. Graceful transitions are enabled through dwell timers, rate limiting, and safety overrides to prevent dynamic instability during mode changes. Volt VAR control is used as a representative distribution automation application to validate the proposed architecture in a cyber-physical Hardware-in-the-loop (HIL) testbed. The proposed approach is evaluated under diverse cyber-physical event-based scenarios, showing region-confined adaptation under localized disturbance, reduced coordination traffic during coordination switching, and node-confined mitigation under cyber attack through edge anomaly detection and neighbor-corroborated impact estimation.