arXiv subjects

Petar Popovski

Publications and source records attributed to Petar Popovski.

At least 55 records · Page 3Linked to original sources

arXiv2026

Resource-Adaptive Teleportation Under Imperfect Entanglement: A Code-Puncturing Framework

Quantum teleportation is a foundational protocol for sending quantum information through entanglement distribution and classical communication. Assuming ideal classical communication, the reliability of quantum teleportation is limited by the fidelity of the shared EPR pairs. This reliability can be improved through two mechanisms: entanglement purification and quantum error correction (QEC). Using both techniques in concert requires flexible QEC rates, since purification alters the structure of errors induced by imperfect-EPR teleportation, and fixed-rate codes cannot be uniformly effective across purification regimes or reliability targets. In this work, we supplement purification with punctured QEC codes, providing a family of code variants that can be adapted to error-channel characteristics and reliability targets. Punctured codes improve teleportation reliability across a broader range of purification regimes, enabling target reliability to be met without hardware-level code switching. This is corroborated by numerical results, showing that different punctured codes achieve the lowest logical error probability in different operating regimes, and that selecting among them reduces logical error relative to fixed-rate encoded teleportation. This reduction relaxes the requirement on the initial EPR fidelity or purification needed to achieve a target reliability. Overall, puncturing enables adaptation to varying entanglement conditions and reliability requirements while reusing a single stabilizer structure.

quant-ph↗

arXiv2026

Temporal Framework for Causality-Preserving Scheduling of Measurements in Quantum Networks

Distributed quantum protocols rely on classical feedforward information to process measurement outcomes, but heterogeneous hardware and uncertain local timing can make the causal order of measurements ambiguous when inferred solely from arrival times. Even in simple line networks with only Pauli measurements, end nodes cannot distinguish whether a missing outcome is caused by slow measurement or by delayed classical propagation. To resolve this ambiguity, we propose a time-division architecture for quantum networks in which nodes perform measurements in pre-assigned slots, ensuring a unique causal interpretation of outcomes. We formalize this temporal framework and derive the feedforward and adjacency constraints required to preserve measurement causality. For simple network topologies, we present an algorithm that yields optimal measurement schedules. Overall, the proposed time-division model provides a practical coordination layer that bridges the classical network timing with quantum measurement processing, enabling reliable and scalable measurement-based quantum networking.

quant-ph↗

arXiv2026

Medium Access for Multi-Cell ISAC Through Scheduling of Radar and Communication Tasks

This paper focuses on communication, radar search, and tracking task scheduling in multi-cell integrated sensing and communication (ISAC) networks under quality-of-service constraints. We propose a medium access control framework that multiplexes these tasks while optimizing radar scan patterns through an interference-aware scheduling algorithm. Specifically, the proposed framework employs time-domain task scheduling and beam selection, formulated as an assignment problem, to mitigate inter-task and inter-cell interference, respectively. Simulations show that our solution guarantees target communication throughput, sensing target detection probability, and sensing signal-to-interference-plus-noise ratio with improved resource efficiency over baseline schemes, highlighting the benefits of coordinated scheduling in multi-cell ISAC.

cs.IT↗

arXiv2026

Interference Detection and Exploitation for Multi-User Radar Sensing

Integrated sensing and communication is a key feature in next-generation wireless networks, enabling joint data transmission and environmental radar sensing on shared spectrum. In multi-user scenarios, simultaneous transmissions cause mutual interference on overlapping frequencies, leading to spurious target detections and degraded sensing accuracy. This paper proposes an interference detection and exploitation algorithm for sensing using spectrally interleaved orthogonal frequency division multiplexing. A statistically rigorous procedure is introduced to detect interference while controlling the familywise error rate. We propose an algorithm that estimates the angle by exploiting interference, while estimating the delay by avoiding the interference. Numerical experiments demonstrate that the proposed method reliably detects interference, and that the delay and angle estimation error approaches the Cramér-Rao lower bound.

eess.SP↗

arXiv2026

Statistical Characterization and Prediction of E2E Latency over LEO Satellite Networks

Low Earth Orbit (LEO) satellite networks are emerging as an essential communication infrastructure, with standardized 5G-based non-terrestrial networks and their integration with terrestrial systems envisioned as a key feature of 6G. However, current LEO systems still exhibit significant latency variations, limiting their suitability for latency-sensitive services. We present a detailed statistical analysis of end-to-end latency based on 500Hz experimental bidirectional one-way measurements and introduce a segmentation of the deterministic 15-second periodic behavior observed in Starlink. We characterize handover-induced boundary regions that produce latency spikes lasting approximately 140 ms at the beginning and 75 ms at the end of each cycle, followed by a stable intra-period regime, enabling accurate short-term prediction. This analysis shows that latency prediction based on long-term statistics leads to pessimistic estimates. In contrast, by exploiting the periodic structure, isolating boundary regions, and applying lightweight parametric and non-parametric models to intra-period latency distributions, we achieve 99th-percentile latency prediction errors below 50 ms. Furthermore, period-level latency prediction and classification enable adaptive transmission strategies by identifying upcoming periods where application latency requirements cannot be satisfied, necessitating the use of alternative systems.

cs.NI↗

arXiv2026

1Q: First-Generation Wireless Systems Integrating Classical and Quantum Communication

We introduce the concept of 1Q, the first wireless generation of integrated classical and quantum communication. 1Q features quantum base stations (QBSs) that support entanglement distribution via free-space optical links alongside traditional radio communications. Key new components include quantum cells, quantum user equipment (QUEs), and hybrid resource allocation spanning classical time-frequency and quantum entanglement domains. Several application scenarios are discussed and illustrated through system design requirements for quantum key distribution, blind quantum computing, and distributed quantum sensing. A range of unique quantum constraints are identified, including decoherence timing, fidelity requirements, and the interplay between quantum and classical error probabilities. Protocol adaptations extend cellular connection management to incorporate entanglement generation, distribution, and handover procedures, expanding the Quantum Internet to the cellular wireless.

cs.NI↗

arXiv2026

Quantize-Sample-and-Verify: LLM Acceleration via Adaptive Edge-Cloud Speculative Decoding

In edge-cloud speculative decoding (SD), edge devices equipped with small language models (SLMs) generate draft tokens that are verified by large language models (LLMs) in the cloud. A key bottleneck in such systems is the limited communication bandwidth between edge and cloud, which necessitates quantization of the information transmitted about generated tokens. In this work, we introduce a novel quantize-sample (Q-S) strategy that provably preserves the output distribution of the cloud-based model, ensuring that the verified tokens match the distribution of those that would have been generated directly by the LLM. We develop a throughput model for edge-cloud SD that explicitly accounts for communication latency. Leveraging this model, we propose an adaptive mechanism that optimizes token throughput by dynamically adjusting the draft length and quantization precision in response to both semantic uncertainty and channel conditions. Simulations demonstrate that the proposed Q-S approach significantly improves decoding efficiency in realistic edge-cloud deployment scenarios.

eess.SP↗

arXiv2026

Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

Current Adaptive Bit Rate (ABR) methods react to network congestion after it occurs, causing application layer buffering and latency spikes in live video streaming. We introduce a proactive semantic control channel that enables coordination between Open Radio Access Network (ORAN) xApp, Mobile Edge computing (MEC), and User Equipment (UE) components for seamless live video streaming between mobile devices. When the transmitting UE experiences poor Uplink (UL) conditions, the MEC proactively instructs downscaling based on low-level RAN metrics, including channel SNR updated every millisecond, preventing buffering before it occurs. A Generative AI (GAI) module at the MEC reconstructs high-quality frames from downscaled video before forwarding to the receiving UE via the typically more robust Downlink (DL). Experimental validation on a live ORAN testbed with 50 video streams shows that our approach reduces latency tail behavior while achieving up to 4 dB improvement in PSNR and 15 points in VMAF compared to reactive ABR methods. The proactive control eliminates latency spikes exceeding 600 ms, demonstrating effective cross-layer coordination for latency-critical live video streaming.

cs.NI↗

arXiv2025

Deflating quantum error-correcting codes

In this work, we introduce a technique for reducing the length of a quantum stabilizer code, and we call this deflation of the code. Deflation can be seen as a generalization of the well-known puncturing and shortening techniques in cases where more than a single qudit is removed. We show that the parameters of the deflated quantum code can be controlled, and argue that a similar approach is not as beneficial when applied to classical linear codes. Furthermore, it is shown that deflation introduces additional freedom compared to applying just puncturing and shortening consecutively. We exemplify that it is possible to obtain better parameters by deflating a code rather than consecutively using puncturing and shortening.

quant-ph↗

arXiv2025

Temporal Windows of Integration for Multisensory Wireless Systems as Enablers of Physical AI

Physical artificial intelligence (AI) refers to the AI that interacts with the physical world in real time. Similar to multisensory perception, Physical AI makes decisions based on multimodal updates from sensors and devices. Physical AI thus operates with a finite spatial footprint of its sensory tributaries. The multimodal updates traverse heterogeneous and unreliable paths, involving wireless links. Throughput or latency guarantees do not ensure correct decision-making, as misaligned, misordered, or stale inputs still yield wrong inferences. Preserving decision-time coherence hinges on three timing primitives at the network-application interface: (i) simultaneity, a short coincidence window that groups measurements as co-temporal, (ii) causality, path-wise delivery that never lets a consequence precede its precursor, and (iii) usefulness, a validity horizon that drops information too stale to influence the current action. In this work, we focus on usefulness and adopt temporal window of integration (TWI)-Causality: the TWI enforces decision-time usefulness by assuming path-wise causal consistency and cross-path simultaneity are handled upstream. We model end-to-end path delay as the sum of sensing/propagation, computation, and access/transmission latencies, and formulate network design as minimizing the validity horizon under a delivery reliability constraint. In effect, this calibrates delay-reliability budgets for a timing-aware system operating over sensors within a finite spatial footprint. The joint choice of horizon and per-path reliability is cast as a convex optimization problem, solved to global optimality to obtain the minimal horizon and per-path allocation of reliability. This is compared favourably to a benchmark based on uniform-after-threshold allocation. Overall, this study contributes to timing-aware Physical AI in next-generation networks.

eess.SP↗

arXiv2025

Quantum-Accelerated Wireless Communications: Concepts, Connections, and Implications

Quantum computing is poised to redefine the algorithmic foundations of communication systems. While quantum superposition and entanglement enable quadratic or exponential speedups for specific problems, identifying use cases where these advantages yield engineering benefits is still nontrivial. This article presents the fundamentals of quantum computing in a style familiar to the communications society, outlining the current limits of fault-tolerant quantum computing and clarifying a mathematical harmony between quantum and wireless systems, which makes the topic more enticing to wireless researchers. Based on a systematic review of pioneering and state-of-the-art studies indicating speedup opportunities, we distill common design trends for the research and development of quantum-accelerated communication systems and highlight lessons learned. The key insight is that quantum algorithms, including their gate-level realizations, can benefit from the design intuition applied in communication engineering. This article aims to catalyze interdisciplinary research at the frontier of quantum information processing and future communication systems.

eess.SP↗

arXiv2025

ISAC-Powered Distributed Matching and Resource Allocation in Multi-band NTN

Scalability is a major challenge in non-geostationary orbit (NGSO) satellite networks due to the massive number of ground users sharing the limited sub-6 GHz spectrum. Using K- and higher bands is a promising alternative to increase the accessible bandwidth, but these bands are subject to significant atmospheric attenuation, notably rainfall, which can lead to degraded performance and link outages. We present an integrated sensing and communications (ISAC)-powered framework for resilient and efficient operation of multi-band satellite networks. It is based on distributed mechanisms for atmospheric sensing, cell-to-satellite matching, and resource allocation (RA) in a 5G Non-Terrestrial Network (NTN) wide-area scenario with quasi-Earth fixed cells and a beam hopping mechanism. Results with a multi-layer multi-band constellation with satellites operating in the S- and K-bands demonstrate the benefits of our framework for ISAC-powered multi-band systems, which achieves 73% higher throughput per user when compared to single S- and K-band systems.

cs.NI↗

arXiv2025

Real-Time Inference for Distributed Multimodal Systems under Communication Delay Uncertainty

Connected cyber-physical systems perform inference based on real-time inputs from multiple data streams. Uncertain communication delays across data streams challenge the temporal flow of the inference process. State-of-the-art (SotA) non-blocking inference methods rely on a reference-modality paradigm, requiring one modality input to be fully received before processing, while depending on costly offline profiling. We propose a novel, neuro-inspired non-blocking inference paradigm that primarily employs adaptive temporal windows of integration (TWIs) to dynamically adjust to stochastic delay patterns across heterogeneous streams while relaxing the reference-modality requirement. Our communication-delay-aware framework achieves robust real-time inference with finer-grained control over the accuracy-latency tradeoff. Experiments on the audio-visual event localization (AVEL) task demonstrate superior adaptability to network dynamics compared to SotA approaches.

cs.LG↗

arXiv2025

Neuromorphic Split Computing via Optical Inter-Satellite Links

We present a neuromorphic split-computing framework for energy-efficient low-latency inference over optical inter-satellite links. The system partitions a spiking neural network (SNN) between edge and core nodes. To transmit sparse spiking features efficiently, we introduce a lossless channel-block-sparse event representation that exploits inter- and intra-channel sparsity. We employ hierarchical error protection using multi-level forward error correction and cyclic redundancy checks to ensure reliable communication without retransmission. The framework uses end-to-end training with sparsity and clustering regularizers, combined with channel-aware stochastic masking to optimize feature compression and channel robustness jointly. In a proof-of-concept implementation on remote sensing imagery, the framework achieves over $10 \times$ reduction in both computational energy and transmission load compared to conventional dense split systems, with less than 1% accuracy loss. The proposed approach also outperforms address-event-based split SNNs by $3.7 \times$ in transmission efficiency and shows superior resilience to optical pointing jitter.

eess.IV↗

arXiv2025

Dynamic Downlink-Uplink Spectrum Sharing between Terrestrial and Non-Terrestrial Networks

6G networks are expected to integrate low Earth orbit satellites to ensure global connectivity by extending coverage to underserved and remote regions. However, the deployment of dense mega-constellations introduces severe interference among satellites operating over shared frequency bands. This is, in part, due to the limited flexibility of conventional frequency division duplex (FDD) systems, where fixed bands for downlink (DL) and uplink (UL) transmissions are employed. In this work, we propose dynamic re-assignment of FDD bands for improved interference management in dense deployments and evaluate the performance gain of this approach. To this end, we formulate a joint optimization problem that incorporates dynamic band assignment, user scheduling, and power allocation in both directions. This non-convex mixed integer problem is solved using a combination of equivalence transforms, alternating optimization, and state-of-the-art industrial-grade mixed integer solvers. Numerical results demonstrate that the proposed approach of dynamic FDD band assignment significantly enhances system performance over conventional FDD, achieving up to 94\% improvement in throughput in dense deployments.

cs.IT↗

arXiv2025

Wireless Memory Approximation for Energy-efficient Task-specific IoT Data Retrieval

The use of Dynamic Random Access Memory (DRAM) for storing Machine Learning (ML) models plays a critical role in accelerating ML inference tasks in the next generation of communication systems. However, periodic refreshment of DRAM results in wasteful energy consumption during standby periods, which is significant for resource-constrained Internet of Things (IoT) devices. To solve this problem, this work advocates two novel approaches: 1) wireless memory activation and 2) wireless memory approximation. These enable the wireless devices to efficiently manage the available memory by considering the timing aspects and relevance of ML model usage; hence, reducing the overall energy consumption. Numerical results show that our proposed scheme can realize smaller energy consumption than the always-on approach while satisfying the retrieval accuracy constraint.

cs.NI↗

arXiv2025

Resilient Radio Access Networks: AI and the Unknown Unknowns

5G networks offer exceptional reliability and availability, ensuring consistent performance and user satisfaction. Yet they might still fail when confronted with the unexpected. A resilient system is able to adapt to real-world complexity, including operating conditions completely unanticipated during system design. This makes resilience a vital attribute for communication systems that must sustain service in scenarios where models are absent or too intricate to provide statistical guarantees. Such considerations indicate that artifical intelligence (AI) will play a major role in delivering resilience. In this paper, we examine the challenges of designing AIs for resilient radio access networks, especially with respect to unanticipated and rare disruptions. Our theoretical results indicate strong limitations of current statistical learning methods for resilience and suggest connections to online learning and causal inference.

cs.IT↗

arXiv2025

Microsecond Federated SVD on Grassmann Manifold for Real-time IoT Intrusion Detection

This paper introduces FedSVD, a novel unsupervised federated learning framework for real-time anomaly detection in IoT networks. By leveraging Singular Value Decomposition (SVD) and optimization on the Grassmann manifolds, FedSVD enables accurate detection of both known and unknown intrusions without relying on labeled data or centralized data sharing. Tailored for deployment on low-power devices like the NVIDIA Jetson AGX Orin, the proposed method significantly reduces communication overhead and computational cost. Experimental results show that FedSVD achieves performance comparable to deep learning baselines while reducing inference latency by over 10x, making it suitable for latency-sensitive IoT applications.

eess.SP↗