Renewable energy source (RES)-powered base stations have received tremendous research interest in recent years because they can expand network coverage without building a power grid. This paper proposes a novel user association (UA), resource allocation (RA), and dynamic power control (PC) scheme to maximize the $\alpha $ -fairness in RES-assisted small cell networks. The $\alpha $ -fairness is a general notion that flexibly adjusts the balance between the throughput, proportional fairness, and max-min fairness according to $\alpha $ . Nevertheless, none of the existing studies has proposed UA, RA, and PC to maximize the $\alpha $ -fairness due to its NP-hardness. Furthermore, fixed-policy-based PC designs cannot consider time-varying environments (e.g., energy harvesting models and wireless channels) of the RES-assisted networks. We first provide a Lagrangian duality-based algorithm to solve the UA and RA problem for a fixed PC. Next, we propose a dynamic PC scheme based on deep reinforcement learning (DRL) that chooses the best PC considering the time-varying environments. However, because the UA and RA algorithm executed in each step of the dynamic PC requires a long computation time, we aim to accelerate the computation of the UA and RA with DRL. Inspired by the Lagrangian duality, we design a DRL-based UA and RA with a low-dimensional continuous variable by relaxing the UA variable, the cardinality of which increases exponentially with the number of base stations and users. The simulation results show that the proposed scheme achieves a 100 times shorter computation time than the optimization-based schemes by computing only two neural networks. In particular, although there have been numerous studies on the proportional fairness maximization, the proposed scheme outperforms the optimization-based schemes in the throughput, proportional fairness, and max-min fairness metrics.

中文翻译：

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α-公平-最大化能量受限小蜂窝网络中的用户关联

可再生能源（RES）供电的基站近年来受到了极大的研究兴趣，因为它们可以在不建设电网的情况下扩大网络覆盖范围。本文提出了一种新颖的用户关联 (UA)、资源分配 (RA) 和动态功率控制 (PC) 方案，以最大化 $\阿尔法$ - RES 辅助的小型蜂窝网络的公平性。这 $\阿尔法$ -fairness 是一个通用概念，根据 $\阿尔法$ . 然而，现有的研究都没有提出 UA、RA 和 PC 来最大化 $\阿尔法$ -公平，因为它的 NP 硬度。此外，基于固定策略的 PC 设计不能考虑 RES 辅助网络的时变环境（例如，能量收集模型和无线信道）。我们首先提供了一种基于拉格朗日对偶的算法来解决固定 PC 的 UA 和 RA 问题。接下来，我们提出了一种基于深度强化学习 (DRL) 的动态 PC 方案，该方案在考虑时变环境的情况下选择最佳 PC。然而，由于在动态 PC 的每一步中执行的 UA 和 RA 算法需要较长的计算时间，我们的目标是使用 DRL 来加速 UA 和 RA 的计算。受拉格朗日对偶的启发，我们设计了一个基于 DRL 的 UA 和 RA，通过放宽 UA 变量，具有低维连续变量，其基数随着基站和用户的数量呈指数增长。仿真结果表明，与基于优化的方案相比，所提出的方案只计算两个神经网络，计算时间缩短了 100 倍。特别是，尽管已经有大量关于比例公平最大化的研究，但所提出的方案在吞吐量、比例公平和最大最小公平指标方面优于基于优化的方案。