arXiv subjects

Muhammad Ali Imran

Publications and source records attributed to Muhammad Ali Imran.

At least 19 recordsLinked to original sources

arXiv2026

RIS-Assisted Proactive Handover for Reliable mmWave Wireless Networks

Millimeter-wave (mmWave) networks are highly susceptible to line-of-sight (LoS) blockages. Vision-aided wireless communications (VAWC) enable proactive handovers (PHO) to mitigate such blockages; however, PHO becomes challenging when no nearby base station (BS) is available. In such cases, reconfigurable intelligent surfaces (RIS) can be used to restore connectivity. To ensure timely PHO, the RIS configuration time must be taken into account, as the large number of RIS elements can limit responsiveness in time-sensitive scenarios. This work proposes a novel RIS-assisted PHO approach that optimizes the number of allocated RIS elements to balance signal processing complexity and link quality under handover timing constraints, making the RIS-assisted link more energy-efficient. An optimization problem based on particle swarm optimization (PSO) is formulated to determine the optimal end-to-end RIS link setup that runs offline to bypass latency constraints. Results show that reducing the number of RIS elements by 12\% leads to a 10\% decrease in dissipated energy without compromising the signal-to-noise ratio (SNR). Moreover, the RIS-assisted link achieves a 15--30 dB improvement in blocked regions while maintaining accurate PHO timing.

cs.CV↗

arXiv2026

Non-Uniform Codebook Design for Optical IRS-Assisted VLC Systems

Optical intelligent reflecting surfaces (OIRS) can improve the coverage of indoor visible light communication (VLC) systems, however, practical deployment requires a finite offline codebook to avoid repeated real-time optimisation of mirror orientations. A uniform codebook with fixed angular steps does not provide uniform coverage on the user plane, because the mapping from steering angles to reflection locations on the user plane is nonlinear. To address this problem, this paper proposes a geometric-optics-based non-uniform codebook design for OIRS-assisted VLC systems. The proposed method constructs an individual codebook for each IRS element according to its geometric position, so that the reflected beams are distributed more uniformly over the user plane. The codebook accuracy is evaluated using the Frobenius norm of the channel error matrix. Simulation results show that the proposed design provides more uniform spatial mapping with fewer codewords than the uniform codebook, and that the sweep-angle resolution has a stronger effect on the codebook accuracy than the tilt-angle resolution.

cs.IT↗

arXiv2026

Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation

Anomaly detection is a core service in the Internet of Underwater Things, yet training accurate distributed models underwater is difficult because acoustic links are low-bandwidth, energy-intensive, and often unable to support direct sensor-to-surface communication. Standard flat federated learning therefore faces two coupled limitations in underwater deployments: expensive long-range transmissions and reduced participation when only a subset of sensors can reach the gateway. This paper proposes an energy-efficient hierarchical federated learning framework for underwater anomaly detection based on three components: feasibility-aware sensor-to-fog association, compressed model-update transmission, and selective cooperative aggregation among fog nodes. The proposed three-tier architecture localises most communication within short-range clusters while activating fog-to-fog exchange only when smaller clusters can benefit from nearby larger neighbours. A physics-grounded underwater acoustic model is used to evaluate detection quality, communication energy, and network participation jointly. In large synthetic deployments, only about 48% of sensors can directly reach the gateway in the 200-sensor case, whereas hierarchical learning preserves full participation through feasible fog paths. Selective cooperation matches the detection accuracy of always-on inter-fog exchange while reducing its energy by 31-33%, and compressed uploads reduce total energy by 71-95% in matched sensitivity tests. Experiments on three real benchmarks further show that low-overhead hierarchical methods remain competitive in detection quality, while flat federated learning defines the minimum-energy operating point. These results provide practical design guidance for underwater deployments operating under severe acoustic communication constraints.

cs.LG↗

arXiv2026

Enabling mmWave Communications with VCSEL-Based Light-Emitting Reconfigurable Intelligent Surfaces

This paper proposes a light-emitting reconfigurable intelligent surface (LeRIS) architecture that integrates vertical cavity surface-emitting lasers (VCSELs) to jointly support user localization and mmWave communication. By leveraging the directional Gaussian beams and dual-mode diversity of VCSELs, we derive a closed-form method for estimating user position and orientation using only three VCSEL sources. These estimates are then used to configure LeRIS panels for directional mmWave beamforming, enabling optimized wave propagation in programmable wireless environments. Simulation results demonstrate that the proposed system achieves millimeter-level localization accuracy and maintains high spectral efficiency. These findings establish VCSEL-integrated LeRIS as a scalable and multifunctional solution for future 6G programmable wireless environments.

cs.IT↗

arXiv2026

Toward Real-Time Mirrors Intelligence: System-Level Latency and Computation Evaluation in Internet of Mirrors (IoM)

The Internet of Mirrors (IoM) is an emerging IoT ecosystem of interconnected smart mirrors designed to deliver personalised services across a three-tier node hierarchy spanning consumer, professional, and hub nodes. Determining where computation should reside within this hierarchy is a critical design challenge, as placement decisions directly affect end-to-end latency, resource utilisation, and user experience. This paper presents the first physical IoM testbed study, evaluating four computational placement strategies across the IoM tier hierarchy under real Wi-Fi and 5G network conditions. Results show that offloading classification to higher-tier nodes substantially reduces latency and consumer resource load, but introduces network overhead that scales with payload size and hop count. No single strategy is universally optimal: the best choice depends on available network, node proximity, and concurrent user load. These findings empirically characterise the computation-communication trade-off space of the IoM and motivate the need for intelligent, adaptive task placement responsive to application requirements and live ecosystem conditions.

cs.NI↗

arXiv2026

Machine Learning for the Internet of Underwater Things: From Fundamentals to Implementation

The Internet of Underwater Things (IoUT) is becoming a critical infrastructure for ocean observation, marine resource management, and climate science. Its development is hindered by severe acoustic attenuation, propagation delays far exceeding those of terrestrial wireless systems, strict energy constraints, and dynamic topologies shaped by ocean currents. Machine learning (ML) has emerged as a key enabler for addressing these limitations, offering data driven mechanisms that enhance performance across all layers of underwater wireless sensor networks. This tutorial survey synthesises ML methodologies supervised, unsupervised, reinforcement, and deep learning specifically contextualised for underwater communication environments. It outlines the algorithmic principles of each paradigm and examines the conditions under which particular approaches deliver superior performance. A layer wise analysis highlights physical layer gains in localisation and channel estimation, MAC layer adaptations that improve channel utilisation, network layer routing strategies that extend operational lifetime, and transport layer mechanisms capable of reducing packet loss by up to 91 percent. At the application layer, ML enables substantial data compression and object detection accuracies reaching 92 percent. Drawing on 300 studies from 2012 to 2025, the survey documents energy efficiency gains of 7 to 29 times, throughput improvements over traditional protocols, and cross layer optimisation benefits of up to 42 percent. It also identifies persistent barriers, including limited datasets, computational constraints, and the gap between theoretical models and real world deployment. The survey concludes with emerging research directions and a technology roadmap supporting ML adoption in operational underwater networks.

eess.SY↗

arXiv2026

The Syntactic-Semantic Internet:Engineering Infrastructures for Autonomous Systems

The Internet has evolved through successive architectural abstractions that enabled unprecedented scale, interoperability, and innovation. Packet-based networking enabled the reliable transport of bits; cloud-native systems enabled the orchestration of distributed computation. Today, the emergence of autonomous, learning-based systems introduces a new architectural challenge: intelligence is increasingly embedded directly into network control, computation, and decision-making, yet the Internet lacks a structural foundation for representing and exchanging meaning. In this paper, we argue that cognition alone: pattern recognition, prediction, and optimization, is insufficient for the next generation of networked systems. As autonomous agents act across safety-critical and socio-technical domains, systems must not only compute and communicate, but also comprehend intent, context, and consequence. We introduce the concept of a Semantic Layer: a new architectural stratum that treats meaning as a first-class construct, enabling interpretive alignment, semantic accountability, and intelligible autonomous behavior. We show that this evolution leads naturally to a Syntactic-Semantic Internet. The syntactic stack continues to transport bits, packets, and workloads with speed and reliability, while a parallel semantic stack transports meaning, grounding, and consequence. We describe the structure of this semantic stack-semantic communication, a semantic substrate, and an emerging Agentic Web, and draw explicit architectural parallels to TCP/IP and the World Wide Web. Finally, we examine current industry efforts, identify critical architectural gaps, and outline the engineering challenges required to make semantic interoperability a global, interoperable infrastructure.

cs.NI↗

arXiv2026

A Survey on Semantic Communication for Vision: Categories, Frameworks, Enabling Techniques, and Applications

Semantic communication (SemCom) emerges as a transformative paradigm for traffic-intensive visual data transmission, shifting focus from raw data to meaningful content transmission and relieving the increasing pressure on communication resources. However, to achieve SemCom, challenges are faced in accurate semantic quantization for visual data, robust semantic extraction and reconstruction under diverse tasks and goals, transceiver coordination with effective knowledge utilization, and adaptation to unpredictable wireless communication environments. In this paper, we present a systematic review of SemCom for visual data transmission (SemCom-Vision), wherein an interdisciplinary analysis integrating computer vision (CV) and communication engineering is conducted to provide comprehensive guidelines for the machine learning (ML)-empowered SemCom-Vision design. Specifically, this survey first elucidates the basics and key concepts of SemCom. Then, we introduce a novel classification perspective to categorize existing SemCom-Vision approaches as semantic preservation communication (SPC), semantic expansion communication (SEC), and semantic refinement communication (SRC) based on communication goals interpreted through semantic quantization schemes. Moreover, this survey articulates the ML-based encoder-decoder models and training algorithms for each SemCom-Vision category, followed by knowledge structure and utilization strategies. Finally, we discuss potential SemCom-Vision applications.

eess.IV↗

arXiv2025

Enhancing PLS of Indoor IRS-VLC Systems for Colluding and Non-Colluding Eavesdroppers

Most intelligent reflecting surface (IRS)-aided indoor visible light communication (VLC) studies ignore the time delays introduced by reflected paths, even though these delays are inherent in practical wideband systems. In this work, we adopt a realistic assumption of IRS-induced time delay for physical layer security (PLS) enhancement. We consider an indoor VLC system where an IRS is used to shape the channel so that the reflected signals add constructively at the legitimate user and create intersymbol interference at eavesdroppers located inside the coverage area. The resulting secrecy capacity maximisation over the IRS element allocation is formulated as a complex combinatorial optimisation problem and is solved using deep reinforcement learning with proximal policy optimisation (PPO). The approach is evaluated for both colluding eavesdroppers, which combine their received signals, and non-colluding eavesdroppers, which act independently. Simulation results are shown for various simulation setups, which demonstrate significant secrecy capacity gains. In a worst-case scenario, where the eavesdroppers have stronger channels than the legitimate user, the proposed PPO-based IRS allocation improves secrecy capacity by 107\% and 235\% in the colluding and non-colluding cases, respectively, compared with allocating all IRS elements to the legitimate user. These results demonstrate that time-delay-based IRS control can provide a strong secrecy advantage in practical indoor VLC scenarios.

cs.IT↗

arXiv2025

Inferring Operator Emotions from a Motion-Controlled Robotic Arm

A remote robot operator's affective state can significantly impact the resulting robot's motions leading to unexpected consequences, even when the user follows protocol and performs permitted tasks. The recognition of a user operator's affective states in remote robot control scenarios is, however, underexplored. Current emotion recognition methods rely on reading the user's vital signs or body language, but the devices and user participation these measures require would add limitations to remote robot control. We demonstrate that the functional movements of a remote-controlled robotic avatar, which was not designed for emotional expression, can be used to infer the emotional state of the human operator via a machine-learning system. Specifically, our system achieved 83.3$\%$ accuracy in recognizing the user's emotional state expressed by robot movements, as a result of their hand motions. We discuss the implications of this system on prominent current and future remote robot operation and affective robotic contexts.

cs.RO↗

arXiv2025

Integrated Localization, Mapping, and Communication through VCSEL-Based Light-emitting RIS (LeRIS)

Light-emitting reconfigurable intelligent surfaces (LeRISs) have recently emerged as a promising solution for providing the spatial awareness required for reliable millimeter-wave (mmWave) communication in programmable wireless environments (PWEs). However, existing LeRIS designs rely on the diffuse emission of light-emitting diodes, while LiDAR-assisted solutions require dedicated sensing modules that hinder their direct integration into RIS panels. In this paper, a vertical-cavity surface-emitting laser (VCSEL)-based LeRIS framework is developed to jointly support user localization, obstacle-aware mapping, and mmWave communication. Specifically, the narrow Gaussian beams and multimode operation of VCSELs are exploited to derive closed-form schemes for the joint recovery of the user position and orientation from received signal strength measurements. According to the provided simulation results, is shown that five VCSELs are sufficient for unique recovery, while this requirement is reduced to three dual-mode VCSELs under specific geometric conditions. Furthermore, the position error bound (PEB) is derived to characterize the achievable localization accuracy, while reflected-signal time-of-arrival measurements are employed to identify obstructed links and enable blockage-resilient LeRIS routing. As a result, the proposed framework achieves cm-level localization accuracy, reliable obstacle detection, and substantial spectral-efficiency and minimum-user-rate gains, thereby establishing VCSEL-based LeRISs as a solution for spatially aware and resilient PWEs.

cs.IT↗

arXiv2025

Adaptive Semantic Communication for UAV/UGV Cooperative Path Planning

Effective path planning is fundamental to the coordination of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems, particularly in applications such as surveillance, navigation, and emergency response. Combining UAVs' broad field of view with UGVs' ground-level operational capability greatly improve the likelihood of successfully achieving task objectives such as locating victims, monitoring target areas, or navigating hazardous terrain. In complex environments, UAVs need to provide precise environmental perception information for UGVs to optimize their routing policy. However, due to severe interference and non-line-of-sight conditions, wireless communication is often unstable in such complex environments, making it difficult to support timely and accurate path planning for UAV-UGV coordination. To this end, this paper proposes a semantic communication (SemCom) framework to enhance UAV/UGV cooperative path planning under unreliable wireless conditions. Unlike traditional methods that transmit raw data, SemCom transmits only the key information for path planning, reducing transmission volume without sacrificing accuracy. The proposed framework is developed by defining key semantics for path planning and designing a transceiver for meeting the requirements of UAV-UGV cooperative path planning. Simulation results show that, compared to conventional SemCom transceivers, the proposed transceiver significantly reduces data transmission volume while maintaining path planning accuracy, thereby enhancing system collaboration efficiency.

cs.NI↗

arXiv2025

Dynamic Control Aware Semantic Communication Enabled Image Transmission for Lunar Landing

The primary challenge in autonomous lunar landing missions lies in the unreliable local control system, which has limited capacity to handle high-dynamic conditions, severely affecting landing precision and safety. Recent advancements in lunar satellite communication make it possible to establish a wireless link between lunar orbit satellites and the lunar lander. This enables satellites to run high-performance autonomous landing algorithms, improving landing accuracy while reducing the lander's computational and storage load. Nevertheless, traditional communication paradigms are not directly applicable due to significant temperature fluctuations on the lunar surface, intense solar radiation, and severe interference caused by lunar dust on hardware. The emerging technique of semantic communication (SemCom) offers significant advantages in robustness and resource efficiency, particularly under harsh channel conditions. In this paper, we introduce a novel SemCom framework for transmitting images from the lander to satellites operating the remote landing control system. The proposed encoder-decoder dynamically adjusts the transmission strategy based on real-time feedback from the lander's control algorithm, ensuring the accurate delivery of critical image features and enhancing control reliability. We provide a rigorous theoretical analysis of the conditions that improve the accuracy of the control algorithm and reduce end-to-end transmission time under the proposed framework. Simulation results demonstrate that our SemCom method significantly enhances autonomous landing performance compared to traditional communication methods.

cs.NI↗

arXiv2025

A Unified Learning-based Optimization Framework for 0-1 Mixed Problems in Wireless Networks

Several wireless networking problems are often posed as 0-1 mixed optimization problems, which involve binary variables (e.g., selection of access points, channels, and tasks) and continuous variables (e.g., allocation of bandwidth, power, and computing resources). Traditional optimization methods as well as reinforcement learning (RL) algorithms have been widely exploited to solve these problems under different network scenarios. However, solving such problems becomes more challenging when dealing with a large network scale, multi-dimensional radio resources, and diversified service requirements. To this end, in this paper, a unified framework that combines RL and optimization theory is proposed to solve 0-1 mixed optimization problems in wireless networks. First, RL is used to capture the process of solving binary variables as a sequential decision-making task. During the decision-making steps, the binary (0-1) variables are relaxed and, then, a relaxed problem is solved to obtain a relaxed solution, which serves as prior information to guide RL searching policy. Then, at the end of decision-making process, the search policy is updated via suboptimal objective value based on decisions made. The performance bound and convergence guarantees of the proposed framework are then proven theoretically. An extension of this approach is provided to solve problems with a non-convex objective function and/or non-convex constraints. Numerical results show that the proposed approach reduces the convergence time by about 30% over B&B in small-scale problems with slightly higher objective values. In large-scale scenarios, it can improve the normalized objective values by 20% over RL with a shorter convergence time.

cs.NI↗

arXiv2025

A Comprehensive Survey of 5G URLLC and Challenges in the 6G Era

As the wireless communication paradigm is being transformed from human centered communication services towards machine centered communication services, the requirements of rate, latency and reliability for these services have also been transformed drastically. Thus the concept of Ultra Reliable and Low Latency Communication (URLLC) has emerged as a dominant theme for 5G and 6G systems. Though the latency and reliability requirement varies from one use case to another, URLLC services generally aim to achieve very high reliability in the range of 99.999\% while ensuring the latency of up to 1 ms. These two targets are however inherently opposed to one another. Significant amounts of work have been carried out to meet these ambitious but conflicting targets. In this article a comprehensive survey of the URLLC approaches in 5G systems are analysed in detail. Effort has been made to trace the history and evolution of latency and reliability issues in wireless communication. A layered approach is taken where physical layer, Medium Access Control (MAC) layer as well as cross layer techniques are discussed in detail. It also covers the design consideration for various 5G and beyond verticals. Finally the article concludes by providing a detailed discussion on challenges and future outlook with particular focus on the emerging 6G paradigm.

cs.NI↗

arXiv2025

Federated Deep Reinforcement Learning for Privacy-Preserving Robotic-Assisted Surgery

The integration of Reinforcement Learning (RL) into robotic-assisted surgery (RAS) holds significant promise for advancing surgical precision, adaptability, and autonomous decision-making. However, the development of robust RL models in clinical settings is hindered by key challenges, including stringent patient data privacy regulations, limited access to diverse surgical datasets, and high procedural variability. To address these limitations, this paper presents a Federated Deep Reinforcement Learning (FDRL) framework that enables decentralized training of RL models across multiple healthcare institutions without exposing sensitive patient information. A central innovation of the proposed framework is its dynamic policy adaptation mechanism, which allows surgical robots to select and tailor patient-specific policies in real-time, thereby ensuring personalized and Optimised interventions. To uphold rigorous privacy standards while facilitating collaborative learning, the FDRL framework incorporates secure aggregation, differential privacy, and homomorphic encryption techniques. Experimental results demonstrate a 60\% reduction in privacy leakage compared to conventional methods, with surgical precision maintained within a 1.5\% margin of a centralized baseline. This work establishes a foundational approach for adaptive, secure, and patient-centric AI-driven surgical robotics, offering a pathway toward clinical translation and scalable deployment across diverse healthcare environments.

cs.RO↗

arXiv2025

A Mathematical Framework of Semantic Communication based on Category Theory

While semantic communication (SemCom) has recently demonstrated great potential to enhance transmission efficiency and reliability by leveraging machine learning (ML) and knowledge base (KB), there is a lack of mathematical modeling to rigorously characterize SemCom system and quantify the performance gain obtained from ML and KB. In this paper, we develop a mathematical framework for SemCom based on category theory, rigorously modeling the concepts of semantic entities and semantic probability space. Within this framework, we introduce the semantic entropy to quantify the uncertainty of semantic entities. We theoretically prove that semantic entropy can be effectively reduced by exploiting KBs, which capture semantic dependencies. Within the formulated semantic space, semantic entities can be combined according to the required semantic ambiguity, and the combined entities can be encoded based on semantic dependencies obtained from KB. Then, we derive semantic channel capacity modeling, which incorporates the mutual information obtained in KB to accurately measure the transmission efficiency of SemCom. Numerical simulations validate the effectiveness of the proposed framework, showing that SemCom with KB integration outperforms traditional communication in both entropy reduction and coding efficiency.

cs.NI↗

arXiv2025

A semantic communication-based workload-adjustable transceiver for wireless AI-generated content (AIGC) delivery

With the significant advances in generative AI (GAI) and the proliferation of mobile devices, providing high-quality AI-generated content (AIGC) services via wireless networks is becoming the future direction. However, the primary challenges of AIGC service delivery in wireless networks lie in unstable channels, limited bandwidth resources, and unevenly distributed computational resources. In this paper, we employ semantic communication (SemCom) in diffusion-based GAI models to propose a Resource-aware wOrkload-adjUstable TransceivEr (ROUTE) for AIGC delivery in dynamic wireless networks. Specifically, to relieve the communication resource bottleneck, SemCom is utilized to prioritize semantic information of the generated content. Then, to improve computational resource utilization in both edge and local and reduce AIGC semantic distortion in transmission, modified diffusion-based models are applied to adjust the computing workload and semantic density in cooperative content generation. Simulations verify the superiority of our proposed ROUTE in terms of latency and content quality compared to conventional AIGC approaches.

cs.LG↗