In multi-user downlink small cell networks, cooperative resource allocation (RA) within a small cell cluster is a key technique to enhance network capacity. However, capacity-maximizing RA in frequency-selective fading channels requires global channel state information (CSI) of users within a small cell cluster, which makes it infeasible in practical networks with limited direct link capacity. To circumvent this global CSI assumption, most of the existing studies on RA have been based on several CSI assumptions such as local CSI and local CSI at the transmitters (CSIT). Nevertheless, cost functions with local CSI or local CSIT in the literature rely on heuristic formulations, because the sum-rate cannot be computed if without global CSI. In this paper, we propose a deep reinforcement learning-based RA algorithm to maximize the sum-rate for any given limited information on instantaneous CSI or sum-rate at the previous period. The proposed scheme is not restricted to certain CSI assumptions, but attempts to find the best RA for any given information such as quantized local CSI and quantized local CSIT; thus, it is applicable to any given direct link capacity. The proposed algorithm is self-adaptive in time-varying channels, since it is not divided into training and test phases. We modify the target neural network (TNN) scheme to enhance the sum-rate and the convergence speed. Numerical simulations confirm that: i) the proposed algorithm outperforms the conventional algorithms even under the same CSI assumption such as local CSI and local CSIT; ii) a flexible trade-off between the amount of CSI and the sum-rate is realizable in practical systems.

中文翻译：

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基于深度强化学习的小基站资源分配和功率控制，信息交换有限

在多用户下行小小区网络中，小小区集群内的协作资源分配（RA）是提升网络容量的关键技术。然而，在频率选择性衰落信道中最大化容量的 RA 需要小小区集群内用户的全局信道状态信息 (CSI)，这使得它在直接链路容量有限的实际网络中不可行。为了规避这种全局 CSI 假设，大多数现有的关于 RA 的研究都基于几个 CSI 假设，例如本地 CSI 和发射机处的本地 CSI (CSIT)。然而，文献中具有局部 CSI 或局部 CSIT 的成本函数依赖于启发式公式，因为如果没有全局 CSI，则无法计算总速率。在本文中，我们提出了一种基于深度强化学习的 RA 算法，以最大化任何给定的有限信息的总和，即前一时期的瞬时 CSI 或总和率。所提出的方案不限于某些 CSI 假设，而是尝试为任何给定信息（例如量化的本地 CSI 和量化的本地 CSIT）找到最佳 RA；因此，它适用于任何给定的直接链路容量。所提出的算法在时变通道中是自适应的，因为它不分为训练和测试阶段。我们修改目标神经网络 (TNN) 方案以提高总和速率和收敛速度。数值模拟证实：i）即使在相同的 CSI 假设（例如本地 CSI 和本地 CSIT）下，所提出的算法也优于传统算法；