论文标题
多模式传感的多访问通道上的NOMA计算
NOMA Computation Over Multi-Access Channels for Multimodal Sensing
论文作者
论文摘要
基于特征向量分解方法的改进的平均误差（MSE）最小化解决方案是针对基于多访问通道（NOMA-COMAC）框架的宽带非正交多辅助计算的。这项工作旨在进一步开发Noma-comac用于下一代多模式传感器网络，其中多模式传感器监视了几个环境参数，例如温度，污染，湿度或压力。我们证明，与平均基于总的频道方法相比，我们提出的方案在E_B/N_O = 1 dB时达到的MSE值大约低于E_B/N_O = 1 dB。此外，由于多样性增益的好处，我们提出的解决方案的MSE性能增益增加了较大的子载体和传感器节点值的增加。作为回报，这表明我们提出的方案非常适合多模式传感器网络。
An improved mean squared error (MSE) minimization solution based on eigenvector decomposition approach is conceived for wideband non-orthogonal multiple-access based computation over multi-access channel (NOMA-CoMAC) framework. This work aims at further developing NOMA-CoMAC for next-generation multimodal sensor networks, where a multimodal sensor monitors several environmental parameters such as temperature, pollution, humidity, or pressure. We demonstrate that our proposed scheme achieves an MSE value approximately 0.7 lower at E_b/N_o = 1 dB in comparison to that for the average sum-channel based method. Moreover, the MSE performance gain of our proposed solution increases even more for larger values of subcarriers and sensor nodes due to the benefit of the diversity gain. This, in return, suggests that our proposed scheme is eminently suitable for multimodal sensor networks.
