Handle Heavy Workload in Penalty-Based Machine-Type Communication: Using ResNet

Zhipeng Feng*, Changhao Du, Jianping An, Zhongshan Zhang

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Although machine-type communication (MTC) will play a crucial role in massive-terminal communication in the future, existing MTC systems often overlook the impact of workload on system performance, leading to the underestimation of the effects of high workload and link retransmission in existing power allocation schemes, ultimately resulting in a deterioration of network capacity. This letter quantifies the penalty caused by retransmission and employs the penalty-weighted sum-rate (PWSR) to evaluate the system performance under different workload conditions, thus studying penalty-based MTC (pMTC) networks. Meanwhile, to obtain the optimal PWSR, we consider both computational cost and performance when designing a power scheme. The residual network (ResNet) adds skip connections on the basis of the deep neural network (DNN), which not only retains the advantages of simple structure and considerable performance but also has the advantages of fast convergence and effective mitigation of gradient vanishment. Thus, we adopt a ResNet-based scheme to allocate the power of each device in the pMTC. The numerical results indicate that under heavy workload conditions, the performance loss generated by the ResNet-based scheme is much lower than that of other investigated schemes, and the former has a better "penalty-robustness"and a lower computational complexity.

源语言英语
期刊IEEE Wireless Communications Letters
DOI
出版状态已接受/待刊 - 2024

指纹

探究 'Handle Heavy Workload in Penalty-Based Machine-Type Communication: Using ResNet' 的科研主题。它们共同构成独一无二的指纹。

引用此

Feng, Z., Du, C., An, J., & Zhang, Z. (已接受/印刷中). Handle Heavy Workload in Penalty-Based Machine-Type Communication: Using ResNet. IEEE Wireless Communications Letters. https://doi.org/10.1109/LWC.2024.3520993