Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
Graph Neural Networks for Distributed Power Allocation in Wireless Networks: Aggregation Over-the-Air
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DOI：
10.1109/twc.2023.3253126
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发表时间：
2022-07
影响因子：
10.4
通讯作者：
Yifan Gu;Changyang She;Z. Quan;Chen Qiu;Xiaodong Xu
中科院分区：
文献类型：
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作者：
Yifan Gu;Changyang She;Z. Quan;Chen Qiu;Xiaodong Xu
分布式功率分配对于具有密集收发器对的干扰受限无线网络是重要的。在本文中，我们的目标是设计低信令开销的分布式功率分配方案，通过使用图神经网络（GNNs），这是可扩展的无线链路的数量。我们首先应用消息传递神经网络（MPNN），GNN的统一框架，来解决这个问题。我们表明，信令开销的增长二次方网络规模的增加。从空中计算（AirComp）的启发，我们然后提出了一个Air-MPNN框架，其中来自相邻节点的消息由导频的发射功率表示，并且可以通过评估总干扰功率来有效地聚合。Air-MPNN的信令开销随着网络规模的增加而线性增长，并且我们证明了Air-MPNN是置换不变的。为了进一步减少信令开销，提出了Air消息传递递归神经网络（Air-MPRNN），每个节点利用前一帧中的图嵌入和局部状态更新当前帧中的图嵌入。由于现有的通信系统在每个帧期间发送导频，因此可以通过调整导频功率将Air-MPRNN集成到现有标准中。仿真结果验证了所提出的框架的可扩展性，并表明，他们优于现有的功率分配算法的总和速率的各种系统参数。
Distributed power allocation is important for interference-limited wireless networks with dense transceiver pairs. In this paper, we aim to design low signaling overhead distributed power allocation schemes by using graph neural networks (GNNs), which are scalable to the number of wireless links. We first apply the message passing neural network (MPNN), a unified framework of GNN, to solve the problem. We show that the signaling overhead grows quadratically as the network size increases. Inspired from the over-the-air computation (AirComp), we then propose an Air-MPNN framework, where the messages from neighboring nodes are represented by the transmit power of pilots and can be aggregated efficiently by evaluating the total interference power. The signaling overhead of Air-MPNN grows linearly as the network size increases, and we prove that Air-MPNN is permutation invariant. To further reduce the signaling overhead, we propose the Air message passing recurrent neural network (Air-MPRNN), where each node utilizes the graph embedding and local state in the previous frame to update the graph embedding in the current frame. Since existing communication systems send a pilot during each frame, Air-MPRNN can be integrated into the existing standards by adjusting pilot power. Simulation results validate the scalability of the proposed frameworks, and show that they outperform the existing power allocation algorithms in terms of sum-rate for various system parameters.