Multi-dimensional resource allocation strategy for LEO satellite communication uplinks based on deep reinforcement learning

Yu Hu, Feipeng Qiu, Fei Zheng, Jilong Zhao
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Abstract

In the LEO satellite communication system, the resource utilization rate is very low due to the constrained resources on satellites and the non-uniform distribution of traffics. In addition, the rapid movement of LEO satellites leads to complicated and changeable networks, which makes it difficult for traditional resource allocation strategies to improve the resource utilization rate. To solve the above problem, this paper proposes a resource allocation strategy based on deep reinforcement learning. The strategy takes the weighted sum of spectral efficiency, energy efficiency and blocking rate as the optimization objective, and constructs a joint power and channel allocation model. The strategy allocates channels and power according to the number of channels, the number of users and the type of business. In the reward decision mechanism, the maximum reward is obtained by maximizing the increment of the optimization target. However, during the optimization process, the decision always focuses on the optimal allocation for current users, and ignores QoS for new users. To avoid the situation, current service beams are integrated with high- traffic beams, and states of beams are refactored to maximize long-term benefits to improve system performance. Simulation experiments show that in scenarios with a high number of users, the proposed resource allocation strategy reduces the blocking rate by at least 5% compared to reinforcement learning methods, effectively enhancing resource utilization.
基于深度强化学习的低地球轨道卫星通信上行链路多维资源分配策略
在低地轨道卫星通信系统中,由于卫星资源受限和流量分布不均匀,资源利用率非常低。此外,低地轨道卫星的快速移动导致网络复杂多变,传统的资源分配策略难以提高资源利用率。为解决上述问题,本文提出了一种基于深度强化学习的资源分配策略。该策略以频谱效率、能效和阻塞率的加权和为优化目标,构建了功率和信道联合分配模型。该策略根据信道数量、用户数量和业务类型分配信道和功率。在奖励决策机制中,通过最大化优化目标的增量来获得最大奖励。然而,在优化过程中,决策总是关注当前用户的最优分配,而忽略了新用户的 QoS。为避免出现这种情况,当前服务波束与高流量波束进行了整合,并对波束状态进行了重构,以实现长期利益最大化,从而提高系统性能。仿真实验表明,在用户数量较多的场景中,与强化学习方法相比,所提出的资源分配策略至少降低了 5%的阻塞率,有效提高了资源利用率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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