Jul 2026· International Conference on Ubiquitous and Future Networks· pp. 1-3· 0 citations· 12 references
Computer Science
Abstract
This paper proposes a self-adaptive channel assignment framework based on Q-learning, where agents learn optimal policies by observing network load, interference conditions, and temporal traffic dynamics within a Markov decision process (MDP). A multi-objective reward function is designed to jointly optimize system throughput, user fairness, and interference mitigation, while an $\epsilon$-greedy strategy is employed to facilitate effective exploration. Simulation results demonstrate stable convergence, achieving an average reward of 37.5 and an average throughput of 28.5 Mbps. Moreover, the proposed approach achieves a Jain’s fairness index of 0.75 and reduces interference by $26.3\%$ compared to random allocation by adaptively responding to dynamic traffic patterns.
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 formulation provides a rigorous and tractable framework for distributed spectrum sharing in 6G O-RAN systems, with the potential to support intelligent and adaptive control in future wireless networks.
E. Spyrou, Chrysostomos D. Stylios, V. Kappatos et al.· Future Internet· 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
This paper proposes a simulation-based dynamic message packing framework that enables runtime MPS selection based on queue occupancy and inter-participant distance while preserving the standard TDMA structure and waveform and provides a practical, standards-compliant runtime optimization for Link 16 systems.
F. Abut, Mehmet Kızıldağ· IEEE Access· 0 citations
A scalable deep reinforcement learning (DRL) framework that exploits environmental-aware knowledge to optimize multi-user scheduling under limited resources, in order to enhance reliability and availability while maintaining fairness, outperforming Round Robin and Proportional Fair schedulers.
Roya Khanzadeh, Fjolla Ademaj-Berisha, Bernhard Etzlinger et al.· IEEE Transactions on Machine...· 0 citations
Simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is crucial to achieve full-space coverage in next-generation wireless networks. However, optimizing resource allocation in STAR-RIS-assisted systems to balance the system sum rate with user fairness, especially in the presence of imperfect channel state information (CSI), remains a significant challenge. To address this issue, this work investigates resource allocation in an STAR-RIS-assisted multiple-input single-output system under imperfect CSI and proposes a novel method based on the deep reinforcement learning (DRL) framework to solve this problem. Specifically, the DRL framework is utilized to solve the maximization problem of the weighted sum of Jain’s fairness index and the normalized system sum rate, and a segmented training strategy is employed to decouple the complexity of the original joint optimization problem. The simulation results demonstrate that the proposed solution achieves a flexible trade-off between the system sum rate and user fairness. Moreover, it effectively mitigates the performance degradation caused by imperfect CSI, thereby ensuring robust system performance.
Lifan Zeng, Yuyang Peng, Mohammad Meraj Mirza et al.· IEEE Wireless Communications...· 0 citations
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