arXiv subjects

Adnan Aijaz

Publications and source records attributed to Adnan Aijaz.

At least 19 recordsLinked to original sources

arXiv2026

Goal-Oriented Communications for Physical AI: Design and Testbed

Physical AI relies on frequently-updated, latency-sensitive video stream to perceive, reason, and interact with the physical world, resulting in strict latency requirements with much higher data volumes that existing 5G networks cannot support. Goal-oriented communication (GoC) offers as a promising approach to solve this challenge by transmitting only task-relevant semantic representations. However, existing GoC frameworks were mainly evaluated in the simulations while their effectiveness has never been validated in a practical deployment of physical AI application. In this work, we develop an end-to-end GoC testbed for Physical AI, which connects a PiPER robot arm equipped with an RGB-D camera and a 5G modem to an NVIDIA Jetson AGX Orin edge server through a 5G OpenAirInterface network. We propose and implement three GoC frameworks that transmit 3D bounding boxes, 2D scene graphs, and 3D scene graphs, as three types of semantic representations, respectively. They share the common functional modules designed for closed-loop Physical AI applications, including semantic extraction, full stack 5G transmission, language model inference, digital twin validation, and robotic control. Extensive experiments on our testbed show that our GoC frameworks reduce the task completion time by up to 52.6% and improve task success probability by up to 45%, compared to the traditional framework that periodically transmits the raw image data. These results validate the practical effectiveness of our GoC framework and pave the way for efficient and reliable Physical AI applications over future 6G networks. Project website: https://sites.google.com/view/goc-physical-ai-testbed.

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arXiv2026

Det-5G: Closing the Determinism Gap in 5G-Advanced for Industrial Closed-Loop Control

The ultra-reliable low-latency communication (uRLLC) capability of 5G has created significant opportunities for industrial wireless connectivity, yet widespread use of cellular networks for closed-loop control remains challenging. Closed-loop control requires more than low packet latency and high reliability: cyclic command/feedback exchanges must complete within predictable time bounds despite changing channel conditions, recovery transmissions, mobility, and multi-device contention. This paper introduces Deterministic-5G (Det-5G), a unified radio resource allocation framework for industrial closed-loop control. Det-5G treats the complete bidirectional control cycle as the scheduling object and combines coordinated downlink/uplink allocation, adaptive bundled transmissions, group-oriented downlink communication, and optimized multi-user uplink scheduling over 5G air-interface. Its performance is evaluated through a combination of closed-form analysis and Monte Carlo scheduling experiments, with comparisons against conventional dynamic grant-based scheduling, semi-persistent scheduling/configured grant operation, and fixed proactive repetition. The evaluation shows that Det-5G improves predictability of cycle completion, maintains the target reliability under changing link conditions, and scales more effectively to multi-device control than conventional reactive scheduling, while adapting radio resource use instead of continuously provisioning for the worst case as in fixed repetition. These characteristics make cycle-oriented scheduling a pragmatic solution for reducing the determinism gap that limits the use of 5G for closed-loop control in different verticals, especially as it evolves through 5G-Advanced toward 6G.

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arXiv2026

Will there be a 7G?

The transition from 5G to 6G is becoming concrete: the ITU-R IMT-2030 framework has established the high-level vision and capability set for 6G, while 3GPP Release 21 has defined the path toward the first 6G specifications. This raises a deliberately provocative question for the research and standards communities: will there be a 7G, and if so, what would justify it? This paper argues that 7G should not be treated as an inevitable numbering exercise or as a catalogue of more ambitious radio targets. Instead, its justification should depend on whether post-6G systems introduce needs or coordination problems that cannot be met by 6G/6G-Advanced, Wi-Fi, NTN, private cellular, neutral-host deployments, edge-cloud platforms, or complementary wireless and software-based systems. To support this assessment, the paper develops a readiness framework covering demand-led need, system-level discontinuity, coordination value, sustainability and circularity, trust, and geopolitical viability. It then applies the framework to candidate 7G discontinuities, including agentic network operation, RF-native computing, quantum-enabled interworking, policy-aware spectrum governance, grid-interactive infrastructure, outcome-assured services, and regionalized standards. The contribution is not a prediction of a fixed 7G architecture, but a structured basis for deciding whether 7G should become a distinct mobile generation, an extension of 6G evolution, or a broader post-6G infrastructure fabric.

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arXiv2026

Powering Net-Zero 6G: Packetized Energy Management for Grid-Interactive Telecom Infrastructure

The transition to net-zero 6G requires energy-management approaches that go beyond conventional RAN efficiency mechanisms. As future networks integrate AI-native operation, edge intelligence, dense deployments, renewables, and storage, the RAN will become both a growing power consumer and a source of distributed energy flexibility. This paper introduces packetized energy management (PEM) as a framework for transforming 6G infrastructure into energy-aware, grid-interactive assets. PEM represents flexible demand as schedulable energy packets that can be admitted, deferred, or reshaped according to local constraints, renewable availability, carbon intensity, price, and communication priorities. We present a PEM-enabled base-station model, a RAN architecture for PEM integration, and the telecoms virtual power plant (VPP) concept for aggregating PEM-enabled sites. Simulation results demonstrate PEM's potential for peak-aware operation, improved renewable utilization, and outage-resilient service continuity. The paper also discusses open challenges for telco-energy co-design.

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arXiv2026

When Robots Exchange Meaning: A Demo of Goal-Oriented Semantic Communications for Collaborative Robotics

Collaborative robotics is a representative task-oriented 6G use-case, where communication quality should be reflected in mission execution, environment understanding, and closed-loop operation rather than packet delivery alone. This demo paper presents a robot-edge semantic communication (SemCom) testbed integrating robot-side visual compression, edge-side semantic mapping, and dashboard-based mission interaction. A mobile robot equipped with RGB-D sensing and LiDAR runs ROS 2, while a Jetson Orin edge node performs reconstruction, RTAB-Map mapping, semantic object handling, and browserbased visualization. As an initial proof of concept, RGB frames are encoded on the robot into VQ-VAE tokens using an ONNX Runtime encoder and reconstructed on the edge using a PyTorch decoder. A 320 X 240 image is represented by an 80 X 60 token grid with a packed payload of 5400 bytes, corresponding to a 42.67X reduction relative to model-input RGB bytes. The reconstructed visual stream is further associated with depth, pose, and 3D mapping information to generate a semantic map for downstream robotic applications. The demo exposes the full path from semantic visual transport to object-level map interaction, and provides a practical platform for future task-aware 6G networking studies at the intersection of SemCom, embodied AI, and physical AI-enabled robotics. A video of the demo is available at https://tinyurl.com/Tos09

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arXiv2026

The Price of Meaning: Quantifying Semantic Communication Overheads in Practice

Semantic communication (SemCom) promises to reduce transmitted payloads by conveying task-relevant meaning instead of raw bits. However, practical SemCom also incurs semantic metadata, control signaling, feedback, model or knowledge-base synchronization, and neural computation costs, which may offset semantic compression gains. This paper develops an overhead-aware analytical framework for quantifying the spectral-resource and energy costs of SemCom under equal task utility. The framework covers point-to-point transmission, user equipment (UE)-to-next-generation NodeB (gNB) uplink, and UE-to-UE communication under a single gNB, and derives closed-form break-even conditions with respect to payload size, semantic compression factor, model reuse, protocol overhead, and computation energy. Simulation results show that SemCom becomes spectrally beneficial only for sufficiently large payloads, while energy gains require larger payloads due to processing and synchronization overheads. The results also show that multi-user downlink is particularly favorable, as shared semantic overheads can be amortized across multiple UEs. These findings provide design guidance for realistic SemCom evaluation and standardization-oriented deployment.

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arXiv2026

Goal-oriented Communication for Fast and Robust Robotic Fault Detection and Recovery

Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require low-latency and robust fault detection and recovery (FDR). However, existing FDR frameworks exhibit various limitations, such as significant delays in communication and computation, and unreliability in robot motion/trajectory generation, mainly because the communication-computation-control (3C) loop is designed without considering the downstream FDR goal. To address this, we propose a novel Goal-oriented Communication (GoC) framework that jointly designs the 3C loop tailored for fast and robust robotic FDR, with the goal of minimising the FDR time while maximising the robotic task (e.g., workpiece sorting) success rate. For fault detection, our GoC framework innovatively defines and extracts the 3D scene graph (3D-SG) as the semantic representation via our designed representation extractor, and detects faults by monitoring spatial relationship changes in the 3D-SG. For fault recovery, we fine-tune a small language model (SLM) via Low-Rank Adaptation (LoRA) and enhance its reasoning and generalization capabilities via knowledge distillation to generate recovery motions for robots. We also design a lightweight goal-oriented digital twin reconstruction module to refine the recovery motions generated by the SLM when fine-grained robotic control is required, using only task-relevant object contours for digital twin reconstruction. Extensive simulations demonstrate that our GoC framework reduces the FDR time by up to 82.6% and improves the task success rate by up to 76%, compared to the state-of-the-art frameworks that rely on vision language models for fault detection and large language models for fault recovery.

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arXiv2026

Toward Quantum-Safe 6G: Experimental Evaluation of Post-Quantum Cryptography Techniques

6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware design for future quantum-secure 6G networks.

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arXiv2026

The Missing Pillar in Quantum-Safe 6G: Regulation and Global Compliance

Sixth-generation (6G) mobile networks are expected to operate for multiple decades, supporting mission-critical and globally federated digital services. This long operational horizon coincides with rapid advances in quantum computing that threaten the cryptographic foundations of contemporary mobile systems. While post-quantum cryptography is widely recognized as a necessary technical response, its effective deployment in 6G depends equally on the evolution of regulatory policy and global compliance frameworks. This article argues that quantum-safe 6G represents a regulatory inflection point for mobile networks, as existing compliance models shaped by static cryptographic assumptions, incremental evolution, and point-in-time certification are poorly suited to long-term quantum risk. Building on an analysis of baseline telecom compliance challenges, the evolution of security regulation from 2G to 5G, and the regulatory impact of post-quantum cryptography adoption, the article shows why incremental regulatory extensions are insufficient. To address this gap, the article advances a compliance-by-design perspective in which regulatory requirements are treated as system-level design constraints, emphasizing cryptographic agility, lifecycle-aware governance, continuous compliance observability, and interoperability-driven global assurance, and concludes by examining the risks of fragmented global compliance for quantum-safe 6G networks.

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arXiv2026

Goal-Oriented Multi-Agent Semantic Networking: Unifying Intents, Semantics, and Intelligence

6G services are evolving toward goal-oriented and AI-native communication, which are expected to deliver transformative societal benefits across various industries and promote energy sustainability. Yet today's networking architectures, built on complete decoupling of the applications and the network, cannot expose or exploit high-level goals, limiting their ability to adapt intelligently to service needs. This work introduces Goal-Oriented Multi-Agent Semantic Networking (GoAgentNet), a new architecture that elevates communication from data exchange to goal fulfilment. GoAgentNet enables applications and the network to collaborate by abstracting their functions into multiple collaborative agents, and jointly orchestrates multi-agent sensing, networking, computation, and control through semantic computation and cross-layer semantic networking, allowing the entire architecture to pursue unified application goals. We first outline the limitations of legacy network designs in supporting 6G services, based on which we highlight key enablers of our GoAgentNet design. Then, through three representative 6G usage scenarios, we demonstrate how GoAgentNet can unlock more efficient and intelligent services. We further identify unique challenges faced by GoAgentNet deployment and corresponding potential solutions. A case study on robotic fault detection and recovery shows that our GoAgentNet architecture improves energy efficiency by up to 99% and increases the task success rate by up to 72%, compared with the existing networking architectures without GoAgentNet, which underscores its potential to support scalable and sustainable 6G systems.

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arXiv2026

Latency-aware Human-in-the-Loop Reinforcement Learning for Semantic Communications

Semantic communication promises task-aligned transmission but must reconcile semantic fidelity with stringent latency guarantees in immersive and safety-critical services. This paper introduces a time-constrained human-in-the-loop reinforcement learning (TC-HITL-RL) framework that embeds human feedback, semantic utility, and latency control within a semantic-aware Open radio access network (RAN) architecture. We formulate semantic adaptation driven by human feedback as a constrained Markov decision process (CMDP) whose state captures semantic quality, human preferences, queue slack, and channel dynamics, and solve it via a primal--dual proximal policy optimization algorithm with action shielding and latency-aware reward shaping. The resulting policy preserves PPO-level semantic rewards while tightening the variability of both air-interface and near-real-time RAN intelligent controller processing budgets. Simulations over point-to-multipoint links with heterogeneous deadlines show that TC-HITL-RL consistently meets per-user timing constraints, outperforms baseline schedulers in reward, and stabilizes resource consumption, providing a practical blueprint for latency-aware semantic adaptation.

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arXiv2025

A Multi-Year Urban Streetlight Imagery Dataset for Visual Monitoring and Spatio-Temporal Drift Detection

We present a large-scale, longitudinal visual dataset of urban streetlights captured by 22 fixed-angle cameras deployed across Bristol, U.K., from 2021 to 2025. The dataset contains over 526,000 images, collected hourly under diverse lighting, weather, and seasonal conditions. Each image is accompanied by rich metadata, including timestamps, GPS coordinates, and device identifiers. This unique real-world dataset enables detailed investigation of visual drift, anomaly detection, and MLOps strategies in smart city deployments. To promtoe seconardary analysis, we additionally provide a self-supervised framework based on convolutional variational autoencoders (CNN-VAEs). Models are trained separately for each camera node and for day/night image sets. We define two per-sample drift metrics: relative centroid drift, capturing latent space deviation from a baseline quarter, and relative reconstruction error, measuring normalized image-domain degradation. This dataset provides a realistic, fine-grained benchmark for evaluating long-term model stability, drift-aware learning, and deployment-ready vision systems. The images and structured metadata are publicly released in JPEG and CSV formats, supporting reproducibility and downstream applications such as streetlight monitoring, weather inference, and urban scene understanding. The dataset can be found at https://doi.org/10.5281/zenodo.17781192 and https://doi.org/10.5281/zenodo.17859120.

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arXiv2025

Satellite Direct-to-Device from Low Earth Orbit: Techno-Economic Analysis of a Global Non-Terrestrial Network

Low Earth orbit (LEO) satellites and satellite direct-to-device (D2D) technology are at the heart of the next-generation global connectivity which promises direct access to space-based broadband services for unmodified 3GPP-compliant handsets. With a rapidly evolving ecosystem, it is important to evaluate the feasibility, cost-effectiveness, and profitability of these services. By assessing the technological aspects as well as economic implications, stakeholders can make informed decisions about investment, development, and deployment strategies. This paper presents a comprehensive techno-economic analysis (TEA) framework for evaluating LEO-based satellite D2D systems. The framework integrates a global satellite constellation model, radio propagation aspects including atmospheric and rainfall attenuation models compliant with ITU-R recommendations, 3GPP-compliant capacity calculations, realistic global population data, and an all-encompassing cost model accounting for both capital and operational expenses associated with space and ground segments. Further, the framework evaluates three different architectural options for realizing a global non-terrestrial network (NTN) for satellite D2D services. With an emphasis on reproducibility, the framework has been implemented through significant enhancements to an open-source tool. The economic assessment reveals that global satellite D2D services can be provided at a monthly cost per subscriber which is comparable to terrestrial services while achieving a positive return on investment (ROI). Moreover, the results show the potential of Open RAN technology for realizing cost-effective satellite D2D services.

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arXiv2025

Building the Self-Improvement Loop: Error Detection and Correction in Goal-Oriented Semantic Communications

Error detection and correction are essential for ensuring robust and reliable operation in modern communication systems, particularly in complex transmission environments. However, discussions on these topics have largely been overlooked in semantic communication (SemCom), which focuses on transmitting meaning rather than symbols, leading to significant improvements in communication efficiency. Despite these advantages, semantic errors -- stemming from discrepancies between transmitted and received meanings -- present a major challenge to system reliability. This paper addresses this gap by proposing a comprehensive framework for detecting and correcting semantic errors in SemCom systems. We formally define semantic error, detection, and correction mechanisms, and identify key sources of semantic errors. To address these challenges, we develop a Gaussian process (GP)-based method for latent space monitoring to detect errors, alongside a human-in-the-loop reinforcement learning (HITL-RL) approach to optimize semantic model configurations using user feedback. Experimental results validate the effectiveness of the proposed methods in mitigating semantic errors under various conditions, including adversarial attacks, input feature changes, physical channel variations, and user preference shifts. This work lays the foundation for more reliable and adaptive SemCom systems with robust semantic error management techniques.

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arXiv2025

Flying Base Stations for Offshore Wind Farm Monitoring and Control: Holistic Performance Evaluation and Optimization

Ensuring reliable and low-latency communication in offshore wind farms is critical for efficient monitoring and control, yet remains challenging due to the harsh environment and lack of infrastructure. This paper investigates a flying base station (FBS) approach for wide-area monitoring and control in the UK Hornsea offshore wind farm project. By leveraging mobile, flexible FBS platforms in the remote and harsh offshore environment, the proposed system offers real-time connectivity for turbines without the need for deploying permanent infrastructure at the sea. We develop a detailed and practical end-to-end latency model accounting for five key factors: flight duration, connection establishment, turbine state information upload, computational delay, and control transmission, to provide a holistic perspective often missing in prior studies. Furthermore, we combine trajectory planning, beamforming, and resource allocation into a multi-objective optimization framework for the overall latency minimization, specifically designed for large-scale offshore wind farm deployments. Simulation results verify the effectiveness of our proposed method in minimizing latency and enhancing efficiency in FBS-assisted offshore monitoring across various power levels, while consistently outperforming baseline designs.

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arXiv2025

RL-Driven Semantic Compression Model Selection and Resource Allocation in Semantic Communication Systems

Semantic communication (SemCom) is an emerging paradigm that leverages semantic-level understanding to improve communication efficiency, particularly in resource-constrained scenarios. However, existing SemCom systems often overlook diverse computational and communication capabilities and requirements among different users. Motivated by the need to adaptively balance semantic accuracy, latency, and energy consumption, this paper presents a reinforcement learning (RL)-driven framework for semantic compression model (SCM) selection and resource allocation in multi-user SemCom systems. To address the challenges of balancing image reconstruction quality and communication performance, a system-level optimization metric called Rate-Distortion Efficiency (RDE) has been defined. The framework considers multiple SCMs with varying complexity and resource requirements. A proximal policy optimization (PPO)-based RL approach is developed to dynamically select SCMs and allocate bandwidth and power under non-convex constraints. Simulations demonstrate that the proposed method outperforms several baseline strategies. This paper also discusses the generalization ability, computational complexity, scalability, and practical implications of the framework for real-world SemCom systems.

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arXiv2025

Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications

The convergence of robotics, advanced communication networks, and artificial intelligence (AI) holds the promise of transforming industries through fully automated and intelligent operations. In this work, we introduce a novel co-working framework for robots that unifies goal-oriented semantic communication (SemCom) with a Generative AI (GenAI)-agent under a semantic-aware network. SemCom prioritizes the exchange of meaningful information among robots and the network, thereby reducing overhead and latency. Meanwhile, the GenAI-agent leverages generative AI models to interpret high-level task instructions, allocate resources, and adapt to dynamic changes in both network and robotic environments. This agent-driven paradigm ushers in a new level of autonomy and intelligence, enabling complex tasks of networked robots to be conducted with minimal human intervention. We validate our approach through a multi-robot anomaly detection use-case simulation, where robots detect, compress, and transmit relevant information for classification. Simulation results confirm that SemCom significantly reduces data traffic while preserving critical semantic details, and the GenAI-agent ensures task coordination and network adaptation. This synergy provides a robust, efficient, and scalable solution for modern industrial environments.

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arXiv2025

Large Generative AI Models meet Open Networks for 6G: Integration, Platform, and Monetization

Generative artificial intelligence (GAI) has emerged as a pivotal technology for content generation, reasoning, and decision-making, making it a promising solution on the 6G stage characterized by openness, connected intelligence, and service democratization. This article explores strategies for integrating and monetizing GAI within future open 6G networks, mainly from the perspectives of mobile network operators (MNOs). We propose a novel API-centric telecoms GAI marketplace platform, designed to serve as a central hub for deploying, managing, and monetizing diverse GAI services directly within the network. This platform underpins a flexible and interoperable ecosystem, enhances service delivery, and facilitates seamless integration of GAI capabilities across various network segments, thereby enabling new revenue streams through customer-centric generative services. Results from experimental evaluation in an end-to-end Open RAN testbed, show the latency benefits of this platform for local large language model (LLM) deployment, by comparing token timing for various generated lengths with cloud-based general-purpose LLMs. Lastly, the article discusses key considerations for implementing the GAI marketplace within 6G networks, including monetization strategy, regulatory, management, and service platform aspects.

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