arXiv subjects

Abdikarim Mohamed Ibrahim

Publications and source records attributed to Abdikarim Mohamed Ibrahim.

5 recordsLinked to original sources

arXiv2026

Large Artificial Intelligence Model Guided Deep Reinforcement Learning for Resource Allocation in Non Terrestrial Networks

Large AI Model (LAM) have been proposed to applications of Non-Terrestrial Networks (NTN), that offer better performance with its great generalization and reduced task specific trainings. In this paper, we propose a Deep Reinforcement Learning (DRL) agent that is guided by a Large Language Model (LLM). The LLM operates as a high level coordinator that generates textual guidance that shape the reward of the DRL agent during training. The results show that the LAM-DRL outperforms the traditional DRL by 40% in nominal weather scenarios and 64% in extreme weather scenarios compared to heuristics in terms of throughput, fairness, and outage probability.

cs.AI↗

arXiv2026

Digital Twin for Ultra-Reliable & Low-Latency 6G Wireless Communications in Dense Urban City

High-frequency deployments in dense cities are difficult to plan because coverage, interference, and service reliability depend sensitively on local morphology. This paper develops a geometric Digital Twin (DT) of the Sunway City and uses it to study the service implications of a multi-site mmWave deployment. The DT is constructed from geo-referenced three-dimensional meshes of buildings, roads, and open areas, assembled in Blender and exported as a mesh scene. A seven-transmitter downlink at 10 GHz is then embedded into this geometry and evaluated using a GPU accelerated ray tracing engine that returns path-gain and Signal-to-Interference-plus-Noise Ratio (SINR) fields over a dense grid of user locations. These fields are mapped to achievable throughput and compared against representative target rates for immersive extended reality (XR), vehicle-to-everything (V2X) services, and ultra-reliable low-latency communication (URLLC). The resulting maps show that favourable streets and courtyards form narrow high rate corridors surrounded by deep shadows, even within a dense area. In the baseline deployment, one fifth of the simulated area can maintain 100 Mbps URLLC rates, and less than 10% of cells can reach 1.7 Gbps for XR, despite the presence of several rooftop sites. By exploiting the DT, we further quantify the macro-diversity margin between the best and second best serving sites and show that most URLLC-feasible cells have several decibels of SINR headroom that could be harvested through dual connectivity. The study shows how a city DT can translate ray tracing output into service centric metrics and planning insights, complementing both analytical models and expensive measurement campaigns.

eess.SY↗

arXiv2026

Geometry-Aware LoRaWAN Gateway Placement in Dense Urban Cities Using Digital Twins

LoRaWAN deployments rely on rough range estimates or simplified propagation models to decide where to place/mount gateways. As a result, operators have limited visibility into how rooftop choice, streets, and building shadowing jointly affect coverage and reliability. This paper addresses the problem of gateway placement in dense urban environments by combining a geometry accurate Digital Twin (DT) with a GPU accelerated ray tracing engine. Existing studies optimize placement on abstract grids or tune models with sparse measurements; few works evaluate LoRaWAN gateways on a full 3D city model using a realistic link budget. In this paper, we develop a DT with ITU radio materials and evaluate eight candidate rooftops for RAK7289 WisGate Edge Pro gateways under a sub-GHz link budget derived from the data sheet. For each rooftop, we obtain Signal-to-Noise Ratios (SNR) on a 5 meter grid, derive robust and edge coverage indicators, and apply a greedy maximum coverage algorithm to rank sites and quantify the benefit of incremental densification. Results show that a single rooftop gateway covers one fifth of the full Sunway twin (i.e., the DT) at a robust SNR threshold, and that six sites still leave large areas of single gateway or out of coverage cells in surrounding residential streets. The findings from this paper shows that DT and ray tracing tools enable network operators to bridge the gap of expensive real-world trials and planning to identify if the planned LoRaWAN gateway is sufficient or additional sites are required.

eess.SY↗

arXiv2026

A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy

Artificial intelligence (AI) and reinforcement learning (RL) have shown significant promise in wireless systems, enabling dynamic spectrum allocation, traffic management, and large-scale Internet of Things (IoT) coordination. However, their deployment in mission-critical applications introduces the risk of unsafe emergent behaviors, such as UAV collisions, denial-of-service events, or instability in vehicular networks. Existing safety mechanisms are predominantly reactive, relying on anomaly detection or fallback controllers that intervene only after unsafe actions occur, which cannot guarantee reliability in ultra-reliable low-latency communication (URLLC) settings. In this work, we propose a proactive safety-constrained RL framework that integrates proof-carrying control (PCC) with empowerment-budgeted (EB) enforcement. Each agent action is verified through lightweight mathematical certificates to ensure compliance with interference constraints, while empowerment budgets regulate the frequency of safety overrides to balance safety and autonomy. We implement this framework on a wireless uplink scheduling task using Proximal Policy Optimization (PPO). Simulation results demonstrate that the proposed PCC+EB controller eliminates unsafe transmissions while preserving system throughput and predictable autonomy. Compared with unconstrained and reactive baselines, our method achieves provable safety guarantees with minimal performance degradation. These results highlight the potential of proactive safety constrained RL to enable trustworthy wireless autonomy in future 6G networks.

cs.NI↗

arXiv2025

URLLC for 6G Enabled Industry 5.0: A Taxonomy of Architectures, Cross Layer Techniques, and Time Critical Applications

The evolution from Industry 4.0 to Industry 5.0 introduces stringent requirements for ultra reliable low latency communication (URLLC) to support human centric, intelligent, and resilient industrial systems. Sixth-generation (6G) wireless networks aim to meet these requirements through sub-millisecond end-to-end delays, microsecond level jitter, and near perfect reliability, enabled by advances such as terahertz (THz) communication, reconfigurable intelligent surfaces (RIS), multi-access edge computing (MEC), and AI driven cross layer optimization. This paper presents a comprehensive review of URLLC solutions for 6G enabled industry 5.0, organized into a structured taxonomy including application domains, key technical enablers, design challenges, and performance enhancements. The survey examines emerging approaches, including digital twin integration, AI/ML based resource orchestration, Network Function Virtualization (NFV) enabled service function chaining, and cross domain networking, while mapping them to critical industrial scenarios such as smart manufacturing, connected healthcare, autonomous mobility, remote control, and next-generation mobile networks. Performance trade-offs between latency, reliability, scalability, and energy efficiency are analyzed in the context of representative state-of-the-art studies. Finally, the paper identifies open challenges and outlines future research directions to realize deterministic, secure, and sustainable URLLC architectures for Industry 5.0.

cs.NI↗