Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 366-371· 0 citations· 13 references
Abstract
As the manufacturing sector increasingly adopts Industry 4.0 technologies, the need for reliable communication among devices, such as autonomous mobile robots (AMRs), or automated guided vehicles (AGVs), becomes a fundamental necessity. To support direct device-to-device communication, the third-generation partnership project (3GPP) introduced 5G new radio sidelink communication mode 2 (NR-SL) in Release 16, and 17. NR-SL allows devices to select transmission resources based on local channel sensing. In NR-SL, however, autonomous resource selection can lead to collisions, particularly in dense industrial environments. In this paper, we propose a learningbased resource allocation scheme (LBRA) for 5G NR Mode-2 sidelink. LBRA is designed to support industrial AGV and AMR communications. It employs multi-agent reinforcement learning (MARL) to improve resource allocation in NR-SL and reduces the collision probability. The results show that LBRA decreases the collision probability by approximately 73% compared to NR-SL.
This paper leverages Open RAN to manage V2X communication and proposes a multi-agent reinforcement learning (MARL) resource-aware system that aims to mitigate interference, optimize resource usage, and enhance quality of service by optimally selecting between sidelink and network transmissions.
M. Barbosa, K. Dias· IEEE Transactions on Vehicul...· 0 citations
The implementation of integrated sensing and communication (ISAC) technology in space-air cooperative systems enhances the spectral efficiency and ensures sensing and communication services for different areas. Although numerous studies have explored resource management in ISAC systems, they generally overlook the uneven spatial distributions of service requirements. This paper introduces beam hopping into space-air cooperative ISAC systems to dynamically schedule resources based on the distribution of service requirements, thereby enhancing resource efficiency. To balance sensing and communication performance, we formulate the joint beam hopping and resource allocation design as a multi-objective optimization problem that jointly maximizes the radar mutual information (RMI) and transmission rate. To address the limited adaptability of existing algorithms to diverse sensing and communication requirements across different tasks, we propose a meta-deep reinforcement learning (meta-DRL) based joint beam hopping and resource allocation algorithm, which is capable of learning a universal initial policy enabling rapid adaptation to various task objectives. Numerical results indicate that the proposed algorithm exhibits fast-adaptation capability and outperforms the benchmark algorithms.
Liming Liang, Gaofeng Cui, Hui Xie et al.· IEEE Transactions on Cogniti...· 0 citations
This work demonstrates the viability of RL for distributed resource management and provides a reproducible simulation toolkit to support further research in AI-driven wireless communication systems.
Mugerwa Joseph, Ajaegbu Chigozirim· International Journal Of Eng...· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
Simulation results position D3QN-PER as a strong candidate for deployment as a near-RT RIC xApp within the O-RAN architecture, advancing the vision of AI-native mobility management for 6G.
Kalpesh Popat, Divyakant T. Meva· Telecommunications Systems· 0 citations
A focused review of power allocation strategies in NOMA is presented, with emphasis on the progression from static and optimization-based dynamic schemes to data-driven Artificial Intelligence (AI) and Machine Learning (ML) driven approaches.
Lekshmi Nair M, Neelakantan Pc· International Journal of Com...· 0 citations
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