MEC-aware Cell Association for 5G Heterogeneous Networks
The need for efficient use of network resources is continuously increasing with the grow of traffic demand, however, current mobile systems have been planned and deployed so far with the mere aim of enhancing radio coverage and capacity. Unfortunately, this approach is not sustainable anymore, as 5G communication systems will have to cope with huge amounts of traffic, heterogeneous in terms of latency among other Qualityof- Service (QoS) requirements. Moreover, the advent of Multiaccess Edge Computing (MEC) brings up the need to more efficiently plan and dimension network deployment by means of jointly exploiting the available radio and processing resources. From this standpoint, advanced cell association of users can play a key role for 5G systems. Focusing on a Heterogeneous Network (HetNet), this paper proposes a comparison between state-of-the-art (i.e., radio-only) and MEC-aware cell association rules, taking the scenario of task offloading in the Uplink (UL) as an example. Numerical evaluations show that the proposed cell association rule provides nearly 60% latency reduction, as compared to its standard, radio-exclusive counterpart.
Code (0)
등록된 구현이 없습니다.
Tasks
Edge-computingSimilar Papers 제목 키워드 기반
Association-Aware GNN for Precoder Learning in Cell-Free Systems
Deep learning has been widely recognized as a promising approach for optimizing multi-user multi-antenna precoders in traditional cellular systems. However, a critical distinction between cell-free and cellular systems l…
Graph Neural NetworkRefinement Contrastive Learning of Cell-Gene Associations for Unsupervised Cell Type Identification
Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusivel…
Representation LearningContrastive LearningMaximization of User Association Deploying IRS in 6G Networks
Prospective mobile networks will be heterogeneous multi-tier networks, with different classes of base stations (BS) installed dependent on user demand. Multi-tier networks enable operators to enhance system capacity and …
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks
Cellular networks are becoming increasingly heterogeneous with higher base station (BS) densities and ever more frequency bands, making BS selection and band assignment key decisions in terms of rate and coverage. In thi…
Model Predictive ControlReinforcement Learning (RL)Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Data
Understanding causal heterogeneity is essential for scientific discovery in domains such as biology and medicine. However, existing methods lack causal awareness, with insufficient modeling of heterogeneity, confounding,…