Planning Versus Learning: Fair Space-Time Scheduling for Unwired Networks

被引:0
|
作者
Peng, Chen [1 ]
Mitra, Urbashi [1 ]
机构
[1] Univ Southern Calif, Dept Elect & Comp Engn, Los Angeles, CA 90089 USA
基金
瑞典研究理事会;
关键词
Underwater acoustics; Planning; Radio frequency; Array signal processing; Numerical models; Multiaccess communication; Mathematical models; Network scheduling; fairness; Markov decision process; approximate dynamic programming; UNDERWATER; ROBUST;
D O I
10.1109/TWC.2024.3444693
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Space-time scheduling for multi-user networks under fairness considerations is investigated. Scheduling is formulated as a sequential decision-making problem under the Markov Decision Processes (MDP) framework. Although the initial focus of the work is underwater acoustic networks, the proposed strategies are also validated for terrestrial radio frequency networks. If environment exploration is expensive, planning is more efficient than online learning. A challenge of the proportional fairness is that the additive structure between current and future rewards does not hold. An approximate reward function that is additive is proposed, enabling dynamic programming. Computational complexity is addressed through sample-based approximations. Error accumulation and error bounds are analyzed to show that error decays with time. As mobility induces model-shifts, a novel re-planning scheme is proposed to optimize the timings of policy updates. Numerical results show that the proposed scheme significantly improves network capacity while maintaining a high level of fairness. Furthermore, the proposed approach yields average capacity and fairness gains as high as 37% and 27%, respectively, compared to current approaches.
引用
收藏
页码:16621 / 16634
页数:14
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