Papers

Wide-area bandwidth per unit of GPU compute falls every hardware generation: in compute-intensity-ratio terms (CIR, bytes per FLOP), the gap between on-package memory and the conventional WAN is four to five orders of magnitude, widening at roughly 12-19% per year. Position papers - including our own - argued this makes elastic optical wide-area capacity necessary for cross-site AI inference. Reviewers correctly objected that such arguments show more bandwidth helps, not that it beats the alternatives: KV recomputation, cache compression, locality-aware routing, scheduling, or an overprovisioned packet backbone. This paper does the comparison. We derive a workload model predicting when moving inference state across sites beats recomputing it - a context-independent crossover at 74-111 Gbps per stream for a 70B multi-head-attention model, falling to 9-14 Gbps under grouped-query attention at 1/8 KV heads - and quantify five sensitivity axes: context length, attention architecture, queueing, agentic compounding, and loss/jitter-induced bandwidth collapse. On economics: at list GPU prices recomputation is cheaper; transfer wins when GPU scarcity and KV reuse multiply effective GPU cost by roughly 5-20x, and modern attention moves the breakeven an order of magnitude in transfer's favour. We position the network levers correctly rather than adversarially: packet networks allocate lit capacity at millisecond timescales; optical fungibility changes how much capacity is lit, at minute timescales, substituting for overprovisioning economically rather than functionally. Finally we specify a ten-metric measurement plan on a three-site production-fibre testbed, framed as an open ecosystem exercise: no single company can - or should - assemble this evidence alone. Every claim is bounded by the regime in which it holds; several findings weaken the naive version of our own thesis, and we state them.

Growing awareness of the impact of quantum threat on classical cryptography directly translates into a growing demand for accurate network simulation tools capable of estimating the integration effects of quantum-safe cryptography in current systems. In particular, the adoption of Post-Quantum Cryptography (PQC) has a direct impact on the performance of network endpoints and transmission overhead. This also affects the scalability of widely adopted security protocols such as TLS and SSH. In this paper, we present a throughput-oriented analytical model that provides a tight upper bound on the maximum sustainable rate of post-quantum secure connection establishment in TLS and SSH. This model takes into account both endpoint and network capacity constraints, decomposing the handshake process into dominant cryptographic operation time and network transmission time. Identifying the bottleneck allows us to derive the achievable throughput in terms of handshakes per second. The experimental results provided show the accuracy of the model against the data obtained from an experimental testbed using, among others, NIST standard primitives from FIPS 203, 204, and 205, including ML-KEM and ML-DSA. Finally, we integrate our model into a network environment and demonstrate how it can be leveraged to enable efficient resource allocation among multiple endpoints, optimizing PQC traffic in multiple-unicast scenarios.

The evolution toward sixth-generation (6G) networks is transforming the radio access network (RAN) into a programmable and intelligent control platform that must continuously adapt to heterogeneous services, dynamic environments, and competing performance objectives. Open Radio Access Network (O-RAN) provides the open interfaces, disaggregated architecture, and multi-timescale control loops needed to support this transformation, while deep reinforcement learning (DRL) offers a natural framework for optimizing sequential decisions under uncertainty. However, existing surveys either address artificial intelligence (AI) and machine learning (ML) in O-RAN broadly or focus on isolated DRL use cases, leaving a gap in the systematic connection between DRL methodology, O-RAN architecture, and operational deployment. To the best of our knowledge, this article presents the first dedicated and comprehensive survey of DRL for Open AI-RAN. We review the foundations of model-free, model-based, offline, safe, multi-agent, federated, and transfer learning, and provide an O-RAN-aware framework for formulating RAN control problems through states, observations, actions, rewards, constraints, and temporal structure. We classify DRL applications across radio resource management, mobility management, interference control, traffic steering, energy efficiency, network slicing, integrated sensing and communication, security, and massive MIMO. We further examine multi-agent and federated coordination, foundation models and agentic AI, trustworthy DRL, sim-to-real transfer, continual adaptation, resource-efficient inference, and reinforcement learning operations. Finally, we review experimental platforms, benchmarks, standards, and industry activities, and identify research directions toward sample-efficient, safe, scalable, interoperable, and deployable DRL control for 6G Open AI-RAN.

3GPP Release 16 enables a 5G system to operate as a transparent IEEE 802.1 TSN bridge, but its scalability under heterogeneous industrial workloads remains insufficiently characterised. This paper uses the nascTime framework on OMNeT++/Simu5G to evaluate how many TSN endpoints a single 5G NR cell can bridge before per-flow QoS degrades. We model closed-loop control, machine vision, bulk telemetry, and IEEE 802.1AS traffic over a four-bearer SDAP architec- ture, varying the number of endpoints from 1 to 40, MAC scheduler, radio bandwidth (10 MHz and 20 MHz), and channel model. Results show three operating regimes. Below saturation, non-DRR schedulers perform similarly; near saturation, QoS- aware PF reduces critical-flow P99 latency by up to two or- ders of magnitude relative to channel-aware and fairness-based schedulers; and under overload, only QoS-PF maintains near- complete delivery for the highest-priority traffic. Across the two evaluated bandwidths, the saturation threshold approximately doubles when bandwidth doubles. We also show that isolating IEEE 802.1AS/gPTP traffic on a dedicated high-priority radio bearer reduces clock-servo instability, although endpoints carry- ing lower-priority data still experience elevated synchronisation delay under saturation because of reduced MAC scheduling frequency. Finally, the evaluated sub-6 GHz, 30 kHz-SCS con- figuration exhibits an effective latency floor of approximately 2.25 ms, indicating that sub-3 ms TSN deadlines may require radio-configuration changes such as configured grants or higher numerology

Tactile Internet applications place strict require- ments on latency, jitter, loss, and responsiveness, which makes transport configuration a critical design factor. Although BBRv2 offers a model-based congestion control framework with strong throughput potential, its default behavior may not be well aligned with delay-sensitive interactive scenarios. This paper presents a robust and constraint-aware tuning framework for BBRv2 in QUIC, where parameter selection is formulated as an expensive black-box optimization problem over multiple emulated network conditions. The tuning process uses Bayesian optimization with the Tree Structured Parzen Estimator to efficiently explore a bounded parameter space under noisy experimental measure- ments. The objective is designed to preserve throughput while enforcing limits on tail latency and loss, while delay instability is evaluated separately through the jitter metric. Experimental results across low, medium, and high impairment scenarios show that the tuned configuration improves tail latency, jitter behavior, and loss performance while maintaining competitive goodput relative to standard QUIC congestion control baselines. These results support robust black-box tuning as a practical method for adapting QUIC transport behavior to tactile Internet style requirements.

HTTPS website fingerprinting (WF) aims to identify visited websites from metadata observable in encrypted traffic. However, real-world deployments introduce a significant out-of-distribution (OOD) problem caused by temporal and geographic changes, while previously unseen websites are common in open-world scenarios. Existing methods primarily learn from raw TCP packet sequences and struggle to capture stable and generalizable website representations, resulting in performance degradation under practical conditions. We propose CipherSight, a TLS-record-based hierarchical framework for robust HTTPS WF. Unlike existing approaches that rely on TCP packet sequences and are sensitive to transport-layer artifacts, CipherSight learns website representations from TLS records by jointly encoding multiple record-level attributes. It introduces a hierarchical architecture that captures both intra-flow dependencies among TLS records and inter-flow interactions across concurrent flows, enabling the model to exploit structural patterns in HTTPS traffic. Besides, to learn robust representations, CipherSight employs a masked record modeling (MRM) task to capture contextual traffic semantics and leverages fine-grained record-resource annotations as privileged supervision through structure-aware objectives and semantic distillation. Experiments show that CipherSight achieves 95.41% accuracy across more than 2,000 website classes in the closed-world setting and maintains over 90% accuracy under both temporal and geographic drift, consistently outperforming all evaluated baselines.

Weird machines are latent computational capabilities that emerge from the composition of architectural components. Prior work has studied this phenomenon extensively in software systems, including x86 instructions, ELF metadata, and page tables, and more recently in cyber-physical systems such as industrial control networks. This paper extends weird machine theory to a new domain: the Transport Layer Security (TLS) handshake and its two dominant implementations, OpenSSL and BoringSSL. We show that legitimate TLS primitives, including session cache entries, renegotiation logic, extension parsing, and certificate verification steps, compose into Turing-complete systems whose computation is coupled to authentication and trust decisions rather than physical actuation. We formalize this coupling, which we call trust actuation, and argue that any TLS implementation providing session storage, arithmetic on sequence counters, conditional branching on handshake state, and iteration through resumption or retry loops satisfies the conditions for arbitrary computation. We validate this theory with two working demonstrations built on real OpenSSL code paths. The first, a sentinel system, composes standard TLS primitives into a defensive mechanism that detects anomalous handshake behavior. The second, an authentication bypass, composes the same class of primitives into an attack that defeats a cipher-strength policy check through mid-connection renegotiation, without any memory corruption or external malware. Both demonstrations run against real server and client binaries in Docker.

Deep joint source-channel coding-enabled (DeepJSCC) semantic communication (SemCom) has excelled at delivering high perceptual quality at low channel-bandwidth ratios, which positions it as a pillar for next-generation wireless networks. However, the existing works have difficulty accommodating user heterogeneity in terms of communication channel quality, expected quality-of-service (QoS) targets, and the available local energy. Therefore, in this paper, we explicitly reflect the heterogeneity of user devices in terms of the differences in expected QoS, channel condition, and local energy, and then mathematically formulate the problem. Next, we propose an energy-aware compression-computation co-adaptation (CoCo) framework, in which the base station can meet the expected user QoS by transmitting a longer signal or offloading the task to a local device. The user has to dedicate energy to denoising the signal to recover higher-fidelity latent features before feeding it to the semantic decoder. To solve the formulated problem, we first decompose it into two sub-problems: parameter optimization and resource allocation problems. Specifically, we propose a robust codec that effectively works under a diversity of compression rates and channel noise without re-training, while the greedy sub-carrier allocation lowers the communication time. Finally, we present simulation results on standard image datasets over additive white Gaussian noise to demonstrate the effectiveness of CoCo, which reduces total latency relative to rate-only adaptive DeepJSCC or denoising-only, thereby ensuring the demands of each individual user are met.

Enterprise security topology design requires translating business intent, regulatory requirements, and risk assumptions into zones, boundary devices, inter-zone paths, and access-control policies. Existing NetOps automation tools mainly operate after this design is fixed, providing limited support for generating structured security topologies from underspecified natural-language requirements. We present TopoIntent, a system that compiles security intent into executable, compliance-checked network topologies. It uses a schema contract to constrain generation, retrieves reference architectures from a curated template library via dense-vector search, and applies staged fusion for intent-template alignment and security completion. The generated topology is checked against CIS Controls v8.1.2 safeguards visible at the topology layer, while unresolved cases are marked for manual review. Structural gaps are repaired through additive schema-preserving edits. The final topology is exported to Mininet scripts with kernel-level iptables ACLs, enabling executable reachability and allow/deny tests. Because no public benchmark exists for this requirement-to-topology task, we construct an evaluation set from reference security architecture diagrams. The retrieval set contains 22 templates and 44 synthetic intents across five scenarios, while the held-out set contains 7 templates and 14 intents from finance and government scenarios excluded from retrieval. On the held-out set, additive repair improves topology-visible CIS satisfaction from 0.78 to 1.00 in fewer than 1.5 rounds on average, and one feedback round raises the post-ACL policy pass rate from 0.78 to 0.88.

We study pull-based remote state estimation of an arbitrary, multi-state Markov source while accounting for both freshness and correctness attributes of information. To that end, we formulate a discounted optimization problem in terms of the age of incorrect information (AoII), and express it as a joint source-AoII belief Markov decision process (MDP) under maximum a posteriori (MAP) estimation. We then exploit the information structure of the model and prove that every reachable belief is represented by the last successfully observed source state and the number of time slots elapsed since that observation. For numerical computation, we truncate the elapsed no-success duration at a finite level and derive an explicit error bound and a criterion for selecting the truncation parameter. For reliable links, we show that an optimal policy can be represented by a look-up table of waiting times. For unreliable links, we propose a persistent policy and derive computable performance bounds. We also show that the MAP estimate stabilizes after a finite number of time slots. To further reduce memory requirements, we introduce a hybrid estimator with an early stationary switch and derive a computable bound on the resulting difference in performance. Finally, we extend the framework to multiple sources, formulate the scheduling problem as a restless multi-armed bandit, establish a sufficient condition for indexability, and develop an approximate Whittle index policy based on interpolation. Our numerical results illustrate the structure of the optimal single-source policy, evaluate the performance of the multi-source policies, and verify that the proposed heuristic policies closely approach the optimal solution while substantially reducing computational efforts.

Challenges associated with densification of radio access networks are motivating exploration of more efficient and scalable architectures. We examine recent progress in one direction that involves moving beyond radio and optical convergence to full confluence.

With the rapid evolution of 5G and emerging 6G networks, reconfigurable intelligent surfaces (RIS) have become a critical technology for enhancing wireless communication scenarios. However, optimizing RIS-assisted multi-user systems typically introduces high-dimensional physical layer variables and non-convex Pareto-optimal rate sets, posing severe computational challenges for real-time applications. To address these limitations, this paper proposes a dimension-reduced, hierarchical reinforcement learning (RL) framework, termed Pareto-aware autoencoder-assisted RL (PAAERL), to optimize online resource allocation in RIS-assisted Internet of Things (IoT) networks. Our approach first substitutes high-dimensional continuous RIS beamforming variables with lower-dimensional weight vectors that strictly represent the Pareto-optimal frontier, theoretically avoiding geometric information loss across both convex and non-convex rate regions. To further mitigate the curse of dimensionality in dense networks, an autoencoder architecture is integrated to execute a secondary, data-driven compression phase, mapping the priority space into a highly condensed continuous latent action space. Extensive simulations conducted across practical communication scenarios, including multi-user mobile edge computing (MEC) networks, demonstrate that the proposed PAAERL framework drastically reduces offline training times, accelerates online policy convergence, and significantly decreases overall network costs compared to state-of-the-art benchmarks, underscoring its exceptional scalability and practical viability for next-generation intelligent IoT environments.

The emerging Internet of Agents enables LLM-powered agents to discover peers, invoke tools, and delegate tasks across organizational boundaries. Existing protocols increasingly define how agents exchange messages, but not how an agent proves its identity, authorization, advertised capabilities, or accountability after delegation. We present InterSAGE, a trust-native protocol suite that supplies this missing security substrate alongside, rather than in place of, communication protocols. InterSAGE comprises four layers: Persistent Identity, Discovery, Trust Negotiation, and Accountability. Its four core primitives are: (1) Agent Identity Cards that bind developer, code package, operator, and deployment context; (2) capability-aware discovery using DID-bound Verifiable Credential manifests; (3) trust negotiation combining monotonic capability attenuation with two-tier access control; and (4) kernel-mediated cryptographic audit trails that bind usage, delegation, and execution traces to agent identity without a consensus ledger. InterSAGE is designed to complement MCP, A2A, ANP, and AG-UI, allowing communication protocols to evolve independently while keeping trust semantics explicit, portable, and verifiable. We compare InterSAGE with more than 50 efforts spanning agent protocols, decentralized identity, OAuth/OIDC extensions, zero-trust governance, delegation, and audit architectures. We show that no prior architecture jointly enforces persistent identity, capability-aware discovery, trust negotiation, and accountability as a unified four-layer trust substrate for secure agent interoperability.

The study presents a systematic machine learning (ML) study of 6G-IoT beamforming optimization (6GBO) using supervised and unsupervised approaches. We compared the predictive power of network, environmental, device, and vision feature groups for 6GBO. Additionally, it addressed other unsupervised perspectives that can enhance 6GBO, including clustering network scenarios using methods such as K-means, DBSCAN, and hierarchical clustering. Several imbalance-aware experiments revealed that network features possess better prediction power than device, environmental, and vision feature groups, as evidenced by their recall, F1-score and ROC-AUC values. For unsupervised ML exploration (assessed using Elbow, Silhouette score, and Davies-Bouldin Index methods), the results indicate that the deployment environment and type of device primarily influence clustering, rather than mobility-based attributes. Furthermore, the explainability analysis showed that bandwidth, IoT sensors, and mobility possess higher global feature importance across the feature groups. In the future, we would apply deep and reinforcement learning techniques to predict throughput/latency or to optimize rewards determined by performance indicators like SNR enhancement

Satellite mega-constellations in Low Earth Orbit (LEO) are becoming an important part of next-generation non-terrestrial networks, but their operation remains challenging because of fast network topology variation, intermittent inter-satellite links, hardware disturbances, and strict Size, Weight, and Power (SWaP) constraints. Existing approaches based on Digital Twin (DT), Digital Twin Network (DTN), Software-Defined Networking (SDN), and Open Radio Access Network (O-RAN) provide useful building blocks for intelligent satellite networking, but they do not fully support real-time, predictive, and platform-aware network operation. In this paper, we propose a Digital Twin Satellite Network (DTSN) framework as a closed-loop architecture for reliable and intelligent management of LEO satellite constellations. The proposed framework connects the physical satellite network with a synchronized virtual twin and combines real-time telemetry, Integrated Sensing and Communication (ISAC), predictive intelligence, and resilience-oriented control. To validate the concept, we develop a constellation-scale cross-domain co-simulation using the NASA 42 spacecraft simulator and a Python-based DT bridge for a LEO constellation. The DT continuously ingests physical telemetry to manage a multi-domain threat environment, encompassing kinematic drift, hardware failures, and adversarial jamming over a 600-second flight window. By leveraging a predictive lookahead mechanism and an exponential sensor recovery model, the framework successfully isolates compromised nodes and triggers proactive network reconfiguration, thereby ensuring uninterrupted service and dynamic network resilience. These results show the potential of DTSN to support predictive and resilience-oriented satellite network operations.

Across many domains (e.g., observability, networking, security), data processing pipelines face what we refer to as the CSP problem: achieving low Cost at large Scale, while maintaining high Performance. In response, we see several efforts to tackle CSP in various stages of the Collect-Transmit-Store-Analyze data lifecycle; such as approximate query processing in databases or sketches in network routers. Our work is driven by the simple insight: "seeing the forest for the trees". These proposed solutions (e.g., AQP, sketching, compression, rollups) addressing CSP share a common property - they exploit semantic-preserving opportunities to support application needs. In this paper, we make a case for ASAP, a paradigm that makes Application Semantic-Aware Processing (ASAP) a first-class design principle in data processing pipelines. We argue that by taking a unified view across ASAP primitives developed in different domains, across the entire data lifecycle, we can unlock new opportunities to tackle the CSP problem. In particular, we can: (i) enable novel cross-lifecycle optimizations such as analytics run directly on sketches computed at the source; (ii) leverage primitives developed in other application domains; and (iii) enable widespread adoption of these powerful techniques. We discuss research challenges in socializing the benefits of the ASAP paradigm, and show preliminary evidence that adopting ASAP can yield up to 3 orders of magnitude improvements in the CSP tradeoff for many application domains.

The demand for ultra-low latency applications necessitates advanced network architectures like the Low Latency, Low Loss, and Scalable Throughput (L4S) standard. A core component of L4S is the DualPI2 Active Queue Management (AQM), which ensures the safe coexistence of scalable and classic traffic. Despite L4S's growing adoption, the ns-3 network simulator lacks a high-fidelity, up-to-date DualPI2 model. This paper presents a comprehensive implementation of the DualPI2 AQM in ns-3, while also mirroring the official Linux Kernel architecture. Our model incorporates representative mechanisms previously absent in simulation, such as credit-based Weighted Round Robin (WRR) scheduling, step-marking, and overload protection. To guarantee simulation accuracy, we validate our implementation against a physical Linux testbed across 25 diverse Bandwidth-Delay Product (BDP) scenarios. Results demonstrate that our ns-3 model replicates real-world behavior, ensuring strict throughput fairness and queue delay isolation. Ultimately, this validated model equips the research community with a robust tool to evaluate and advance L4S performance across diverse network topologies, ranging from data centers to wireless home and office environments.

Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances. Although multi-agent reinforcement learning (MARL) enables decentralized coordination through centralized training, existing methods suffer from high-dimensional joint state-action modeling, noise-sensitive policy generation, leading to unstable training and degraded tracking. To address these issues, we propose VGG-MADiffRL, a value-gradient-guided multi-agent diffusion RL algorithm, and MDCA, a diffusion?based hierarchical control architecture. Leveraging underwater mission characteristics, we model sonar detection mechanisms and ocean current disturbances, formulating cooperative tracking for multi-AUV ad-hoc networks as an MDP. The proposed MDCA constitutes a three-tier closed-loop control framework: a global intelligent control layer, a local online training layer, and a physical action execution layer. This structure enables synergistic optimization across task allocation, local decision processes, and execution feedback. Within MDCA, the local online training layer is the policy learning framework; VGG-MADiffRL builds on diffusion policies and incorporates value gradients to guide action generation in the reverse denoising process, steering the generated actions towards higher expected returns. It employs twin value networks with joint optimization and soft target updates to mitigate overestimation and training oscillations, promoting more stable convergence. Experimental results show that VGG-MADiffRL consistently achieves faster convergence, higher tracking accuracy, and smoother training dynamics in cooperative tracking scenarios, validating its effectiveness and practical engineering value in dynamic underwater settings.

IEEE 802.11bn (11bn) introduces Coordinated Spatial Reuse (Co-SR), a Multi-AP Coordination (MAPC) scheme in which two Access Points (APs) coordinate to control the transmit power for a simultaneous transmission. This letter introduces a Proportional Fairness (PF)-driven framework for allocating Co-SR transmit power on a per-Transmission Opportunity (TXOP) basis. We prove that any Pareto-optimal power pair keeps at least one AP at its maximum power, reducing the joint two-dimensional search to two cheap one-dimensional line searches that fit comfortably within TXOP timing, and show that this collapses to an exact closed-form expression in a real, discrete-rate system. We validate the resulting policies, together with a low-complexity selfish baseline, against a brute-force oracle and through packet-level simulations in Kom8ndor, an 11bn network simulator. Results show that jointly evaluating both APs' links is key to unlocking Co-SR's spatial reuse gains, that the coordinated AP's fairness-optimal power depends critically on whether the rate is modeled continuously or through real, discrete Modulation and Coding Scheme (MCS) steps, and that current 11bn signaling supports the selfish policy but not the fairness-optimal ones, which would need additional per-TXOP channel reporting.

Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users. Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional growth in centralized infrastructure. To capture stochastic and dynamic interactions among users, resources, tasks, and policies, we develop a high-dimensional generative Markov model with structured temporal factorization. The model supports simulation and provides a foundation for task scheduling and QoS-aware resource allocation optimization. We evaluate the system across user populations, resource capacities, and centralized and distributed scheduling policies. Simulations show that distributed scheduling becomes increasingly advantageous as the user population grows, improving request completion and P99 latency while substantially reducing dedicated resource consumption. These results demonstrate the feasibility of user-assisted collaborative inference for infrastructure-efficient autoscaling.

Recent advances in Large Language Models (LLMs) have spurred cross-modal solutions for time-series forecasting. However, existing methods rely heavily on textual prompts for modality alignment-introducing nontrivial computational overhead and failing to leverage the rich spectral dynamics inherent in time-series data. To enable prompt-free, frequency-aware adaptation of frozen LLMs, we propose FM-LLM (Frequency-Enhanced Mixture-of-Experts for adapting LLMs to Time Series Forecasting), an autoregressive framework grounded in constrained asymmetric coupling. A Fourier Analysis Network (FAN)-based spectral token aligner injects structured harmonic representations directly into the frozen LLM with numerical compatibility. An asymmetric Mixture-of-Experts (MoE) decoder enforces role separation: shared experts with lightweight FAN layers reconstruct the global periodic backbone, while routed experts-restricted to standard FFNs-specialize in modeling non-periodic residual dynamics. A time-frequency hybrid loss function jointly optimizes temporal accuracy and spectral consistency, mitigating error accumulation during long-horizon autoregressive rollouts. Evaluated across eleven public benchmarks, FM-LLM achieves state-of-the-art performance on 59 out of 78 evaluation metrics. Compared to the strongest autoregressive LLM-based baseline, it delivers average improvements of 5.3% in MSE and 5.6% in MAE, with maximum gains reaching 8.0% for MSE and 8.4% for MAE. FM-LLM also demonstrates robust transferability, maintaining superior performance in 10% few-shot and zero-shot forecasting scenarios.

Integrated sensing and communication (ISAC) extends mobile networks from information transfer toward perception of passive objects and environments. By exploiting propagation delay, Doppler, angle, and temporal variation, a mobile network can support detection and tracking while reusing licensed spectrum, infrastructure, and edge computing at network scale and under operator control. Unlike conventional radar, ISAC must additionally address multi-application access, heterogeneous sensing entities, uncertainty, privacy, trust, and integration with communication services. This article reviews the evolution of ISAC in 3GPP from 5G-Advanced to 6G. It explains Release-19 ISAC service requirements and channel models, the Release-20 sensing function architecture and reporting abstractions, and the 5G monostatic baseline for drone detection and tracking. It also examines 6G-native sensing, including passive object sensing, communication assistance, multiple sensing modes, and multi-source data integration. By connecting the evolving 3GPP architecture and radio studies across 5G-Advanced and 6G, this article provides a unified technical perspective on current standardization choices, their implementation tradeoffs, and the open challenges shaping network-grade sensing.

Enterprise wide-area networks (WANs) use quantum-vulnerable public-key cryptography to authenticate peers and establish keys for Internet Protocol Security (IPsec), Transport Layer Security (TLS), and software-defined WAN services. Harvest-now, decrypt-later collection already threatens data whose protection lifetime may exceed the time needed to migrate, while a future cryptographically relevant quantum computer would also endanger certificates and other authentication dependencies. Post-quantum standards and government directives now define algorithms and transition milestones, but they do not tell an enterprise which WAN services to migrate first or what evidence is sufficient for deployment. This paper proposes a vendor-neutral framework that separates exposure priority, migration readiness, and evidence confidence, and maps those outputs to staged security, operational, and governance gates. It also defines a complete evaluation design for classical and ML-KEM hybrid key establishment in TLS 1.3 and IKEv2/IPsec. The retained artifact is narrower: it contains author-reported aggregate TLS p50, p95, and p99 latencies at configured loss settings of 0%, 1%, 3%, and 5%, an arithmetic checker, and limited environment and MTU/MSS notes. At those settings, the TLS-H minus TLS-C p95 differences are 0.00, 0.16, 0.38, and 0.63 ms, corresponding to 0.00%, 1.32%, 3.07%, and 5.00% relative to TLS-C. These values are descriptive aggregate observations, not inferential results, because per-trial data, sample counts, original captures, and timestamped logs were not retained. Protocol-size and MTU/MSS calculations are reported separately from measured behavior, and unsupported IKE, resource, throughput, retransmission, and downgrade outcomes are excluded.

Root cause analysis (RCA) in large datacenter networks is challenging because telemetry is noisy, partial, and asynchronous. Score-based approaches degrade under these conditions, often yielding unstable or incorrect attributions. We present CoreSec, a production RCA system that replaces weighted fusion with a PAM-style abstention algebra. Telemetry agents are composed using control flags that yield deterministic decisions and explicit abstention when evidence is ambiguous. CoreSec combines this algebra with topology-aware configurations that capture failure surfaces across Clos fabrics and converge monotonically as evidence accumulates. Deployed at hyperscale, CoreSec provides stable and explainable RCA behavior across diverse environments without retuning. Our experience shows that structured composition with abstention forms a practical foundation for automated RCA in real-world cloud networks.

Federated interconnected QKD networks are becoming the norm, and with several standards already in place, interoperability is now technically feasible. Many aspects related to cross-domain key distribution, however, are left unresolved: breach announcement and mitigation, integration with the space segment, extension to a future QKD-as-a-Service commercial model, custom requests referring to key freshness, jurisdictions, security levels, etc. We argue that, to move from syntactic to semantic interoperability, a common language must be developed to ensure transfer of meaning along with the keys. We posit this is possible via QKD key metadata, and we propose a 6-step process to develop it as a standard.

Entanglement distribution in quantum networks must jointly account for limited link capacities, probabilistic entanglement swapping, and heterogeneous link fidelities. In this paper, we study multi-pair fidelity-aware rate allocation in quantum networks. We formulate three rate-allocation problems: rate sum, rate sum subject to minimum-rate constraints, and max-min fairness. Prior work has studied a special case of the rate sum problem, where all links have identical fidelity. This special case admits a polynomial-time algorithm. We prove that all three problems are NP-hard. We then study optimization versions of these problems which maximize the minimum end-to-end fidelity subject to throughput or fairness requirements. We present fully polynomial-time approximation schemes (FPTAS) for solving these optimization problems. Experiments on randomly generated networks demonstrate the computational effectiveness of the proposed schemes.

Symbolic network verifiers can reason about correctness across vast spaces of routing inputs and failures, but only for the protocols and features an expert has encoded by hand. Creating and maintaining a faithful model of the control plane is both difficult and never-ending, since no written source specifies perfectly what a network does: vendor implementations deviate from the RFCs, and behaviour shifts with releases. The burden of constant upkeep ultimately keeps verification out of many networks that need it. We argue that the model should instead evolve automatically to faithfully capture the actual network behaviour. To achieve that, we leverage the only source that specifies it unambiguously: the router software itself. In a counterexample-guided loop, a coding agent proposes extensions to the verifier's symbolic encoding, while a trusted oracle (e.g., emulated routers) supplies the ground-truth routing state. The agent iteratively refines the network model using each disagreement with the oracle. As early evidence, a prototype of this system taught a 3,000-line SMT-based verifier three features it did not support: OSPF areas, BGP route reflection, and L3VPN over EVPN, converging autonomously on models that match the oracle, even noticing vendor-specific behaviour. Automating model growth shifts the hard problem from writing verification systems to systematically testing them; we propose a research agenda for trusting and harnessing automatically evolved verifiers.

With the surge in privacy-sensitive data from sources such as social media and IoT devices, there is a pressing need for formal, automated methods to assess privacy risks within these intricate systems. This paper formally investigates root sources of pairing-based privacy threats exploited using replay/relay techniques in wireless communication. Our research harnesses condition-oblivious responses, replay-resistance, and distance bounding measures vital for protocols utilizing shared keys in allowlists for authenticated reconnections. Particularly, the paper uses formal modeling of notable wireless networks, like the Wi-Fi P2P persistent group formation and the Bluetooth Low Energy reconnection procedure, to illustrate the root causes and countermeasures. Our model rigorously validates the proposed solution against association inference attacks, along with existing formalizations of well-authentication, frame opacity, and no-desynchronization. The ensuing analysis reveals not only uncharted privacy realms in wireless communication but also identifies old and new vulnerabilities. Our proposed design changes are acknowledged by Wi-Fi Alliance and Bluetooth SIG, paving the way for future advancements in resilient, privacy-preserving wireless protocols.

Low Power Wide Area Networks like LoRa are increasingly deployed for smart city applications, requiring accurate path loss prediction for effective network planning. Traditional (empirical) propagation models often exhibit limited accuracy in these scenarios. We investigate machine learning models for LoRa path loss prediction, systematically analyzing how prediction accuracy scales with training set size using real-world measurements from an urban deployment. Our approach employs a Random Forest with LiDAR-derived terrain features and k-Nearest Neighbors with coordinate data, comparing their performance against established empirical models and specialized LPWAN models. Under random pooled splits, both ML models consistently outperform the considered baseline models across the evaluated training-set sizes. At maximum training size, they achieve RMSE values below 6.5 dB compared to 9.7 dB for the best baseline, indicating accurate within-deployment interpolation. A leave-one-gateway-out check qualifies this result: RF shows placement-dependent transfer to held-out gateways, with moderate degradation for several gateways but larger errors for others, whereas coordinate-only k-NN degrades substantially when the gateway location is unseen

Laser diodes (LDs) promise an order-of-magnitude bandwidth advantage over light-emitting diodes for indoor optical wireless access, but reported prototype studies frequently leave the gap between hardware demonstrations and system-level performance unquantified. This paper develops a complete, reproducible system model of a diffused-beam LD LiFi transceiver - a 500-mW laser source beam-shaped by a holographic diffuser, an intensity-modulation/ direct-detection (IM/DD) receiver, and adaptive M-QAM signaling - and embeds it in two cross validated simulators: an open ns-3 module providing full-stack network simulation (channel, PHY, ARQ MAC, Net Device, IP/UDP/TCP) and a Python link-level engine used for Monte Carlo validation of all analytical error models. Starting from a hardware prototype that transferred data, real-time voice, and images over a 14-m line-of-sight link, we identify and close the technical gaps typical of prototype-class reports: serial-interface throughput ceilings misread as optical-link capacity, absent noise modeling, unmeasurable error floors, and unexamined beamwidth/coverage trade-offs. The framework shows that the same optical front end, freed of its 2-Mbaud UART bottleneck and driven at its 250-MHz electrical bandwidth, supports 930 Mb/s net at 14 m under a $3.8 \times 10^{-3}$ HD-FEC threshold with 16-QAM, scales to 1.86 Gb/s at 5 m with 256-QAM, and sustains on-off keying to 23.3 m; a $20^\circ$ diffuser covers a 4.2-m-radius cell of a standard room at desk height. Network simulations over the ns-3 stack yield saturation goodput within 7% of the PHY line rate and sub-0.11-ms 99th-percentile latency at 70% load. All models, code, and figures are released for reproduction.