Search

The combination of Multi-access Edge Computing (MEC) and New Radio Unlicensed (NR-U) can provide more powerful computing power and wireless access for the Internet of Things (IoT). An essential function in NR-U is to allocate time-frequency resources for IoT devices while ensuring coexistence with wireless local area networks (Wi-Fi) in the same spectrum. However, in the licensed assisted access (LAA) based NR-U, the listen-before-talk (LBT) mechanism makes the nodes located at the edges of the NR-U suffer from the hidden node problem. This can lead to a waste of time-frequency resources in NR-U and degrade the system performance. In this paper, we propose a determinantal point process (DPP) based link scheduling to improve the terminals downlink performance in the hidden nodes zone. Instead of scheduling relay links based on assignment methods, we introduce a recommendation scheme that recommends the D2D transmission nodes from a set of candidate nodes for hidden nodes and establishes D2D relay links. The recommendation scheme is designed based on a DPP model. The simulation results demonstrate that the proposed DPP method is capable of effectively selecting the optimal transmitting nodes and optimizing more hidden nodes while maximizing the sum rate. [ABSTRACT FROM AUTHOR]

Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages "principal component analysis (PCA)" to analyze the contribution of each energy consumption parameter and selects "representative parameter", and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model. • Designing the power modeling process, including parameter selection, parameter analysis and screening, model training and evaluation. • Using the "principal component analysis (PCA)" to analyze the factor contribution of each parameter. • Building a new power model named DSBF based on BP neural network and feature selection. [ABSTRACT FROM AUTHOR]