Learning from Peers at the Wireless Edge
Learning from Peers at the Wireless Edge
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DOI：
10.1109/comsnets48256.2020.9027318
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Shuvam Chakraborty;Hesham Mohammed;D. Saha
中科院分区：
文献类型：
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作者：
Shuvam Chakraborty;Hesham Mohammed;D. Saha
最后一英里连接以无线链路为主，其中异构节点共享有限且已经拥挤的电磁频谱。当前基于竞争的分散式无线接入系统本质上是反应性的以减轻干扰。在本文中，我们建议使用神经网络以协作方式学习和预测频谱可用性，以便可以高精度地预测其可用性，以最大化无线接入并最小化同时链路之间的干扰。边缘节点具有广泛的传感和计算能力，同时经常使用不同的运营商网络，而这些运营商网络可能不愿意共享其模型。因此，我们引入了一种点对点联邦学习模型，其中基于每个节点的感知结果训练本地模型并在其对等点之间共享以创建全局模型。通过将边缘节点授权为本地模型的聚合器并最小化模型传输的通信开销，取代了基站或接入点充当集中式参数服务器的需要。我们生成无线信道访问数据，用于训练本地模型。本地和全局模型的仿真结果显示，在预测各种网络拓扑中的信道机会方面，准确度超过 95%。
The last mile connection is dominated by wireless links where heterogeneous nodes share the limited and already crowded electromagnetic spectrum. Current contention based decentralized wireless access system is reactive in nature to mitigate the interference. In this paper, we propose to use neural networks to learn and predict spectrum availability in a collaborative manner such that its availability can be predicted with a high accuracy to maximize wireless access and minimize interference between simultaneous links. Edge nodes have a wide range of sensing and computation capabilities, while often using different operator networks, who might be reluctant to share their models. Hence, we introduce a peer to peer Federated Learning model, where a local model is trained based on the sensing results of each node and shared among its peers to create a global model. The need for a base station or access point to act as centralized parameter server is replaced by empowering the edge nodes as aggregators of the local models and minimizing the communication overhead for model transmission. We generate wireless channel access data, which is used to train the local models. Simulation results for both local and global models show over 95 % accuracy in predicting channel opportunities in various network topology.