Energy Efficient Resource Allocation for Mobile-Edge Computation Networks with NOMA
원문 초록
This paper investigates an uplink non-orthogonal multiple access (NOMA)-based mobile-edge computing (MEC) network. Our objective is to minimize the total energy consumption of all users including transmission energy and local computation energy subject to computation latency and cloud computation capacity constraints. We first prove that the total energy minimization problem is a convex problem, and it is optimal to transmit with maximal time. Then, we accordingly proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. The proposed algorithm is successfully shown to be globally optimal. Numerical results show that the proposed algorithm achieves better performance than the conventional methods.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
Edge-Enabled Collaborative Object Detection for Real-Time Multi-Vehicle Perception
FedSKC: Federated Learning with Non-IID Data via Structural Knowledge Collaboration
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
Cooperative Inference for Real-Time 3D Human Pose Estimation in Multi-Device Edge Networks
PolygoNet: Leveraging Simplified Polygonal Representation for Effective Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.