Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks
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DOI：
10.1109/gcwkshps52748.2021.9681985
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Yasar Sinan Nasir;Dongning Guo
中科院分区：
文献类型：
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作者：
Yasar Sinan Nasir;Dongning Guo
无线网络运营商通常将其无线电频谱划分为多个子带，并重用它们来服务于许多小区中的业务。为了减轻同信道干扰，频谱和功率资源的分配需要适应整个网络中随时间变化的信道和业务条件。基于标准模型的网络效用最大化算法受到计算复杂度和难以获取瞬时全局信道状态信息的限制。在本文中，提出了一种基于学习的方法来优化离散子带分配和连续功率分配使用（一般延迟和不准确）在本地和附近的小区的信道状态信息。对于这两种类型的分配，两个互补的深度强化学习算法被设计为同时执行和训练，以最大化联合目标。仿真结果表明，该方法优于最先进的分数规划算法以及以前的解决方案基于深度强化学习。
A wireless network operator typically divides its radio spectrum into a number of subbands and reuse them to serve traffic in many cells. To mitigate co-channel interference, allocation of spectrum and power resources needs to be adapted to time-varying channel and traffic conditions throughout the network. Standard model-based network utility maximization is severely limited by the computational complexity and the difficulty of acquiring instantaneous global channel state information. In this paper, a learning-based method is proposed to optimize discrete subband allocations and continuous power allocations using (generally delayed and inaccurate) channel state information in local and nearby cells. For these two types of allocations, two complementary deep reinforcement learning algorithms are designed to be executed and trained simultaneously to maximize a joint objective. Simulation results show that the proposed method outperforms a state-of-the-art fractional programming algorithm as well as a previous solution based on deep reinforcement learning.