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arXiv2026

WIP: Energy-Efficient LLM-Based Serving Cluster Formulation in Cell-Free Massive MIMO

One way to increase the Energy Efficiency (EE) of 6G wireless networks is to utilize existing network infrastructure more efficiently. This can be achieved by introducing User-Centric Cell-Free Massive Multiple-Input-Multiple-Output (UCCF MMMIMO), which allows for simultaneously serving a single user by multiple Base Stations (BSs). From this perspective, the key challenge is to decide which BSs should serve a given user, known as the Serving Cluster Formulation (SCF). In this paper, we propose to deal with this problem by using an Artificial Intelligence (AI) agent based on a Large Language Model (LLM), targeting improvement of EE. We evaluated the proposed AI agent using a complex, 3D Ray Tracer-based, cellular network simulator, comparing a few GPT models and state-of-the-art algorithms. The results show up to 32% gain in EE of the proposed AI agent compared to the baseline.

cs.NI↗

arXiv2026

Indirect Estimation of SINR via SSB and CSI-RS RSRP in 5G NR

Predicting user equipment (UE) performance is essential for proactive network control, resource management, and digital twin sandboxes. However, the inherent flexibility and complexity of beam-based 5G new radio (NR) networks make accurate performance forecasting highly challenging. This paper proposes a data-driven approach to predict the average downlink signal-to-interference-plus-noise ratio (SINR) relying exclusively on standardized reference-signal measurements, namely synchronization signal block (SSB) and channel state information-reference signal (CSI-RS) reference signal received power (RSRP). We formulate this prediction as a supervised learning problem and evaluate various input feature representations using a third generation partnership project (3GPP)-compliant synthetic dataset. Our analysis reveals that filtering measurements based on active CSI-RS beams significantly enhances prediction accuracy while reducing input dimensionality. This activity-aware strategy demonstrates the strong viability of machine learning models for proactive network optimization.

cs.NI↗

arXiv2026

Uncertainty-Aware Multi-Task Learning for Joint Modulation Recognition and SINR Estimation

Joint modulation recognition and signal-to-interference-plus-noise ratio (SINR) estimation can reduce duplicated processing in intelligent receivers, but the two tasks have different uncertainty characteristics. This letter proposes an uncertainty-aware multi-task model that transforms each short normalized in-phase/quadrature window into 36 deterministic, label-free statistics, learns a shared representation, and uses task-specific adapters for modulation classification and heteroscedastic SINR regression. A joint uncertainty score combines classification entropy and predicted regression variance to support selective inference. Simulations cover QPSK, 8PSK, 16QAM, and 64QAM under matched additive white Gaussian noise/Rayleigh channels and an unseen frequency-selective Rician channel. Over five independent seeds, the proposed model improves matched and unseen-channel accuracy over conventional multi-task learning by 14.86 and 8.61 percentage points, respectively, while reducing SINR mean absolute error by 1.60 and 1.61 dB. Confidence-based rejection further lowers modulation error under channel mismatch.

cs.NI↗

arXiv2026

Toward AI-Native 6G Air Interface: A 3GPP Perspective on Protocol Framework

Artificial intelligence (AI) is expected to play an important role in the sixth-generation (6G) air interface design, but making the air interface truly AI-native requires more than applying learning algorithms to individual radio functions. The deeper challenge is architectural: once AI influences how the user equipment and network interpret, predict, and adapt radio behavior, the air interface must provide common protocol semantics for coordinating such intelligence across vendors and deployments. This article presents a 3rd generation partnership project (3GPP) oriented perspective on the protocol framework for AI-native 6G air interface. We argue that standardization should preserve implementation freedom by avoiding prescription of model architectures, training methods, or model weights. Instead, 6G should define the protocol framework needed for interoperable AI operation, including how AI-enabled functions are configured, validated, activated, monitored, and safely reverted to conventional operation. Neural receiver assisted reference signal adaptation is used as a case study to concretely show this broader architectural shift.

cs.NI↗

arXiv2026

LEMONS: Leveraging Model-Based Techniques to Enable Non-Intrusive Semantic Enrichment in Wireless Sensor Networks

The paper presents an efficient approach to the semantic enrichment of measured sensor data in Wireless Sensor Networks (WSNs), by bridging techniques from Model-driven Software Development (MDSD) and Semantic Web Technology (SWT). Our approach reinforces data interoperability, fostering data sharing and reuse, by utilizing SWT. Model-based and type-agnostic configuration reduces the overall effort for WSN setup and maintenance, which are traditionally complex and time-consuming tasks. The presented approach addresses the problem of large-scale WSN management through the application of SWT in WSN configuration and management without requiring expert knowledge. Additionally, we present a generic architecture and an implementation which is also supplemented by hands-on descriptions of an illustrative use case. Our experimental results demonstrate that our model-based approach provides non-intrusive semantic enrichment with sub-millisecond computational overhead, as well as partially automated configuration of WSNs.

cs.NI↗

arXiv2026

QUASAR: Quantum Satellite Architecture and Routing Simulator

The deployment of Low Earth Orbit (LEO) satellite constellations is an important step toward global-scale quantum networking. However, evaluating satellite quantum network protocols under spatiotemporal orbital dynamics and quantum physical constraints remains computationally expensive and challenging. In this paper, we propose QUASAR, a lightweight simulator for evaluating entanglement distribution in satellite-based quantum networks. QUASAR provides a decoupled architecture that integrates dynamic orbital topologies, time-varying optical transmittance, and quantum memory decoherence into network- layer attributes. To demonstrate its capabilities, we abstract and implement two representative hardware architectures: Simultaneous Downlink and On-Orbit Stitching. We further introduce an Entanglement Distribution Rate (EDR)-Aware Spatiotemporal Routing (EASR) heuristic as a reference workload. Our case study examines how QUASAR supports different satellite architectures, routing workloads, realistic orbital traces, concurrent requests, and scalable event-driven execution. With over 85% lower network-layer update latency than continuous polling, QUASAR provides a practical and extensible framework for future satellite quantum network protocol evaluation.

cs.NI↗

arXiv2026

Predictive Traffic Shaping as a UE Network Control Loop in Wireless Systems

Wireless systems usually react to current channel conditions, queue state, and policy. Yet service conditions can often be anticipated seconds ahead. This paper studies predictive traffic shaping, a slower user-equipment (UE) control loop that changes when flexible demand reaches the radio access network (RAN). The UE estimates useful pre-event demand and releases it across a lookahead window. Cooperative deployments may also send a compact future-risk descriptor to the network. The trigger uses prediction confidence. Predictive service is confined to available surplus, while a debt account preserves long-term fairness after temporary pre-event preference. A bandwidth-time model captures efficiency gains and load smoothing. It also accounts for prediction waste and shared-resource cost. In a stylized shared- cell simulation, paced demand release substantially expands the stable-feasible region. A safe service cap and admission control provide further gains at longer windows. PRISM, an application-owned middleware prototype, implements the local control policy using ordinary mobile transfer mechanisms.

cs.NI↗

arXiv2026

Comparative Performance of Graphene-Enabled Transmitarray Antenna and Reflectors for Wide-Angle Terahertz Beam Steering

This work compares a hemispherical graphene-based transmitarray antenna with its planar reflector counterpart for wide-angle beam steering in the THz regime. The theoretical framework of the planar reflector is formulated and numerically evaluated, yielding an elevation beam-steering range of \pm60°. In contrast, the transmitarray extends the elevation steering range to \pm78° while maintaining full 360° azimuthal coverage. The planar reflector exhibits a larger HPBW variation of 26.48°, compared with 13.1° for the transmitarray, resulting in a broader reflected-beam distribution and reduced directional power density, directivity, and gain. Meanwhile, the transmitarray maintains a more stable and controllable beamwidth response with greater directional power concentration over a wide steering range. The performance advantages of the transmitarray are demonstrated through comparisons with experimental results reported in the literature for planar reflectors and antennas. We further provide a comprehensive assessment of the performance advantages of the transmitarray over planar configurations across the remaining metrics.

cs.NI↗

arXiv2026

Data-Driven Case Study of gNB Placement Optimization in a Private Indoor 5G Testbed

Accurate radio planning is a fundamental requirement for the deployment of wireless networks in indoor environments, where signal propagation is strongly affected by walls, partitions, and other structural obstacles. Despite the availability of standardized propagation models, their ability to represent the characteristics of specific deployment scenarios is often limited, motivating the use of measurement-driven approaches. In this context, this paper presents a data-driven case study of next generation NodeB (gNB) placement optimization in an office using measurements collected from an experimental fifth generation (5G) testbed. A propagation model is trained from reference signal received power (RSRP) measurements using distance and wall count as input features and integrated with a combinatorial search framework. The proposed workflow is used to evaluate alternative deployment strategies under different optimization criteria. Results indicate that satisfactory indoor coverage and improved cell-edge conditions can be achieved with a small number of gNBs.

cs.NI↗

arXiv2026

Performance Analysis of Dynamic Equilibria in Joint Path Selection and Congestion Control in Path-Aware Networks

Path-aware networking (PAN) architectures, such as SCION and emerging LEO constellations, expose tens to hundreds of verifiable paths to endpoints. When multipath protocols like MPTCP and MPQUIC greedily exploit this diversity, uncoordinated migration can induce persistent, high-amplitude load oscillations. Although this instability is well-known, its quantitative performance impact remains poorly understood. In this paper, we apply a discrete-time axiomatic framework to the joint dynamics of loss-based congestion control and greedy path selection. By deriving the system's dynamic equilibria (stable periodic oscillations), we prove a fundamental trade-off: high Responsiveness improves Fairness but necessarily degrades Efficiency and Convergence. Conversely, we demonstrate that Efficiency, Convergence, and Loss Avoidance are simultaneously achievable at a critical lossless operating point. Furthermore, we find that while migration de-synchronizes traffic in high-diversity environments, realistic limited-visibility constraints transform coherent oscillations into persistent spatial load imbalance, rather than eliminating instability entirely. These results yield concrete design guidelines for robust multipath transport over the future path-aware Internet.

cs.NI↗

arXiv2026

AI-Powered CPS-Enabled Vulnerable-User-Aware Urban Transportation Digital Twin: Methods and Applications

We present methods and applications for the development of digital twins (DT) for urban traffic management. While the majority of studies on the DT focus on its ``eyes," which is the emerging sensing and perception like object detection and tracking, what really distinguishes the DT from a traditional simulator lies in its ``brain," the prediction and decision making capabilities of extracting patterns and making informed decisions from what has been seen and perceived. In order to add value to urban transportation management, DTs need to be powered by artificial intelligence and complement with low-latency high-bandwidth sensing and networking technologies, in other words, cyberphysical systems. This paper can be a pointer to help researchers and practitioners identify challenges and opportunities for the development of DTs; a bridge to initiate conversations across disciplines; and a road map to exploiting potentials of DTs for diverse urban transportation applications.

eess.SY↗

arXiv2026

Biased Backpressure Routing for Multihop Wireless Networks with Heterogeneous Interfaces

Heterogeneous-interface multihop wireless networks (Het-MuNets) are emerging as a promising paradigm for tactical networks and for infrastructure-light applications such as vehicular communications, wireless backhaul, and non-terrestrial connectivity. To exploit the diverse profiles of heterogeneous communication technologies in penetration, interference, and bandwidth, packet-to-interface assignment must be determined on a per-hop basis, making routing and scheduling highly complex. In this work, we develop a unified framework for joint packet routing, link scheduling, and interface assignment in Het-MuNets with multiple concurrent flows. By modeling packet-to-interface assignment as transmission between virtual subnodes, we transform interface assignment into intra-device virtual routing, which is solved jointly with physical routing and scheduling under a unified multi-layer shortest path-biased Backpressure (SP-BP) scheme. Numerical results demonstrate that the proposed framework outperforms SP-BP operating on other baseline graph models and non-backpressure routing schemes in goodput, latency, and packet delivery rate.

cs.NI↗

arXiv2026

CM2: Multimodal Cultural Reasoning via an Integrated Multi-Agent Framework

Multimodal Large Language Models (MLLMs) have shown remarkable success in STEM domains, where progress is often driven by vertical, step-by-step deduction under relatively stable symbol systems. Their horizontal, interdisciplinary cultural reasoning, however, remains underexplored.We propose CM2, a multi-agent framework grounded in the cognitive pathway of human cultural interpretation. CM2 integrates multimodal perception, retrieval-augmented generation, networked reasoning, gated fusion, and reward-driven feedback.Experiments on CM2D across multiple MLLM backbones show consistent gains over CoT and typical reasoning paradigms; ablations validate each module's contribution, and conflict analyses confirm genuine cross-modal arbitration.

cs.AI↗

arXiv2026

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems

This paper investigates the temporal analysis of NetFlow datasets for machine learning (ML)-based network intrusion detection systems (NIDS). Although many previous studies have highlighted the critical role of temporal features, such as inter-packet arrival time and flow length/duration, in NIDS, the currently available NetFlow datasets for NIDS lack these temporal features. This study addresses this gap by creating and making publicly available a set of NetFlow datasets that incorporate these temporal features [1]. With these temporal features, we provide a comprehensive temporal analysis of NetFlow datasets by examining the distribution of various features over time and presenting time-series representations of NetFlow features. This temporal analysis has not been previously provided in the existing literature. We also borrowed an idea from signal processing, time frequency analysis, and tested it to see how different the time frequency signal presentations (TFSPs) are for various attacks. The results indicate that many attacks have unique patterns, which could help ML models to identify them more easily.

cs.LG↗

arXiv2026

ResLearn-XR: Residual Learning for Network Traffic and Quality-of-Experience-Aware Modeling in Extended Reality

We present ResLearn-XR, a residual learning framework for predicting eXtended Reality (XR) network traffic and estimating Quality-of-Experience (QoE) risk. ResLearn-XR adopts a two-stage temporal learning structure comprising a base sequence prediction model augmented with task-specific residual learning components to improve adaptability to bursty, non-stationary XR traffic dynamics. The residual learning stages operate in the value space for continuous XR traffic forecasting and in the logit space for probabilistic QoE risk estimation. \rev{For the QoE-risk branch, we introduce a Data Descriptor Algorithm (DDA), a causal feature-construction module that converts packet-level application-layer observables into frame-timing-aware descriptors suitable for encrypted traffic analysis. We also construct an XR Traffic-QoE dataset that pairs continuous XR traffic traces with session-level user-reported QoE labels. ResLearn-XR reduces SMAPE by up to 17.84% across frame-count, frame-size, and inter-arrival-time prediction, while reducing QoE-risk estimation SMAPE by up to 87.8% over single-stage baselines.

cs.AI↗

arXiv2026

SecDT: A Profile-Based Security Layer for TRDP Communications

The Train Real-time Data Protocol (TRDP) is widely used on rolling stock but it provides limited native support for cryptographic protection. Furthermore, the multicast traffic profile used in TRDP Process Data to exchange critical information between onboard subsystems makes the introduction of cryptographic protection a challenge. This paper presents a lightweight security layer for secure TRDP communication that implements a number of security profiles built around modern cryptographic algorithms. This additional layer relies on an On-board Key Management System (OKMS) for both security profile negotiation, dynamic key distribution and key lifecycle management. The security profiles allow for cryptographic agility and flexibility, ranging from simple authentication to Authenticated Encryption with Associated Data (AEAD) algorithms. The security profile negotiation procedure guarantees all TRDP End Devices (ED) on a common Communication ID (ComID) share the same security profile and can therefore process each other's messages. A prototype implementation based on mbedTLS and Arm Platform Security Architecture (PSA) was developed and evaluated. Experimental results demonstrate manageable overhead, suitable for the real-time and time-sensitive communication found on rolling stock.

cs.CR↗

arXiv2026

Dynamic Modeling of Target Cell Location for Mobility Robustness Analysis in Cellular Networks: Technical Report

Mobility robustness optimization (MRO) requires an appropriate selection of handover (HO) parameters such as the time-to-trigger (TTT) and offset margin to balance HO failures and ping-pong HOs. Existing stochastic geometry-based analyses for MRO have treated the angular position of the target base station (BS) as uniformly distributed over a feasible region. However, this treatment does not explicitly capture the spatial distribution of the target BS dynamically selected as a user equipment (UE) moves through the network. In this paper, we develop a stochastic geometry-based analytical framework for MRO in sub-6 GHz cellular networks. We derive the distribution of the HO triggering time and the spatial distribution of the dynamically selected target BS under straight-line UE mobility. Based on these distributions, we formulate too-late HO and ping-pong HO events as mutually exclusive events and analytically derive their probabilities. Numerical results validate the analysis, demonstrate improved accuracy over the conventional uniform-angle model, and reveal the tradeoff between the two HO events and the dependence of the optimal TTT on BS density.

cs.NI↗

arXiv2026

Waves on the Walls: Empirical Characterization of mmWave Lateral Waves for Enhanced Indoor Coverage

High-frequency millimeter-wave (mmWave) communication systems are constrained by the surrounding environment, where walls are traditionally treated as obstacles that block or reflect signals indoors. Consequently, current beamforming strategies are tailored to circumvent these obstructions. In this paper, a paradigm shift is introduced that leverages lateral wave propagation along building interfaces to extend mmWave coverage. Unlike traditional reflections, lateral waves travel along the boundary between two media of different refractive indices and decay algebraically with distance, offering a potential alternative path for mmWave connectivity. While well-established at low frequencies in natural media, the existence of lateral waves at mmWave frequencies along engineered building materials has not been demonstrated before. To this end, the first experimental characterization of mmWave lateral waves along a wall is reported. Extensive controlled measurements are employed to characterize the signal-grazing geometry and to establish a frequency and distance-dependent path-loss model for this phenomenon. The results provide the first empirical foundation for a new class of interface-guided mmWave links.

cs.NI↗